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International Journal of Lean Six Sigma

ISSN : 2040-4166

Article publication date: 11 January 2021

Issue publication date: 16 July 2021

This paper aims to develop an initial understanding of the Lean Six Sigma methodology since its inception and examine the few Lean Six Sigma dimensions as a research domain through a critical review of the literature.

Design/methodology/approach

The paper is structured in two-part. The first part of the paper attempts to dwell on the evolution of the Lean Philosophy and Six Sigma methodology individually and the emergence of Lean Six Sigma methodology, covered under the Lean Six Sigma: a historical outline section. The second part of the study examines the dimensions associated with Lean Six Sigma such as frameworks, critical success factors, critical failure factors, type of industry, performance metric, year, publisher and journal, based on a total of 223 articles published in 72 reputed journals from the year 2000 to 2019 as a literature review.

The adoption of Lean Six Sigma, as a continuous improvement methodology, has grown enormously in the manufacturing and few service sectors such as health care and higher education during the past decade. The study revealed that researchers came out with conceptual frameworks for the implementation of Lean Six Sigma, whereas the validation through case studies seems to be lacking. The integration of Lean Six Sigma and other approaches with a focus on sustainability and the environment has emerged as a research field. A few of the most common critical success and failure factors were identified from the articles studied during the study.

Research limitations/implications

This paper may not have included some of the studies due to the inaccessibility and selection criteria followed for the study.

Originality/value

This paper will provide an initial introduction on Lean, Six Sigma and Lean Six Sigma and research insights Lean Six Sigma to beginners such as students, researchers and entry-level professionals.

• Lean Six Sigma
• Success factors
• Failure factors

Patel, A.S. and Patel, K.M. (2021), "Critical review of literature on Lean Six Sigma methodology", International Journal of Lean Six Sigma , Vol. 12 No. 3, pp. 627-674. https://doi.org/10.1108/IJLSS-04-2020-0043

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Lean Six Sigma for the automotive industry through the tools and aspects within metrics: a literature review

• Critical Review
• Published: 22 November 2021
• Volume 119 , pages 1357–1383, ( 2022 )

Cite this article

• Iris Bento da Silva 1 ,
• Marcio Gonçalves Cabeça 2 ,
• Gustavo Franco Barbosa 3 &
• Sidney Bruce Shiki 3  

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4 Citations

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Nowadays, the automotive industries seek to consider increasingly higher standards of competitiveness. This sector is looking for a proper management methodology to solve a given problem in its kind of organization. In this sense, continuous improvement is essential for any business environment, due to provide conditions for getting excellence levels. Based on this strategy, the Lean Six Sigma (LSS) appears as an option to be applied. Although Lean was originated in the automotive industry, systematic review about Lean Six Sigma in this industry has a lack of research, considering tools and aspects within metrics. For that, the objective of this work is to carry out a literature review on the implementation of Lean Six Sigma in the automotive industry. The databases adopted for this research were Web of Science and Google Scholar, resulting in 69 selected articles. The results indicated that the implementation of this methodology contributes to the continuous improvement process and problem-solving in the automotive branch.

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da Silva, I.B., Cabeça, M.G., Barbosa, G.F. et al. Lean Six Sigma for the automotive industry through the tools and aspects within metrics: a literature review. Int J Adv Manuf Technol 119 , 1357–1383 (2022). https://doi.org/10.1007/s00170-021-08336-0

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Received : 28 July 2021

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Published : 22 November 2021

Issue Date : March 2022

DOI : https://doi.org/10.1007/s00170-021-08336-0

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This compilation of data on abortion in the United States draws mainly from two sources: the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, both of which have regularly compiled national abortion data for approximately half a century, and which collect their data in different ways.

The CDC data that is highlighted in this post comes from the agency’s “abortion surveillance” reports, which have been published annually since 1974 (and which have included data from 1969). Its figures from 1973 through 1996 include data from all 50 states, the District of Columbia and New York City – 52 “reporting areas” in all. Since 1997, the CDC’s totals have lacked data from some states (most notably California) for the years that those states did not report data to the agency. The four reporting areas that did not submit data to the CDC in 2021 – California, Maryland, New Hampshire and New Jersey – accounted for approximately 25% of all legal induced abortions in the U.S. in 2020, according to Guttmacher’s data. Most states, though,  do  have data in the reports, and the figures for the vast majority of them came from each state’s central health agency, while for some states, the figures came from hospitals and other medical facilities.

Discussion of CDC abortion data involving women’s state of residence, marital status, race, ethnicity, age, abortion history and the number of previous live births excludes the low share of abortions where that information was not supplied. Read the methodology for the CDC’s latest abortion surveillance report , which includes data from 2021, for more details. Previous reports can be found at  stacks.cdc.gov  by entering “abortion surveillance” into the search box.

For the numbers of deaths caused by induced abortions in 1963 and 1965, this analysis looks at reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. In computing those figures, we excluded abortions listed in the report under the categories “spontaneous or unspecified” or as “other.” (“Spontaneous abortion” is another way of referring to miscarriages.)

