proactive problem solving framework

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PROACTIVE PROBLEM SOLVING: QUICKLY, CREATIVELY, AND PERMANENTLY SUPPORT

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Today’s technology companies face the difficulties of the digital era, including rising user expectations, technological changes, and severe rivalry. Hence, managers and IT professionals frequently push technology solutions to proactive problem-solving .

However, many companies still lack an appropriate and rigorous strategy for developing issues, identifying core causes, implementing necessary remedial measures, assessing the consequences, and ultimately creating a better knowledge of the task that enhances people’s day-to-day employment practices. This is where proactive problem management methods come in.

To prevent issues from happening in the first place, you must have a plan for every possible scenario. This blog post will explore the importance of proactive problem-solving and how to do it quickly, creatively, and permanently.

What is proactive problem management?

What is proactive problem-solving?

It is a problem-solving approach that focuses on identifying solutions before they occur by employing proactive problem solving techniques.

The key is not necessarily the reaction but how you react to it. Preventing issues is what proactive problem-solving entails. The emphasis is on resolving the root source of the problem rather than its consequences.

A proactive development team is a team of developers who don’t wait for solutions; they are proactive about discovering problems to solve. This involves ensuring that all team members are trained in dealing with any issue that may arise and having a backup plan in place in case something goes wrong.

When teams are proactive, they solve problems preemptively for two main reasons: firstly, so that problems don’t affect their team’s productivity or output, and secondly, their organization can gain a competitive advantage and satisfy clients.

How to build a proactive problem-solving team

Problem-solving teams are created to work together permanently

Identify root causes

Once the fundamental cause is identified, a team can remediate the defect at its source and prevent it from future occurrences.

For example in proactive problem management , developers can review the design and requirements documentation to make corrections if the defect results from a design error. If a testing mistake causes the defect, developers can update the test cases and metrics.

Hence, a proactive problem-solving team is a team that can identify the root causes.

Types of defect

[su_spoiler title=”Errors, omissions, or gaps in the original requirements.” style=”fancy”]These flaws can arise when a need is missed or forgotten when it is written incorrectly, when stakeholders are not adequately understood, or when developers are misinterpreted.[/su_spoiler]

[su_spoiler title=”Errors in the design or architecture.” style=”fancy”]These issues arise when software designers build an inefficient software algorithm or process, or when that algorithm or process fails to provide results with the requisite accuracy.[/su_spoiler]

[su_spoiler title=”Errors in the coding or implementation.” style=”fancy”]These defects include traditional bugs caused by everything from missing brackets to ungraceful error handling.[/su_spoiler]

[su_spoiler title=”Errors in test planning or test execution.” style=”fancy”]These defects are the result of inadequately tested features and functions.[/su_spoiler]

[su_spoiler title=”Errors in deployment.” style=”fancy”]An example of one of these problems is when a team allocates insufficient VM resources.[/su_spoiler]

[su_spoiler title=”Errors in the process or policies a team uses to govern the development cycle.” style=”fancy”]For example, this defect crop up when a team obtains signoffs or approvals without good design, coding, or testing review.[/su_spoiler]

How to implement proactive problem management process

Approaches to root cause analysis

The Fishbone diagram is one of the most popular techniques.

A fishbone analysis, also known as an Ishikawa diagram or a cause-and-effect diagram, is intended to assist analysts in visualizing a root cause by categorizing potential reasons into categories that branch out from the initial issue. The resultant graphic resembles a fish skeleton, thus the name.

The underlying problem or issue is usually written at the “head” of the fish. The “bones” are categories of possible causes. Then we can find out the principal reasons under each group; if necessary, the diagram might include secondary and tertiary factors.

Proactive approach to problem solving

Learn more: When do you need to hire a professional software QA team?

Proactively determine solutions

Once you’ve identified the issue, it’s time to devise a solution. It is sometimes possible to become so engrossed in identifying issues that solution definition becomes secondary.

When delivering a fix for the identified problem, we must consider two factors: resolving the issue and preventing it from recurring in the future. We’ve all seen “hotfixes” that last forever and cause technical debt.

Furthermore, enablement needs to propose solutions among team members first to ensure they continue to understand the context of the issue through the eyes of the stakeholders and connect the solution to stakeholders’ pain points. Then continuing to communicate with stakeholders proactively early and during the implementation will assist in creating further trust and enthusiasm in solutions.

Empower open communication and ongoing feedback

Proactive problem-solving begins with getting everyone on the same page about an overall plan for how you’re going tackle the project. This includes setting specific goals and objectives. Hence, communication is key here—be sure that everyone knows their role and what they are expected to do throughout the entire project life cycle.

The evolution of management is an ongoing process of open communication and feedback. Team members will receive the support needed for any improvements or changes in direction from management if necessary.

• Feedback from all members of the development group should be given regularly, even if it’s negative or positive. Developing a clear feedback process with the team puts everyone on the same playing field for future progress.
• Encourage open communication among peers by making space for discussion in meetings. The team may focus on what went right and wrong in a productive and non-occupational way through meetings.
• Encourage members of the team to ask questions. Never disregard a question or make someone feel insufficient for posing one. Questions contribute to critical explanations, discoveries, and, in many cases, process improvements that the team would not have identified otherwise.

Read more: Proactive communication – successful key of all offshore development team

Characteristics of InApps’ proactive problem-solving team

We win our client’s trust with high skills, market knowledge, well-communication, and 24/7 dedicated support.

Proactive problem solver

Flexible approach

We provide each of our clients with a unique custom solution. We always have meetings to deeply understand our client’s business models and requirements or the pain points before making the proposals.

With InApps, clients can participate in projects by prioritizing, defining functions, developing iteration plans and reviews, and developing software versions that incorporate new features.

Proactive support

We handle issues and fix urgent to minimize complaints.

Our team uses platforms like Slack for internal conversations between meetings. When we require the client’s feedback, we use technologies like Basecamp to facilitate communication proactively.

This is also useful if the client needs to bring anything to our notice for discussion. We can communicate, ensure information is distributed, and plan spontaneous conversations to walk through more complex issues.

High troubleshooting skills

Need to fix bugs to launch your web/app as soon as possible? We offer dedicated teams with proactive troubleshooting skills to quickly fix all your urgent issues.

Trusted and high technical skills are the factors that made InApps build a successful high-performing offshore team .

Rapid response & quickly fix all urgent issues

We have a unique program to train talents to become a SWAT team that works effectively with clients. Our offshore team quickly solves the problems from the root causes and responds to the client within 24 hours. 

Read more: InApps’ Automation Management: Proactive Solution for Software Development

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Problem management: 8 steps to better problem solving

Problem management is an 8 step framework most commonly used by IT teams. You can use problem management to solve for repeating major incidents. By organizing and structuring your problem solving, you can more effectively get to the root cause of high-impact problems—and devise a solution. Solving the root cause prevents recurrence and creates a repeatable solution to use on similar errors in the future.

In an IT department, errors and mishaps are part of the job. You can't always control these problems, but you can control how you respond to them with problem management. Problem management helps you solve larger problems and reduce the risk that they’ll happen again by identifying all connected problems, solving them, and planning for the future.

What is problem management?

Problem management is an 8 step framework most commonly used by IT teams. Your team can use problem management to solve for repeating major incidents. By organizing and structuring your problem solving, you can more effectively get to the root cause of high-impact problems—and devise a solution. Problem management is a process—used mostly by IT teams—to identify, react, and respond to issues. It’s not for every problem, but it’s a useful response when multiple major incidents occur that cause large work interruptions. Unlike problem solving, problem management goes beyond the initial incident to discover and dissect the root causes, preventing future incidents with permanent solutions.

The goals of problem management are to:

Prevent problems before they start.

Solve for repetitive errors.

Lessen each incident’s impact. 

Problem management vs. incident management 

Example: Someone leaves their unprotected laptop in a coffee shop, causing a security breach. The security team can use incident management to solve for this one, isolated event. In this case, the team could manually shut down the accounts connected to that laptop. If this continues to happen, IT would use problem management to solve the root of this issue—perhaps installing more security features on each company laptop so that if employees lose them, no one else can access the information.

Problem management vs. problem solving

While similar in name, problem management differs slightly from problem-solving. Problem management focuses on every aspect of the incident—identifying the root cause of the problem, solving it, and prevention. Problem solving is, as the name implies, focused solely on the solution step. 

Example: You’re launching a new password management system when it crashes—again. You don’t know if anything leaked, but you know it could contain confidential information. Plus, it’s happened before. You start the problem management process to ensure it doesn’t happen again. In that process, you’ll use problem solving as a step to fix the issue. In this case, perhaps securing confidential information before you try to launch a new software.

Problem management vs. change management 

Change management targets large transitions within your workplace, good and bad. These inevitable changes aren’t always negative, so you can’t always apply problem management as a solution. That’s where change management comes in—a framework that helps you adjust to any new scenario.

Example: Your company is transitioning to a new cloud platform. The transition happens incident-free—meaning you won’t need problem management—but you can ease the transition by implementing some change management best practices. Preparing and training team members in the new software is a good place to start.

Problem management vs. project management

Project management is the framework for larger collections of work. It’s the overarching method for how you work on any project, hit goals, and get results. You can use project management to help you with problem management, but they are not the same thing. Problem management and project management work together to solve issues as part of your problem management process.

Example: During problem management, you uncover a backend security issue that needs to be addressed—employees are using storage software with outdated security measures. To solve this, you create a project and outline the tasks from start to finish. In this case, you might need to alert senior executives, get approval to remove the software, and alert employees. You create a project schedule with a defined timeline and assign the tasks to relevant teams. In this process, you identified a desired outcome—remove the unsafe software—and solved it. That’s project management.

The 8 steps of problem management

It’s easy to get upset when problems occur. In fact, it’s totally normal. But an emotional response is not always the best response when faced with new incidents. Having a reliable system—such as problem management—removes the temptation to respond emotionally. Proactive project management gives your team a framework for problem solving. It’s an iterative process —the more you use it, the more likely you are to have fewer problems, faster response times, and better outputs. 

1. Identify the problem

During problem identification, you’re looking at the present—what’s happening right now? Here, you’ll define what the incident is and its scale. Is this a small, quick-fix, or a full overhaul? Consider using problem framing to define, prioritize, and understand the obstacles involved with these more complex problems. 

2. Diagnose the cause

Use problem analysis or root cause analysis to strategically look at the cause of a problem. Follow the trail of issues all the way back to its beginnings.

To diagnose the underlying cause, you’ll want to answer:

What factors or conditions led to the incident?

Do you see related incidents? Could those be coming from the same source?

Did someone miss a step? Are processes responsible for this problem?

3. Organize and prioritize

Now it’s time to build out your framework. Use an IT project plan to organize information in a space where everyone can make and see updates in real time. The easiest way to do this is with a project management tool where you can input ‌tasks, assign deadlines, and add dependencies to ensure nothing gets missed. To better organize your process, define:

What needs to be done? 

Who’s responsible for each aspect? If no one is, can we assign someone? 

When does each piece need to be completed?

What is the final number of incidents related to this problem?

Are any of these tasks dependent on another one? Do you need to set up dependencies ?

What are your highest priorities? How do they affect our larger business goals ? 

How should you plan for this in the future?

4. Create a workaround

If the incident has stopped work or altered it, you might need to create a workaround. This is not always necessary, but temporary workarounds can keep work on track and avoid backlog while you go through the problem management steps. When these workarounds are especially effective, you can make them permanent processes.

5. Update your known error database

Every time an incident occurs, create a known error record and add it to your known error database (KEDB). Recording incidents helps you catch recurrences and logs the solution, so you know how to solve similar errors in the future. 

6. Pause for change management (if necessary)

Larger, high-impact problems might require change management. For example, if you realize the problem’s root cause is a lack of staff, you might dedicate team members to help. You can use change management to help them transition their responsibilities, see how these new roles fit in with the entire team, and determine how they will collaborate moving forward.

7. Solve the problem

This is the fun part—you get to resolve problems. At this stage, you should know exactly what you’re dealing with and the steps you need to take. But remember—with problem management, it’s not enough to solve the current problem. You’ll want to take any steps to prevent this from happening again in the future. That could mean hiring a new role to cover gaps in workflows , investing in new softwares and tools, or training staff on best practices to prevent these types of incidents.

Read: Turn your team into skilled problem solvers with these problem-solving strategies

8. Reflect on the process

The problem management process has the added benefit of recording the process in its entirety, so you can review it in the future. Once you’ve solved the problem, take the time to review each step and reflect on the lessons learned during this process. Make note of who was involved, what you needed, and any opportunities to improve your response to the next incident. After you go through the problem management process a few times and understand the basic steps, stakeholders, workload, and resources you need, create a template to make the kickoff process easier in the future.

5 benefits of problem management

Problem management helps you discover every piece of the problem—from the current scenario down to its root cause. Not only does this have an immediate positive impact on the current issue at hand, it also promotes collaboration and helps to build a better product overall. 

Here are five other ways ‌problem management can benefit your team:

Avoids repeat incidents. When you manage the entire incident from start to finish, you will address the foundational problems that caused it. This leads to fewer repeat incidents.

Boosts cross-functional collaboration. Problem management is a collaborative process. One incident might require collaboration from IT, the security team, and legal. Depending on the level of the problem, it might trickle all the way back down to the product or service team, where core changes need to be made.

Creates a better user experience. It’s simple—the fewer incidents you have, the better your customer’s experience will be. Reducing incidents means fewer delays, downtime, and frustrations for your users, and a higher rate of customer satisfaction.

Improves response time. As you develop a flow and framework with a project management process, you’ll be better equipped to handle future incidents—even if they’re different scenarios.

Organizes problem solving. Problem management provides a structured, thoughtful approach to solving problems. This reduces impulsive responses and helps you keep a better problem record of incidents and solutions.

Problem management leads to better, faster solutions

IT teams will always have to deal with incidents, but they don’t have to be bogged down by them. That’s because problem management works. Whether you employ a full problem management team or choose to apply these practices to your current IT infrastructure, problem management—especially when combined with a project management tool—saves you time and effort down the road.

With IT project plans, we’ve made it easier than ever to track your problem management work in a shared tool. Try our free IT project template to see your work come together, effortlessly.

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What is root cause analysis? A proactive approach to change management

Root cause analysis (rca) focuses on fostering a proactive approach to solving problems before they happen and eliminating the potential for flaws to reoccur in the future..

Root cause analysis definition

Root cause analysis (RCA) is a problem-solving process that focuses on identifying the root cause of issues or errors with the goal of preventing them from reoccurring in the future. RCA is typically part of service management methodologies and frameworks, such as ITIL , TQM , and Kanban , that focus on continuous process improvement . This type of analysis can help identify flaws in IT processes, potential security breaches, and faults in business processes.

When a problem is identified and removed, it is considered a “root cause” if it prevents the problem from reoccurring. If, however, a problem is removed and it impacts the event’s outcome, but not in the way intended, then it is a “causal factor.” RCA is typically used to find the root cause of software or infrastructure problems to improve the quality and efficiency of processes, and thereby to save time and money. Every potential cause in a given process is identified and analyzed to ensure the organization is treating the disease, rather than just the symptoms.

Reactive vs. proactive problem management

Reactive management and proactive management are the two main approaches organizations take to repairing issues and solving problems. With reactive management, problems are fixed soon after they occur, often called “putting out fires.” The goal is to act quickly to resolve issues and alleviate any effects of a problem as soon as possible.

Proactive management, on the other hand, aims to prevent problems from reoccurring. It is focused less on quickly solving problems and instead on analyzing them to find ways to prevent them from happening again. That’s where root cause analysis comes in. Its methodology is best suited to support proactive problem management’s goal of identifying and fixing underlying issues, rather than just reacting to problems as they happen.

Root cause analysis steps

While there’s no strict rulebook on how to conduct a root cause analysis, certain guidelines can help ensure your root cause analysis process is effective. The four main steps that most professionals agree are essential for RCA to be successful include the following:

• Identification and description: Organizations must first identify the failures, errors, or events that triggered the problem in question and then establish event descriptions to explain what happened.
• Chronology: After identifying these issues, organizations must then create a sequential timeline of events to better visualize the root cause and any contributing causal factors. Here, it’s important to establish the nature of the event, the impact it had, and where and when the problem occurred.
• Differentiation: Once the sequence of events is established, data involved with a particular issue can be matched to historical data from past analysis to identify the root cause, causal factors, and non-causal factors.
• Causal graphing: Those investigating the problem should be able to establish key events that explain how the problem occurred and convert that data into a causal graph.

Root cause analysis takes a systematic approach to identifying problems and requires the effort of full teams to properly perform the analysis. Those tasked with the analysis typically work backwards to determine what happened, why it happened, and how to reduce the chances of it happening again. They can trace triggered actions to find the root cause that started the chain reaction of errors in a process to remedy it. These steps help guide the process and give organizations a framework for how to successfully complete a root cause analysis.

