A Systematic Review on Lean Applications' in Emergency Departments

Leveraging Kaizen with Process Mining in Healthcare Settings: A Conceptual Framework for Data-Driven Continuous Improvement

Background/Objectives: Healthcare systems face persistent challenges in improving efficiency, optimizing resources, and delivering high-quality care. Traditional continuous improvement methodologies often rely on subjective assessments, while data-driven approaches typically lack human-centered adaptability. This study aims to develop an integrated framework combining Kaizen principles with Process Mining capabilities to address these limitations in healthcare process optimization. Methods: This research employed a structured literature review approach to identify key concepts, methodologies, and applications of both Kaizen and Process Mining in healthcare settings. The study synthesized insights from the peer-reviewed literature published in the last two decades to develop a conceptual framework integrating these approaches for healthcare process improvement. Results: The proposed framework combines Kaizen's employee-driven approach to eliminating inefficiencies with Process Mining's ability to analyze workflow data and identify process deviations. The integration is structured into four key phases: data collection, process analysis, Kaizen events, and continuous monitoring. This structure creates a feedback loop where data-driven insights inform collaborative problem-solving, resulting in sustained improvements validated through objective process analysis. Conclusions: The integration of Kaizen and Process Mining offers a promising approach to enhancing workflow efficiency, reducing operational errors, and improving resource utilization in healthcare settings. While challenges such as data quality concerns, resource constraints, and potential resistance to change must be addressed, the framework provides a foundation for more effective process optimization. Future research should focus on empirical validation, AI-enhanced analytics, and assessing adaptability across diverse healthcare contexts.

A Mixed-Methods Analysis of Negative Patient Experiences in Emergency Department Care: Identifying Challenges and Evidence-Informed Strategies Across the Care Continuum

This mixed-methods study explores negative patient experiences within emergency departments (EDs), aiming to uncover systemic challenges and propose evidence-informed solutions. Of 2114 shared ED experiences, 306 (14.5%) were reported as "bad" or "very bad." Younger age, Indigenous status, financial instability, mental health disabilities, and non-heteronormative sexual identities were associated with negative ED experiences. Our research highlights key issues across the ED care continuum. During triage and registration, patients felt judged and perceived that their health concerns were under prioritized. Prolonged wait times contributed to feelings of neglect. During assessments, privacy concerns and lack of communication were prominent. Perceptions of misdiagnosis and stigmatization emerged as major concerns during the diagnosis and treatment phases. At discharge, insufficient follow-up and unclear instructions were frequently reported. Our findings underscore the need for improved communication, enhanced training to reduce stigma, and multi-pronged strategies to address the root causes of patient dissatisfaction. These insights can guide healthcare practitioners and policymakers in fostering a more inclusive and supportive ED environment, ultimately improving patient experiences and outcomes.

A SEIPS-Based Analysis to Understand Safety Culture During Postpartum Hemorrhage

Background/Objectives: Maternal mortality occurs at alarming rates in the United States. In 2018, there were 17 maternal deaths for every 100,000 births-double that of other high-income countries, including France and Canada. Postpartum hemorrhage (i.e., excessive blood loss during delivery or within the 24 h following) is a leading cause of maternal mortality and is a treatable condition if identified and managed in a timely manner. One aspect of work that impacts patient care during postpartum hemorrhage is the safety culture. The safety culture is the beliefs, values, and norms shared by members of the organization that influence their actions and behaviors. In this study, we use the Systems Engineering Initiative for Patient Safety (SEIPS) model to understand and describe how the sociotechnical system shapes safety culture during postpartum hemorrhage. Methods: We conducted interviews and focus groups with 29 clinicians to describe the work system and the barriers and facilitators during postpartum hemorrhage. Then, we inductively categorized the barriers and facilitators into emergent properties of sociotechnical systems related to safety culture. Results: We identified 45 barriers and 158 facilitators into five emergent properties related to the safety culture (i.e., staffing, communication, organizational management and leadership, organizational processes, and teamwork). The participants identified more positive aspects than negative, suggesting that the safety culture positively influences their actions and behaviors. Conclusions: Our results indicate that safety culture could be improved by redesigning the work system to mitigate barriers related to staffing, communication, organizational management, and teamwork that hinder the safety culture.