Guttmacher data in this post comes from national surveys of abortion providers that Guttmacher has conducted 19 times since 1973. Guttmacher compiles its figures after contacting every known provider of abortions – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, and it provides estimates for abortion providers that don’t respond to its inquiries. (In 2020, the last year for which it has released data on the number of abortions in the U.S., it used estimates for 12% of abortions.) For most of the 2000s, Guttmacher has conducted these national surveys every three years, each time getting abortion data for the prior two years. For each interim year, Guttmacher has calculated estimates based on trends from its own figures and from other data.

The latest full summary of Guttmacher data came in the institute’s report titled “Abortion Incidence and Service Availability in the United States, 2020.” It includes figures for 2020 and 2019 and estimates for 2018. The report includes a methods section.

In addition, this post uses data from StatPearls, an online health care resource, on complications from abortion.

An exact answer is hard to come by. The CDC and the Guttmacher Institute have each tried to measure this for around half a century, but they use different methods and publish different figures.

The last year for which the CDC reported a yearly national total for abortions is 2021. It found there were 625,978 abortions in the District of Columbia and the 46 states with available data that year, up from 597,355 in those states and D.C. in 2020. The corresponding figure for 2019 was 607,720.

The last year for which Guttmacher reported a yearly national total was 2020. It said there were 930,160 abortions that year in all 50 states and the District of Columbia, compared with 916,460 in 2019.

• How the CDC gets its data: It compiles figures that are voluntarily reported by states’ central health agencies, including separate figures for New York City and the District of Columbia. Its latest totals do not include figures from California, Maryland, New Hampshire or New Jersey, which did not report data to the CDC. ( Read the methodology from the latest CDC report .)
• How Guttmacher gets its data: It compiles its figures after contacting every known abortion provider – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, then provides estimates for abortion providers that don’t respond. Guttmacher’s figures are higher than the CDC’s in part because they include data (and in some instances, estimates) from all 50 states. ( Read the institute’s latest full report and methodology .)

While the Guttmacher Institute supports abortion rights, its empirical data on abortions in the U.S. has been widely cited by  groups  and  publications  across the political spectrum, including by a  number of those  that  disagree with its positions .

These estimates from Guttmacher and the CDC are results of multiyear efforts to collect data on abortion across the U.S. Last year, Guttmacher also began publishing less precise estimates every few months , based on a much smaller sample of providers.

The figures reported by these organizations include only legal induced abortions conducted by clinics, hospitals or physicians’ offices, or those that make use of abortion pills dispensed from certified facilities such as clinics or physicians’ offices. They do not account for the use of abortion pills that were obtained  outside of clinical settings .

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The annual number of U.S. abortions rose for years after Roe v. Wade legalized the procedure in 1973, reaching its highest levels around the late 1980s and early 1990s, according to both the CDC and Guttmacher. Since then, abortions have generally decreased at what a CDC analysis called  “a slow yet steady pace.”

Guttmacher says the number of abortions occurring in the U.S. in 2020 was 40% lower than it was in 1991. According to the CDC, the number was 36% lower in 2021 than in 1991, looking just at the District of Columbia and the 46 states that reported both of those years.

(The corresponding line graph shows the long-term trend in the number of legal abortions reported by both organizations. To allow for consistent comparisons over time, the CDC figures in the chart have been adjusted to ensure that the same states are counted from one year to the next. Using that approach, the CDC figure for 2021 is 622,108 legal abortions.)

There have been occasional breaks in this long-term pattern of decline – during the middle of the first decade of the 2000s, and then again in the late 2010s. The CDC reported modest 1% and 2% increases in abortions in 2018 and 2019, and then, after a 2% decrease in 2020, a 5% increase in 2021. Guttmacher reported an 8% increase over the three-year period from 2017 to 2020.

As noted above, these figures do not include abortions that use pills obtained outside of clinical settings.

Guttmacher says that in 2020 there were 14.4 abortions in the U.S. per 1,000 women ages 15 to 44. Its data shows that the rate of abortions among women has generally been declining in the U.S. since 1981, when it reported there were 29.3 abortions per 1,000 women in that age range.

The CDC says that in 2021, there were 11.6 abortions in the U.S. per 1,000 women ages 15 to 44. (That figure excludes data from California, the District of Columbia, Maryland, New Hampshire and New Jersey.) Like Guttmacher’s data, the CDC’s figures also suggest a general decline in the abortion rate over time. In 1980, when the CDC reported on all 50 states and D.C., it said there were 25 abortions per 1,000 women ages 15 to 44.

That said, both Guttmacher and the CDC say there were slight increases in the rate of abortions during the late 2010s and early 2020s. Guttmacher says the abortion rate per 1,000 women ages 15 to 44 rose from 13.5 in 2017 to 14.4 in 2020. The CDC says it rose from 11.2 per 1,000 in 2017 to 11.4 in 2019, before falling back to 11.1 in 2020 and then rising again to 11.6 in 2021. (The CDC’s figures for those years exclude data from California, D.C., Maryland, New Hampshire and New Jersey.)

