The study goal was to prospectively characterize the demographics, mechanisms of injury, diagnose... more The study goal was to prospectively characterize the demographics, mechanisms of injury, diagnoses, and outcomes among trauma patients presenting to Mbarara Regional Referral Hospital (MRRH) prior to planned trauma training interventions. Methods: All patients presenting to the Accidents & Emergency (A&E) area at MRRH over a ten-week period who met the World Health Organization definition of major trauma were prospectively enrolled. Patient demographics, mechanism of injury, arrival mode, referral site, initial vital signs, and diagnoses were collected by direct observation and chart review. Disposition from A&E, vital status and therapeutic actions were determined by daily chart review for the first 14 days of hospitalization. Outpatients or their caregivers were contacted by phone at 2 weeks and 30 days to determine vital status. Ethical/research approval was obtained from the Mbarara University of Science and Technology (Mbarara, Uganda) and Partners Healthcare (Boston, USA). Results: 497 patients with traumatic injuries presented during the 10-week study period. We included the 415 who met WHO criteria for major trauma. Mean age was 26.5 years (range 1-76, SD 14.6), and 77.8% were male. Patients most frequently arrived by car (61.7%), followed by boda/motorbike (21.2%), and ambulance (13.5%). 122 patients arrived in transfer (29.4%). Mechanisms of injury included motor vehicle accidents (60.2%), pedestrians struck (18.3%), assaults (11.1%) and penetrating trauma (4.8%). Only 30.4% of patients had any single vital sign recorded during their trauma evaluation, with only 2.6% having a documented heart rate, blood pressure, respiratory rate and oxygen saturation. Invasive interventions were rare and limited to Foley catheters (6.3%), nasogastric tubes (2.4%), and chest tubes (1.7%). There were no interosseous, central or arterial lines placed and no intubations in A&E. Patients remained in A&E for variable periods but were then discharged to: home (45.5%), surgery ward (33.7%), operating theatre (8.2%), or morgue (5.3%). Only 1.7% (7) patients went to the ICU. Head injury was the most common discharge diagnosis (44.6%) in the 186 hospitalized patients with follow-up, followed by fracture/dislocation (29% closed, 13.4% open). Abdominal trauma was a rare diagnosis (5.9% blunt, 1.6% penetrating), as was chest trauma (2.7% blunt, 1.1% penetrating). There were 35 in-hospital deaths (8.4%, 95% CI ¼ 5.9%-11.5%); 48.6% were within 24 hours of presentation. Head injury was among the major diagnoses in 84% of deaths. While the lost-to-follow-up rate at 2 weeks was high (40%), 3 additional deaths were identified bringing the overall mortality estimate to 9.2% (95% CI ¼ 6.6%-12.4%). There were no known additional deaths at 30-day follow-up. Conclusions: The trauma burden and mortality at this referral hospital are high. The site manages critically ill patients but has limited resuscitation capacity in A&E including no functioning CT scanner, limited critical care if patients cannot pay for ICU care, and inconsistent diagnostic, medical, operative and ICU supplies. Training in the algorithmic management of trauma patients and emergency medicine is currently limited but planned.