Root cause analysis methods

RCA is already baked into several IT frameworks and methodologies as a step for change, problem, or risk management. It’s been established as a proven, effective way to support continuous process and quality improvement. But if you are conducting a root cause analysis outside of a separate process management framework, organizations typically employ the following methods to ensure a successful RCA:

• Form a team to conduct the RCA and evaluate processes and procedures in the organization that have flaws. This team should be built by bringing together employees who work in relevant business areas or who work directly with the broken processes.
• Once the analysis begins, it can take upwards of two months to complete. Each step of the process is given equal weight whether it’s defining and understanding the problem, identifying possible causes, analyzing the effects of the problem, or determining potential solutions.
• Teams should meet at least once per week, if not more often, with meetings being kept to no longer than two hours with a loose agenda. The meetings are intended to be relatively creative, so you want to avoid bogging people down with too much structure.
• Team members should be assigned specific roles or tasks so everyone has a clear understanding of what they should be investigating.
• Upon finding a potential solution, it’s crucial to follow up to make sure that the solution is effective and that it’s implemented successfully.

Root cause analysis tools

You don’t need much to conduct a root cause analysis, but there are several tools that are helpful and commonly used to help make the process easier. Commonly used tools to perform an effective root cause analysis include:  

• Fishbone diagrams: A fishbone diagram is mapped out in the shape of a fishbone, allowing you to group causes into sub-categories to be analyzed.
• Failure mode and effects analysis (FMEA): FMEA is a technique that can be used to map out a system or process and identify the failures within it. It can be used not only to identify flaws but also to map out how often they happen, what actions have already been taken, and what actions have been effective in remedying the issue.
• Pareto charts: A Pareto chart is a simple bar chart that maps out related events and problems in order of how often they occur. This helps identify which problems are more significant than others and where to focus process improvement efforts.
• Scatter diagrams: A scatter diagram plots data on a chart with an x and y axis. This is another useful tool for mapping out problems to understand their impact and significance.
• Fault tree analysis: A fault tree analysis uses Boolean logic to identify the cause of problems or flaws. They are mapped out on a diagram that looks like a tree, where every potential cause is included as its own “branch.”
• 5 whys analysis: With 5 whys analysis, you will ask the question “why” five times too delve deeper into a problem to develop a clearer picture of its root cause.

Root cause analysis training

While RCA is a part of other frameworks and methodologies, there are training programs and courses designed to focus on helping people better understand how to perform the analysis. If you want to get more training on RCA, here are a handful of programs designed to help:

• Workhub Root Cause Analysis training
• Udemy Root Cause Analysis course
• Pink Elephant Problem Management: Root Cause Analysis Specialist certification course
• NSF Root cause analysis CAPA training and certification
• Coursera Root Cause Analysis course
• ASQ root cause analysis course
• Lean Six Sigma Root cause analysis online training

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Home > assyst Blog > How to Quick-Start Proactive Problem Management

How to Quick-Start Proactive Problem Management

by Hannah Mandapat   |   Sep 8, 2022   |   Estimated reading time: 12 minutes  |   in assyst Blog   |   tagged dashboard , Incident Management , ITIL 4 , Practices , Problem Management , reporting

No matter how efficient your incident management process, you will always have more incidents than you can handle—until you get proactive about problem management .

Where do you start?

Part of our Making ITIL 4 Simple series.

8m Deep Dive

Developing a mature problem management practice can have a transformational impact on your service desk and the broader IT group—because it greatly reduces unplanned work and avoids operational costs. In turn, it gives you back time and money which can be reallocated to improvement and innovations.

Problem management is your friend because it’s the #1 way to end the firefighting—the unplanned work that disrupts an IT person’s day. By eliminating a chunk of unplanned work (as much as 50% in just a few months of running a proactive problem management practice), you give IT people time back—so they have the bandwidth to push innovation projects forward. It’s a virtuous cycle.

Unfortunately, many organisations aren’t where they need to be with the problem management practice. Research from HDI indicates 61% of organizations are “doing” ITIL problem management. However, most of these are only doing reactive PM, driven by frequent major incidents.

Why are organizations not where they need to be with problem management? Firefighting gets in the way. It’s a Catch-22 situation: a mature problem management practice ends the firefighting, but the constant firefighting prevents them from maturing their problem management practice. This is a vicious cycle.

So how can we shift from vicious cycle to virtuous cycle?

Be agile. Find the single biggest problem. Solve it. Use the time that released to scale-up your problem management practice a little and solve the next most painful problems. Start redirecting operational resources from the “daily grind” work to transformative project work. Keep going.

ITIL 4 guidance can help:

• Start small (See ITIL 4: Start Where You Are , ITIL 4: Keep it Simple and Practical )
• Focus on the problems causing the most damage (see ITIL 4: Focus on Value ).
• Use the time gained to iterate (See ITIL 4: Progress Iteratively with Feedback )

RELATED : See all our ITIL 4 Articles

What is a Problem?

Before we dig into the detail, and what ITIL 4 has to say about problem management, let’s quickly re-cap to make sure we’re all on the same page with definitions. According to ITIL 4, a problem is:

A cause, or potential cause, of one or more incidents ITIL 4 Foundation Volume, Page 130

The word “potential” appears because—in the context of modern, automated ITSM technology—a problem is the underlying root cause of none or more incidents. We say none (instead of one) because in a mature problem management environment (supported by mature event management ), a problem can be automatically discovered and automatically resolved before a service consumer experiences an issue. Before an incident record is ever created.

This is the realm of AIOps/AITSM, where immediate detect-and-correct automations and predictive AI can be applied to automate much of IT’s daily ITSM / ITOM workload. You can find out more about this in our forthcoming AIOps and AITSM series. Subscribe to this blog to make sure you don’t miss these.

Now back to the issue of improving the ITIL problem management practice….

Why Does Problem Management Matter?

Where incident management is about speed , problem management is about quality : taking time to properly investigate the problem, identify and validate a root cause, propose a solution, apply it, test it, deploy it, document it, and so on. Problem management is a fastidious, detail-oriented, technical practice.

You could argue it’s about stopping incidents flowing into the service desk, but that’s an inside-out IT perspective, not a service consumer perspective. Problem management is about improving service quality for customers—preventing service disruptions which reduce employee productivity—that’s what is really important here.

Customers care more about always-on services than they do about the stress levels in your service desk . The focus should be on solving problems which are impacting the customer experience. Reducing the number of calls coming into the service desk is a secondary benefit.

Problem management matters because it’s a force multiplier. Incident management is focused on solving one incident . Problem management is about solving many . So, the impact of problem management on business productivity is greater—by an order of magnitude.

A Brief Guide to Problem Management

In ITIL 4 , the problem management practice is made up of three sub-practices— Problem Identification , Problem Control , and Error Control —each of which has a small set of clear responsibilities. Proactive problem management isn’t as scary as people sometimes think.

This can help you assign responsibilities in a way which spreads the load evenly across members of your team. This will help ensure your problem management practice is sustainable—as well as avoiding potential conflicts between tasks. The diagram below sets out the main activities in each of the problem management sub-practices:

Now that we’ve covered the basics, we’ve set the scene to talk about where you can start, and how you can gain some quick traction with ITIL problem management.

Start by Attacking Your Top-10 Incident Pains

An easy place to start is to pull a report of your top 10 most frequent incident types from your service desk/ITSM solution . E.g. find out where your service desk agents are spending large amounts of time on the most frequent recurring issues.

SOLUTIONS : ITSM Dashboards & Reporting

Calls to the service desk follow a pareto distribution , sometimes called an 80:20 chart (see the diagram below), meaning the majority of all calls come from just a handful of causes—seen here as the “head” of the chart, marked in red. The remaining calls come from a much larger set of different causes—the “tail” of the chart, marked in blue.

Problem management is all about understanding cause and effect. By pulling a report of the top-10 calls by volume, we can identify the “head” (marked in red). These ten issues are likely causing 30%-50% of all calls to the service desk.

This is powerful because it allows us to solve a large chunk of the total call volume coming in to the service desk by addressing just a handful of issues.

In many organizations, the biggest stack is caused by password reset requests (which can be easily solved by providing self-service password reset tools to end users via a web and mobile…but let’s not get ahead of ourselves).

The “long tail” (marked in blue) represents a smaller number of calls than the head, but there will be hundreds or potentially thousands of underlying problems. Eliminating these requires exponentially more effort. Focusing on the head gets you the biggest reduction in calls—in the fastest time. Ignore the tail until you have dealt with the head.

There is a nuance here to be aware of. Is a report of your top 10 calls really business-focused, or is it more focused on reducing stress on the service desk? Solving these top 10 problems might reduce the volume of calls (or self-service loggings ) coming in to the service desk, but what impact will solving them have on the service consumer experience? Are they the top priority for the customer?

Look again at your report. What are the business priorities of these incidents (as measured using your priority matrix )? Do the priorities match up against the call volumes? If not, reorganise them based on the business priorities to ensure you are fully aligned with business demand. Now go and talk to your business unit heads to validate this list before you get to work on doing something about it.

They’ll really appreciate the chance to provide input on priorities, and they may even get excited about you closing-off some annoying recurring issues which are hurting their productivity. This means they are more likely to provide valuable support if you need it (e.g. when you are asking other IT teams to help out with applying the relevant fixes). More on that later.

When you have this business-validated list, these problems will be the focus of planned work for your problem management practice.

This planned work should be the core of what your problem management practice does, but it can be over-ruled in the event of a major incident —one which is causing catastrophic loss to the organization. Major incidents are Priority 1 incidents which require an all-hands-on-deck response. The problem management team will park planned work and instantly divert to investigating the major incident.

But remember…with a mature problem management practice in place, the number of major incidents will reduced over time —tipping the balance from unplanned to planned work. Once again, we’re back to the benefit of reducing unplanned work to make room for improvement and innovation .

Pro-active problem management is your friend because—although it doesn’t seem glamorous—it helps you accelerate innovation and execute the digital agenda.

Doing Something About It

So far we’ve looked at Problem Identification and the prioritisation element of Problem Control . Now you know what to focus on. It’s time to do something about root causes so you can systematically close off incidents permanently. We’re now going deeper into Problem Control , where things start to get more technical.

We need to investigate two things:

• Is there a workaround that will restore the service quickly, sidestepping the need for detailed analysis and getting service users back online faster?
• Where is the root cause? And why did a service interruption happen? This is the detailed analysis that needs to happen to close off the source of these incidents for good.

This is where a CMDB can accelerate you to both of these goals. If you have a complete and accurate digital view of your IT ecosystem—one which shows you both the relationships between service components and the status of those components—you can get an understanding of cause and effect more quickly (versus sending people to physically inspect hardware or manually parse system log files).

Once you have identified the root cause, you may need to create a change record to trigger the work that needs to happen to apply a permanent solution.

The challenge is that many of the actions that need to be taken to eliminate these flaws happen outside of the service desk; beyond the reach and power of the problem management team. Service desk agents don’t have direct access to the different technologies: privileged systems like databases, servers, network devices, storage, and cloud management. In large organizations there are teams dedicated to each of these types of system—and for the sake of stability and security they don’t share access. That means that problem management practitioners need to reach out to effect a change. That’s what a change ticket is—a formal request from one team to another team to take an action that they themselves cannot take.

Ultimately, problems are eliminated through this process. With each problem solved, the IT ecosystem is made more robust and another stack of calls to the service desk are avoided.

Don’t Stop Now

Once you’ve solved your top-10 pain points, you should now have taken some pressure off your service desk—perhaps enough already to reassign some of the more tech-savvy agents to your problem management practice to make even more progress, faster . Business stakeholders will have noticed a difference too—now that a significant number of disruptions to employee productivity have been avoided.

If you pull your incident-volumes-by-category report once again, it will probably look quite a bit like your original report—but with a shallower slope. You have “cut off the head” but not slayed the beast. The “long tail” issues will still be popping up at the service desk again and again. There’s more to be done. With problem management there’s always more to be done.

Where to Start

Taking the first step towards improvement often means taking a step back from the firefighting. Something has to give way to make room for improvement. That means there might need to be a temporary drop in incident management performance to create the slack you need to create a permanent up-tick in performance. After that, you should have created enough bandwidth to easily catch-up with the backlog (now that the volume of firefighting is reduced) as well as have some spare capacity to tackle more. From there, each problem you tackle reduces the volume of unplanned work a little bit more.

In the meantime, you need to communicate with your business stakeholders. They need to know what you’re planning and why. They need to know that they might need to take a temporary hit in order to help IT turn a corner which will ultimately enable a much faster pace of innovation in the long run. When they know what’s coming, they can plan a way around it.

Proactive problem management is your friend. It has the power to transform your support operations in just a few months. Proactive problem management activity makes time for more proactive problem management activity. Each step forward creates a ripple of positive effects across IT. It’s a virtuous cycle and it’s one of the keys to achieving high IT maturity .

To see ITIL 4 expert Troy DuMoulin and ITSM Solutions Consultant talk about how you can kick-start pro-active problem management (and 4x other ways you can quickly apply ITIL 4 best practices), watch this on-demand webinar now :

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Published: 10 April 2024 Contributor: Camilo Quiroz-Vázquez

Problem management is the process of identifying, managing and finding solutions for the root causes of incidents on an IT service. Problem management is a critical aspect of IT service management (ITSM).

The problem management process is both proactive and reactive and improves an IT team’s ability to find the root cause of issues while offering continuous service delivery to users. Crucially, problem management goes beyond identifying issues and delivering a quick fix; successful problem management operates on a comprehensive understanding of all underlying factors that contribute to incidents and solutions that address the root cause.

IT operations  (ITOps) involves managing a complex system of interdependent applications, software, hardware, IT infrastructure and other technologies. Ideally, incidents and problems would not occur in the first place, but when they do, it is necessary to solve issues and identify known errors before they cascade into larger ones. Service disruptions prevent organizations from providing continual service improvements and can cause serious reputational and financial issues.

Proactive problem management helps enterprises stop problems before they occur and reduce downtime.  IT automation solutions help manage the impact of incidents by automating incident detection and the workflows that lead to resolution. IT issues can include long load times, inefficient or broken code, or database queries that fetch unnecessary data. Proactively addressing problems leads to reduced costs and improved customer satisfaction.

Effective problem management requires observability into IT systems and rigorous categorization of problems and incidents. By classifying instances that might lead to major incidents, organizations can address issues likely to have the largest business impact. Problem management strategies address incidents across an organization’s tech stack and compel organizations to explore better ways to address incidents across operations.

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Problem management requires a well-thought-out approach to ensure that teams are allocating resources as efficiently as possible. Problem management teams and other stakeholders use several levers to address problems effectively and efficiently. These levers help teams identify the root cause of the problem and create solutions that can stop the problem from recurring.

Most problem management approaches follow a similar pattern of assessment, logging, analysis and solution.

IT professionals identify recurring incidents that are classified as problems, often by using automation . Automated systems help find anomalies by sifting through large data sets and identifying data points that might be out of the ordinary.

Anomalous data can lead IT team members to the potential causes of incidents. Incident reports and automated notifications are sent to the service desk, which can identify whether the incident is new or if a team has identified and resolved it in the past.

Teams or automated systems identify and categorize incidents as problem records or as unrelated issues likely to occur again. This categorization helps an organization determine whether it can solve a problem immediately or if the problem requires deeper analysis.

Problem management teams log problems, often by using self-service platforms, and create problem records. Problem records consist of comprehensive accounting for the problem, including any related incidents, where and how the problem occurred, the root cause analysis and the solution. This logging system creates a known error record and enters it into the known error database (KEDB). Enterprises should connect their problem-management and knowledge management approaches. Knowledge management creates a library of solutions for known problems.

Organizations study the underlying issues behind identified problems and develop roadmaps leading to long-term solutions. Understanding the root cause allows organizations to prevent the problem from repeating, reducing the long-term impact.

When an IT team understands the problem and its root cause, it can address the problem (also known as problem control) and find a resolution. This can involve a quick or protracted response depending on the severity or complexity of the problem. Quick resolutions are made by finding workarounds that shorten downtimes while IT teams find the root cause.

Problem management can also use templates, such as ones focused on escalation information and problem reviews, to minimize human resources previously dedicated to key problem management tasks.

Error control is another facet of problem control. Error control focuses on finding resolutions to known errors with the goal of removing them from the known error database (KEDB).

The goal of problem management is to minimize downtime, increase efficiency and improve service delivery. Some of the more impactful benefits of problem management include:

Identifying the underlying cause of incidents is an important part of  cyberrisk management . Organizations that merely patch or resolve individual incidents without exploring their root cause might be overlooking significant security issues. Problem management teams can work in coordination with security professionals to understand which incidents and problems result from malicious actors or security flaws, both of which can create major problems for an organization.

Customer retention relies on the consistent delivery of quality services. Sustained downtime and the inability to access applications or websites can drive customers elsewhere. By prioritizing problem identification and problem resolution, organizations can minimize downtime and increase customer satisfaction.

Organizations that prioritize knowledge management, the process of identifying, organizing, storing and disseminating information in a knowledge base, as part of their problem management approach have a better chance of avoiding repeat incidents. By capturing this information in a problem record, organizations can create known error databases so they can avoid future incidents and create permanent solutions.

Implementing problem management strategies helps maintain the efficiency of IT departments and improve employee experience . Problem management prevents employees from having to repeatedly fix and maintain the same issues, allowing them to boost productivity on higher value work.