Comprehensive Risk Analysis of Emergency Medical Response Systems in Serbian Healthcare: Assessing Systemic Vulnerabilities in Disaster Preparedness and Response

BACKGROUND/OBJECTIVES

Emergency Medical Response Systems (EMRSs) play a vital role in delivering medical aid during natural and man-made disasters. This quantitative research delves into the analysis of risk and effectiveness within Serbia's Emergency Medical Services (EMS), with a special emphasis on how work organization, resource distribution, and preparedness for mass casualty events contribute to overall disaster preparedness.

METHODS

The study was conducted using a questionnaire consisting of 7 sections and a total of 88 variables, distributed to and collected from 172 healthcare institutions (Public Health Centers and Hospitals). Statistical methods, including Pearson's correlation, multivariate regression analysis, and chi-square tests, were rigorously applied to analyze and interpret the data.

RESULTS

The results from the multivariate regression analysis revealed that the organization of working hours (β = 0.035) and shift work (β = 0.042) were significant predictors of EMS organization, explaining 1.9% of the variance (R2 = 0.019). Furthermore, shift work (β = -0.045) and working hours (β = -0.037) accounted for 2.0% of the variance in the number of EMS points performed (R2 = 0.020). Also, the availability of ambulance vehicles (β = 0.075) and financial resources (β = 0.033) explained 4.1% of the variance in mass casualty preparedness (R2 = 0.041). When it comes to service area coverage, the regression results suggest that none of the predictors were statistically significant. Based on Pearson's correlation results, there is a statistically significant correlation between the EMS organization and several key variables such as the number of EMS doctors (p = 0.000), emergency medicine specialists (p = 0.000), etc. Moreover, the Chi-square test results reveal statistically significant correlations between EMS organization and how EMS activities are conducted (p = 0.001), the number of activity locations (p = 0.005), and the structure of working hours (p = 0.001).

CONCLUSIONS

Additionally, the results underscore the necessity for increased financial support, standardized protocols, and enhanced intersectoral collaboration to strengthen Serbia's EMRS and improve overall disaster response effectiveness. Based on these findings, a clear roadmap is provided for policymakers, healthcare administrators, and EMS personnel to prioritize strategic interventions and build a robust emergency medical response system.

New horizons in systems engineering and thinking to improve health and social care for older people

Existing models for the safe, timely and effective delivery of health and social care are challenged by an ageing population. Services and care pathways are often optimised for single-disease management, while many older people are presenting with multiple long-term conditions and frailty. Systems engineering describes a holistic, interdisciplinary approach to change that is focused on people, system understanding, design and risk management. These principles are the basis of many established quality improvement (QI) tools in health and social care, but implementation has often been limited to single services or condition areas. Newer engineering techniques may help reshape more complex systems. Systems thinking is an essential component of this mindset to understand the underlying relationships and characteristics of a working system. It promotes the use of tools that map, measure and interrogate the dynamics of complex systems. In this New Horizons piece, we describe the evolution of systems approaches while noting the challenges of small-scale QI efforts that fail to address whole-system problems. The opportunities for novel soft-systems approaches are described, along with a recent update to the Systems Engineering Initiative for Patient Safety model, which includes human-centred design. Systems modelling and simulation techniques harness routine data to understand the functioning of complex health and social care systems. These tools could support better-informed system change by allowing comparison of simulated approaches before implementation, but better effectiveness evidence is required. Modern systems engineering and systems thinking techniques have potential to inform the redesign of services appropriate for the complex needs of older people.

Diagnosing patient flow issues in the emergency department: an Australasian hospital case study

PURPOSE

This study investigates how a hospital can increase the flow of patients through its emergency department by using benchmarking and process improvement techniques borrowed from the manufacturing sector.

DESIGN/METHODOLOGY/APPROACH

An in-depth case study of an Australasian public hospital utilises rigorous, multi-method data collection procedures with systems thinking to benchmark an emergency department (ED) value stream and identify the performance inhibitors.