Joint Commission journal on quality and patient safety, May 1, 2019
Background: In hospitals and health systems across the country, patient flow bottlenecks delay ca... more Background: In hospitals and health systems across the country, patient flow bottlenecks delay care delivery-emergency department boarding and operating room exit holds are familiar examples. In other industries, such as oil, gas, and air traffic control, command centers proactively manage flow through complex systems. Methods: A systems engineering approach was used to analyze and maximize existing capacity in one health system, which led to the creation of the Judy Reitz Capacity Command Center. This article describes the key elements of this novel health system command center, which include strategic colocation of teams, automated visual displays of real-time data providing a global view, predictive analytics, standard work and rules-based protocols, and a clear chain of command and guiding tenets. Preliminary data are also shared. Results: With proactive capacity management, subcycle times decreased and allowed the health system's flagship hospital to increase occupancy from 85% to 92% while decreasing patient delays. Conclusion: The command center was built with three primary goals-reducing emergency department boarding, eliminating operating room holds, and facilitating transfers in from outside facilities-but the command center infrastructure has the potential to improve hospital operations in many other areas. PROBLEM DEFINITION AND CONTEXT I n the inpatient setting, hospital gridlock often delays patient movement to the optimal location for care. Emergency department (ED) crowding and boarding, which stem from systemwide inefficiencies, 1-3 have been directly linked to higher inpatient morbidity, preventable harm, and overall mortality, as well as increased total inpatient length of stay and decreased patient satisfaction. 4-16 Operating room (OR) exit holds, in which patients are unable to move from the OR to the postanesthesia care unit (PACU) or the ICU due to capacity constraints, are associated with worse patient outcomes and avoidable expense. 17,18 Delays in moving patients to critical care units are associated with increased mortality. 19-21 At tertiary care centers, inefficiencies often limit the number of patients eligible for transfer in from surrounding community hospitals, thereby delaying or preventing access to care. Many approaches have been tried to address boarding and crowding, such as monitoring of bed turnaround time, OR schedule smoothing, telemedicine consults, Lean and Plan-Do-Study-Act (PDSA) rapid cycle improvement, and many more. 2,5,22,23 Despite this, crowding continues to increase, leading The Joint Commission to require hospitals to measure and address ED boarding and the Centers for Medicare & Medicaid Services to include related metrics
Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant ti... more Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant time-stamped data from electronic medical records and other information technologies. However, the various characterizations of patient flow, cohort decisions, sub-processes, and the diverse stakeholders requiring data visibility create further overlying complexity. We use the Donabedian model to prioritize patient flow metrics and build an electronic dashboard for enabling communication. Ten metrics were identified as key indicators including outcome (length of stay, 30-day readmission, operating room exit delays, capacityrelated diversions), process (timely inpatient unit discharge, emergency department disposition), and structural metrics (occupancy, discharge volume, boarding, bed assignation duration). Dashboard users provided real-life examples of how the tool is assisting capacity improvement efforts, and user traffic data revealed an uptrend in dashboard utilization from May to October 2017 (26 to 148 views per month, respectively). Our main contributions are twofold. The former being the results and methods for selecting key performance indicators for a unit, department, and across the entire hospital (i.e., separating signal from noise). The latter being an electronic dashboard deployed and used at The Johns Hopkins Hospital to visualize these ten metrics and communicate systematically to hospital stakeholders. Integration of diverse information technology may create further opportunities for improved hospital capacity.
Background The ability to provide medical care during sudden increases in patient volume during a... more Background The ability to provide medical care during sudden increases in patient volume during a disaster or other high-consequence event is a serious concern for health-care systems. Identifi cation of inpatients for safe early discharge (ie, reverse triage) could create additional hospital surge capacity. We sought to develop a disposition classifi cation system that categorises inpatients according to suitability for immediate discharge on the basis of risk tolerance for a subsequent consequential medical event. Methods We did a warfare analysis laboratory exercise using evidence-based techniques, combined with a consensus process of 39 expert panellists. These panellists were asked to defi ne the categories of a disposition classifi cation system, assign risk tolerance of a consequential medical event to each category, identify critical interventions, and rank each (using a scale of 1-10) according to the likelihood of a resultant consequential medical event if a critical intervention is withdrawn or withheld because of discharge. Findings The panellists unanimously agreed on a fi ve-category disposition classifi cation system. The upper limit of risk tolerance for a consequential medical event in the lowest risk group if discharged early was less than 4%. The next categories had upper limits of risk tolerance of about 12% (IQR 8-15%), 33% (25-50%), 60% (45-80%) and 100% (95-100%), respectively. The expert panellists identifi ed 28 critical interventions with a likelihood of association with a consequential medical event if withdrawn, ranging from 3 to 10 on the 10-point scale. Interpretation The disposition classifi cation system allows conceptual classifi cation of patients for suitable disposition, including those deemed safe for early discharge home during surges in demand. Clinical criteria allowing real-time categorisation of patients are awaited.