The CDC broadly divides abortions into two categories: surgical abortions and medication abortions, which involve pills. Since the Food and Drug Administration first approved abortion pills in 2000, their use has increased over time as a share of abortions nationally, according to both the CDC and Guttmacher.

The majority of abortions in the U.S. now involve pills, according to both the CDC and Guttmacher. The CDC says 56% of U.S. abortions in 2021 involved pills, up from 53% in 2020 and 44% in 2019. Its figures for 2021 include the District of Columbia and 44 states that provided this data; its figures for 2020 include D.C. and 44 states (though not all of the same states as in 2021), and its figures for 2019 include D.C. and 45 states.

Guttmacher, which measures this every three years, says 53% of U.S. abortions involved pills in 2020, up from 39% in 2017.

Two pills commonly used together for medication abortions are mifepristone, which, taken first, blocks hormones that support a pregnancy, and misoprostol, which then causes the uterus to empty. According to the FDA, medication abortions are safe  until 10 weeks into pregnancy.

Surgical abortions conducted  during the first trimester  of pregnancy typically use a suction process, while the relatively few surgical abortions that occur  during the second trimester  of a pregnancy typically use a process called dilation and evacuation, according to the UCLA School of Medicine.

In 2020, there were 1,603 facilities in the U.S. that provided abortions,  according to Guttmacher . This included 807 clinics, 530 hospitals and 266 physicians’ offices.

While clinics make up half of the facilities that provide abortions, they are the sites where the vast majority (96%) of abortions are administered, either through procedures or the distribution of pills, according to Guttmacher’s 2020 data. (This includes 54% of abortions that are administered at specialized abortion clinics and 43% at nonspecialized clinics.) Hospitals made up 33% of the facilities that provided abortions in 2020 but accounted for only 3% of abortions that year, while just 1% of abortions were conducted by physicians’ offices.

Looking just at clinics – that is, the total number of specialized abortion clinics and nonspecialized clinics in the U.S. – Guttmacher found the total virtually unchanged between 2017 (808 clinics) and 2020 (807 clinics). However, there were regional differences. In the Midwest, the number of clinics that provide abortions increased by 11% during those years, and in the West by 6%. The number of clinics  decreased  during those years by 9% in the Northeast and 3% in the South.

The total number of abortion providers has declined dramatically since the 1980s. In 1982, according to Guttmacher, there were 2,908 facilities providing abortions in the U.S., including 789 clinics, 1,405 hospitals and 714 physicians’ offices.

The CDC does not track the number of abortion providers.

In the District of Columbia and the 46 states that provided abortion and residency information to the CDC in 2021, 10.9% of all abortions were performed on women known to live outside the state where the abortion occurred – slightly higher than the percentage in 2020 (9.7%). That year, D.C. and 46 states (though not the same ones as in 2021) reported abortion and residency data. (The total number of abortions used in these calculations included figures for women with both known and unknown residential status.)

The share of reported abortions performed on women outside their state of residence was much higher before the 1973 Roe decision that stopped states from banning abortion. In 1972, 41% of all abortions in D.C. and the 20 states that provided this information to the CDC that year were performed on women outside their state of residence. In 1973, the corresponding figure was 21% in the District of Columbia and the 41 states that provided this information, and in 1974 it was 11% in D.C. and the 43 states that provided data.

In the District of Columbia and the 46 states that reported age data to  the CDC in 2021, the majority of women who had abortions (57%) were in their 20s, while about three-in-ten (31%) were in their 30s. Teens ages 13 to 19 accounted for 8% of those who had abortions, while women ages 40 to 44 accounted for about 4%.

The vast majority of women who had abortions in 2021 were unmarried (87%), while married women accounted for 13%, according to  the CDC , which had data on this from 37 states.

In the District of Columbia, New York City (but not the rest of New York) and the 31 states that reported racial and ethnic data on abortion to  the CDC , 42% of all women who had abortions in 2021 were non-Hispanic Black, while 30% were non-Hispanic White, 22% were Hispanic and 6% were of other races.

Looking at abortion rates among those ages 15 to 44, there were 28.6 abortions per 1,000 non-Hispanic Black women in 2021; 12.3 abortions per 1,000 Hispanic women; 6.4 abortions per 1,000 non-Hispanic White women; and 9.2 abortions per 1,000 women of other races, the  CDC reported  from those same 31 states, D.C. and New York City.

For 57% of U.S. women who had induced abortions in 2021, it was the first time they had ever had one,  according to the CDC.  For nearly a quarter (24%), it was their second abortion. For 11% of women who had an abortion that year, it was their third, and for 8% it was their fourth or more. These CDC figures include data from 41 states and New York City, but not the rest of New York.