Problem management and incident management are closely related processes. IT departments perform both functions with the goal of providing continuous service and eradicating issues. The main difference between these two functions lies in the technical definitions of “incident” and “problem.”

•   An incident is a singular event that causes a disruption and hinders a system’s ability to deliver a specific service. 
• Problems are the root cause of that incident. A problem can consist of a single incident or multiple concurring incidents.

The incident management process has its roots in the IT service desk , which provides a single point of contact between IT operations and users, and handles the entire lifecycle of IT service delivery. Incident resolution happens reactively and involves quickly resolving incidents before they disrupt service. Problem management is concerned with finding the underlying cause of each incident and offering a permanent solution to the cause of the problem. IT teams set standards for problem analysis, allowing them to trace the root cause of incidents. The most effective problem management strategies are proactive and can identify the potential cause of a problem before it occurs. 

Efficient problem management strategies involve an emphasis on knowledge management. Knowledge management strategies use organizational experience to resolve issues more quickly or avoid them entirely. Robust documentation of solutions, protocols and common workarounds is a key aspect of knowledge management. IT departments store documentation in a centralized location and ensure that documentation is easily accessible across teams. Knowledge management repositories help IT teams focus on more complex work and the optimization of existing services. They are also an important tool for proactive problem management.

A problem management team can either engage in reactive or proactive problem management, depending on what incidents they observe and what historical data they have. Reactive problem management is concerned with identifying the problem when it occurs and solving it as quickly as possible. The problem must first occur before organizations can apply reactive problem management.

Proactive problem management involves more investigative work on why a problem is occurring and creating a solution to prevent it from happening again. The more proactive an enterprise can be, the more likely it is to avoid large issues, security threats and service interruptions.

The Information Technology Infrastructure Library (ITIL) is a repository of best practices for optimizing IT operations and improving service level functions. The ITIL is an integral part of the configuration management database (CMDB), which is the centralized authority for every component needed to provide and manage IT services. IT teams use the ITIL when implementing IT service management (ITSM).

ITSM is how an organization ensures its IT services work in the way that its users and business need them to work. ITSM strategy aims to enable and maintain optimal deployment, operation and management of IT resources. Problem management is a core component of ITSM. ITIL is the most widely adopted guidance framework for implementing and documenting ITSM.

ITIL problem management uses ITIL processes to minimize the foundational work that addressing any one problem requires. Many problems that organizations face, such as server outages and cybersecurity issues, have happened before to other organizations. Often, standardized responses exist. Therefore, ITSM approaches often incorporate ITIL to minimize the new work needed to solve IT problems. ITSM also encompasses the process of change management.

Change management is the process of managing and implementing organizational change. Change management can occur throughout migrations, digital transformations or organizational mergers. DevOps teams use ITIL to guide them through these changes and measure KPIs and metrics related to the successful implementation of changes to IT systems. Ideally the change management process should be seamless. When it isn’t, problem management strategies can help smooth the transition.

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Incidents are errors or complications in IT service. Those that point to underlying or more complicated issues that require more comprehensive addressing are called problems.

IT operations and AIOps oversee and automate the management, delivery and support of IT services throughout an organization.

ITSM is how an organization ensures its IT services work the way users and the business need them to work.

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ITIL Reactive and Proactive Problem Management: Two sides of the same coin

While ITIL Problem Management has a logical and easy-to-understand description, implementing Problem Management within your own organization is extremely challenging. It happens more often than not, that Problem Management doesn’t produce any of the desired outputs upon implementation. In order to prevent that, you must recognize the importance of both the reactive and proactive parts of ITIL Problem Management.

At this point, I’d recommend reading ITIL Problem Management: getting rid of problems  just to establish a general overview of the relationship between Incident Management and Problem Management.

Reactive Problem Management

Figure 1: Reactive Problem Management

Reactive Problem Management reacts to incidents that have already occurred, and focuses effort on eliminating their root cause and reoccurrence. The main focus of Problem Management is to increase long-term service stability and, consequently, customer satisfaction.

When incidents start to occur, IT organizations want Problem Management involved early, but Incident Management strives to resolve the incident and restore service to usable levels as quickly as possible, and during that process, some important indications about root cause may be lost. So, in order to effectively pinpoint root cause, Problem Management may block Incident Management efforts to restore service. This is where confusion may arise regarding the difference between Incident Management and Problem Management.

What we need is clear and well-defined hand-over procedure, with agreed time frames within which Incident Management stops, and Problem Management starts. There should also be an agreed set of information that Incident Management passes to Problem Management during the hand-over, which includes what has been done so far, whether any workarounds are in place, information about affected Configuration Items (CIs), or other important information.

Problem Management processes all that information and outputs Requests for Change, updates the Known Error Database (KEDB) and Work-Arounds, updates Problem Records and produces management information.

Proactive Problem Management

Figure 2: Proactive Problem Management

Even though Reactive Problem Management relies heavily on other Service Management components, Proactive Problem Management relies even more. Proactive Problem Management is a continuous process that doesn’t wait for an incident (or series of incidents) to happen in order to react; it’s always active and always on guard.

Proactive Problem Management is extremely challenging in an environment where you have lots of services, different technologies, and many things going on at the same time. So, what makes efficient Proactive Problem Management?

With Proactive Problem Management, the focus is on continuous data analysis, and in order to do that, you need a large volume of quality data. There are several data analysis techniques that Proactive Problem Management uses in daily operation:

• Pain Value Analysis – Instead of analyzing the number of incidents related to a specific CI or system over time, Pain Value Analysis is focused on the “level of pain” those incidents brought to the business. The formula for calculating “pain level” is: Pain value = (No. of incidents) x (duration) x (1/severity) x (weighting factor) . It’s very useful for detecting problems with equipment that is invisible to end-users (network routers, VOIP gateways, etc.).
• Pareto Analysis – This is another great method for finding root cause for most common trivial issues. Group the incident/problem data by common group type, and create a cumulative percentage table. Drawing a graph will reveal the common group type that generates 80% of all incidents/problems, and you can focus further investigation from there.
• Kepner-Tregoe® method – Kepner-Tregoe is a Registered Trademark of Kepner-Tregoe, Inc. in the United States and other countries, and is mentioned within ITIL materials related to Problem Management as one of the data analysis techniques. It revolves around: defining the problem, describing the problem in terms of identity, location, time (duration) and size (impact), establishing possible causes, testing the most probable cause, and verifying the true cause.

So, what’s so confusing about ITIL Problem Management?

You may be aware that ITIL Service Management practice components deeply rely on and interact with each other. Some may be observed in more “independent” fashion, but some can’t exist even on the drawing board without other components being implemented first.

One of the greatest examples of heavily dependent component is ITIL Problem Management . It’s closely related to Incident Management, and Incident Management is one of the first ITIL components that IT organizations implement. With basic Incident Management in place, organizations believe that Problem Management is simply an add-on, which can be used to “upgrade” Incident Management with Problem Management.

But, Problem Management can hardly be of any use if there is no Change Management, Asset Management, Configuration Management, Event Management, Availability Management, Capacity Management, Knowledge Management and many more components in place. Problem Management heavily relies on data stored throughout the Service Lifecycle in order to be effective.

I can give you a good example of Problem Management reliance on other Service Management components: A customer had repeatedly reported issues with his laptop performance, and the Incident Management team repeatedly resolved it by simply reinstalling the computer, over and over again. The customer was obviously not thrilled with the solution, but each incident was resolved within the SLA, and on the surface, everything looked peachy. However, repeated occurrence of the incident on the same asset triggered the Problem Management process, and after brief analysis, the results were very surprising. The customer initially had a SSD drive installed, but a year ago ordered a new one with larger capacity. At roughly the same time, the first incident reports about slow performance started. After deeper analysis, Problem Management discovered that the new hard drive installed was, in fact, not a SSD, and moreover, it was the large capacity variant of the slowest model possible. Even deeper analysis revealed that the customer, when ordering the new drive, never stated that it should be SSD, and the vendor delivered a regular, slow, high-capacity type.

Without quality data from the Incident, Asset, Change, and Configuration Management – Problem Management would be useless in this situation.

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More From Forbes

15 tips to become a proactive business problem-solver.

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While every business has problems, the solutions that you use to solve these issues is what really matters. As a working professional, it can be easy to react rather than prepare for the challenges your business faces. Having a plan in place that proactively takes the reins and addresses these problems can make a difference in how well equipped you are to handle and manage these situations without any interruption in your business’ operations.

Below, 15 members of Forbes Coaches Council share the best tips for planning ahead so that you are proactive rather than reactive when it comes to solving problems at work. Here’s what they recommend:

Members discuss ways to stay ahead of problems.

1. Reflect With Others

I engage others on a regular basis to discuss what we should continue doing, stop doing or start doing. Being proactive requires an understanding of reality by managing perceptions. This means getting feedback and actively seeking solutions, no matter how small. Set up regular reflections with individuals or teams. - Alan Trivedi, MBA PCC , Trivedi Coaching & Consulting Group

2. Research And Anticipate

Take time to research projects fully -- not only in regards to execution but also how they fit into broader strategic goals. This will give you additional insight that will allow you to anticipate potential needs and opportunities that may not be immediately evident and get out in front of them. - Tonya Echols , Thrive Coaching Solutions

3. Use The Buffer Technique

An admin used to schedule back-to-back meetings, sometimes including those with a drive to get there. Even if I kept up, I was always rushed. Now all meetings and focused activities have "buffer time" --15 or 30 minutes, unscheduled, in between meetings. This allows for meetings that go over, time to get a snack or time to drive and arrive relaxed and early. Put the buffer between meetings. - John Hittler , Evoking Genius

4. Seek Feedback From Co-workers

Get to know the people on your team and ask them about what they find difficult or challenging in their jobs. See if you can get them to pinpoint an area where they need support on a particular project, with a client or on an assignment. Once you understand their pain points, offer your support and follow through with helping them tackle their challenges. - Beth Kuhel , Get Hired, LLC

5. Train Unemotionally

Most problems aren't new. You have seen this movie. When you are in the problem, the crisis, you react emotionally. You are living it. As an EMT, you know how to stem the flow of blood safely. You were trained by the book. Similarly, in your business and life, you can train and create ways you will handle a problem client on any issue or a product setback. Write that SOP now before the emergency. - John M. O'Connor , Career Pro Inc.

6. Have Systems And Processes

Staying organized with systems and processes allows you to anticipate issues or be ready to handle any issues that arise unexpectedly. With systems in place, you can notice when a project isn't aligning and more quickly catch a potential issue. With processes to follow, people can know when a modified step provides better results for a finished project. - Rosie Guagliardo , InnerBrilliance Coaching

7. Use The Decision Tree Method

A sure way to anticipate and be prepared for potential issues is to understand and utilize the"decision tree" methodology. Think of it as playing out different outcomes based on different decisions. If you apply it consistently to all major decisions, you will very likely be able to be proactive when potential issues arise. - Kamyar Shah , World Consulting Group

8. Separate People From The Problem

Focusing on the problem rather than the personalities behind it helps us find win-win solutions without devolving into personal attacks. Focusing on sharing information and building relationships prepares us for the inevitable challenges that come up at work. This will always be a more constructive approach to solving problems and leveraging power than wielding it like a club. - Tracey Grove , Pure Symmetry Coaching and Consulting

9. Ask The Right Questions

Great leaders ask great questions. Many leaders cannot plan ahead because they cannot think about the questions that they should be asking themselves or their teams. Every month, pause to ask yourself the right questions that will lead you to working on the right things for the future and not just for the urgent. - Ken Gosnell , CEO Experience

10. Develop A 100-Day Plan

Planning ahead requires you understand what types of challenges your team is facing. Spend time on the floor observing, listening and talking to your team. Then, use a 100-day calendar, with 10-day increments, and map out what is due and what might come up. Make this visible so everyone can be prepared. This helps your team learn to review, revise and think ahead. - Cynthia Howard RN, CNC, PhD. , EI Leadership

11. Stay Organized

Staying organized is key to being proactive and keeping things under control. Being organized is a delicate balance between managing your time and not being a slave to it. Develop an organizational system that works for you and fits your personal style. Then use it. Create "footballs" for projects and team engagements so you can dedicate time to proactive discussions and critical thinking. - Tony Mickle , Big Box Coaching

12. Remember 'Busy' Is A State Of Mind

I do everything in batches by task instead of being victim to my to-do list or inbox. If I am writing or focusing on a strategic piece, I log out of email and turn off Wi-Fi and put my phone on "do not disturb." I commit to spending 50% of my time on working in the business and 50% of my time working on the business (strategy). I remind myself that being overly "busy" isn't a sign of achievement or success. - Courtney Feider , Courtney Feider, LLC

13. Focus On The Important, Not Just The Urgent

It's easy to get caught up in the firefighting, but to be proactive, you need to find some time for fire prevention. Review everything that you work on; not everything that is urgent is important. Learn to delegate the non-important work or even eliminate it, if possible. This will help take you out of reactive mode and give you the opportunity to focus on just the important and be proactive. - Gordon Tredgold , Leadership Principles LLC

14. Use Walt Disney's Planning Strategy

I love to use Walt Disney's strategy to planning. He would have three chairs in his office that represented a different perspective to view any idea. In the Dreamer chair, he would allow himself to create visions without constraints. In the Realist chair, he would consider the resources available to him. And, finally, in the Spoiler chair, he would consider all the pitfalls and plan accordingly. - Carolina Caro , Carolina Caro

15. Be Deliberate

To be proactive, you must own the situation. Detail how time is utilized and include time for strategic thinking where you look to the future and learn from the past. Make notes about where you want the organization to go, how to lead, and learn from others by reading their journeys. This simple, deliberate act drives continuous improvement to prepare you for the situations to come. - Chris Stricklin , Afterburner

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Reactive problem management vs proactive problem management

Everything you need to know about effective problem management

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• Reactive vs Proactive

In this section, we will expand upon the two common approaches in practicing problem management and how your IT environment maturity decides which approach is more suitable for your organization.

Reactive problem management vs. proactive problem management

What is reactive problem management?

Reactive problem management reacts to the incidents that show up, then proceeds with the problem management process. Essentially, a reactive problem management approach aims to find and eliminate the root causes of known errors, and deals with a problem only when it shows up as major or recurring incidents.

What is proactive problem management?

Proactive problem management seeks out issues, faults, and known errors in IT systems by going through past incidents, network monitor data logs, and other sources of information, then proceeds to solve them permanently before they arise as incidents. This process is a part of continuous service improvement. Proactive problem management also aims to solve all known errors under the KEDB if it is feasible to do so.

Both types of problem management follow the same phases of problem-solving once presented with a problem: problem identification, problem control, and error control. The only difference is the approach towards identifying the problem. Nonetheless, both processes offer distinct advantages to service management, and require unique resources to function.

Choosing between reactive and proactive problem management approaches

Organizations that are new to problem management should focus their efforts on implementing a reactive problem management process. It's sensible to use the problem-solving talent of the existing service desk staff when they aren't occupied with daily incidents; in doing this, they gain valuable experience before implementing proactive problem management.

As an organization's service delivery matures, it should transition to a proactive problem management process. This transition should be carried out by a team with a good analytical skill set that's highly proficient in IT infrastructure and the tools and technology that support the organization.

However, many organizations don't undergo this transition since it's tricky to quantify the benefits of proactive problem management, which can be perceived as solving potential problems and not actual ones. Nevertheless, some of the world's most effective organizations practice proactive problem management and find tremendous benefit in it.

Despite reactive and proactive problem management following the same phases of problem-solving once presented with a problem, there are multiple techniques to get to the root cause of a problem. Let's move on to the various techniques used in problem management.

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What is the SCR Framework? Your In-depth Guide to Situation-Complication-Resolution

The SCR Framework helps businesses identify problems, understand their complexities, and devise solutions. It's a three-step approach: defining the current situation, pinpointing the challenge, and strategizing a resolution.

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Introduction

The Situation-Complication-Resolution (SCR) framework is a powerful tool for businesses to define challenges and develop solutions. It provides a concise structure that can help organizations identify a problem's root cause, analyze its impact, and develop strategies to overcome it. The framework is widely used in various industries, including marketing, sales, and project management.

The SCR framework comprises three elements: situation, Complication, and Resolution. The situation refers to the current state of affairs, the Complications to the problem or challenge that need to be addressed, and the resolution to the solution or strategy that will be implemented to overcome the challenge. Following this structured approach, businesses can better understand their challenges and develop practical solutions.

In this in-depth guide, we will explore the SCR framework in detail, including its benefits, how to use it, and examples of its application in real-world scenarios. Whether you're a business owner, manager, or employee, this guide will provide the knowledge and tools you need to effectively implement the SCR framework and drive success in your organization.

Related Posts:

• Situation-Complication-Resolution Framework: Free template

The Fundamental Aspects of the SCR Framework

The SCR framework is a problem-solving approach widely used in various industries, including business, healthcare, and education. It is based on the Situation-Complication-Resolution (SCR) model, which helps individuals and teams to identify, analyze, and resolve complex problems.

The SCR framework has three fundamental aspects: situation, Complications, and resolution. Situation refers to the problem or challenge that needs to be addressed. This could be anything from a business process that needs fixing to a patient experiencing health issues.