Any opinions, findings, conclusions, or recommendations expressed in this publication are those o... more Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not represent the policy or position of the Department of Homeland Security. Few tools exist that are sufficiently robust to allow manipulation of key input variables to produce casualty estimates resulting from high-consequence events reflecting local or specific regions of concern. This article describes the design and utility of a computerized modeling simulation tool, Electronic Mass Casualty Assessment and Planning Scenarios (EMCAPS), developed to have broad application across emergency management and public health fields as part of a catastrophic events preparedness planning process. As a scalable, flexible tool, EMCAPS is intended to support emergency preparedness planning efforts at multiple levels ranging from local health systems to regional and state public health departments to Metropolitan Medical Response System jurisdictions. Designed around the subset of the National Planning Scenarios with health effects, advanced by the US Department of Homeland Security, the tool's platform is supported by the detailed descriptions and readily retrievable evidence-based assumptions of each scenario. The EMCAPS program allows the user to manipulate key scenario-based input variables that would best reflect the region or locale of interest. Inputs include population density, vulnerabilities, event size, and potency, as applicable. Using these inputs, EMCAPS generates the anticipated population-based health surge influence of the hazard scenario. Casualty estimates are stratified by injury severity/types where appropriate. Outputs are graph and table tabulations of surge estimates. The data can then be used to assess and tailor response capabilities for specific jurisdictions, organizations, and health care systems. EMCAPS may be downloaded without cost from https://www.hopkins-cepar.org/EMCAPS/EMCAPS.html as shareware. [
Background: Emergency Departments (ED) are challenged with excess demand for services and inadequ... more Background: Emergency Departments (ED) are challenged with excess demand for services and inadequate system capacity. Crowding at two independent EDs within a health system prompted an examination of the potential effects of improving patient throughput. The objective of this study was to determine the effects of reducing ED dwell time on temporal patterns of patient flow and demand for ED resources. Methods: Separate discrete event simulation (DES) models were developed for the EDs of a 1,000-bed urban medical center and a 560-bed community medical center using patient flow information. These models characterized the effects of reducing patient dwell time on ED care area census (i.e., staffing needs), waiting room census, total length of stay (LOS) and waiting time. Dwell time was defined as the time interval from when a patient entered the main ED care area to when the patient exited the ED by discharge or hospital admission. Total LOS is defined as the entire time interval from ED from arrival to exit (including waiting time). Results: DES results for each site demonstrate how natural patient arrivals and common hospital admission processes generate common temporal patterns of decreased crowding. Improving flow translates to most substantial reductions in waiting time and waiting room census during evening hours (17:00 to 22:00 hours). Significant effects on ED care area census and staffing demands are lagged, not occurring until overnight hours (2:00 to 8:00 hours). We reduced patient dwell time in 5% increments within the urban ED (16.2 min) and community ED (13.5 min) from 5% to 15%. For example, a 10% decrease in dwell time at the urban ED (32.4 min) and community ED (27.0 min) resulted in respective decreases in evening waiting room census by 49% (10.8 patients) and 26% (3.5 patients) during evening hours and ED care area census by 16% (3.6 patients) and 11% (2.0 patients) overnight. Conclusions: DES results suggest that increasing ED efficiency will most significantly decrease delays experienced by evening arrivals and provide opportunities to decrease care area census and reduce staff overnight.
Additional file 1: Table S1. AAAEM/AACEM ED Benchmarking Survey, Select Questions and Definitions... more Additional file 1: Table S1. AAAEM/AACEM ED Benchmarking Survey, Select Questions and Definitions. List of AAAEM/AACEM survey questions and associated definitions
Additional file 2: Table S2. EDBA Benchmarking Survey, Select Questions and Definitions. List of ... more Additional file 2: Table S2. EDBA Benchmarking Survey, Select Questions and Definitions. List of EDBA survey questions and associated definitions
Healthcare organizations face challenges in efficiently accommodating increased patient demand wi... more Healthcare organizations face challenges in efficiently accommodating increased patient demand with limited resources and capacity. The modern reimbursement environment prioritizes the maximization of operational efficiency and the reduction of unnecessary costs (i.e., waste) while maintaining or improving quality. As healthcare organizations adapt, significant pressures are placed on leaders to make difficult operational and budgetary decisions. In lieu of hard data, decision makers often base these decisions on subjective information. Discrete event simulation (DES), a computerized method of imitating the operation of a real-world system (e.g., healthcare delivery facility) over time, can provide decision makers with an evidence-based tool to develop and objectively vet operational solutions prior to implementation. DES in healthcare commonly focuses on (1) improving patient flow, (2) managing bed capacity, (3) scheduling staff, (4) managing patient admission and scheduling proced...