Nearly four-in-ten women who had abortions in 2021 (39%) had no previous live births at the time they had an abortion,  according to the CDC . Almost a quarter (24%) of women who had abortions in 2021 had one previous live birth, 20% had two previous live births, 10% had three, and 7% had four or more previous live births. These CDC figures include data from 41 states and New York City, but not the rest of New York.

The vast majority of abortions occur during the first trimester of a pregnancy. In 2021, 93% of abortions occurred during the first trimester – that is, at or before 13 weeks of gestation,  according to the CDC . An additional 6% occurred between 14 and 20 weeks of pregnancy, and about 1% were performed at 21 weeks or more of gestation. These CDC figures include data from 40 states and New York City, but not the rest of New York.

About 2% of all abortions in the U.S. involve some type of complication for the woman , according to an article in StatPearls, an online health care resource. “Most complications are considered minor such as pain, bleeding, infection and post-anesthesia complications,” according to the article.

The CDC calculates  case-fatality rates for women from induced abortions – that is, how many women die from abortion-related complications, for every 100,000 legal abortions that occur in the U.S .  The rate was lowest during the most recent period examined by the agency (2013 to 2020), when there were 0.45 deaths to women per 100,000 legal induced abortions. The case-fatality rate reported by the CDC was highest during the first period examined by the agency (1973 to 1977), when it was 2.09 deaths to women per 100,000 legal induced abortions. During the five-year periods in between, the figure ranged from 0.52 (from 1993 to 1997) to 0.78 (from 1978 to 1982).

The CDC calculates death rates by five-year and seven-year periods because of year-to-year fluctuation in the numbers and due to the relatively low number of women who die from legal induced abortions.

In 2020, the last year for which the CDC has information , six women in the U.S. died due to complications from induced abortions. Four women died in this way in 2019, two in 2018, and three in 2017. (These deaths all followed legal abortions.) Since 1990, the annual number of deaths among women due to legal induced abortion has ranged from two to 12.

The annual number of reported deaths from induced abortions (legal and illegal) tended to be higher in the 1980s, when it ranged from nine to 16, and from 1972 to 1979, when it ranged from 13 to 63. One driver of the decline was the drop in deaths from illegal abortions. There were 39 deaths from illegal abortions in 1972, the last full year before Roe v. Wade. The total fell to 19 in 1973 and to single digits or zero every year after that. (The number of deaths from legal abortions has also declined since then, though with some slight variation over time.)

The number of deaths from induced abortions was considerably higher in the 1960s than afterward. For instance, there were 119 deaths from induced abortions in  1963  and 99 in  1965 , according to reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. The CDC is a division of Health and Human Services.

Note: This is an update of a post originally published May 27, 2022, and first updated June 24, 2022.

Support for legal abortion is widespread in many countries, especially in Europe

Nearly a year after roe’s demise, americans’ views of abortion access increasingly vary by where they live, by more than two-to-one, americans say medication abortion should be legal in their state, most latinos say democrats care about them and work hard for their vote, far fewer say so of gop, positive views of supreme court decline sharply following abortion ruling, most popular.

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Lean Six Sigma methodology to improve the discharge process in a Brazilian intensive care unit

Metodologia lean seis sigma para melhoria do processo de alta em uma unidade de terapia intensiva, metodología lean six sigma para mejorar el proceso de alta en una unidad de cuidados intensivos, guilherme dos santos zimmermann.

I Hospital Alemão Oswaldo Cruz. São Paulo, São Paulo, Brazil

Elena Bohomol

II Universidade Federal de São Paulo. São Paulo, São Paulo, Brazil

Objectives:

to describe the Lean Six Sigma implementation process to improve the discharge process in a Brazilian health institution’s ICU.

prospective study following the Define-Measure-Analyse-Improve-Control project development method. This method consists of five phases, namely: project definition, measurement of the starting point and data collection, analysis of results, improvement in processes, and statistical control.

applying Lean Six Sigma methodology following the Define-Measure-Analyse-Improve-Control in the discharge process from the intensive care unit to the inpatient unit was effective in improving processes. This improvement represented a reduction in the mean patient transfer time to the inpatient unit from 189 minutes to 75 minutes, representing a 61% improvement in discharge time.

Conclusions:

this article demonstrates the effectiveness of applying Lean Six Sigma methodology to improve the discharge flow in a critical unit, resulting in time and waste reduction.

descrever o processo de implementação do Lean Seis Sigma para melhoria do processo de alta em uma unidade de terapia intensiva brasileira.

Método:

foi realizado um estudo prospectivo, que seguiu o método de desenvolvimento de projetos intitulado DMAIC (Define-Measure-Analyze-Improve-Control). Este método constituiu cinco fases, sendo elas: a definição do projeto, mensuração do ponto inicial e coleta de dados, análise dos resultados, melhoria dos processos e controle estatístico.

Resultados:

a aplicação da metodologia Lean Seis Sigma foi efetiva para melhoria do processo de alta da unidade de terapia intensiva para a unidade de internação. Esta melhoria representou uma redução no tempo médio de alta de 189 para 75 minutos, totalizando uma melhoria de 61%.