Complication is the factor that makes the situation more complex or difficult to solve. For example, in a business setting, the Complications could be needing more resources or a poorly designed process, or in a healthcare setting, a patient's medical history or a complex diagnosis.

The resolution is the solution or action taken to address the situation and the Complication. This could involve implementing a new process, providing additional resources, or developing a treatment plan for a patient.

The SCR framework is designed to help individuals and teams to approach complex problems systematically. By breaking down the problem into its fundamental aspects, the framework enables individuals to identify the root cause of the problem and develop an effective solution.

Overall, the SCR framework is a powerful problem-solving tool that can be applied in various settings. By following its fundamental aspects, individuals and teams can work together to identify and resolve complex problems, leading to improved outcomes and tremendous success.

Significance of SCR in Business Planning

The SCR framework is a powerful business tool for planning and executing effective strategies. By following the Situation-Complication-Resolution model, businesses can better understand their current situation, identify potential complications, and develop solutions to overcome them.

The significance of SCR in business planning lies in its ability to provide a structured approach to problem-solving. It helps businesses break down complex issues into smaller, more manageable parts, making it easier to identify the root cause of a problem and develop practical solutions.

Moreover, the SCR framework encourages businesses to consider multiple perspectives when analyzing a situation. By considering the views of different stakeholders, companies can gain a more comprehensive understanding of the problem and develop more effective solutions.

In addition, the SCR framework can help businesses to communicate their strategies more effectively. By breaking down a complex issue into a simple and structured format, companies can ensure all stakeholders easily understand their plans.

Overall, the SCR framework is valuable for businesses looking to develop effective strategies and solve complex problems. By following this structured approach, companies can improve their decision-making processes and increase their chances of success.

Unpacking the 'Situation' in the SCR Framework

The 'Situation' in the SCR Framework refers to the context or background of the problem that needs to be resolved. It is the starting point of the framework and provides a clear understanding of the problem at hand.

To unpack the 'Situation' in the SCR Framework, one needs to identify the following:

• The problem statement: Clearly define the problem to be solved. This will help identify the root cause and develop an effective solution.
• The stakeholders: Identify all the stakeholders involved in the problem. This includes the individuals or groups affected by it and those who can influence the solution.
• The scope: Define the size of the problem. This will help identify the problem's boundaries and ensure the solution is focused and effective.
• The impact: Identify the problem's impact on the stakeholders. This will help prioritize the solution and ensure it addresses the most critical issues.

Unpacking the' Situation' in the SCR Framework can help one understand the problem and its context. This will help build a practical solution that addresses the root cause of the problem and meets the needs of all stakeholders involved.

Understanding the 'Complication' in the SCR Framework

The 'Complication' stage is the most crucial part of the SCR framework. It is where the problem or issue is identified and analyzed. This stage requires a thorough examination of the situation to determine the root cause of the problem.

To identify the Complication, one must ask questions such as 'What went wrong?' and 'Why did it go wrong?'This stage requires a deep understanding of the situation and the factors that contributed to it.

Once the Complication is identified, it is essential to analyze it thoroughly. This analysis should include a detailed examination of the problem and its impact on the situation. It identifies any potential risks or consequences arising from the Complication.

One can use tools such as SWOT analysis, Fishbone diagram, and Pareto chart to analyze the complication effectively. These tools can help identify the root cause of the problem and provide a clear understanding of the situation.

In conclusion, the 'Complication' stage is critical in the SCR framework as it helps to identify and analyze the problem. I need to review the situation to find the root cause of the problem. Using various tools and techniques, one can effectively analyze the Complication and develop a resolution.

A Deep Dive into the 'Resolution' of the SCR Framework

The third and final part of the SCR framework is the 'Resolution' stage. This is where the protagonist resolves the Complications they face in the 'Complications' stage. The resolution can take many forms, depending on the situation and the protagonist's abilities.

Sometimes, the resolution might involve the protagonist overcoming a personal flaw or learning a valuable lesson. In other cases, the protagonist might use their skills or resources to outsmart their opponent. Regardless of the specific approach, the resolution should feel satisfying and believable to the reader.

One effective way to create a satisfying resolution is to use foreshadowing. By hinting at the solution earlier in the story, the author can make the answer feel more earned and less like a deus ex machina. For example, if the protagonist defeats their opponent using a specific skill, the author could show the protagonist practicing that skill earlier in the story.

Another critical aspect of the resolution is the aftermath. The reader should understand how the solution affects the protagonist and the world around them. This could involve showing the protagonist's emotional reaction to the resolution or demonstrating how the fundamental changes the status quo.

Ultimately, the resolution is the payoff for the tension and conflict established in the 'Situation' and 'Complication' stages. By creating a satisfying and believable solution, the author can leave the reader feeling fulfilled and satisfied with the story.

Comparison: SCR and other Planning Frameworks

When it comes to planning frameworks, there are many options available. The SCR framework is just one of them. Here is a brief comparison of the SCR framework with some other popular planning frameworks:

SWOT Analysis

SWOT analysis is a popular planning framework for Strengths, Weaknesses, Opportunities, and Threats. It is often used to evaluate a business or project and identify areas of improvement. While the SCR framework focuses more on problem-solving, SWOT analysis focuses more on evaluation. ( free StrategyPunk SWOT Template )

The PDCA (Plan, Do, Check, Act) cycle is a problem-solving framework often used in quality management. Similar to the SCR framework, it involves identifying a problem, developing a plan, and implementing a solution. However, the PDCA cycle is more iterative and involves ongoing monitoring and improvement.

DMAIC Process

The DMAIC (Define, Measure, Analyze, Improve, Control) process is a problem-solving framework often used in Six Sigma. It is similar to the SCR framework, which involves identifying a problem, analyzing data, and implementing a solution. However, the DMAIC process is more data-driven and involves statistical analysis.

Overall, the SCR framework is a simple and effective problem-solving tool that can be applied to various situations. While other frameworks are available, the SCR framework is an excellent option for those looking for a straightforward approach.

Implementing the SCR Framework in Business Strategy

Businesses can use the SCR Framework to develop an effective strategy to help them achieve their goals. The implementation process involves the following steps:

• Situation Analysis:  The first step is to conduct a thorough analysis of the current situation, including the market, customers, competitors, and internal resources. This analysis will help businesses identify their strengths and weaknesses and determine the market's opportunities and threats.
• Complication Identification:  The next step is identifying the complications preventing the business from achieving its goals. These complications could be internal or external, such as a lack of resources, poor customer service, or increased competition.
• Resolution Development:  Once the complications have been identified, businesses can develop a resolution plan to address them. This plan should be based on the strengths identified in the situation analysis. It should be designed to overcome the identified complications.
• Implementation and Monitoring:  The final step is implementing the resolution plan and monitoring its effectiveness. This will involve assigning responsibilities, setting timelines, and tracking progress.

By implementing the SCR Framework, businesses can develop a comprehensive strategy that addresses their challenges and leverages their strengths to achieve their goals.

SCR and Business Storytelling

The Situation-Complication-Resolution (SCR) framework is a powerful tool for business storytelling. It allows companies to present their successes and challenges clearly and concisely, making it easier for stakeholders to understand the context and outcomes of a particular situation.

When using the SCR framework for business storytelling, it's essential to keep the following tips in mind:

• Start by clearly and concisely describing the situation. This should include relevant background information, such as the time, place, and people involved.
• Use specific and concrete examples to illustrate the Complication. This will help stakeholders understand the challenges faced by the company and how they were overcome.
• Finally, describe the resolution, including the actions and results. This will help stakeholders understand the impact of the company's efforts and the lessons learned.

By following these guidelines, companies can use the SCR framework to effectively communicate their successes and challenges to stakeholders, building trust and credibility.

SCR Framework - Case Study Analysis

Let's look at a case study analysis to understand better how the SCR framework works.

A small business owner is experiencing a decline in sales. They have tried various marketing strategies but need help getting everything working. They are feeling frustrated and unsure of what to do next.

Complication

The business owner realizes they must more effectively target their ideal customers. They must cast a wider net and tailor their marketing efforts to their specific audience. Additionally, they need to track their marketing data to determine which strategies are working and which are not.

The business owner implements the SCR framework to address their marketing issues. They begin by analyzing their current situation and identifying their ideal customer. They then create a targeted marketing plan that speaks directly to that customer. They also start tracking their marketing data to measure the success of their strategies.

Implementing the SCR framework increases sales and a more engaged customer base. The business owner can make data-driven decisions and adjust their marketing efforts accordingly.

In conclusion, the SCR framework can be a powerful tool for businesses looking to address complex issues. Companies can achieve a more effective and sustainable solution by breaking down the problem into its components and addressing each one systematically.

The Role of SCR in Problem-solving and Decision-making

The SCR framework plays a crucial role in problem-solving and decision-making. It provides a structured approach to analyzing and resolving complex problems by breaking them into three main components: Situation, Complication, and Resolution.

Using the SCR framework, individuals can identify the root cause of the problem and develop an effective solution. The framework helps ensure that all relevant information is considered and all potential solutions are evaluated before deciding.

The following are some ways in which the SCR framework can be used in problem-solving and decision-making:

• Identifying the Situation:  The first step in using the SCR framework is to identify the situation or problem that needs to be addressed. This involves gathering all relevant information and understanding the context in which the issue has arisen.
• Analyzing the Complication:  Once the Situation has been identified, the next step is to analyze the Complication. This involves identifying the factors contributing to the problem and understanding how they are interconnected.
• Developing a resolution:  The final step in using the SCR framework is developing an answer. This involves evaluating all potential solutions and selecting the most likely effective ones. It is crucial to consider each solution's possible consequences and choose the one with the most positive impact.

Overall, the SCR framework provides a structured approach to problem-solving and decision-making to help individuals make more informed and effective decisions. By breaking down complex problems into manageable components, the framework can ensure that all relevant information is considered and all potential solutions are evaluated before making a decision.

Tips to Effectively Use the SCR Framework for Business Planning

When using the SCR framework for business planning, please remember a few tips to make the most of this approach. Here are some suggestions to help you use the SCR framework effectively:

• Define the situation clearly:  Understanding it before analyzing it is crucial. Define the problem or opportunity you are trying to address and ensure everyone involved in the planning process is on the same page.
• Identify the key complications:  Once you have defined the situation, identify the key complications preventing you from achieving your goals. These complications could be internal or external factors that are impacting your business.
• Prioritize the complications:  Not all difficulties are created equal. Prioritize the complications based on their impact on your business and the resources required to address them. This will help you focus your efforts on the most critical issues.
• Brainstorm potential resolutions:  Once you have identified the key complications, brainstorm possible resolutions to address them. Encourage creativity and innovation during this process to generate various solutions.
• Evaluate the potential resolutions:  After generating possible answers, evaluate them based on their feasibility, impact on your business, and the resources required to implement them. Select the solution that best addresses the complications and aligns with your business goals.

By following these tips, you can effectively use the SCR framework for business planning and make informed decisions that drive your business forward.

Benefits of SCR Framework to Organizations

The SCR framework offers several benefits to organizations that adopt it. Here are some of the key advantages:

Improved Clarity and Focus

The SCR framework provides a clear and structured approach to problem-solving. By breaking down complex situations into their parts, organizations can better understand their issues and develop targeted solutions. This can help teams focus their efforts and resources more effectively, leading to better outcomes.

Enhanced Collaboration

The SCR framework encourages collaboration and communication among team members. Organizations can leverage their team members' diverse perspectives and expertise by involving all stakeholders in problem-solving. This can lead to more innovative solutions and improved buy-in from all stakeholders.

Increased Efficiency and Effectiveness

Organizations can streamline their processes and reduce waste using a structured approach to problem-solving. The SCR framework helps teams identify the root causes of problems and develop targeted solutions that address those causes. This can lead to more efficient and effective operations and improved customer satisfaction.

Better Risk Management

The SCR framework helps organizations identify and manage risks more effectively. By analyzing the potential complications of a situation, teams can develop strategies to mitigate those risks and avoid adverse outcomes. This can help organizations to avoid costly mistakes and improve their overall risk management practices.

Overall, the SCR framework provides a powerful tool for organizations looking to improve their problem-solving capabilities. By adopting this structured approach, teams can enhance their collaboration, focus, efficiency, and risk management practices, leading to better outcomes for the organization and its stakeholders.

Challenges and Solutions in Implementing the SCR Framework

Implementing the SCR framework can be challenging, especially for those new to it. Here are some common challenges and solutions.

Challenge: Identifying the Situation

Identifying the situation is one of the biggest challenges in implementing the SCR framework. It can be difficult to locate the case accurately, especially if there are multiple situations or the problem is complex.

Solution: Ask Questions

It is vital to ask questions. Asking questions helps clarify the situation and ensure all relevant information is considered. This helps ensure that the Complications and resolutions are appropriate for the problem.

Challenge: Identifying the Complication

Another challenge in implementing the SCR framework is identifying the complications. It can be difficult to locate the Complication, especially if there are multiple complications or the Complication is not immediately apparent.

Solution: Brainstorm Possible Complications

It can be helpful to brainstorm possible complications. This can ensure that all potential complications are considered and that the most appropriate Complication is identified.

Challenge: Identifying the Resolution

Identifying the resolution can also be a challenge in implementing the SCR framework. Determining the most appropriate resolution can take time, primarily if multiple solutions exist or the answer is complex.

Solution: Evaluate Possible Resolutions

Evaluating possible resolutions is essential. This helps ensure that the solution is appropriate for the situation and effectively addresses the Complexity.

Implementing the SCR framework can be challenging, but asking questions, brainstorming possible complications, and evaluating possible resolutions can help implement the framework effectively.

SCR Framework in Action: Real-Life Examples

The SCR framework can be applied to various real-life situations, from personal to professional scenarios. Here are a few examples of how the SCR framework has been used in practice:

Example 1: Resolving a Customer Complaint

Situation:  A customer has complained about a product not meeting their expectations.

Complication:  The customer has already tried to resolve the issue through customer service but has not received a satisfactory response.

Resolution:  The company's customer service representative follows up with the customer and offers a refund or replacement for the product and a sincere apology.

Example 2: Improving Team Communication

Situation:  A team needs help communicating effectively, leading to misunderstandings and delays in completing tasks.

Complication:  The team members come from different departments and have different communication styles and preferences.

Resolution:  The team leader implements a communication plan that includes regular team meetings, clear expectations for communication, and training on effective communication techniques.

Example 3: Managing a Project Delay

Situation:  A project is behind schedule and at risk of missing the deadline.

Complication:  The delay is caused by unforeseen circumstances, such as a supplier issue or a team member's absence.

Resolution:  The project manager assesses the situation and develops a plan to mitigate the delay, such as finding alternative suppliers or redistributing tasks among team members.

By using the SCR framework, individuals and teams can approach problems in a structured and efficient way, leading to better outcomes and more effective decision-making.

SCR Framework Relevance in the Digital Age

The SCR framework is relevant in the digital age because it provides a structured approach to problem-solving that can be applied to a wide range of digital situations. The digital age is characterized by rapid technological change, and organizations must be able to adapt quickly to stay competitive. The SCR framework helps organizations to identify problems, analyze them, and develop practical solutions.

One key benefit of the SCR framework in the digital age is its flexibility. The framework can be applied to various digital situations, from software development to social media marketing, making it a valuable tool for digital space organizations.

Another benefit of the SCR framework is that it encourages collaboration and communication. In the digital age, teams are often distributed across different locations and time zones. The SCR framework provides a common language and structure to help teams work together more effectively.

Finally, the SCR framework is relevant in the digital age because it emphasizes the importance of continuous improvement. In the digital space, organizations must adapt quickly to changing circumstances. The SCR framework helps organizations identify areas for improvement and develop strategies for constant improvement.

Overall, the SCR framework is valuable for organizations in the digital age. It provides a structured approach to problem-solving that can be applied to a wide range of digital situations, encourages collaboration and communication, and emphasizes the importance of continuous improvement.

The Future of the SCR Framework: Beyond 2023

The SCR framework has been valuable for many businesses, helping them navigate various situations and find effective resolutions. As we move beyond 2023, the future of the SCR framework looks bright, with many exciting developments on the horizon.

One trend likely to continue is the increasing use of technology in the SCR framework. With the rise of artificial intelligence and machine learning, businesses can analyze data more quickly and accurately, making it easier to identify potential complications and find solutions. Additionally, technology will enable enterprises to automate many aspects of the SCR framework, freeing up time and resources for other essential tasks.

Another trend is the growing emphasis on collaboration and teamwork in the SCR framework. As businesses become more complex and global, it is increasingly important to have a diverse team of experts working together to address complicated situations. Companies must invest in training and development programs that help employees build the skills they need to work effectively in teams.

Finally, the future of the SCR framework will depend on businesses' ability to adapt to changing circumstances. As the world becomes more unpredictable and volatile, companies must be agile and responsive, able to pivot quickly when unexpected complications arise. This means proactively identifying potential risks and developing contingency plans to address them.

Overall, the future of the SCR framework looks bright, with many exciting developments on the horizon. By embracing new technologies, fostering collaboration and teamwork, and staying agile and responsive in the face of change, businesses can continue leveraging the SCR framework's power to achieve their goals and overcome challenges.