Any opinions, findings, conclusions, or recommendations expressed in this publication are those o... more Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not represent the policy or position of the Department of Homeland Security. Few tools exist that are sufficiently robust to allow manipulation of key input variables to produce casualty estimates resulting from high-consequence events reflecting local or specific regions of concern. This article describes the design and utility of a computerized modeling simulation tool, Electronic Mass Casualty Assessment and Planning Scenarios (EMCAPS), developed to have broad application across emergency management and public health fields as part of a catastrophic events preparedness planning process. As a scalable, flexible tool, EMCAPS is intended to support emergency preparedness planning efforts at multiple levels ranging from local health systems to regional and state public health departments to Metropolitan Medical Response System jurisdictions. Designed around the subset of the National Planning Scenarios with health effects, advanced by the US Department of Homeland Security, the tool's platform is supported by the detailed descriptions and readily retrievable evidence-based assumptions of each scenario. The EMCAPS program allows the user to manipulate key scenario-based input variables that would best reflect the region or locale of interest. Inputs include population density, vulnerabilities, event size, and potency, as applicable. Using these inputs, EMCAPS generates the anticipated population-based health surge influence of the hazard scenario. Casualty estimates are stratified by injury severity/types where appropriate. Outputs are graph and table tabulations of surge estimates. The data can then be used to assess and tailor response capabilities for specific jurisdictions, organizations, and health care systems. EMCAPS may be downloaded without cost from https://www.hopkins-cepar.org/EMCAPS/EMCAPS.html as shareware. [
Introduction Nationally, there has been more than a 40% decrease in Emergency Department (ED) pat... more Introduction Nationally, there has been more than a 40% decrease in Emergency Department (ED) patient volume during the coronavirus disease 2019 (Covid-19) crisis, with reports of decreases in presentations of time-sensitive acute illnesses. We analyzed ED clinical presentations in a Maryland/District of Columbia regional hospital system while health mitigation measures were instituted. Methods We conducted a retrospective observational cohort study of all adult ED patients presenting to five Johns Hopkins Health System (JHHS) hospitals comparing visits from March 16 through May 15, in 2019 and 2020. We analyzed de-identified demographic information, clinical conditions, and ICD-10 diagnosis codes for year-over-year comparisons. Results There were 36.7% fewer JHHS ED visits in 2020 compared to 2019 (43,088 vs. 27,293, P<.001). Patients 75+ had the greatest decline in visits (−44.00%, P<.001). Both genders had significant decreases in volume (−41.9%, P<.001 females vs −30.6%...
Background Academic and non-academic emergency departments (EDs) are regularly compared in clinic... more Background Academic and non-academic emergency departments (EDs) are regularly compared in clinical operations benchmarking despite suggestion that the two groups may differ in their clinical operations characteristics. and outcomes. We sought to describe and compare clinical operations characteristics of academic versus non-academic EDs. Methods We performed a descriptive, comparative analysis of academic and non-academic adult and general EDs with 40,000+ annual encounters, using the Academy of Academic Administrators of Emergency Medicine (AAAEM)/Association of Academic Chairs of Emergency Medicine (AACEM) and Emergency Department Benchmarking Alliance (EDBA) survey results. We defined academic EDs as primary teaching sites for emergency medicine (EM) residencies and non-academic EDs as sites with minimal resident involvement. We constructed the academic and non-academic cohorts from the AAAEM/AACEM and EDBA surveys, respectively, and analyzed metrics common to both surveys. Resu...