FINDINGS

High levels of value stream uncertainty result from inefficient processes and weak controls. Reduced patient flow arises from senior management's commitment to simplistic government targets, clinical staff that lack basic operations management skills, and fragmented information systems. High junior/senior staff ratios aggravate the lack of inter-functional integration and poor use of time and material resources, increasing the risk of a critical patient incident.

RESEARCH LIMITATIONS/IMPLICATIONS

This research is limited to a single case; hence, further research should assess value stream maturity and associated performance enablers and inhibitors in other emergency departments experiencing patient flow delays.

PRACTICAL IMPLICATIONS

This study illustrates how hospital managers can use systems thinking and a context-free performance benchmarking measure to identify needed interventions and transferable best practices for achieving seamless patient flow.

ORIGINALITY/VALUE

This study is the first to operationalise the theoretical concept of the seamless healthcare system to acute care as defined by Parnaby and Towill (2008). It is also the first to use the uncertainty circle model in an Australasian public healthcare setting to objectively benchmark an emergency department's value stream maturity.

Improving the emergency services using quality improvement project and Donabedian model in a quaternary teaching hospital in South India

INTRODUCTION

The primary goal of quality improvement is to enhance patient outcomes, particularly in the emergency department (ED). Timely and effective care is crucial in these situations. By comprehending the challenges, evaluating current performance and implementing quality improvement projects, areas in need of enhancement can be pinpointed and addressed, resulting in better outcomes.

METHODOLOGY

This interventional study explores the implementation of quality improvement in the ED of a quaternary care teaching hospital in South India. It follows the Plan-Do-Check-Act (PDCA) cycle guided by the Donabedian model. Descriptive statistics were employed to measure changes in outcomes before and after implementation. To improve processes, Donabedian principles were applied, and a performance audit was conducted based on patient feedback and stakeholder input. Various ED indicators were measured. To address identified issues, formal root cause analysis was performed, leading to the generation of PDCA rapid change cycles. These cycles were implemented over 6 months, with two cycles executed, followed by postimplementation evaluation.

RESULTS

Post implementation, improvements were observed in several aspects of ED operations. These included reduced ED average length of stay, decreased time to analgesia, shorter cross-consultation time, faster transfer time from ED and improved investigation turnaround time (TAT). Additionally, there was a reduction in revisits to ED within 72 hours and a decrease in patients who left without being seen. These positive changes demonstrate the effectiveness of the quality improvement intervention using the PDCA cycle.

CONCLUSION

A comprehensive understanding of patient profile in the ED and factors influencing care is essential for the hospital to ensure sufficient resources and skilled emergency medicine physicians are available 24/7. By enhancing services in the ED, reducing patient waiting times and improving TAT, the overall efficiency of services can be improved. This leads to provision of timely quality care to patients and ultimately improves their outcomes.

Applying Lean Kaizen to Improve Timely Computed Tomography Scan Appointments for Oncology Patients in a Safety Net Hospital

PURPOSE

Timely radiographic studies are essential to oncology care. At our institution, a safety net hospital in a large metropolitan area, baseline assessment determined that the overwhelming majority of outpatient computed tomographic (CT) scans for oncology patients were overdue and not scheduled within 2 weeks of their first requested date.

METHODS

We conducted a series of structured, interdisciplinary meetings including staff from radiology, oncology, scheduling, and administration to critically review the scheduling process utilizing Lean Kaizen quality improvement methods. A new workflow was developed in which clinic staff scheduled CT scans before clinic discharge. Three months after our initial meeting, the new workflow was launched. We set a target of decreasing the percentage of overdue scans to below 20%.

RESULTS

At baseline, 87% (65 of 75) of CT scans awaiting scheduling were overdue. Data were gathered at 5 and 10 weeks after implementation of our workflow. The percentage of CT scans overdue for scheduling was 17% (9 of 53) at 5 weeks and 0.97% (1 of 103) at 10 weeks after implementation. Clinic visit durations were not affected.

CONCLUSION

The Lean Kaizen QI model was successful in decreasing the rate of oncology patients overdue for CT scan scheduling with minimal effects on clinic visit durations. This study demonstrated the importance of interdepartmental collaboration and continuous monitoring for improvement. Given the success of this project, this workflow will be expanded to other outpatient clinics within our institution.