Conclusion: SCR Framework as a Game-changer in Planning

The SCR framework is a powerful planning tool that can help individuals and organizations navigate complex situations. By breaking down a problem into its parts—Situation, Complications, and Resolution—individuals can identify critical issues and develop effective strategies to address them.

Using the SCR framework can help individuals and organizations make more informed decisions. They can identify potential complications and develop practical solutions to address them. Additionally, the framework can help individuals and organizations communicate more effectively, providing a common language and structure for discussing complex issues.

Overall, the SCR framework is valuable for anyone looking to improve their planning and decision-making skills. Whether you want to make better decisions in your personal life or an organization looking to develop more effective strategies, the SCR framework can help you achieve your goals.

Frequently Asked Questions

How does the scr framework work.

The SCR framework is a problem-solving approach that involves breaking down a situation into three parts: Situation, Complication, and Resolution. The case is the current state of things, the Complication is the problem or issue that needs to be addressed, and the resolution is the solution or action plan to solve the problem.

What are the benefits of using the SCR framework?

The SCR (Situation-Complication-Resolution) framework stands as a pinnacle in business communication, offering a structured methodology that brings clarity, focus, and impact to narratives. Its benefits, while numerous, can be distilled into a few key points that underscore its value in the corporate landscape.

Firstly, the SCR framework ensures clarity and alignment . Starting with the Situation sets a clear context, ensuring that all stakeholders have a shared understanding of the current state. This foundational clarity is pivotal in avoiding misinterpretations and ensuring that subsequent discussions are rooted in a common ground. The Complication phase then introduces challenges, emphasizing the urgency or significance of the matter. This captures attention and highlights the stakes, making the narrative more compelling. Finally, the Resolution offers a clear path forward, showcasing proactive problem-solving and strategic thinking. This structured approach ensures the message is effectively conveyed and positions the communicator as a thought leader. The SCR framework is more than just a communication tool; it's a strategic instrument that fosters understanding, drives engagement, and catalyzes action.

What are some everyday situations where the SCR framework can be applied?

The SCR (Situation-Complication-Resolution) framework seamlessly translates into many everyday business scenarios. Its structured approach offers a blueprint for addressing challenges and crafting solutions, making it an indispensable tool for various business situations.

Imagine a retail business noticing a sudden drop in sales ( Situation ). They found that a new competitor had entered the market with aggressive pricing ( Complication ). The Resolution might involve introducing a loyalty program, enhancing customer service, or diversifying product offerings. In another instance, after launching its app (Situation), a tech startup receives user feedback about specific performance issues ( Complication ). The Resolution then focuses on software updates and enhanced user engagement strategies. Similarly, a manufacturing company experiencing consistent product quality ( Situation ) encounters supply chain disruptions due to unforeseen global events ( Complication ). The Resolution might involve seeking alternative suppliers or optimizing inventory management. Through the SCR lens, businesses can navigate the ever-evolving challenges of the corporate world, ensuring they remain proactive, adaptive, and solution-driven.

How can the SCR framework be used in problem-solving?

The SCR framework can be used in problem-solving by first identifying the situation, then identifying the Complication, and finally developing a Resolution to address the Complication. This approach ensures that all aspects of the problem are considered and that the solution is comprehensive.

How does the SCR framework promote sustainability?

In the evolving sustainability narrative, the SCR (Situation-Complication-Resolution) framework offers a unique lens through which businesses and organizations can align their sustainability goals with clear communication strategies. At its core, the SCR method identifies challenges and proposes solutions, a process intrinsically linked to sustainable development ethos.

Organizations can lay out the current environmental, social, or economic landscapes starting with the Situation, setting a clear sustainability benchmark. The Complication phase then delves into the pressing challenges—climate change, resource depletion, or social inequalities—that disrupt this status quo. By highlighting these complications, organizations can underscore the urgency of sustainable interventions. The narrative culminates in the Resolution , where actionable, sustainable solutions are proposed, showcasing an organization's commitment to a greener, more equitable future.

Through the SCR lens, sustainability is not just a buzzword; it's a structured journey from recognizing challenges to implementing solutions, ensuring that the message of sustainability is clear and actionable.

What is the SCR framework for communication?

The SCR framework emerges as a beacon for structured, impactful messaging in the vast landscape of communication strategies. An acronym for Situation-Complication-Resolution, this methodology, pioneered by McKinsey & Company, is more than just a storytelling tool—it's a communication powerhouse.

The framework commences with the Situation , clearly depicting the existing scenario. This clarity ensures the audience is aligned, setting the stage for what's coming. The narrative then delves into the Complication , highlighting the challenges or disruptions that necessitate a shift in perspective or action. Here, the communicator underscores the urgency or gravity of the situation. The crescendo is the Resolution , where solutions are articulated, showcasing foresight and proactive problem-solving. By leveraging the SCR framework, communicators convey information and master the art of persuasion, making their message resonate profoundly with their audience.

How do you tell a business story using the McKinsey situation-complication-resolution?

In business communication, the McKinsey situation-complication-resolution (SCR) framework stands out as a powerful tool for crafting compelling narratives. The process begins with the Situation , where one sets the stage by describing the existing environment or context. This gives the audience a clear understanding of the baseline or the 'status quo.'

However, no story is complete without a twist. Enter the Complication . This is where you introduce a challenge, problem, or unexpected turn of events that disrupt the initial situation. By highlighting this complication, you're capturing the audience's attention and emphasizing the urgency or significance of the matter.

Concluding with the Resolution is crucial for me. You can present a solution, strategy, or action to tackle the complication. This wraps up your narrative neatly and positions you or your organization as proactive problem solvers. By employing the SCR framework, professionals can ensure their business stories are engaging and compelling, conveying their message with clarity and impact.

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Problem-Solving Frameworks: Go Down to the Root

Problems of all shapes and sizes pop up on a daily basis. So the big question is: How to solve them? We bring you several frameworks that could help.

Do you consider yourself a problem-solver? Well, you certainly should. Because that's what you and your team do every day. 

First and foremost, you solve the problems that your prospective customers have, for which they want to find a solution (i.e. your product).

Then, there are unexpected errors and usability issues that your existing users face while using your product, or the bugs that your engineers encounter.

On a higher level, you need to find the right solution for the new features you want to develop, discover new opportunities for growth, and so much more. 

Now, the big question is: How to solve all those problems?  

We bring you several problem-solving frameworks that could help.

In this chapter

• Icons 300 The Phoenix Checklist
• Icons 300 Root Cause Analysis
• Icons 300 CIRCLES Method
• Icons 300 The mathematician’s “universal” way

The Phoenix Checklist #

Have you ever wondered how the CIA goes about solving problems ? Well, they’ve developed The Phoenix Checklist to “encourage agents to look at a challenge from many different angles”.

The Phoenix Checklist was popularized by Michael Michalko, a former CIA creative consultant, in his book Thinkertoys , as a blueprint for dissecting the problem into knowns and unknowns to find the best possible solution.     

Some of the questions of The Phoenix Checklist are:

Why is it necessary to solve this particular problem?

What benefits will you receive by solving it?

What is the information you have?

Is the information sufficient? 

What is the unknown?

What isn't a problem?

Should you draw a diagram of the problem? A figure?

Where are the boundaries of the problem?

What are the constants of the problem?

Have you seen this problem before?

If you find a similar problem that has already been solved, can you use its method?

Can you restate the problem? How many different ways can you restate it?

What are the best, worst, and most probable solutions you can imagine?

There’s no doubt that The Phoenix Checklist can be a complementary problem-solving technique for your product team, even though it wasn’t developed with product managers in mind. Use it to frame, deconstruct, and reframe the problems you encounter.

Root Cause Analysis #

Root Cause Analysis (RCA) is a problem-solving method that aims at identifying the root cause of a problem by moving back to its origin, as opposed to techniques that only address and treat the symptoms.

The RCA is corrective in its nature with a final goal to prevent the same problem from happening again in the future. But that doesn’t mean that root cause investigation is simple or that it only needs to be done once. 

The starting questions are: 

What is currently the problem?

Why does this problem occur?

But don’t stop at the first why. Keep asking why that happened , until you get to the bottom and the real cause.

When you first start using the RCA method, it will be a reactive approach to solving problems. It is typically in use when something goes wrong. But once you perfect this technique, you can use it as a proactive action towards identifying problems before they happen and preventing them from happening. The end goal of the Root Cause Analysis is continuous improvement.

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CIRCLES Method #

The CIRCLES method is a problem-solving framework that helps product managers provide a meaningful response to any questions coming from design, marketing, customer success, or other teams. 

The creator of the CIRCLES method is Lewis C. Lin, author of the book Decode and Conquer . The way he explains it , you should always start by clarifying the goal, identifying the constraints up front, and understanding the context of the situation.

The seven steps of the CIRCLES method are:

Comprehend the situation: Understand the context of the problem you’re solving

Identify the customer: Know who you’re building the product for

Report customer’s needs: Rely on the customer research to uncover pain points 

Cut, through prioritization: Omit unnecessary ideas, tasks, and solutions

List solutions: Keep the focus on the most feasible solutions

Evaluate tradeoffs: Consider the impact, cost of delay, and other factors

Summarize your recommendation: Make a decision and explain your reasoning

The main goal of the CIRCLES method is to help you keep an open mind as you move through the steps, as well as to avoid jumping straight into the conclusions.

The mathematician’s “universal” way #

Although there isn’t exactly a universal way to solve problems that would perfectly fit every situation and scenario, mathematician Claude Shannon developed a strong problem-solving system that has given results across disciplines.

The essential part of his framework involves creative thinking to get out of standard mental loops, critical thinking to question every answer and every possible solution, and the process of restructuring a problem , whether it’s by maximizing it, minimizing it, contrasting it, inverting it, or anything else. 

As explained in the article from Quartz :

"Claude Shannon didn’t just formulate a question and then look for answers. He was methodological in developing a process to help him see beyond what was in sight."

Shannon’s problem-solving process includes:

Finding a problem

Understanding a problem

Going beyond obvious questions

Defining a shape and a form of a problem

Focusing on essential details, but always keeping a bigger picture in mind

Changing a reference point and reframing a point of view

Uncovering insights from the sea of information

That said, Claude Shannon certainly developed a methodology that is relevant for every problem-solving situation, not only math problems. 

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Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals

Gabriel souto fischer.

1 Programa de Pós-Graduação em Computação Aplicada—PPGCA, Universidade do Vale do Rio dos Sinos—Unisinos, Av. Unisinos 950, Bairro Cristo Rei, São Leopoldo CEP 93022-750, Rio Grande do Sul, Brazil

Rodrigo da Rosa Righi

Cristiano andré da costa, guilherme galante.

2 Programa de Pós-Graduação em Ciência da Computação—PPGComp, Universidade Estadual do Oeste do Paraná–Unioeste, Rua Universitária 2069, Bairro Jardim Universitário, Cascavel CEP 85819-110, Paraná, Brazil

Dalvan Griebler

3 Parallel Applications Modeling Group—GMAP, Pontifical Catholic University of Rio Grande do Sul—PUCRS, Av. Ipiranga 6681, Bairro Partenon, Porto Alegre CEP 90619-900, Rio Grande do Sul, Brazil

4 Laboratory of Advanced Research on Cloud Computing–LARCC, Três de Maio Educational Society—SETREM, Av. Santa Rosa 2405, Três de Maio CEP 98910-000, Rio Grande do Sul, Brazil

Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.

1. Introduction

The Internet-of-Things (IoT) is a concept where physical objects (i.e., things) are connected through a network structure and are part of the internet activities in order to exchange information about themselves and about objects and things around themselves [ 1 , 2 ]. A particularly relevant scenario for IoT is healthcare [ 3 , 4 , 5 ]. IoT-assisted patients can be supervised uninterruptedly, thus allowing risky situations to be detected and appropriately treated right away [ 6 ]. According to Butean et al. [ 7 ], no matter how easy or complicated a situation is, if the medical staff do not react in an appropriate time, everything regarding patients’ health might become doubtful and unsafe. Hence, health professionals play a major role towards patients’ well-being [ 8 ]. In this kind of scenario, a static allocation of health professionals to health sectors may be inefficient, since some professionals may be misallocated to low demanding sectors, while leading to a lack of professionals in highly demanding sectors. Such a problem is illustrated in Figure 1 , where the set of available attendants are statically assigned to two service sectors, one for exams and another for medication. In the example, more attendants are examining than medicating patients, even though the number of patients waiting for exams is considerably smaller than those waiting to receive some medication. In this context, if each room has a required specialty, and if each health professional has a list with all its specialties, the idle attendants who have the required destination room specialty could be moved from the low demanding room to the high demanding one. In fact, the allocation of attendants should always adapt to the current conditions of the health sectors.

Example scenario where there are more attendants examining than medicating patients, even though the number of patients waiting for exams is considerably smaller than those waiting to receive some medication, generating dissatisfaction for patients awaiting medication.

Therefore, it is necessary to find effective strategies to adapt human resources in real-time. Elasticity in cloud computing is one of the key strategies for adapting on-demand computational resources [ 9 , 10 , 11 ]. According to Rostirolla et al. [ 12 ], the elasticity concept can be extended to other areas besides computing. Today, most resources control approaches can be classified as reactive or proactive (also named by some authors as predictive) [ 9 , 10 , 13 , 14 ]. Reactive approaches are based on both static bounds and if-condition-then rules to manage elasticity [ 9 ]. Typically, users define an upper and a lower threshold on a target performance metric (e.g., CPU utilization, memory, response time) to trigger activation and deactivation, respectively, of a certain resources number [ 15 ]. A problem of using fixed thresholds is related to application overloading, illustrated in Figure 2 . After the system reaches an upper bound, there is a time interval for the delivery of the resource. During that period, we have an application overload [ 9 ]. Also, another problem is the lack of reactivity when using these parameters. There are situations in which is possible to anticipate the (de)allocation of resources, however, the resource configuration remains the same due to bad choices on setting the lower and upper thresholds [ 9 , 15 ].

Elasticity approaches: ( a ) reactive; ( b ) proactive.

A proactive approach employs prediction techniques to anticipate system behavior (its load) and thereby decide the adapting actions [ 9 ]. This capability enables the application to be ready to handle the increase when it actually occurs [ 15 ]. To accomplish this approach, it is common to use time-series-based prediction techniques (such as Exponential Smoothing, Moving Averages and Autoregressive models) and machine learning algorithms (including Neural Network, Linear Regression, Support Vector Machine, Reinforcement Learning and Pattern Matching techniques) [ 9 , 15 ]. This approach is typically classified adversely as time-consuming for sensitive performance applications [ 9 , 16 ]. Also, Netto et al. [ 17 ] affirm that proactive elasticity strategies focus on method accuracy and ignore limitations such as the scaling up operation time, although it dependents on the workload characteristics. Hence, the reactive approach performs faster because there is no concurrent processing concerning the application. In the proactive approach, for each monitoring step, it runs a given prediction algorithm that can impact the normal execution of the application, since the background task can be costly.

Considering this background, we present a model of Elastic allocation of human resources in Healthcare environments (ElHealth, for short) as an alternative to the traditional static allocation of medical staff. ElHealth works by adjusting the medical staff allocation of smart hospitals (equipped with IoT sensors) based on reactive and proactive elasticity approaches. In particular, ElHealth uses IoT sensors to keep track of patients demand, which is modeled as a time series and is used to estimate demands. Such estimations allow to identify situations where the staff availability is unlikely to meet the demand. Building upon such estimations, ElHealth proposes an efficient allocation of the medical staff by moving such professionals and also allocating new human resources to the most demanding areas while taking into account their time constraints. The idea is to always offer a reasonable waiting time for patients regardless of the workload (number of them in the hospital room). In resources elasticity, there are advantages and disadvantages in reactive and predictive methods. Using ElHealth, we propose an evaluation of proactive and reactive approaches for reorganizing human resources in smart hospitals to identify which significantly decreases the waiting time regarding healthcare. ElHealth supports both elasticity approaches at run-time. The main scientific contributions of this article are threefold:

• (i) We devise Human Resources IoT-based Elasticity, for automatic management of human resources in healthcare environments, making use of elasticity for smart, IoT-enabled hospitals;
• (ii) A cost-benefit analysis of the use of reactive and predictive strategies (of elasticity in cloud computing) for human resources reorganization. The cost refers to the health staff allocation costs in each approach, and the benefit is the anticipation of problems, based on the reduction of waiting time for care.
• (iii) We introduce Human resources cost and Elastic number of human resources used metrics for evaluating human resources elasticity.

This article is organized as follows. Section 2 presents the work related to our study. Section 3 presents ElHealth as well as the concepts of Multi-level Reactive and Proactive Elasticity of Human Resources. Section 4 expresses the methodology of evaluation of the model. Section 5 presents an evaluation performed with the developed implementation, as well as the results found. Finally, Section 6  presents the conclusions and future work directions.

2. Related Work

This section describes some approaches to manage elasticity in cloud and overviews approaches to managing the deficiency of resources to attend patients’ demand in healthcare environments. They were divided into two groups: reactive and proactive systems in Section 2.1 (where we discuss two papers of elasticity in cloud computing, one for each approach, and all articles found that extend the concept of elasticity to other areas) and human resources in Section 2.2 (where we discuss some works that focus on human resources lack in healthcare environments). Lastly, the initiatives were compared and analyzed in order to detach the current gaps in the research area.