The study goal was to prospectively characterize the demographics, mechanisms of injury, diagnose... more The study goal was to prospectively characterize the demographics, mechanisms of injury, diagnoses, and outcomes among trauma patients presenting to Mbarara Regional Referral Hospital (MRRH) prior to planned trauma training interventions. Methods: All patients presenting to the Accidents & Emergency (A&E) area at MRRH over a ten-week period who met the World Health Organization definition of major trauma were prospectively enrolled. Patient demographics, mechanism of injury, arrival mode, referral site, initial vital signs, and diagnoses were collected by direct observation and chart review. Disposition from A&E, vital status and therapeutic actions were determined by daily chart review for the first 14 days of hospitalization. Outpatients or their caregivers were contacted by phone at 2 weeks and 30 days to determine vital status. Ethical/research approval was obtained from the Mbarara University of Science and Technology (Mbarara, Uganda) and Partners Healthcare (Boston, USA). Results: 497 patients with traumatic injuries presented during the 10-week study period. We included the 415 who met WHO criteria for major trauma. Mean age was 26.5 years (range 1-76, SD 14.6), and 77.8% were male. Patients most frequently arrived by car (61.7%), followed by boda/motorbike (21.2%), and ambulance (13.5%). 122 patients arrived in transfer (29.4%). Mechanisms of injury included motor vehicle accidents (60.2%), pedestrians struck (18.3%), assaults (11.1%) and penetrating trauma (4.8%). Only 30.4% of patients had any single vital sign recorded during their trauma evaluation, with only 2.6% having a documented heart rate, blood pressure, respiratory rate and oxygen saturation. Invasive interventions were rare and limited to Foley catheters (6.3%), nasogastric tubes (2.4%), and chest tubes (1.7%). There were no interosseous, central or arterial lines placed and no intubations in A&E. Patients remained in A&E for variable periods but were then discharged to: home (45.5%), surgery ward (33.7%), operating theatre (8.2%), or morgue (5.3%). Only 1.7% (7) patients went to the ICU. Head injury was the most common discharge diagnosis (44.6%) in the 186 hospitalized patients with follow-up, followed by fracture/dislocation (29% closed, 13.4% open). Abdominal trauma was a rare diagnosis (5.9% blunt, 1.6% penetrating), as was chest trauma (2.7% blunt, 1.1% penetrating). There were 35 in-hospital deaths (8.4%, 95% CI ¼ 5.9%-11.5%); 48.6% were within 24 hours of presentation. Head injury was among the major diagnoses in 84% of deaths. While the lost-to-follow-up rate at 2 weeks was high (40%), 3 additional deaths were identified bringing the overall mortality estimate to 9.2% (95% CI ¼ 6.6%-12.4%). There were no known additional deaths at 30-day follow-up. Conclusions: The trauma burden and mortality at this referral hospital are high. The site manages critically ill patients but has limited resuscitation capacity in A&E including no functioning CT scanner, limited critical care if patients cannot pay for ICU care, and inconsistent diagnostic, medical, operative and ICU supplies. Training in the algorithmic management of trauma patients and emergency medicine is currently limited but planned.