Conclusões:

este artigo demonstra a efetividade da aplicação da metodologia Lean Seis Sigma para melhoria do fluxo de alta em uma unidade crítica, possibilitando ganhos na redução de tempo e desperdícios.

describir el proceso de implementación de Lean Six Sigma para mejorar el proceso de alta en una unidad de cuidados intensivos brasileña.

Métodos:

estudio prospectivo siguiendo el método de desarrollo de proyectos denominado DMAIC (Define-Measure-Analyze-Improve-Control) . Este método consta de cinco fases, a saber: definición del proyecto, medición del punto de partida y recolección de datos, análisis de resultados, mejora en los procesos y control estadístico.

la aplicación de la metodología Lean Six Sigma fue efectiva para mejorar el proceso de alta de la unidad de cuidados intensivos a la unidad de hospitalización. Esta mejora representó una reducción en el tiempo promedio de alta de 189 a 75 minutos, totalizando una mejora del 61%.

Conclusiones:

este artículo demuestra la efectividad de la aplicación de la metodología Lean Six Sigma para mejorar el flujo de descarga en una unidad crítica, lo que resulta en la reducción de tiempo y desperdicio.

INTRODUCTION

Continuous improvement in processes or manufacturing is a strategy adopted in many organisations, including healthcare. It requires steps such as problem identification, data collection, cause analysis, planning and implementation of improvements, verification, evaluation, and control of results. These steps can be taken by using specific methodologies, such as Lean Six Sigma (LSS), that incorporate tools and continuously consider quality assurance ( 1 - 2 ) .

This methodology stems from a business philosophy and aims to reduce waste and costs, while simultaneously improving processes with rates of 3.4 defects per million opportunities. The strategy to reduce errors to this proportion is to reduce process variation to a capability of ± six standard deviations (sigma) within specified limits ( 3 - 4 ) .

To achieve this result, LSS projects suggest applications of DMAIC (Define, Measure, Analyse, Improve and Control) cycles. These cycles follow all necessary phases to improve services or products, aiming to improve work processes, quality, satisfaction and to reduce waste rates ( 5 ) .

Studies have demonstrated improvements in healthcare processes that applied this methodology. One of them, conducted in a medical centre in California, United States of America (USA), showed a reduction in nurses turnaround time in a paediatric care unit, which increased the time dedicated to patient and family care and increased patient satisfaction from 87% to 95% ( 6 ) . Another study conducted in the state of Indiana (USA) showed a 44% reduction in the cost of assembling surgical instruments, resulting in savings of approximately one and a half million dollars for the institution ( 7 - 8 ) .

The transfer of care within hospital units is a complex situation, especially when patients are transferred from the intensive care unit (ICU) to the inpatient unit (IU). The association of clinical frailty and patients’ lack of autonomy with the time of transfer may result in adverse events due to factors such as high variability in the transfer process, lack of standardisation, communication failures, and difficulties in the transition of care ( 9 ) . When these factors are combined, the discharge process poses risk to the patient and must be analysed from the point of view of quality, safety, financial impacts, and the experience of the patient and family. This holistic analysis allows the development and implementation of improvement actions ( 10 ) .

Delays in the intra-hospital transfer process can negatively impact patients and their family. For example, research has shown that 10% of patients transferred from ICUs to the ward had an adverse event in the transfer of care, and 52% of such were avoidable ( 9 ) . Similar to patient safety, a poorly structured discharge process can have negative financial impacts. For example, a systematic review showed four main costs related to delays in discharge: patients who occupied beds after being clinically fit for discharge, delays in admission resulting from beds that were still occupied, pressure on the nursing staff to arrange discharge, and administrative interventions associated with organisation delays ( 11 ) .

The LSS methodology proves to be versatile for application in several areas, including critical environments. A study conducted in a USA ICU reduced the inpatient length of stay by 24% and the mean cost of mechanical ventilation per case by 27% ( 12 ) . Another study, also conducted in the USA, demonstrated the effectiveness of using the LSS methodology by showing a reduction in medical transfer time to the ICU from 214 to 84 minutes ( 13 ) .

The Six Sigma methodology was initially introduced in Brazil only in the mid-2000s, although without Lean tools. This methodology was mainly used in the automotive, electronics, and manufacturing sectors. Since its application in the Brazilian healthcare system is still limited, there is limited research in that field to demonstrate the Lean Six Sigma effectiveness in Brazilian healthcare institutions and to expose intersections between the nursery and management fields ( 7 ) . Therefore, this research question is: How is the LSS implementation process to improve the discharge process in a Brazilian health institution’s ICU?

To describe the LSS implementation process to improve the discharge process in a Brazilian health institution’s ICU.

Ethical aspects

The project was submitted and approved by the Research Ethics Committee and all research subjects agreed to participate by signing an Informed Consent Form.