2.1. Reactive and Proactive Systems

Reactive managers are those based only on thresholds to take elasticity decisions; more precisely, resource reconfiguration takes place when the lower or the upper threshold is violated. In the reactive scope, we highlight three initiatives: Al-Dhuraibi et al. [ 18 ], Elastic-RAN [ 19 ] and ElCity  [ 12 ]. Al-Dhuraibi et al. [ 18 ] presents a new elasticity management system powering both vertical and horizontal elasticities, both VM and Container virtualization technologies, multiple cloud providers simultaneously, and various elasticity policies based on a dynamic configuration during the execution of the application. The experiments demonstrated that their model covers the elasticity policies provided by the well-known cloud public providers with negligible overhead. Elastic-RAN [ 19 ] proposes a multi-level and adaptable elasticity for Cloud Radio Access Networks (C-RANs). The adaptive algorithm feature refers to the moldable elasticity grain where resources in BBU pools level and BBU level are provisioned as close as possible to the current processing needs. Elastic-RAN might achieve gains up to 64% in the execution time when compared to a traditional C-RAN. ElCity [ 12 ] is a model that combines citizens and city devices data to enable an automatic and elastic multi-level management of energy consumption for a particular city. ElCity explores the cloud elasticity concept in multiple target levels (smartphones from citizens, city devices involved in the public lighting, and data center nodes), turning on or off the target levels resources on each level regarding their demands, estimated based on energy consumption monitoring and citizens movement. ElCity achieved a reduction of more than 90 percent of the energy spent in public lightning in the studied city.

Proactive managers try to predict the cloud behavior to anticipate elasticity decisions before any under or overload situation. In the proactive elasticity, we highlight two works: Hanafy et al. [ 20 ] and Proliot [ 21 ]. Hanafy et al. [ 20 ] proposed an elasticity control algorithm for a containerized cloud using two agents. The host agent monitors and predicts its utilization using Autoregressive Moving Average (ARMA) [ 22 ], while the master agent performs elasticity by handling failures in load interchange scenarios. The results demonstrated the algorithm capabilities to elasticate and handle flash crowds along with decreasing the management overhead and maintaining proximate load balancing. Proliot [ 21 ] combines cloud and high-performance computing to address the IoT scalability problem in a novel EPCglobal-compliant architecture. The model offers an elastic EPCIS component that is automatically allocated or deallocated concerning the system load. Proliot uses Autoregressive Integrated Moving Average (ARIMA) [ 23 ] and Weighted Moving Average (WMA) [ 24 ] to predict the IoT load behavior, anticipating scaling in or out operations. Proliot improves 300% the response time when compared with the scenario that is not using elasticity. Table 1 summarizes the aforementioned related work. Reactive approaches have a low computational cost compared to proactive approaches. However, proactive approaches can avoid overloading in applications by taking elasticity actions in advance.

Reactive and proactive related work comparison.

2.2. Human Resources in Healthcare Environments

Some approaches focused on optimizing the flow of patients to properly allocate health resources [ 25 , 26 , 27 ]. Cappoci et al. [ 25 ] used discrete event simulation technique in order to improve patients’ waiting times. To this end, using data from a Brazilian polyclinic, and queueing theory [ 28 ], the authors proposed some changes to balance the occupancy levels of the health unit’s staff and, at the same time, reach a shorter waiting time for patients. Results showed a significant improvement in the performance of the Polyclinic’s system. Vieira and Hollmén [ 26 ] investigated ways of minimizing bottlenecks in the flow of patients due to appointments, visits, usage of resources, etc. The objective was to improve patients’ satisfaction and maximize the hospital’s profit. To this end, using data from a Finnish hospital, the authors used k-Nearest Neighbours [ 29 , 30 ] and Random Forests [ 31 ] to predict such a flow. In the same line of thinking, Graham et al. [ 27 ] aimed at predicting the arrival of patients in the emergency department of a hospital to properly prepare the allocation of medical staff. To accomplish such a task, the authors used logistic regression [ 32 ], decision trees [ 33 ], and gradient boosted machines [ 34 ] with data from a British hospital. In both works [ 26 , 27 ], the objective was exclusively on identifying specific data patterns, instead of proposing counter-measures to improve the allocation of health resources.

In an attempt to increase health coverage, some studies proposed forecasting models to understand the evolution of doctors supply and patients demand to better adjust one to the other. Ishikawa et al. [ 35 ] concentrated on training enough physicians to meed the patients demand in Japan until 2030. Liu et al. [ 36 ] focused on a similar problem, but from a global perspective. In contrast to our work, the adaptation of the hospital’s resources to the patients’ flow was left aside for these works. Table 2 summarizes the aforementioned human resources related work. As we can see, there are several approaches to analyze and estimate the use of human resources in healthcare environments so that that patient flow can be improved, or to understand the evolution of the problem of the health professionals lack.

Human resources in healthcare related work comparison.

2.3. Comparison and Research Opportunities

Table 1 and Table 2 presents a comparison of the collected papers, presenting some of their main characteristics, and pointing out some of their gaps. Based on the selected papers, we can identify that despite the elasticity being proposed for cloud computing, and being employed in reactive [ 37 ] and proactive [ 20 , 38 ] approaches, the same can also be employed in other areas such as energy [ 12 ], IoT [ 21 ] and C-RAN [ 19 ]. In this way, we can see the potential of elasticity to be extended to other contexts, such as human resources. When we have the problem of the lack of resources in hospital environments, the articles found just focus on predicting the future demand of patients or the future quantity of available doctors, not proposing solutions to the problem, leaving others in charge of decision-making. The approaches that propose solutions, such as physician training [ 35 ], or the movement of a nurse between two rooms [ 25 ], are very specific and can not be used in other medical environments. In this context, we can enumerate some of the main gaps in the area as follows:

• In the best of our knowledge, there are no approaches that evaluate the use of reactive and predictive elasticity for human resource management;
• Although several models are capable of identifying current and future demand in a hospital environment, these models lack solutions to help to solve the problem of deficiency of hospital resources;

The lack of enough human resources in healthcare environments is not new and, based on studied works, we can see that this problem will remain in the future [ 35 , 36 ]. Hence, finding ways of optimizing the use of existing resources and adjust hospitals’ capacity to meet patients demand are challenges that can make all the difference. The use of data prediction and Internet of Things contributes towards future solutions or automation of processes in the health area. However, the potential of the technologies is being underused since it is possible to propose solutions such as optimization and better use of existing human resources.

3. ElHealth Model

According to the literature review, most of the approaches concentrate only on identifying the location and current/future health status of patients, neglecting the potential benefits that efficient health resources allocation could bring to the patients [ 39 , 40 ]. As presented in Section 1 , one of the major challenges faced in hospital environments refers to the large waiting queues. Moreover, considering that doctors reaction time plays a role in patients recovery [ 7 ], long waiting times may compromise patients’ future health.

Based on this background, we introduce ElHealth, a multi-level model for efficient allocation of human resources based on patients’ flow within hospital environments. In particular, ElHealth adapts the concept of elasticity in cloud computing to the context of human resources, adjusting the hospital’s attendance capacity to the demand of patients, where professionals are allocated, deallocated and reallocated according to the hospital needs. ElHealth groups information from several sources: patients arrivals and needs (using IoT sensors spread over the hospital environment and a hospital dataset), patients movement (using IoT sensors), and medical staff availability (from a dataset). Using these data, we measure real-time demand of patients, on reactive approach (which we discuss in Section 3.3.1 ), and we employ a time-series prediction algorithm to anticipate the future demand of patients, on proactive approach (as we discuss in Section 3.3.2 ). This information is then useful for applying the concept of elasticity-based allocation of resources. Based on that model, ElHealth computes an efficient allocation of hospital resources (medical staff and equipment), which contributes towards minimizing patients’ waiting queues. Hence, ElHealth introduces the concept of Human Resources IoT-based Elasticity in healthcare environments, which can be defined as follows.

Human Resources IoT-based Elasticity is an extension of the concept of resource elasticity in Cloud Computing [ 13 ] to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patients’ demand. The Human Resources IoT-based Elasticity uses IoT sensors to keep track of patients’ demand and, based on proactive and reactive elasticity approaches, proposes an efficient allocation of the medical staff by moving such professionals to the most demanding areas, always considering the quality of services currently offered by these healthcare environments.

The next subsections detail our model, bringing the main design decisions ( Section 3.1 ), the proposed architecture ( Section 3.2 ), and the Multi-level Elasticity of Human Resources concept using reactive ( Section 3.3.1 ) and proactive ( Section 3.3.2 ) approaches.

3.1. Design Decisions

We based our model on the premise that there are sensors scattered around the hospital, which can identify patients who pass through them. Firstly, they must be in all the entrances and exits, so that whenever a patient enters or leaves the hospital, it is possible to identify it. To detect the movement and location of patients, we assume the presence of sensors at the doors of all hospital rooms. Each patient must have a Patient Identification Wristband linked in the system and must carry it through all time in the hospital’s internal environment. The attendant responsible for the reception of patients should be able to perform the linking of a wristband to a given patient as soon as the patient is admitted in the hospital. Thus it is possible to identify when and where a given patient is as soon as he enters at the healthcare environment, along with the time he remains in each room while being attended to. Also, each healthcare professional must have a tag linked to him in the system and must carry it with him throughout his active period in the hospital. Thus, all available attendants can also be located inside the hospital in the same way as patients.

We use a Real-Time Location System (RTLS) [ 41 ] with room-level localization accuracy. According to Boulos and Berry [ 41 ] and Jachimczyk et al. [ 42 ], RTLS are systems for identifying and tracking the location of assets and/or people in real-time or near real-time. Furthermore, RTLS provides an automated means of collecting operational data on clinic activity such as room utilization rates, or patient wait times [ 43 ]. We based the choice of an RTLS on its ability to allow automatic identification, avoiding the existence of a human error in identification processes. ElHealth should be transparent to patients, in the sense that it does not need to report any conditions related to its movement through the hospital environment, being an activity performed automatically by the system.

With respect to the data prediction strategy, ElHealth uses a statistical-based approach through the implementation of the ARIMA model. According to Nisha and Sreekumar [ 44 ], ARIMA model uses historical information to predict future patterns. ARIMA is the most general class of model for forecasting a time series. Since we can describe the number of patients waiting for care over time as a time series, we chose to use the approach through ARIMA because it is a very flexible mathematical model, with an excellent predictive performance of time series when compared with other approaches [ 44 ]. ARIMA models are extremely useful in predicting different sectorial series since they can represent stationary series, and also non-stationary series. We use a non-stationary model based on seasonality in demand for medical staff, since accidents, epidemics, holidays, and other events, can alter patients’ demand for care.

3.2. Architecture

ElHealth architecture model three services: (i) a Web service, responsible for visualization layer, and ElHealth Web Interface; (ii) an inference service, responsible for data processing, movement records handling, patients demand prediction, and human resources allocation decisions; and (iii) a database service. These three services are part of our proposed ElHealth Service. Figure 3 presents the components and the network view in the proposed model.

Components and network view in ElHealth model with ( i ) ElHealth Web Interface; ( ii ) ElHealth Service, for information processing and decision making; ( iii ) a RTLS, for track users’ tags; and ( iv ) Hospital managers, patients, or human resources.

ElHealth model is subdivided into five modules responsible for information handling from its capture by sensors to the final result displayed in the Web application. Each module has a specific function, having an input information and a specific output result that can be used as input from other modules. Figure 4 presents the proposed modules, detailing the architecture of the model.

ElHealth model architecture detail where the information flow starts in ElHealth_Capture module that receives users’ movement records from RTLS sensors, and goes through different handlings over proposed modules, until the exhibition of elasticity notifications in ElHealth Web Pages.

ElHealth_Capture receives and pre-process data captured by sensors scattered around the hospital and sends to ElHealth_Formatter , responsible for process data, and identify patients’ movement through hospital environments and rooms. After, ElHealth_Predict identifies patients movement through the hospital environment. Based on previously generated movement records, the path that patients travel during their movement through the hospital, and the time spent in each environment are identified. Thus, this module identifies patterns related to the arrival of patients in these environments, and patterns related to the waiting time for care, using this information to predict future patients arrivals in each hospital environment.

ElHealth_Elastic manages system’s elasticity. It verifies human resources allocation in each of hospital environments, check the current patients’ movement (in reactive approach) and the predictions made by the previous module (on proactive approach). This module generates an intelligent and automatic allocation of human resources to meet patient demand better. We want to emphasize that the system generates notifications for human resources to reallocate, but effective reallocation depends on the people accomplishing what was indicated by the application. ElHealth_Elastic and ElHealth_Predict modules are the most important part and the core of our proposed model, since ElHealth_Elastic can request predictions from the ElHealth_Predict module to take elastic actions, performing resources analysis based on predictions performed by the previous module, and also can perform elastic actions based on current patient demand. In Section 3.3 will be detailed the algorithms and how the elastic management of the human resources in the hospital environment are performed. Finally, ElHealth Web Interface displays to human resources the elasticity notifications generated before.

3.3. Human Resources Elasticity

ElHealth employs the term elasticity with a slightly different meaning from that used in cloud computing. Here, it refers to the system’s ability to allocate/reallocate/deallocate human resources capable of attending patients in order to adapt to varying patient demand in real-time. In particular, in the context of this work, elasticity refers to:

• Allocation , which denotes the capacity of the system to request health professionals who are not in the hospital to attend the current patients’ demand;
• Reallocation (or migration) , which denotes the ability of the system to migrate professionals who are allocated to a particular hospital environment to some other environment where more professionals are needed;
• Deallocation which denotes the capacity of the system to release human resources no longer needed to attend the current patients’ demand.

In order to perform allocation, deallocation, and reallocation of human resources, ElHealth model makes use of reactive or proactive approaches to monitor the demand of patients and the use of rooms in the hospital. Our model considers elasticity differently for: (i) the reactive approach, where our model must verify the use of any given room, and propose human resources movement if an upper or lower threshold is reached (as discussed next, in Section 3.3.1 ), and for (ii) the proactive approach, where ElHealth should verify if there are sufficient attendants to meet patients’ future demand from any given room in the hospital environment, with attendants moving between rooms (as detailed forward in Section 3.3.2 ). For this process, ElHealth should be able to alert people to allocate. However, the final decision should always be made by the health professional or hospital manager.

3.3.1. Reactive Elasticity

In reactive mode, ElHealth uses a multi-level approach where our model considers elasticity differently at (i) the room-level, where ElHealth must verify the use of a given room, and check if is necessary more or fewer attendants to meet patients’ demand, and at (ii) the hospital-level, where our model proposes attendants movement between rooms to meet patients’ demand. We use this multi-level strategy, since different rooms may have different time thresholds for care. In this way, a prior analysis of the need for each room-level is necessary in order to perform the load-balancing procedure (hospital-level). An example of these two levels is presented in Figure 5 .

Multi-level Reactive Elasticity of Human Resources example with ( i ) room-level reactive elasticity, and ( ii ) hospital-level reactive elasticity.

ElHealth model adapts the reactive elasticity strategy using upper and lower thresholds for the context of people, based on the waiting time for care in each of waiting queues of a hospital environment. Figure 6 illustrates the use of thresholds where an upper threshold is reached (meaning that human resources should be increased to fulfill that needs) and soon after a lower threshold is reached (meaning that human resources could be released to other sectors). So, at room-level, in each monitoring cycle, ElHealth first checks the specific time thresholds of each analyzed room and compares with the waiting time in that room. In those where time is outside the upper or lower bounds, our model defines the need for allocation or deallocation of human resources.

Reactive elasticity example based on waiting time for care adopted by ElHealth, where the delivery and release of human resources occur after the thresholds are reached.

At the hospital-level, ElHealth considers the possibility of moving health professionals between different hospital environments in order to optimize medical care time. To this end, the available options refer to: allocating new attendants, reallocating health professionals between different sectors, or deallocating human resources that are no longer necessary. ElHealth’s first option should always be the possibility of reallocating human resources already allocated to hospital care. The reallocation is prioritized because it is the option that brings fewer costs to the hospital since it performs adjustment of medical care without additional attendants. Algorithm 1 presents the pseudo-code for hospital-level reactive elasticity.

In what follows, we firstly discuss the reallocation concept, followed by the allocation procedures, rules, and algorithms. Lastly, we present the deallocation process. We note that, although deallocation appears first in the algorithm (line 9), it actually builds upon the human resources allocated during the preceding iteration of the algorithm. In ElHealth model, each room has a required specialty to the human resources that are allocated in it. In parallel, each health professional has a list of all its specialties. The process of reallocating or allocating human resources is only performed between professionals who have the required destination room specialty. This is necessary because in a laboratory exams room is required a nursing professional accustomed to blood tests for example, and even if we have X-ray technicians available for reallocation, they are not able to improve the attendance in the aforementioned room. In order to achieve human resources reallocation, all hospital rooms are in a list ordered by the attendants available for reallocation. In that way, whenever a room r needs a new human resource, the elasticity manager checks for available attendants, with room r specialty, in the first room of the list. If there is an available attendant, then it is reallocated to the needed room.