Joint Commission journal on quality and patient safety, May 1, 2019
Background: In hospitals and health systems across the country, patient flow bottlenecks delay ca... more Background: In hospitals and health systems across the country, patient flow bottlenecks delay care delivery-emergency department boarding and operating room exit holds are familiar examples. In other industries, such as oil, gas, and air traffic control, command centers proactively manage flow through complex systems. Methods: A systems engineering approach was used to analyze and maximize existing capacity in one health system, which led to the creation of the Judy Reitz Capacity Command Center. This article describes the key elements of this novel health system command center, which include strategic colocation of teams, automated visual displays of real-time data providing a global view, predictive analytics, standard work and rules-based protocols, and a clear chain of command and guiding tenets. Preliminary data are also shared. Results: With proactive capacity management, subcycle times decreased and allowed the health system's flagship hospital to increase occupancy from 85% to 92% while decreasing patient delays. Conclusion: The command center was built with three primary goals-reducing emergency department boarding, eliminating operating room holds, and facilitating transfers in from outside facilities-but the command center infrastructure has the potential to improve hospital operations in many other areas. PROBLEM DEFINITION AND CONTEXT I n the inpatient setting, hospital gridlock often delays patient movement to the optimal location for care. Emergency department (ED) crowding and boarding, which stem from systemwide inefficiencies, 1-3 have been directly linked to higher inpatient morbidity, preventable harm, and overall mortality, as well as increased total inpatient length of stay and decreased patient satisfaction. 4-16 Operating room (OR) exit holds, in which patients are unable to move from the OR to the postanesthesia care unit (PACU) or the ICU due to capacity constraints, are associated with worse patient outcomes and avoidable expense. 17,18 Delays in moving patients to critical care units are associated with increased mortality. 19-21 At tertiary care centers, inefficiencies often limit the number of patients eligible for transfer in from surrounding community hospitals, thereby delaying or preventing access to care. Many approaches have been tried to address boarding and crowding, such as monitoring of bed turnaround time, OR schedule smoothing, telemedicine consults, Lean and Plan-Do-Study-Act (PDSA) rapid cycle improvement, and many more. 2,5,22,23 Despite this, crowding continues to increase, leading The Joint Commission to require hospitals to measure and address ED boarding and the Centers for Medicare & Medicaid Services to include related metrics
Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant ti... more Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant time-stamped data from electronic medical records and other information technologies. However, the various characterizations of patient flow, cohort decisions, sub-processes, and the diverse stakeholders requiring data visibility create further overlying complexity. We use the Donabedian model to prioritize patient flow metrics and build an electronic dashboard for enabling communication. Ten metrics were identified as key indicators including outcome (length of stay, 30-day readmission, operating room exit delays, capacityrelated diversions), process (timely inpatient unit discharge, emergency department disposition), and structural metrics (occupancy, discharge volume, boarding, bed assignation duration). Dashboard users provided real-life examples of how the tool is assisting capacity improvement efforts, and user traffic data revealed an uptrend in dashboard utilization from May to October 2017 (26 to 148 views per month, respectively). Our main contributions are twofold. The former being the results and methods for selecting key performance indicators for a unit, department, and across the entire hospital (i.e., separating signal from noise). The latter being an electronic dashboard deployed and used at The Johns Hopkins Hospital to visualize these ten metrics and communicate systematically to hospital stakeholders. Integration of diverse information technology may create further opportunities for improved hospital capacity.
Background The ability to provide medical care during sudden increases in patient volume during a... more Background The ability to provide medical care during sudden increases in patient volume during a disaster or other high-consequence event is a serious concern for health-care systems. Identifi cation of inpatients for safe early discharge (ie, reverse triage) could create additional hospital surge capacity. We sought to develop a disposition classifi cation system that categorises inpatients according to suitability for immediate discharge on the basis of risk tolerance for a subsequent consequential medical event. Methods We did a warfare analysis laboratory exercise using evidence-based techniques, combined with a consensus process of 39 expert panellists. These panellists were asked to defi ne the categories of a disposition classifi cation system, assign risk tolerance of a consequential medical event to each category, identify critical interventions, and rank each (using a scale of 1-10) according to the likelihood of a resultant consequential medical event if a critical intervention is withdrawn or withheld because of discharge. Findings The panellists unanimously agreed on a fi ve-category disposition classifi cation system. The upper limit of risk tolerance for a consequential medical event in the lowest risk group if discharged early was less than 4%. The next categories had upper limits of risk tolerance of about 12% (IQR 8-15%), 33% (25-50%), 60% (45-80%) and 100% (95-100%), respectively. The expert panellists identifi ed 28 critical interventions with a likelihood of association with a consequential medical event if withdrawn, ranging from 3 to 10 on the 10-point scale. Interpretation The disposition classifi cation system allows conceptual classifi cation of patients for suitable disposition, including those deemed safe for early discharge home during surges in demand. Clinical criteria allowing real-time categorisation of patients are awaited.