Managing inpatient bed setup: an action-research approach using lean technical practices and lean social practices

PURPOSE

This article aims to introduce a guide to improving hospital bed setup by combining lean technical practices (LTPs), such as kaizen and value stream mapping (VSM) and lean social practices (LSPs), such as employee empowerment.

DESIGN/METHODOLOGY/APPROACH

Action research approach was employed to analyze the process of reconfiguration of bed setup management in a Brazilian public hospital.

FINDINGS

The study introduces three contributions: (1) presents the use of VSM focused specifically on bed setup, while the current literature presents studies mainly focused on patient flow management, (2) combines the use of LSPs and LTPs in the context of bed management, expanding current studies that are focused either on mathematical models or on social and human aspects of work, (3) introduces a practical guide based on six steps that combine LSPs and LSPs to improve bed setup management.

RESEARCH LIMITATIONS/IMPLICATIONS

The research focused on the analysis of patient beds. Surgical beds, delivery, emergency care and intensive care unit (ICU) were not considered in this study. In addition, the process indicators analyzed after the implementation of the improvements did not contemplate the moment of the COVID-19 pandemic. Finally, this research focused on the implementation of the improvement in the context of only one Brazilian public hospital.

PRACTICAL IMPLICATIONS

The combined use of LSPs and LTPs can generate considerable gains in bed setup efficiency and consequently increase the capacity of a hospital to admit new patients, without the ampliation of the physical space and workforce.

SOCIAL IMPLICATIONS

The improvement of bed setup has an important social character, whereas it can generate important social benefits such as the improvement of the admission service to patients, reducing the waiting time, reducing hospitalization costs and improving the hospital capacity without additional physical resources. All these results are crucial for populations, their countries and regions.

ORIGINALITY/VALUE

While the current literature on bed management is more focused on formal models or pure human and social perspectives, this article brings these two perspectives together in a single, holistic framework. As a result, this article points out that the complex bed management problem can be efficiently solved by combining LSPs and LTPs to present theoretical and practical contributions to the important social problem of hospital bed management.

Transformational Performance Improvement: Why Is Progress so Slow?

In this chapter, we identify three distinct transformational performance improvement (TPI) approaches commonly used to redesign work processes in health care organizations. We describe the unique components or tools that each approach uses to improve the delivery of health services. We also summarize what is empirically known about the effectiveness of each TPI approach according to systematic reviews and recent studies published in the peer-reviewed literature. Based on examination of this research, we discuss what knowledge is still needed to strengthen the evidence for whole system transformation. This involves the use of conceptual frameworks to assess and guide implementation efforts, and facilitators and barriers to change as revealed in a recent evaluation of one major initiative, the Lean Enterprise Transformation (LET) at the Veterans Health Administration. The analysis suggests ways in which TPI facilitators can be developed and barriers reduced to improve the effectiveness and sustainability of quality initiatives. Finally, we discuss appropriate study designs to evaluate TPI interventions that may strengthen the evidence for their effectiveness in real world practice settings.

Road to Better Work-Life Balance? Lean Redesigns and Daily Work Time among Primary Care Physicians

PURPOSE

To assess the impact of Lean primary care redesigns on the amount of time that physicians spent working each day.

METHODS

This observational study was based on 92 million time-stamped Epic® EHR access logs captured among 317 primary care physicians in a large ambulatory care delivery system. Seventeen clinic facilities housing 46 primary care departments were included for study. We conducted interrupted time series analysis to monitor changes in physician work patterns over 6 years. Key measures included total daily work time; time spent on "desktop medicine" outside the exam room; time spent with patients during office visits; time still working after clinic, i.e., after seeing the last patient each day; and remote work time.

RESULTS

The amount of time that physicians spent on desktop EHR activities throughout the day, including after clinic hours, decreased by 10.9% (95% CI: -22.2, -2.03) and 8.3% (95% CI: -13.8, -2.12), respectively, during the first year of Lean implementation. Total daily work hours among physicians, which included both desktop activity and time in office visits, decreased by 20% (95% CI: -29.2, -9.60) by the third year of Lean implementation.