A potential problem that arises in the context of elasticity is the so-called hysteresis [ 45 ], which refers to the tendency of the system to return to the previous state in the absence of the impulse that caused the change. In the context of human resources elasticity, hysteresis occurs if a resource reallocated from a given room A to another room B and, in the subsequent time-step, room A needs that resource back. This kind of situation happens when the stimulus that led to the reallocation ceases to exist. However, when the resource is returned to the original room, the stimulus will emerge once again, leading the resource to be reallocated continuously between the two rooms. In order to prevent hysteresis of human resources, we employ a cooldown-based strategy [ 46 ]. In particular, whenever a resource is reallocated from a given room A to another room B, and if room B need a resource in the subsequent monitoring cycle, its need will only be met if another room has free resources, or by the allocation of a new attendant. In other words, the resource reallocated previously cannot be immediately returned, which avoids the hysteresis effect.

In some situations, the reallocation process may not be enough to improve the attendance level of the hospital. In such situations, the allocation of new resources may be necessary. We emphasize that, in order to minimize operational costs, the allocation is only performed if reallocation is not able to meet the patients’ demand. In an emergency situation, or exceptional cases, where all hospital staff are already in care and not available for reallocation, ElHealth proposes the allocation of new human resources. Thus, our model allocates health professionals who are not in the hospital but are available for allocation. We highlight that the hospital must have a strategy to define human resources available for external allocation. Since different countries have different labor laws, the rules that can make available for allocation the hospital staff on rest time can vary. Finally, if the algorithm identifies that the demand for care of all hospital rooms is very low and that the deallocation of attendants of some room does not harm the whole, ElHealth must identify which attendants were allocated outside of their regular working hours and deallocate them to lower the hospital’s financial costs. In the same way as reallocation, both allocation and deallocation are also protected by the cooldown-period. Also, if a given human resource is deallocated, it can no longer be allocated in the same work shift.

3.3.2. Proactive Elasticity

In proactive elasticity, ElHealth model uses a multi-level approach, slightly different of reactive elasticity, where (i) in the room-level, our model must identify the future use of a given room, and check if the number of attendants is sufficient to meet patients’ demand, and in (ii) the hospital-level, where ElHealth should verify if there are sufficient attendants to meet patients’ demand from all rooms in the hospital environment, with attendants moving between rooms. An example of these two levels is presented in Figure 7 .

Multi-level Proactive Elasticity of Human Resources example with ( i ) room-level proactive elasticity, and ( ii ) hospital-level proactive elasticity.

ElHealth model adapts the proactive elasticity strategy using upper and lower thresholds for the context of people, based on the waiting time for care in each of waiting queues of a hospital environment. Figure 8 illustrates the use of thresholds, where ElHealth forecasts that the upper threshold will be reached and soon after ElHealth forecasts that the lower threshold will be reached.

Proactive elasticity based on predicted waiting time for care adopted by ElHealth, where the delivery and release of human resources occur before the thresholds are reached.

At the room-level, in each monitoring cycle, ElHealth needs to predict patients arrival rate at any room based on current and previous arrivals on that room. The prediction is made using the ARIMA model based on the average care time with the current attendants’ allocation, and the estimated waiting time for the care queue. When ElHealth identifies that the waiting time will become higher or lower than the threshold values set by hospital manager, ElHealth should compute the number of health resources required to meet patients’ demand through the Proactive Human Resources Elastic Speedup. Proactive Elastic Speedup uses a predictive approach to determine the future demand of patients and dynamically define the adequate number of attendants, identifying the gain of future medical care time in a hospital environment. ElHealth proposes some mathematical formalism to estimate the Proactive Human Resources Elastic Speedup, which will be described in the sequence. Table 3 presents a summary of such mathematical notation.

Mathematical notation of ElHealth.

Let C V ( r , t i , t f ) denote the care vector of room r for the time interval between t i and t f . The size of any such vector is defined by s i z e ( x ) . Using these two functions, the average care time in the hospital’s room r between t i and t f times can be formulated as in Equation ( 1 ), where C D T ( x [ i ] ) refers to a care duration time x [ i ] that has already occurred in that room and x [ ] = C V ( r , t i , t f ) is a care vector that occurred in that room.

Equation ( 1 ) results in a numerical value of time. An example would be any room r , between 1 and 5 times, where the result could be defined as: A C T ( r , 1 , 5 ) = 15 minutes. Using this equation, it is possible to estimate the average time of a care in a particular hospital room. Due to the elasticity of human resources, at different time instants, there is a different number of attendants allocated to care in each of the hospital rooms. The average number of attendants in the hospital’s room r between times t i and t f is defined as in Equation ( 2 ), where N A ( r , t n ) refers to the number of attendants allocated to care in the room r at the instant of time n .

The same idea of the previous function is useful for patients’ reality because in different moments of time there are different amounts of patients awaiting care in each of the hospital rooms. Thus, the estimated number of patients waiting for care in the hospital’s room r between t i and t f times is defined by Equation ( 3 ), where N W P ( r , t i ) refers to the number of waiting patients for care in a room r at t i time instant, and N I P ( r , t n ) refers to the number of incoming patients in a room r at t n time instant.

Using the equations previously proposed, our model calculates the estimated care time of all patients waiting, and estimates the time that a new incoming patient needs to wait to be attended. The E C T ( r , t i , t f ) is defined by Equation ( 4 ), where A C T ( r , t i , t f ) refers to the average care time for room r between t i and t f times, and E N P ( r , t i , t f ) refers to the estimated number of patients who are waiting for care in a room r between t i and t f instants. An example would be the room r 1 , between two times t i and t f that would result in an average number of 4 patients and an average care time of 10 min as shown in Figure 9 .

Calculating E C T in a hospital room r 1 with 4 patients waiting, and average care time of 10 min. In this hypothetical situation, at 0 min instant the first patient was called to the care. In 10 min instant, the first patient ends their care and goes away, so the second patient is designated to care, and so on, until instant 40 min, when the last patient is released. Thereby, all patients are attended within 40 min. Applying Equation ( 4 ), we obtain E C T ( r 1 , t i , t f ) = A C T ( r 1 , t i , t f ) · E N P ( r 1 , t i , t f ) = 10 × 4 = 40 min.

Knowing E C T ( r , t i , t f ) , we can analyze the average time for care of all patients waiting in the room r between t i and t f times. However, this value refers to a hospital room with a single attendant allocated for care, but in most cases will be more than one health professional working in that room, making it necessary to identify the average time with different numbers of attendants. In this context, ElHealth model uses a parallel allocation of human resources, such as the parallel allocation of virtual machines used in elastic systems [ 13 ] or the use of parallel processors in high-performance computing [ 47 ]. Thus, based on the Elastic Speedup proposed by [ 47 ], ElHealth introduces Equation ( 5 ) for Human Resources Elastic Speedup. Considering again the previous example ( Figure 9 ), with room r 1 between two times t i and t f with an average number of 4 patients, an average care time of 10 min and with two health professionals allocated, as shown in Figure 10 .

Calculating the E C T in a hospital room using parallel allocation of attendants, with 4 patients waiting, average care time of 10 min, and 2 attendants. In this hypothetical situation, at 0 min time instant, there were 4 patients waiting and none in attendance by doctors, so the first two patients were called to care. In 10 min instant, the first two patients are released, and the last two patients are designated to care. Thus, at 20 min instant, the last two patients are released. Thereby, all patients are attended in only 20 min. Using Equation ( 5 ), we obtain: H R E S ( r 1 , t i , t f ) = E C T ( r 1 , t i , t f ) A N A ( r 1 , t i , t f ) = A C T ( r 1 , t i , t f ) · E N P ( r 1 , t i , t f ) A N A ( r 1 , t i , t f ) = 10 × 4 2 = 20 min.

H R E S ( r , t i , t f ) returns the estimated care time of a room r between the t i and t f times, considering a parallel allocation of attendants in that period of time, through the use of A N A ( r , t i , t f ) function. Thus, with the increase in the average number of attendants allocated, the estimated care time decreases, inversely proportional.

A problem of reactive elasticity is that the elasticity actions are taken after the upper threshold are reached, causing a state of overload in the hospital throughout the professionals’ movement period. Thus, an alternative to this problem is the use of proactive elasticity [ 48 ]. Thus, anticipating the moment when the upper threshold will be reached, people’s movement can occur in advance, minimizing or avoiding patients’ overloads in the hospital. In this context, we propose Equation ( 6 ) for Proactive Human Resources Elastic Speedup as follows:

where a is the number of attendants allocated between the future times f i and f f , and E C T ( r , f i , f f ) ′ is a prediction of the future care time for this room using ARIMA. We can compute E C T ′ as:

where A C T ( r , f i , f f ) ′ and E N P ( r , f i , f f ) ′ are predictions of the average care time and future patients at room r , respectively. Thus, for each room r being calculated, we generate a time series of A C T ( r , t i , t f ) that occurred in the past, and we use it to predict A C T ( r , f i , f f ) ′ . In addition, for each room we also generate a time series for N I P ( r , t i , t f ) , and can predict future patient input and find E N P ( r , f i , f f ) ′ .

Using the aforementioned equations, ElHealth can predict the waiting time of any hospital room. Varying a attribute in P H R E S equation, with the increase and decrease of the number of health professionals in attendance, ElHealth can identify how many attendants would be needed to adjust the waiting time of any room to the proposed thresholds, as defined by the hospital manager. Algorithm 2 presents our method to verify the need to allocate or deallocate human resources in any room r in a smart hospital.

At the hospital-level, ElHealth needs to test different allocations for the attendants to ensure that all rooms identified in the previous step (local-level) have enough attendants, and to minimize overcrowding. Our algorithm considers the possibility of moving health professionals between different hospital environments in order to optimize medical care time. As in the reactive strategy, between allocation or reallocation, ElHealth prioritizes the possibility of reallocating human resources already allocated to hospital care, to minimize hospital’s costs. To redistribute such health attendants between different hospital rooms, our model uses some strategies known from other contexts of scientific computing and adapts them to the proactive elasticity of human resources needs. Algorithm 3 presents the pseudo-code for hospital-level proactive elasticity. As in the reactive strategy, each room has a required specialty, and the process of reallocating or allocating human resources is only performed between professionals who have the required destination room specialty. A point to be observed is that those rooms where they need a specialty that no other hospital’s professional has, only the allocation of new human resources is performed.

In order to achieve a balanced reallocation of human resources, we developed a variation of the dynamic List Scheduling algorithm [ 49 ], which was originally used for process scheduling. Here, all hospital rooms are in a list ordered by the number of attendants available for reallocation. In that way, whenever a room r needs more attendants, the elasticity manager checks for available attendants, with room r specialty, in the first room of the list. If attendants are available, then they are reallocated to the room lacking them, and the list is sorted again. If more attendants are needed, the algorithm checks the first room in the list again, and so forth, until the room obtains all the required attendants.

Figure 11 illustrates the reallocation process, where Room 1 needs three more attendants and Rooms 2 and 4 have some free attendants. Following the logic of the adapted List Scheduling algorithm, in the first round, Room 2 is the first in the list, with three available attendants, and gives an attendant for Room 1. In the second round, even though all rooms in the list have the same number of free attendants, Room 2 remains at the top of the list, so another attendant is reallocated. Finally, in the third round, Room 4 becomes the first on the list, since it has two free human resources (as opposed to Room 2, which has only one), and an attendant of Room 4 is reallocated to Room 1.

Reallocation through the adapted List Scheduling algorithm, with a sorted list of 4 rooms, and 12 attendants, where Room 1 needs to allocate more 3 attendants.

As in the reactive strategy, in proactive elasticity, we employ a cooldown-based strategy to prevent hysteresis of human resources. If the reallocation process is not enough to improve the attendance level of the hospital, ElHealth proposes the allocation of new human resources to hospital care. Lastly, if ElHealth identifies that the future demand for care of all hospital rooms is very low and that the deallocation of attendants of some room does not harm the whole, ElHealth proposes the deallocation of attendants allocated outside of their regular working hours.

4. Evaluation Methodology

We assess the performance of ElHealth through simulations in a virtual hospital environment. Considering the unavailability of data, the hospital environment was defined based on synthetic workloads. These data and its parameters are detailed in Section 4.2 . According to [ 50 ], synthetic workloads can be considered a representative form to evaluate elasticity in computational clouds. ElHealth was implemented mainly in Java, except for the ARIMA method, which was implemented in Python. For hospital queues simulation, we used a clock with discrete increments of ten seconds. At each advance in the simulation clock, our simulator verifies the patients who are in care and those who should leave the care. At each monitoring cycle, the arrival of patients should be checked. The data probability distributions were generated using triangular distributions (more details in Section 4.2 ), as implemented by StdRandom [ 51 ].

4.1. Considered Scenarios

Given the hospital simulation procedure, we consider three different scenarios for analysis. In all scenarios, we used the same input parameters. The differences in the scenarios are related to the use of the proposed model in the hospital environment and will be described as follows:

• S1:   Hospital without ElHealth: in order to have data for comparison, the first test scenario is based on the simulation of a non-elastic hospital
• S2:   Smart hospital with ElHealth’s reactive elasticity: the second scenario focuses on the simulation of the hospital environment with the use of the allocation, reallocation, and deallocation of human resources proposed in the ElHealth model, using reactive elasticity approach.
• S3:   Smart hospital with ElHealth’s proactive elasticity: the third scenario is similar to the second, based on the simulation of the hospital environment with ElHealth’s elasticity model, but unlike the previous scenario, using proactive elasticity approach.

4.2. Performance Evaluation Parameters

To perform the simulation of the hospital environment, we use the data collected in the study of Capocci et al. [ 25 ] performed in a hospital environment located in Guarulhos City, in the state of São Paulo in Brazil. According to Capocci et al. [ 25 ], all patients upon entering the unit first go through reception, where a Personal Health Record (PHR) [ 52 ] is prepared. After this preparation, patients are referred to waiting for triage. In the triage procedure, the patients are examined by the nursing team and classified into priorities according to the urgency of the health problem and are referred to waiting for medical attention. In polyclinic analyzed by Capocci et al. [ 25 ], after first medical attention, 24% of patients are referred for x-ray exam, 37% for laboratory examinations (blood test, for example), 8% for electrocardiograms (ECG) exam, and 31% do not need more than physician examination. Also after doctor treatment room, only 1% of patients do not take medication and are released with only one prescription, but 50% of patients require intravenous medication, 30% intramuscular injection and 19% inhalation medication. After the exams, 60% of patients need to return to the doctor, and 40% are released. After a return care, 78% of patients are released, 2% need new exams, and 20% require new medication.

Also, according to Capocci et al. [ 25 ], the care time in each room of the hospital environment follows a triangular distribution, with minimum and maximum times and a more frequent average time. Table 4 shows the distributions for all possible care in this hospital unit, as identified by [ 25 ] in their study. All other parameters used in our simulation can be found in [ 25 ].

Triangular distributions of probability for care times.

In Brazil, the working model adopted for hospital environments is the so-called 12 × 36 h. According to Brazilian Law No. 13,467 [ 53 ], under this work regime, an employee can work for twelve consecutive hours (with a one-hour pause for lunch) and must rest for 36 h before a new work shift of 12 h starts. Under this regime, four health professions alternating shifts is enough to ensure a single position for 24 h, seven days a week. Also, according to the understanding of the law, if for any reason an employee needs to work within their rest period, it should be treated as overtime, unless the hours are compensated at another time. Thus, while a human resource of the hospital is in working time, three other employees who perform the same function are in their paid-rest period. According to Brazilian Decree-Law No. 5,452 [ 54 ], the minimum rest period between two working days must be eleven consecutive hours. In that way, even if there are overtime hours, an employee must rest eleven hours to return to the next work shift. Thus, these three resting employees shall not be arbitrarily available to a new allocation. In particular, any resting employee is only available under the following rules:

• Rule   1: The minimum rest period for a human resource to be available for allocation is eleven hours;
• Rule   2: An allocated employee cannot works outside of the regular work shift for a long time period. The largest possible work period allowed in Brazilian legislation is twelve hours. Thus, an allocated employee cannot work more than twelve hours;
• Rule   3: Allocated employees must be deallocated no later than 11 h before they next normal work shift; and
• Rule   4: Each employee must meet one of the 36 h rest periods within the same week in order to comply with a law determination that requires all workers to have a 24 h paid-rest period per week.

As our case study is based on Brazilian hospital data, we have set thresholds appropriate to our reality. So, based in Brazilian Law Project of 14 June 2018 [ 55 ] that proposes a maximum waiting time for care in hospitals, clinics, and laboratories of 30 min on regular days (from Monday to Sunday), we define ElHealth’s maximum load (i.e., 100%) in 30 min. Based on several works [ 12 , 47 , 56 , 57 ], we are using 4 combinations of thresholds when evaluating the second scenario, so considering 30% (9 min) and 50% (15 min) for lower threshold, and considering 70% (21 min) and 90% (27 min) for upper threshold. For proactive elasticity, we set ElHealth’s upper threshold in 30 min, (i.e., maximum load previously defined), and we set ElHealth’s lower threshold in 9 min (30% of maximum waiting time). For elasticity actions, we set 10 min for reallocation process (human resources movement between rooms), and 60 min for allocation process (to simulate the movement of a new human resource to the hospital).