Any opinions, findings, conclusions, or recommendations expressed in this publication are those o... more Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not represent the policy or position of the Department of Homeland Security. Few tools exist that are sufficiently robust to allow manipulation of key input variables to produce casualty estimates resulting from high-consequence events reflecting local or specific regions of concern. This article describes the design and utility of a computerized modeling simulation tool, Electronic Mass Casualty Assessment and Planning Scenarios (EMCAPS), developed to have broad application across emergency management and public health fields as part of a catastrophic events preparedness planning process. As a scalable, flexible tool, EMCAPS is intended to support emergency preparedness planning efforts at multiple levels ranging from local health systems to regional and state public health departments to Metropolitan Medical Response System jurisdictions. Designed around the subset of the National Planning Scenarios with health effects, advanced by the US Department of Homeland Security, the tool's platform is supported by the detailed descriptions and readily retrievable evidence-based assumptions of each scenario. The EMCAPS program allows the user to manipulate key scenario-based input variables that would best reflect the region or locale of interest. Inputs include population density, vulnerabilities, event size, and potency, as applicable. Using these inputs, EMCAPS generates the anticipated population-based health surge influence of the hazard scenario. Casualty estimates are stratified by injury severity/types where appropriate. Outputs are graph and table tabulations of surge estimates. The data can then be used to assess and tailor response capabilities for specific jurisdictions, organizations, and health care systems. EMCAPS may be downloaded without cost from https://www.hopkins-cepar.org/EMCAPS/EMCAPS.html as shareware. [
Background: Emergency Departments (ED) are challenged with excess demand for services and inadequ... more Background: Emergency Departments (ED) are challenged with excess demand for services and inadequate system capacity. Crowding at two independent EDs within a health system prompted an examination of the potential effects of improving patient throughput. The objective of this study was to determine the effects of reducing ED dwell time on temporal patterns of patient flow and demand for ED resources. Methods: Separate discrete event simulation (DES) models were developed for the EDs of a 1,000-bed urban medical center and a 560-bed community medical center using patient flow information. These models characterized the effects of reducing patient dwell time on ED care area census (i.e., staffing needs), waiting room census, total length of stay (LOS) and waiting time. Dwell time was defined as the time interval from when a patient entered the main ED care area to when the patient exited the ED by discharge or hospital admission. Total LOS is defined as the entire time interval from ED from arrival to exit (including waiting time). Results: DES results for each site demonstrate how natural patient arrivals and common hospital admission processes generate common temporal patterns of decreased crowding. Improving flow translates to most substantial reductions in waiting time and waiting room census during evening hours (17:00 to 22:00 hours). Significant effects on ED care area census and staffing demands are lagged, not occurring until overnight hours (2:00 to 8:00 hours). We reduced patient dwell time in 5% increments within the urban ED (16.2 min) and community ED (13.5 min) from 5% to 15%. For example, a 10% decrease in dwell time at the urban ED (32.4 min) and community ED (27.0 min) resulted in respective decreases in evening waiting room census by 49% (10.8 patients) and 26% (3.5 patients) during evening hours and ED care area census by 16% (3.6 patients) and 11% (2.0 patients) overnight. Conclusions: DES results suggest that increasing ED efficiency will most significantly decrease delays experienced by evening arrivals and provide opportunities to decrease care area census and reduce staff overnight.
Additional file 1: Table S1. AAAEM/AACEM ED Benchmarking Survey, Select Questions and Definitions... more Additional file 1: Table S1. AAAEM/AACEM ED Benchmarking Survey, Select Questions and Definitions. List of AAAEM/AACEM survey questions and associated definitions
Additional file 2: Table S2. EDBA Benchmarking Survey, Select Questions and Definitions. List of ... more Additional file 2: Table S2. EDBA Benchmarking Survey, Select Questions and Definitions. List of EDBA survey questions and associated definitions
Healthcare organizations face challenges in efficiently accommodating increased patient demand wi... more Healthcare organizations face challenges in efficiently accommodating increased patient demand with limited resources and capacity. The modern reimbursement environment prioritizes the maximization of operational efficiency and the reduction of unnecessary costs (i.e., waste) while maintaining or improving quality. As healthcare organizations adapt, significant pressures are placed on leaders to make difficult operational and budgetary decisions. In lieu of hard data, decision makers often base these decisions on subjective information. Discrete event simulation (DES), a computerized method of imitating the operation of a real-world system (e.g., healthcare delivery facility) over time, can provide decision makers with an evidence-based tool to develop and objectively vet operational solutions prior to implementation. DES in healthcare commonly focuses on (1) improving patient flow, (2) managing bed capacity, (3) scheduling staff, (4) managing patient admission and scheduling proced...