CONCLUSIONS

These findings suggest that Lean redesign may be associated with time savings for primary care physicians. However, since this was an observational analysis, further study is warranted (e.g., randomized trial) -to determine the impact of Lean interventions on physician work experiences.

Modelling Granular Process Flow Information to Reduce Bottlenecks in the Emergency Department

Increasing demand and changing case-mix have resulted in bottlenecks and longer waiting times in emergency departments (ED). However, many process improvement efforts addressing the bottlenecks have limitations, as they lack accurate models of the real system as input accounting for operational complexities. To understand the limitation, this research modelled granular procedural information, to analyse processes in a Level-1 ED of a 1200-bed teaching hospital in the UK. Semi-structured interviews with 21 clinicians and direct observations provided the necessary information. Results identified Majors as the most crowded area, hence, a systems modelling technique, role activity diagram, was used to derive highly granular process maps illustrating care in Majors which were further validated by 6 additional clinicians. Bottlenecks observed in Majors included awaiting specialist input, tests outside the ED, awaiting transportation, bed search, and inpatient handover. Process mapping revealed opportunities for using precedence information to reduce repeat tests; informed alerting; and provisioning for operational complexity into ED processes as steps to potentially alleviate bottlenecks. Another result is that this is the first study to map care processes in Majors, the area within the ED that treats complex patients whose care journeys are susceptible to variations. Findings have implications on the development of improvement approaches for managing bottlenecks.

Exosomes Derived From Mesenchymal Stem Cells: Novel Effects in the Treatment of Ischemic Stroke

Ischemic stroke is defined as an infarction in the brain, caused by impaired cerebral blood supply, leading to local brain tissue ischemia, hypoxic necrosis, and corresponding neurological deficits. At present, revascularization strategies in patients with acute ischemic stroke include intravenous thrombolysis and mechanical endovascular treatment. However, due to the short treatment time window (<4.5 h) and method restrictions, clinical research is focused on new methods to treat ischemic stroke. Exosomes are nano-sized biovesicles produced in the endosomal compartment of most eukaryotic cells, containing DNA, complex RNA, and protein (30-150 nm). They are released into surrounding extracellular fluid upon fusion between multivesicular bodies and the plasma membrane. Exosomes have the characteristics of low immunogenicity, good innate stability, high transmission efficiency, and the ability to cross the blood-brain barrier, making them potential therapeutic modalities for the treatment of ischemic stroke. The seed sequence of miRNA secreted by exosomes is base-paired with complementary mRNA to improve the microenvironment of ischemic tissue, thereby regulating downstream signal transduction activities. With exosome research still in the theoretical and experimental stages, this review aims to shed light on the potential of exosomes derived from mesenchymal stem cells in the treatment of ischemic stroke.

Lean ICU Layout Re-Design: A Simulation-Based Approach

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility.

Lean-ing Method in an Emergency Department of the Italian Epicenter of the COVID-19 Outbreak: When the Algorithm Makes Difference

The Lean method entails a set of standardized processes intending to optimize resources, reduce waste, and improve results. Lean has been proposed as an operative model for the COVID-19 outbreak. Herein, we summarized data resulted from the Lean model adoption in an Emergency Department of the Lombardy region, the Italian epicenter of the pandemic, to critically appraise its effectiveness and feasibility. The Lean algorithm was applied in the Humanitas Clinical and Research Hospital, Milan, north of Italy. At admission, patients underwent outdoor pre-triage for fever, respiratory, and gastrointestinal symptoms, with a focus on SpO2. Based on these data, they were directed to the most appropriate area for the COVID-19 first-level screening. High-risk patients were assisted by trained staff for second-level screening and planning of treatment. Out of 7.778 patients, 21.9% were suspected of SARS-CoV-2 infection. Mortality was 21.9% and the infection rate in health workers was 4.8%. The lean model has proved to be effective in optimizing the overall management of COVID-19 patients in an emergency setting. It allowed for screening of a large volume of patients, while also limiting the health workers’ infection rate. Further studies are necessary to validate the suggested approach.