Design, period, and place of study

This is a quantitative study to evaluate the impact of an intervention aimed to improve the discharge flow of an intensive care centre using the Lean Six Sigma methodology. The study was carried out in an Adult ICU in a tertiary high complexity private hospital in the city of São Paulo, SP, Brazil, from October 2018 to May 2019. The SQUIRE 2.0 (Standards for Quality Improvement Reporting Excellence) was used as reference during the development and structuring of the study.

The ICU has seven units - five general, one neurological, and one cardiac -, totalling 44 beds. All admitted patients have a clinical and surgical treatment profile and are mostly from emergency and operating rooms.

Population or sample, inclusion and exclusion criteria

Professionals of the ICU with experience in bedside care and leadership were invited to collaborate in the study. The researcher and the unit manager agreed to invite thirteen professionals to compose the core team of the project, consisting of seven nurses, two physiotherapists, an administrative assistant, a medical coordinator, and two nursing coordinators.

Study protocol and analysis of results and statistics

The DMAIC project development method was used to apply the LSS methodology:

Phase D - Define

A core working team of professionals from the unit was created. It was coordinated by the first researcher with a Green Belt background. The project scope, goals, process metrics, performance limits, and expected results were defined.

Phase M - Measure

This phase consisted of the preparation of the operational definition, that is, the description and measurement criteria established to collect data. Information on availability time of the inpatient bed (provided by the admission to the nursing coordinator) until discharge from the ICU was collected, measured by the transfer time to the IU recorded in the hospital’s computerised system.

Process data were tabulated and analysed using the Minitab ® statistical software, using the mean, median, standard deviation, and sigma capability of the sample. The initial sample consisted of the total number of discharges from two general intensive care units over a three-month period (October to December 2018).

The sigma capability was determined using the Z Bench value approximation method. This value is equivalent to the standardised index of the normal curve and was obtained by the proportion of defects in the process. A 1.5 sigma detachment was added to the Z Bench value to estimate the sigma capability of the process.

Phase A - Analyse

In this phase, we used an analysis model based on the concept of process management, in which the set of causes (x) generates effects on the output (y) of processes. Based on that, the core team used the cause-and-effect diagram tool to determine the relationships between x and y. Initially, a brainstorming session was conducted to highlight the problems encountered in the process of patient transfer from the ICU to the IU.

The traditional 6 M categorisation was used to create the cause-and-effect diagram: Measurements, Materials, Environment/Mother Nature, Method, Machine and Manpower. However, we decided to replace Manpower with Personnel, as it better describes the hospital environment. The GUT Matrix was used to implement and prioritise the possible causes identified through the analyses of three aspects: Gravity, Urgency, and Tendency. Problems are scored from 1 to 5 on each of the characteristics, and then multiplied to identify priorities ( 14 ) .

After prioritising the potential causes selected by the team, statistical tests were applied to confirm whether the identified problems affected the discharge process. Using the Minitab ® software, three hypotheses were tested for two samples. The Welch t test was used to compare two independent groups with normal means when their variances were not equal. The Minitab ® software report card assistant was used to determine sample power and normality pattern. A significance level (type I error probability) of < 0.05 was used.

Phase I - Improve

The improve phase began after identifying causes statistically associated with the ICU discharge flow. It aimed to determine and implement actions and re-analyse the improved process. Another brainstorming session was conducted with the core team to propose improvements in the elements that had a negative impact on the process and to construct action plans.

Phase C - Control

In the control phase, the aim was to standardise the improvements made in the previous phase and monitor the performance. For one month (May/2019), the individual data chart and moving range (I-MR) were prepared to monitor the mean and variation of the process.

Results will be presented in sections, each of them corresponding to a phase in the DMAIC cycle.

For this phase, the project was defined as “Lean Six Sigma to improve discharge flow” and its objective was to optimise the availability of intensive care beds after medical discharge, reduce waste, and improve operational efficiency. The first researcher and the unit manager agreed to invite thirteen professionals to form the core team of the project, consisting of seven nurses, two physiotherapists, an administrative assistant, a medical coordinator, and two nursing coordinators. The defined project scope was:

• Project name: Lean Six Sigma for High Flow Improvement.
• Justification: Improving ICU discharge flow refers to optimising the availability of intensive care beds after discharge. It is necessary mainly due to the high cost of maintaining the profile of patients, as keeping patients without real clinical need generates a waste of financial resources. Moreover, there may be an improvement in the unit’s renewal rate, which directly impacts revenue generation.
• Purpose: Improve patient discharge flow in the ICU.
• Goal: Reduce the time between availability of the ready room and the patient’s departure to 90 minutes or less.
• Client: Hospital and patients.
• Customer Impact: Reduced discharge time will make patients spend less time in the ICU environment, which can contribute to a better experience and reduce possible damage caused by the environment.
• Impact on the Company: The main advantages in improving the discharge flow are the reduction of unnecessary expenses and the indirect generation of income.