Any opinions, findings, conclusions, or recommendations expressed in this publication are those o... more Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not represent the policy or position of the Department of Homeland Security. Few tools exist that are sufficiently robust to allow manipulation of key input variables to produce casualty estimates resulting from high-consequence events reflecting local or specific regions of concern. This article describes the design and utility of a computerized modeling simulation tool, Electronic Mass Casualty Assessment and Planning Scenarios (EMCAPS), developed to have broad application across emergency management and public health fields as part of a catastrophic events preparedness planning process. As a scalable, flexible tool, EMCAPS is intended to support emergency preparedness planning efforts at multiple levels ranging from local health systems to regional and state public health departments to Metropolitan Medical Response System jurisdictions. Designed around the subset of the National Planning Scenarios with health effects, advanced by the US Department of Homeland Security, the tool's platform is supported by the detailed descriptions and readily retrievable evidence-based assumptions of each scenario. The EMCAPS program allows the user to manipulate key scenario-based input variables that would best reflect the region or locale of interest. Inputs include population density, vulnerabilities, event size, and potency, as applicable. Using these inputs, EMCAPS generates the anticipated population-based health surge influence of the hazard scenario. Casualty estimates are stratified by injury severity/types where appropriate. Outputs are graph and table tabulations of surge estimates. The data can then be used to assess and tailor response capabilities for specific jurisdictions, organizations, and health care systems. EMCAPS may be downloaded without cost from https://www.hopkins-cepar.org/EMCAPS/EMCAPS.html as shareware. [
Introduction Nationally, there has been more than a 40% decrease in Emergency Department (ED) pat... more Introduction Nationally, there has been more than a 40% decrease in Emergency Department (ED) patient volume during the coronavirus disease 2019 (Covid-19) crisis, with reports of decreases in presentations of time-sensitive acute illnesses. We analyzed ED clinical presentations in a Maryland/District of Columbia regional hospital system while health mitigation measures were instituted. Methods We conducted a retrospective observational cohort study of all adult ED patients presenting to five Johns Hopkins Health System (JHHS) hospitals comparing visits from March 16 through May 15, in 2019 and 2020. We analyzed de-identified demographic information, clinical conditions, and ICD-10 diagnosis codes for year-over-year comparisons. Results There were 36.7% fewer JHHS ED visits in 2020 compared to 2019 (43,088 vs. 27,293, P<.001). Patients 75+ had the greatest decline in visits (−44.00%, P<.001). Both genders had significant decreases in volume (−41.9%, P<.001 females vs −30.6%...
Background Academic and non-academic emergency departments (EDs) are regularly compared in clinic... more Background Academic and non-academic emergency departments (EDs) are regularly compared in clinical operations benchmarking despite suggestion that the two groups may differ in their clinical operations characteristics. and outcomes. We sought to describe and compare clinical operations characteristics of academic versus non-academic EDs. Methods We performed a descriptive, comparative analysis of academic and non-academic adult and general EDs with 40,000+ annual encounters, using the Academy of Academic Administrators of Emergency Medicine (AAAEM)/Association of Academic Chairs of Emergency Medicine (AACEM) and Emergency Department Benchmarking Alliance (EDBA) survey results. We defined academic EDs as primary teaching sites for emergency medicine (EM) residencies and non-academic EDs as sites with minimal resident involvement. We constructed the academic and non-academic cohorts from the AAAEM/AACEM and EDBA surveys, respectively, and analyzed metrics common to both surveys. Resu...
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