4.3. Workload

We use the human resources allocation found in [ 25 ] research, where 11 health professionals were allocated, 24 h a day, seven days a week, through more than one work shift. To be specific, health professionals were allocated as follows: 2 attendants in a reception; 1 nurse working in patient triage; 2 doctors acting in doctors treatment rooms; 2 nurses working with collection exams; 2 nurses working throughout the medication area; 1 nurse acting on the electrocardiogram; and 1 radiology technician acting with the X-ray exams.

Regarding patients load, we modeled four workloads: constant, ascending, descending, and wave. The idea of using different load behaviors for the same application is used to observe how the input load can impact saturation points, bottlenecks, and the addition or removal of resources [ 56 ]. These four behaviors of workload are based on those proposed by [ 56 ]. Besides these four loads are representative to evaluate elasticity, wave workload is the most closely to the hospital reality, and the ascending workload represents the behavior of the model in a situation of increased patient load, which could be caused, for example, by a viral outbreak or epidemics. We want to emphasize that the ascending workload demonstrates the worst possible case, with an increasing entry of patients into a hospital. Figure 12 presents a representation of each workload of the model. The x axis expresses the time available in one day of care in the hospital unit, while the y axis represents the arrival of patients at each instant of time.

Graphical representation of workloads used in ElHealth tests, where x axis expresses time available in one day of care, while y axis represents the arrival of patients at each time instant.

Since the workloads generate decimal numbers, we established a strategy to generate integers for the arrival of the patients in the hospital environment. This occurs because, in a real environment, it is not possible the arrival of 0.2 patients or 1.7 patients, for example. Thus, we adopted a load accumulation strategy, where if at any given moment there is something between 0.1 and 0.9 patient, this value is accumulated with next instant load. An example would be an instant with a load of 0.6 patient. Since there would not be an integer charge, a patient would not be introduced into the system, and the charge would accumulate for the next instant of time. At the next moment, with a new load of 0.6 patient, the accumulated load would be 1.2 patient, resulting in the entry of 1 patient in the hospital. Thus, there would be still 0.2 patient, which would be accumulated for the next instant and so on.

4.4. Performance Evaluation Metrics

In order to evaluate the proposed model, the following metrics are considered:

• Maximum waiting time for care;
• Human resources cost;
• Elastic number of human resources used.

To evaluate the waiting time, we used as parameter the variation of the maximum waiting time between the scenarios and the adequacy of the maximum waiting time to the established limits. To determine the human resources cost, we had to propose a way to measure the cost of a human resource in normal working hours and the cost of a human resource outside of its working hours. According to Brazilian Law No. 13,467 [ 53 ] and Brazilian Decree-Law No. 5452 [ 54 ], the overtime pay will be at least 50% (fifty percent) higher than the normal hour. In this way, a health professional allocated outside of its working shift costs 50% more than an employee during its working shift. Based on this, we devised Equation ( 7 ) for Human resources cost as follows:

where H R ( t n ) refers to all human resources in their working shift at t n time instant, and A l l o c a t e d H R ( t n ) refers to all allocated, or in the process of allocation, human resources outside their regular working hours at t n time instant. With regard to human resources number, we proposed a metric for compare elastic and non-elastic health professional allocation, where we expect that our model uses the existing health professionals in the hospital in an optimized way. Thus, static allocation of S1, with eleven employees working, can be compared to ElHealth elastic allocation, with the number of human resources varying throughout the day. Table 5 presents all the evaluation metrics described above, relating the results expected for the second and third scenario with the use of ElHealth, when compared to the current hospital environment, without the ElHealth model.

Evaluation metrics and expected results in each scenario.

5. Performance Evaluation and Results Analysis

Based on the evaluation methodology proposed for the ElHealth model, we performed twelve simulations of the proposed hospital environment in order to collect results for analysis. For each proposed scenario, between S1, S2, and S3, a simulation was performed for each of the workloads, constant, ascending, descending, and wave.

For the maximum waiting time metric, we expected a decrease in patients’ waiting for care. Figure 13 shows the maximum waiting time identified for each workload in the proposed scenarios over the simulated one-week period. We perceive a significant reduction in the maximum waiting time between S1 and S2, and a second diminution when comparing S2 and S3, regardless of the workload used. After a thorough analysis, we can identify that in S3 for reception, triage, doctor treatment, and collection exams rooms, at no time was measured waiting time longer than 30 min, regardless of the workload used. As for medication, X-ray, and electrocardiogram rooms, there were a few moments when this limit was exceeded. Through the collected data, we identify a significant reduction in waiting time with the use of the reactive and proactive elasticity approaches for human resources organization when compared to the hospital without the use of the elasticity. Thanks to the reactive procedures, ElHealth has shown to decrease the waiting time by 96.13%, 95.27%, 96.05% and 93.4% for constant, ascending, descending, and wave workloads, respectively, as compared to the scenario where no human resources reorganizations are performed. In proactive procedures, ElHealth has shown to decrease the waiting time by 96.66%, 96.73%, 97.06% and 96.65% for constant, ascending, descending and wave workloads, respectively, as compared to the non-elastic hospital.

Maximum waiting time at the hospital for each of the proposed scenarios, S1 (in red), best result between thresholds for S2 (in green for 70 × 50 and in orange for 70 × 30), and S3 (in purple), using ( a ) constant, ( b ) ascending, ( c ) descending and ( d ) wave workloads.

For human resources cost metric, we expected an increase in the cost between scenarios. Figure 14 presents the human resources cost for each workload in S2 and S2, the scenarios where the cost can variate. We can observe that cost ranged from 11 to 17.77 per hour, in the reactive approach, and ranged from 11 to 18.47 in the proactive approach. Furthermore, as exposed in the aforementioned Figure 14 , whenever ElHealth costs increase, the patients’ waiting time decreases. We can also see that the proactive approach achieved the most significant reduction in waiting time, with more cost than the reactive approach. In reactive procedures, the cost increased by 0.64%, 5%, 13.27% and 9.27% for constant, ascending, descending, and wave workloads, respectively. In proactive procedures, the cost increased by 7.09%, 7.36%, 22.82% and 3.27% for constant, ascending, descending, and wave workloads, respectively.

Human resources cost compared with maximum waiting time at the hospital using ( a ) constant, ( b ) ascending, ( c ) descending and ( d ) wave workloads in S2 (best result between thresholds) and S3.

For the elastic number of human resources used metric, we expected an increase in the number of professionals in the hospital, as well as a variation of this number over the hospital care period. Figure 15 presents the elastic number of human resources used for hospital care in S3, the only scenario where the number of employees can variate. We can observe that the elastic number of human resources ranged from 11 to 14 per hour. Although there are moments with the allocation of up to 14 health professionals in care, the average per hour of care professionals turns out to be slightly lower depending on the time it takes for an employee to be allocated or reallocated in the hospital. Furthermore, as exposed in the aforementioned Figure 15 , whenever ElHealth reallocates or allocates peoples for care, the patients’ waiting time decreases.

Elastic number of human resources used compared with maximum waiting time at the hospital for ( a ) constant workload in best result between thresholds for S2, ( b ) constant workload in S3, ( c ) ascending workload in best result for S2, ( d ) ascending workload in S3, ( e ) descending workload in best result for S2, ( f ) descending workload in S3, ( g ) wave workload in best result for S2 and ( h ) wave workload in S3.

Based on established metrics, we can note that the ElHealth model was able to improve the performance of the simulated hospital environment in all workloads used. Table 6 presents all the results found in each of the proposed evaluation metrics, highlighting the best results in green and the worst in red. As proposed in our evaluation methodology, we expected that the maximum waiting time presented a gradual decrease between scenarios S1, S2, and S3, and this in fact occurred, fulfilling the objective of this metric. For human resources cost, we expected an increase between scenarios S2 and S3, and our model has met expectations. For the elastic number of human resources used, an increase in the result was expected between scenarios S2 and S3, and our model once again was able to meet the proposed goal. Thus, the expected results in the evaluation methodology were achieved through the use of the ElHealth model in the proposed hospital environment.

Evaluation metrics and results found in each of the proposed scenarios, using constant, ascending, descending and wave workloads, where the best results for each metric are highlighted in green and the worst in red.

For maximum waiting time metric, our objective was the time reduction. As already shown, the ElHealth model was able to reduce the waiting time for the proposed hospital environment significantly. However, although the average maximum waiting times for the S3 scenario were within the established limit (9.42 min with constant workload, 12.7 min with ascending workload, 15.65 min with descending workload and 12.8 with wave workload), when we analyzed the longer waiting time identified in all the simulation period, the upper limit was exceeded (39, 48, 86 and 70 min with constant, ascending, descending and wave workloads, respectively). We believe that this occurred due to the limitations of the hospital environment used as the basis for this simulation. As there were not many care stations available to be allocated new human resources, our model was not able to reach the goal in this hospital environment. For human resources cost metric, we expected an increase among the proposed scenarios, and that is precisely what happened. When we compare with the previous metric, the increase in human resources cost is inversely proportional to the waiting time decrease. In the reactive approach, we had a considerable improvement in waiting time reduction, with little increase in cost. For proactive elasticity, we have a new improvement in waiting time reduction, with a new increase in the cost. Although the proactive approach has a higher cost, we believe it is still more efficient than the reactive approach because it can further decrease waiting time for medical care, anticipating more potential health problems. For the elastic number of human resources used metric, we expected an increase in the average number of human resources between scenarios C2 and C3, and this also actually occurred.

6. Conclusions and Future Work

IoT sensors allow smart hospitals capable of tracking people and objects in real-time. With this data, computer systems can be used to generate knowledge and value for hospital managers. This work puts efforts in this direction, taking data captured from IoT sensors and generating decision-making value on them. Thus, this article presented the ElHealth model. Unlike related work, ElHealth not only proposes the use of elasticity to anticipate eventual problems in the future but also presents a model to allocate, migrate and deallocate people in hospitals in such a way to provide benefits at patients viewpoint. Using IoT-sensors and an ARIMA-based prediction engine, we can instrument a smart hospital to collect data in time-series, so better arranging professionals and either preventing or mitigating patient treatment problems, which sometimes are related to life or death issues. In this way, we extended the concept of elasticity from cloud computing to the context of human resources management, while proposing new mathematical formalisms, algorithms, and definitions to provide a dynamic and elastic allocation of professionals in hospital environments.

We expect that the model proposed in this work can help to decrease the waiting time of patients for healthcare. The idea is to provide such facility in a transparent way for the patients, i.e., they do not need to follow additional procedures in the hospital, but only wear a wristband which serves as identification. We also hope to, with the use of ElHealth, we can identify bottlenecks in the patients care flow and help optimize processes in healthcare environments. Moreover, the provided data can also be used for decision making in terms of changes in hospital capacity and infrastructure. In ElHealth’s case study, the waiting time is decreased by 96.4% and 96.73% for reactive and proactive approaches, respectively. In the reactive approach, we had a considerable improvement in terms of waiting time reduction, with little cost increasing. On the other hand, with the proactive approach, we had more waiting time reduction, with an increase in the cost. Even with the higher cost, we believe that proactive elasticity is more efficient than the reactive approach since with a shorter waiting time, more potential health problems can be anticipated.

Although presenting encouraging results, we envisage some limitations that must be addressed on implementing ElHealth model in a real hospital environment: (i) employees and patients must carry their identification tags throughout their time in the smart hospital; (ii) ElHealth only generates notifications for human resource, but the effective movement of staff in hospital environments depends on their individual decision to follow the recommended guidance; (iii) previous installation of RTLS sensors in corridors and doors of the hospital.

As future work, we envisage the implementation of the IoT system, as well as the development of a prototype that implements all the modules and algorithms proposed by ElHealth, so enabling the deploying in a real hospital environment. Another possibility concerns the adaptation of the model to use other prediction algorithms on the proactive approach, including Artificial Neural Networks and Random Forest approaches. Also, we visualize a new approach to perform an evaluation based on a function incorporating constant, ascending, descending and wave workloads with different coefficients, since in an actual hospital environment a mix of these workloads could appear and modify over time.

Abbreviations

The following abbreviations are used in this manuscript:

Author Contributions

G.S.F. is the designer and developer of ElHealth and wrote the majority of the paper. R.d.R.R. and C.A.d.C. contributed to ElHealth proposing and design. G.S.F. and G.G. provided related work discussion. G.S.F. and R.d.R.R. conceived and designed the experiments. G.S.F. performed the experiments. G.S.F. and R.d.R.R. analyzed the data and wrote the results section. R.d.R.R., C.A.d.C., D.G. and G.G. contributed with the paper structuring, language and detailed review. R.d.R.R. also contributed with the paper proposal, scientific contributions and conclusion evaluation.

This research was partially funded by Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES), Finance Code 001; the Foundation for Research of the State of Rio Grande do Sul (FAPERGS), Brazil; and by Brazilian National Council for Research and Development (CNPq).

Conflicts of Interest

The authors declare no conflict of interest.

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How a proactive approach to care management will improve member health and reduce costs

Machine learning technologies offer providers opportunities to address rising risk and deliver better care.

By design, healthcare is largely reactive in nature. After experiencing symptoms, a patient sees a provider, receives a diagnosis, and is treated. That provider is then paid for services delivered and measured on reactive performance (i.e., how quickly a heart attack was treated appropriately or how well post-op pain was controlled), which means there is no incentive to promote healthy behaviors and avoid high-cost treatments.

But the shift to value-based care, with its promise of lower costs and better population health, has opened the door to new models that engage with people proactively-when they are at “rising risk” for worsening health and skyrocketing costs, rather than waiting until they are already high-cost, high-acuity patients. And luckily, today’s advanced technology is here to help.

Leveraging technology to assess future risk

Traditional prospective risk tools are driven by current risk and grounded in the heuristic, rule-based model that high-risk members today will be high-risk tomorrow. While adequately identifying persistently high-risk members, they fail to identify individuals who are low cost today and may seem “healthy,” but will become high cost tomorrow. 

It no longer has to be this way. Advances in technology-including predictive analytics, artificial intelligence (AI), and machine learning-are a making the dream of proactive care management a reality. Using sophisticated algorithms to analyze numerous data streams, these systems can accurately predict future risk by pairing pattern recognition with known outcomes. Providers are able to match machine learning predictions that identify rising-risk patients with suggested actions that can be taken, then engage with and support them in proactively changing behaviors before they experience an acute episode. 

Consider the ability to proactively engage with individuals at rising risk of worsening diabetes. In the past, a provider would need to identify and reach out to every patient diagnosed with pre-diabetes, diabetes, or metabolic syndrome. Such a practice would have identified many diabetic members who are either well-controlled or unwilling to engage, and would have offered little opportunity to engage with the small subset who were both in need of and willing to change their clinical trajectories. AI-enabled technology now makes finding and focusing on that small group of rising-risk members open to engagement and change a reality. 

Benefits of a proactive care management model 

Beyond improving patient health and lowering costs, this proactive model enables care managers to take an integrated approach aligned with the shift to value-based holistic care. By training the data sets to understand the characteristics of patient who are likely to engage, and those who are not, care managers will be able to spend more effective time with the members they can help the most, and less time with those they cannot help. Consider how fundamentally an entire organizational structure and culture could be redesigned around proactive engagement.  

Imagine a patient in her 50s with heart disease, chronic pain treated with opiates, tobacco use, anxiety, and depression. She hasn’t been hospitalized, but she’s had more than 20 outpatient visits over 12 months and her risk of experiencing an acute episode is rising every day. A reactive care management model wouldn’t find her until she was hospitalized.  But what if an AI-enabled partnership identified her unique, individual risks and suggested that a provider engage with her? The provider might then reach out, discover that she needed a much deeper connection with counseling, and help her transition off opiates and quit smoking.  

This isn’t an imaginary scenario – it’s a real-world example of a patient who would have fallen through the cracks of our reactive healthcare system. Instead, using AI-enabled predictive analytics, the care manager got to the patient at the right time, in the right way to both dramatically improve her health trajectory and reduce her total cost of care.  

Ready or not, the AI-enabled future of healthcare is here. By finding the most engageable and clinically impactable members before they experience acute issues, machine learning technologies offer providers opportunities to address rising risk and deliver better care sooner. 

Chris DeRienzo is chief medical officer for Cardinal Analytx Solutions, leading the company’s clinical portfolio and helping connect Cardinal’s world-class data science to better patient outcomes. Formerly chief quality officer for Mission Health, Dr. DeRienzo is Board Certified in both Pediatrics and Neonatology, and completed his MD, Masters in Public Policy and post-graduate medical training at Duke.

Erica Kaitz is care management lead for Cardinal Analytx Solutions, contributing to the company’s clinical portfolio and helping connect Cardinal’s world-class data science to better patient outcomes. Formerly the behavioral health director for Cigna Health-Spring of Illinois’ government programs, Erica Kaitz is a licensed clinical social worker, and completed her Masters of Social Work at Columbia University and post-graduate training in psychiatric crisis and community mental health settings. 

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