Capability data were collected for three months to determine the baseline. In this sample, the mean discharge time from the moment the room was ready was 189 minutes, with a standard deviation of 119.1. Capability was calculated by approximation of Z Bench, thus obtaining a sigma capability of 0.66 sigma. A histogram and a normal curve show these results graphically ( Figure 1 ).

In Phase A, the possible causes of defects in the process were determined by the core team. The main problems due to the extended time for the transfer of patients from the ICU to the IU are shown in Figure 2 .

Another meeting with the core team was held for performing cause-and-effect analyses to identify the most important causes of delays in the intra-hospital transfer. A GUT matrix was used to identify the ten most important problems: professionals’ procrastination in the transfer (Score 100), refusal of the destination IU to receive the shift (Score 60), IU staff and the nurse not being aware whether the room was clear (Score 48), family members request room change before transfer (Score 48), elevator delay during patient’s transfer (Score 48), doctors discharge request without clinical assessment (Score 36), absence of transport team for transfer (Score 36), patients remain in an ICU room in the electronic medical record (Score 36), patients request eating before transfer (Score 27) and incomplete medical orders before discharge (Score 24).

The team selected three of the eighteen causes, namely: professionals’ procrastination in the transfer; refusal of the destination IU to receive the shift, IU staff and the nurse not being aware whether the room was clear. Thus, three hypothesis tests were designed to confirm if they significantly affect the process or not.

Cause 1: In the procrastination test, the Active Procrastination Scale was used as a reference. It uses four factors to explain the situation: preference for pressure, ability to meet deadlines, satisfaction with results, and intentional decision ( 15 ) . We decided to use only two factors, the preference for pressure and the ability to meet deadlines because they have a greater impact on procrastination. Group I was challenged to refer the patient to the IU within 90 minutes from the moment the room was ready, with support of the nursing coordinator and other units (Group II), without receiving any intervention. Group I had a sample mean of 74 minutes versus 237 minutes compared to Group II, with a standard deviation of 119 for the group without a goal and 28 for the group with a goal (p < 0.001).

Cause 2: In the test for evaluating the refusal of the destination IU to receive the shift, discharge samples refused in the shift transfer call and discharge samples accepted in the shift transfer call were selected. The sample of refused discharges had a sample mean of 205 minutes versus 97 minutes for the sample of accepted discharges, with a standard deviation of 102 versus and 50, respectively (p < 0.001).

Cause 3: In the test of awareness of clean rooms, one sample of discharge was collected for clean rooms (which posed no need for the nurse to periodically check the system) and another sample for rooms that were not clean (which posed a need for the nurse to periodically check the system). There was a lower mean (92 minutes) of discharges in rooms in which the fourth status were “clean” compared to rooms in which fourth status were “not clean” (133 minutes), with no statistically significant difference between mean values (p= 0.056). Although the core team believed that receiving the fourth “not clean” status of bed management would delay the patient’s departure from the ICU, this hypothesis was not confirmed because the variable was not statistically significant.

Thus, a statistically significant cause-and-effect relationship was found in two of the three problems analysed by the core team, confirming that these causes are real and not institutional paradigms.

In the improvement phase, the core team met to establish actions to reduce the delay in the discharge flow. After conducting a brainstorming session considering the criteria of not adding costs to the institution and establishing a shorter implementation period, the team decided for the following improvements: establishment of room release goals, revision of the reason for delays, process adjustments, implementation of discharge checklist, and on-site training.

After the proposal and implementation of improvements, data of discharge times were collected again. This time, all discharges from the seven ICUs (n=287) were analysed for 30 days and the sigma capability of the discharge process was calculated again, as shown in Figure 3 .

In the new process, the mean time for the patient to leave the ICU for the IU was 75.7 minutes, with a standard deviation of 31.6 (maximum 200 minutes; 10 and median of 75 minutes). The new discharge process capability determined by the selected sample using the Z Bench approximation method was 1.95 sigma.

The preand post-intervention comparative analysis showed an improvement in process times, with reduction of a previous mean of 189 minutes to 75 minutes, in addition to a reduction in process variability with an improvement in sigma capability from 0.66 to 1.95 sigma, which corresponds to a 62.8% increase in discharges in up to 90 minutes. ( Figure 4 ):

In the control phase, improvements were standardised and a monthly verification of the indicator and I-MR control chart was established to determine the stability of the process. The control chart created revealed the non-stability of the process with some points being outside the control limits ( Figure 5 ).

Two out of the six observations outside the control limits were related to the absence of family members in the ICU for discharge (cases where patients could not be discharged unaccompanied to the IU), one was related to work overload and three of them had no justification, which reinforces the difficulty in dealing with notifications. As there was no stability in the process, we reinforced the need to investigate other possible causes for various other improvements not considered and to keep controlling the statistical process.

The initial sigma capability of the discharge process was 0.4 sigma, which represents 82% of items out of specification. After setting goals, training the team, and changing the flows, the post-intervention sigma capability was 1.93 sigma, representing 19% of items out of specification. Before the intervention, the mean time taken to transfer the patients from the ICU to the IU was 189 minutes, with a standard deviation of 119. After the intervention, it was 75 minutes, with a standard deviation of 31 minutes. There was a 113-minute gain in the process, representing a 61% improvement in discharge time.

This study allowed the full method application and the development of the project using the DMAIC. The first phase consisted of listing the guidelines, the project objectives, problem identification, expected results, and goals.

This tool was useful to guide the project team and contributed to activities of the organisation. This was also found in a study conducted in an emergency service in the USA aimed at reducing the inpatient length of stay. The reported results were positive, as the tool helped the project team with problem solving during planning and subsequent phases ( 16 ) .

The baseline was established by determining the sigma capability of the discharge time. In this study, the capability was 0.44σ, which is out of specification of 85.44%. In a similar study conducted in Kuwait, aimed at reducing the waiting time of patients in an obstetric hospital, the sigma capability was also used as a baseline for measuring time, with initial values of 0.5σ, which corresponds to 81.48% of non-compliance ( 17 ) .

The choice of measuring time during the project was part of the institution’s strategic planning. This variable is inferred to be closely related to the user experience in the health service in a competitive environment that prioritises customer satisfaction. Thus, a consideration of this indicator represents a strategic decision of organisations. The 60% reduction in discharge time from ICU to IU is supported by a study that also used LSS to reduce time in an obstetric hospital, finding a 67% reduction in patient care time on weekends and 63% on weekdays, improving patient and family satisfaction ( 17 ) . In another study conducted in an oncology centre in Jordan in which time was measured, a 50% reduction in waiting time for medical appointments was found, improving patient care ( 18 ) .

Optimisations in the discharge process can bring benefits to the institution, such as the increase in bed turnover, waste reduction, and increased satisfaction. This trend is supported by the results of a study conducted in a hospital in Jordan, proposing the reduction of discharge time without increasing unit costs, which found an increase of 57% in unit costs up to the limit of 50 minutes ( 19 ) .

Another study conducted in a paediatric hospital in the USA aimed to reduce the request and discharge time using LSS. It had positive results in anticipating discharge requests and a 53% reduction in discharge time, with positive impacts on bed turnover despite the high hospital occupancy rate ( 20 ) . While all these studies established time goals in the discharge process, the research institution did not control this metric, which highlights the importance of quantitative goals for improvement of this process.

This study opted for training, encouraging care, and supporting staff to minimise procrastination, envisioning a consequent reduction in the process time. Currently, there is a trend in healthcare organisations to prioritise workers within the care process, based on the method developed by the Institute for Healthcare Improvement, the Quadruple Aim. Aiming at improving the quality of the health system, one of the dimensions of this method is the team’s wellbeing, which integrates with the others, namely: improving the patient’s experience in care, improving population health, and reducing health care costs ( 21 - 22 ) .

Thus, the application of DMAIC in its entirety brought significant gains for the institution that go beyond what was initially proposed in this study. Data show that attention was paid to bed release time throughout the organisation, discharge in IU became more predictable, proposals were made for automating processes that were human-dependent, and goals related to patient flow in the institutional remuneration program were proposed.

Study limitations

This article demonstrates the effectiveness of applying Lean Six Sigma methodology to improve the discharge flow of a critical unit, resulting in time and waste reduction. One of the study limitations was the failure to investigate all the causes of delays. Additionally, the continuity of statistical control of the process could not be maintained due to the limited time of the research, which could have improved the sigma capability beyond the implementation of other actions. Finally, quantifying financial waste was a challenge and detailed analyses from this perspective could improve the actions that required financial investment.

Contributions to the fields of Nursing, Health or Public Policy

This study highlights the importance of using Lean Six Sigma methodology to improve processes and quality tools in health care institutions. Also, it underlines the significance of statistical thinking and agile methods proposed by Lean Six Sigma. It contributes to the care practice through (1) encouragement to implement methods to improve processes and quality tools in healthcare institutions and (2) engagement of care professionals in methods and quality management tools, which broadens their view of the healthcare system. Finally, in management and leadership, this study develops statistical thinking and LSS agile methods and contributes to professional development in interdisciplinary methods of improvement.

CONCLUSIONS

Applying Lean Six Sigma methodology following the DMAIC in the discharge process from the intensive care unit to the inpatient unit was effective in improving processes. Despite its origins in production, the methodology is a promising alternative for healthcare institutions, especially in patient care settings.

The initial sigma capacity of the discharge process was .44 sigma, which represents 82% of off-specification items. After goal setting, training and process adjustment, the post-intervention sigma capacity was 1.93 sigma, which accounts for 19% of off-specification items. The initial average time to transfer an ICU outpatient to the original hospital unit was 189 minutes, with a standard deviation of 119 minutes. The post-intervention average transfer time was 75 minutes, with a standard deviation of 31 minutes. The intervention warranted a total gain of time of 113 minutes, which accounts for an improvement of 61% in discharge time.

This work was carried out with support of the Coordination for the Improvement of Higher Education Personnel ( Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES).