HEALTH POLICY/ORIGINAL RESEARCH
Emergency Department Crowding and Outcomes After Emergency Department Discharge Gelareh Z. Gabayan, MD, MSHS*; Stephen F. Derose, MD, MSHS; Vicki Y. Chiu, MS; Sau C. Yiu, MS; Catherine A. Sarkisian, MD, MSPH; Jason P. Jones, PhD; Benjamin C. Sun, MD, MPP *Corresponding Author. E-mail: [email protected].
Study objective: We assess whether a panel of emergency department (ED) crowding measures, including 2 reported by the Centers for Medicare & Medicaid Services (CMS), is associated with inpatient admission and death within 7 days of ED discharge. Methods: We conducted a retrospective cohort study of ED discharges, using data from an integrated health system for 2008 to 2010. We assessed patient transit-level (n¼3) and ED system-level (n¼6) measures of crowding, using multivariable logistic regression models. The outcome measures were inpatient admission or death within 7 days of ED discharge. We defined a clinically important association by assessing the relative risk ratio and 95% confidence interval (CI) difference and also compared risks at the 99th percentile and median value of each measure. Results: The study cohort contained a total of 625,096 visits to 12 EDs. There were 16,957 (2.7%) admissions and 328 (0.05%) deaths within 7 days. Only 2 measures, both of which were patient transit measures, were associated with the outcome. Compared with a median evaluation time of 2.2 hours, the evaluation time of 10.8 hours (99th percentile) was associated with a relative risk of 3.9 (95% CI 3.7 to 4.1) of an admission. Compared with a median ED length of stay (a CMS measure) of 2.8 hours, the 99th percentile ED length of stay of 11.6 hours was associated with a relative risk of 3.5 (95% CI 3.3 to 3.7) of admission. No system measure of ED crowding was associated with outcomes. Conclusion: Our findings suggest that ED length of stay is a proxy for unmeasured differences in case mix and challenge the validity of the CMS metric as a safety measure for discharged patients. [Ann Emerg Med. 2015;-:1-10.] Please see page XX for the Editor’s Capsule Summary of this article. 0196-0644/$-see front matter Copyright © 2015 by the American College of Emergency Physicians. http://dx.doi.org/10.1016/j.annemergmed.2015.04.009
SEE EDITORIAL, P.
.
---
INTRODUCTION Background and Importance The Institute of Medicine and the American College of Emergency Physicians have identified emergency department (ED) crowding as a critical threat to the quality of emergency care.1 ED crowding adversely affects clinical outcomes and is associated with poor patient satisfaction and clinical care experience.2,3 Since January 2014, the Centers for Medicare & Medicaid Services (CMS) has mandated the collection and public reporting of ED crowding measures for both admitted and discharged patients as quality-of-care markers. For discharged patients, this includes waiting time and length of stay.4 Although several studies suggest that ED crowding leads to greater mortality and worse patient outcomes in hospitalized patients,5-8 its effect on discharged patients is less clear. Given that more than 85% of patients who visit an ED in the United States are discharged,9 even small Volume
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no.
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:
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2015
effects of ED crowding on patient safety may have large aggregate effects. A single Canadian study reported an association between the average ED length of stay for a given shift and increased short-term deaths and unscheduled hospital admissions for discharged patients.10 However, these findings have not been replicated in US health care settings and are limited by the use of the use of single measures of ED crowding. Because of CMS payfor-reporting requirements and public reporting of ED timeliness measures, assessing the link with outcomes has important policy implications. Goals of This Investigation Using data from a large integrated health system, we assessed the relationship between multiple measures of ED crowding and their association with 7-day admissions and deaths after discharge. We evaluated 9 measures of ED crowding, including those that correspond to the CMS ED throughput measures of waiting time and length of stay.4 We hypothesized that both patient transit and ED system Annals of Emergency Medicine 1
Gabayan et al
Emergency Department Crowding and Outcomes After Discharge
Editor’s Capsule Summary
What is already known on this topic Regulatory bodies use emergency department (ED) crowding measures as a quality measure. What question this study addressed Are ED crowding factors or patient care intervals related to hospital admission or death within 7 days of discharge? What this study adds to our knowledge In a cohort of 625,096 discharged ED patients within a mature, integrated health care system in 1 California region, prolonged times to evaluate and to discharge patients linked to 7-day admission or death. No ED crowding measure linked to these outcomes. How this is relevant to clinical practice In this system, ED crowding metrics did not correlate with bad outcomes for discharged patients.
measures would be associated with higher rates of postdischarge hospitalizations and death. Possible causal pathways for this hypothesis include delays in diagnosis and treatment, missed diagnoses because of cognitive overload, and pressure to decompress the ED, leading to premature discharge. MATERIALS AND METHODS Study Design and Setting We conducted a multisite retrospective cohort study of ED visits. This study was approved by the institutional review boards of Kaiser Permanente Southern California and the University of California, Los Angeles. We analyzed administrative data of Kaiser Permanente Southern California, an integrated health system that provides comprehensive care to 3.5 million members at 14 medical centers and 197 offices throughout Southern California. There were 13 health system EDs in operation during the study period. All members have similar health care benefits, including coverage of emergency services both within and outside the health system. Members of the health plan are generally representative of the population of Southern California, which is a racially and socioeconomically diverse region. Approximately 7% of members enroll through Medicaid and 10% through Medicare. All members are assigned a unique health number that can be used for data linkage. All study medical centers had training programs for residents but none had emergency 2 Annals of Emergency Medicine
medicine residency programs. None of the study EDs were American College of Surgeons Level I or Level II trauma centers. The number of staffed ED beds ranged from 12 to 62. Selection of Participants Study subjects were members of Kaiser Permanente Southern California, with at least 1 ED visit and discharge from January 1, 2008, to December 31, 2010. A subject had to be a member of the health plan at the ED visit; however, no minimum enrollment history was required. We restricted analyses to adults (>18 years), given that rates of events are low in children and care processes differ. All subjects were discharged from the ED to home or a non–acute care facility such as a nursing home or rehabilitation facility. Transfers to observation status from the ED were also excluded. We excluded patients receiving hospice care because the goal of this care is to provide palliative services rather than prolong life. Patients who were transferred to and from other hospitals were also excluded. If a patient had multiple ED visits during the study period, then we selected the first ED visit to analyze for this study. Patients who left without being seen by a health care provider were excluded. The small number (48 hours) were dropped from the analysis. Data Collection and Processing The complete electronic health record (termed HealthConnect) contained records of all member visits to health plan EDs during the study period. This system contains history, mode of arrival, vital signs, staff notes, orders, diagnoses, and test results. Standardized data fields from ED visits provided time stamps for patient registration, triage, assignment to provider, and disposition order (discharge to home or a care facility). HealthConnect was also used to identify the International Classification of Diseases, ninth revision, diagnoses and Current Procedural Terminology codes associated with the ED visit. Outcome Measures The primary outcome was an inpatient admission within 7 days of discharge from the ED. The admission could be to either to a Kaiser Permanente Southern California or non–Kaiser Permanente Southern California hospital bed. Observation stays were excluded from the outcome. We focus on inpatient admissions because the coding of “observation” was inconsistent during the study period. Volume
-,
no.
-
:
-
2015
Gabayan et al
Emergency Department Crowding and Outcomes After Discharge
Hospital case managers also use objective, proprietary criteria to identify patients who meet inpatient versus observation criteria, implying that the 2 categories are qualitatively different. We chose the 7-day follow-up period as the primary time frame for several reasons. Admission within a week is more likely to be a consequence of the index ED visit than more distant periods, and preliminary analyses suggested sufficient time for accumulating outcomes. Moreover, 1 week is a reasonable period for arranging follow-up care and has clinical relevance and implications for health policy decisions, and has been used in previous studies.10-12 Although the frequency of death after discharge was spread more equally within 1 month after discharge, we chose the 7-day time frame to capture death for consistency. In sensitivity analyses, we also evaluated the outcomes of admission and death at 30 days after ED discharge. Information about admissions after discharge was obtained through billing data. The secondary outcome was death within 7 days of ED discharge. Mortality data were linked to the patient records with the California Vital Statistics files for in-state death and the Social Security Death Index for out-of-state deaths. For the final week of 2010, the 7-day follow-up period included the first week of 2011. There is currently no single, consensus measure of ED crowding.13 We created and assessed a panel of 9 crowding measures at the patient transit level (3) and ED system level (6) according to the input-throughput-output model of ED crowding.14 These measures represent an individual patient’s experience (patient transit level) and surrounding exposure (ED system level) to crowding while in the ED. All measures were derived with electronic time stamps. Patient transit-level measures were straightforward to measure and included the patient’s overall or total length of stay and its subdivision of waiting time and evaluation time.14,15 Two of these measures correspond to the CMS measures of ED throughput: waiting time (corresponds to OP-20, door to diagnostic evaluation by a qualified medical professional) and median length of stay (corresponds to OP-18b, median time from ED arrival to ED departure for discharged ED patients).4 The ED system-level measures were created with a composite of individual-level data on all patients in the ED, excluding the index patient. Because these measures evaluated the ED environment during the index patient’s visit, they were obtained when the index patient checked in to the ED (termed “at entry”) and during their entire ED stay (termed “time averaged”), which describe the mean time of the crowding measure during the index patient’s entire evaluation. These measures avoid direct confounding by index patient-level characteristics and included occupancy, Volume
-,
no.
-
:
-
2015
length of stay, and boarding time. To calculate the occupancy rates, we divided the number of patients in the ED at a given time by the number of staffed beds at a given site. For example, “boarding time at entry” describes the boarding time of all patients in the ED when the index patient checks in to the ED, whereas “time averaged boarding time” describes the mean boarding time of all patients who are boarding during the duration of the index patient’s ED stay. Figure 1 describes the measures. We adjusted for the demographic variables of age, sex, and race or ethnicity. We identified 30 preexisting comorbidities from inpatient and outpatient discharge diagnosis codes for health encounters before the ED visit and organized comorbidities with the Elixhauser classification system.16 ED primary discharge diagnoses were classified into 39 categories with the Clinical Classification Software developed by the Agency for Healthcare Research and Quality. We have previously described the rationale and development of this classification system.11 We also adjusted for mode of arrival, ED triage vital signs (pulse rate, blood pressure), weekend versus weekday visit, shift of visit (midnight to 8 AM, 8 AM to 4 PM, and 4 PM to midnight), month of the visit, and the Emergency Severity Index score (categorized as 1 to 2, 3, and 4)17 in the analysis. The Emergency Severity Index triage category 1 to 5 classifies illness severity, with 1 indicating the most acutely ill patients. Because different medical centers started using electronic data at different times, we also adjusted for medical center and year, as well as the interaction term between medical center and year. Primary Data Analysis We estimated the incidence and descriptive statistics of admissions and mortality within 7 days of discharge from the ED. We compared patient characteristics with the Wilcoxon’s rank sum test and c2 test, with significant testing at P
Emergency Department Crowding and Outcomes After Emergency Department Discharge Gelareh Z. Gabayan, MD, MSHS*; Stephen F. Derose, MD, MSHS; Vicki Y. Chiu, MS; Sau C. Yiu, MS; Catherine A. Sarkisian, MD, MSPH; Jason P. Jones, PhD; Benjamin C. Sun, MD, MPP *Corresponding Author. E-mail: [email protected].
Study objective: We assess whether a panel of emergency department (ED) crowding measures, including 2 reported by the Centers for Medicare & Medicaid Services (CMS), is associated with inpatient admission and death within 7 days of ED discharge. Methods: We conducted a retrospective cohort study of ED discharges, using data from an integrated health system for 2008 to 2010. We assessed patient transit-level (n¼3) and ED system-level (n¼6) measures of crowding, using multivariable logistic regression models. The outcome measures were inpatient admission or death within 7 days of ED discharge. We defined a clinically important association by assessing the relative risk ratio and 95% confidence interval (CI) difference and also compared risks at the 99th percentile and median value of each measure. Results: The study cohort contained a total of 625,096 visits to 12 EDs. There were 16,957 (2.7%) admissions and 328 (0.05%) deaths within 7 days. Only 2 measures, both of which were patient transit measures, were associated with the outcome. Compared with a median evaluation time of 2.2 hours, the evaluation time of 10.8 hours (99th percentile) was associated with a relative risk of 3.9 (95% CI 3.7 to 4.1) of an admission. Compared with a median ED length of stay (a CMS measure) of 2.8 hours, the 99th percentile ED length of stay of 11.6 hours was associated with a relative risk of 3.5 (95% CI 3.3 to 3.7) of admission. No system measure of ED crowding was associated with outcomes. Conclusion: Our findings suggest that ED length of stay is a proxy for unmeasured differences in case mix and challenge the validity of the CMS metric as a safety measure for discharged patients. [Ann Emerg Med. 2015;-:1-10.] Please see page XX for the Editor’s Capsule Summary of this article. 0196-0644/$-see front matter Copyright © 2015 by the American College of Emergency Physicians. http://dx.doi.org/10.1016/j.annemergmed.2015.04.009
SEE EDITORIAL, P.
.
---
INTRODUCTION Background and Importance The Institute of Medicine and the American College of Emergency Physicians have identified emergency department (ED) crowding as a critical threat to the quality of emergency care.1 ED crowding adversely affects clinical outcomes and is associated with poor patient satisfaction and clinical care experience.2,3 Since January 2014, the Centers for Medicare & Medicaid Services (CMS) has mandated the collection and public reporting of ED crowding measures for both admitted and discharged patients as quality-of-care markers. For discharged patients, this includes waiting time and length of stay.4 Although several studies suggest that ED crowding leads to greater mortality and worse patient outcomes in hospitalized patients,5-8 its effect on discharged patients is less clear. Given that more than 85% of patients who visit an ED in the United States are discharged,9 even small Volume
-,
no.
-
:
-
2015
effects of ED crowding on patient safety may have large aggregate effects. A single Canadian study reported an association between the average ED length of stay for a given shift and increased short-term deaths and unscheduled hospital admissions for discharged patients.10 However, these findings have not been replicated in US health care settings and are limited by the use of the use of single measures of ED crowding. Because of CMS payfor-reporting requirements and public reporting of ED timeliness measures, assessing the link with outcomes has important policy implications. Goals of This Investigation Using data from a large integrated health system, we assessed the relationship between multiple measures of ED crowding and their association with 7-day admissions and deaths after discharge. We evaluated 9 measures of ED crowding, including those that correspond to the CMS ED throughput measures of waiting time and length of stay.4 We hypothesized that both patient transit and ED system Annals of Emergency Medicine 1
Gabayan et al
Emergency Department Crowding and Outcomes After Discharge
Editor’s Capsule Summary
What is already known on this topic Regulatory bodies use emergency department (ED) crowding measures as a quality measure. What question this study addressed Are ED crowding factors or patient care intervals related to hospital admission or death within 7 days of discharge? What this study adds to our knowledge In a cohort of 625,096 discharged ED patients within a mature, integrated health care system in 1 California region, prolonged times to evaluate and to discharge patients linked to 7-day admission or death. No ED crowding measure linked to these outcomes. How this is relevant to clinical practice In this system, ED crowding metrics did not correlate with bad outcomes for discharged patients.
measures would be associated with higher rates of postdischarge hospitalizations and death. Possible causal pathways for this hypothesis include delays in diagnosis and treatment, missed diagnoses because of cognitive overload, and pressure to decompress the ED, leading to premature discharge. MATERIALS AND METHODS Study Design and Setting We conducted a multisite retrospective cohort study of ED visits. This study was approved by the institutional review boards of Kaiser Permanente Southern California and the University of California, Los Angeles. We analyzed administrative data of Kaiser Permanente Southern California, an integrated health system that provides comprehensive care to 3.5 million members at 14 medical centers and 197 offices throughout Southern California. There were 13 health system EDs in operation during the study period. All members have similar health care benefits, including coverage of emergency services both within and outside the health system. Members of the health plan are generally representative of the population of Southern California, which is a racially and socioeconomically diverse region. Approximately 7% of members enroll through Medicaid and 10% through Medicare. All members are assigned a unique health number that can be used for data linkage. All study medical centers had training programs for residents but none had emergency 2 Annals of Emergency Medicine
medicine residency programs. None of the study EDs were American College of Surgeons Level I or Level II trauma centers. The number of staffed ED beds ranged from 12 to 62. Selection of Participants Study subjects were members of Kaiser Permanente Southern California, with at least 1 ED visit and discharge from January 1, 2008, to December 31, 2010. A subject had to be a member of the health plan at the ED visit; however, no minimum enrollment history was required. We restricted analyses to adults (>18 years), given that rates of events are low in children and care processes differ. All subjects were discharged from the ED to home or a non–acute care facility such as a nursing home or rehabilitation facility. Transfers to observation status from the ED were also excluded. We excluded patients receiving hospice care because the goal of this care is to provide palliative services rather than prolong life. Patients who were transferred to and from other hospitals were also excluded. If a patient had multiple ED visits during the study period, then we selected the first ED visit to analyze for this study. Patients who left without being seen by a health care provider were excluded. The small number (48 hours) were dropped from the analysis. Data Collection and Processing The complete electronic health record (termed HealthConnect) contained records of all member visits to health plan EDs during the study period. This system contains history, mode of arrival, vital signs, staff notes, orders, diagnoses, and test results. Standardized data fields from ED visits provided time stamps for patient registration, triage, assignment to provider, and disposition order (discharge to home or a care facility). HealthConnect was also used to identify the International Classification of Diseases, ninth revision, diagnoses and Current Procedural Terminology codes associated with the ED visit. Outcome Measures The primary outcome was an inpatient admission within 7 days of discharge from the ED. The admission could be to either to a Kaiser Permanente Southern California or non–Kaiser Permanente Southern California hospital bed. Observation stays were excluded from the outcome. We focus on inpatient admissions because the coding of “observation” was inconsistent during the study period. Volume
-,
no.
-
:
-
2015
Gabayan et al
Emergency Department Crowding and Outcomes After Discharge
Hospital case managers also use objective, proprietary criteria to identify patients who meet inpatient versus observation criteria, implying that the 2 categories are qualitatively different. We chose the 7-day follow-up period as the primary time frame for several reasons. Admission within a week is more likely to be a consequence of the index ED visit than more distant periods, and preliminary analyses suggested sufficient time for accumulating outcomes. Moreover, 1 week is a reasonable period for arranging follow-up care and has clinical relevance and implications for health policy decisions, and has been used in previous studies.10-12 Although the frequency of death after discharge was spread more equally within 1 month after discharge, we chose the 7-day time frame to capture death for consistency. In sensitivity analyses, we also evaluated the outcomes of admission and death at 30 days after ED discharge. Information about admissions after discharge was obtained through billing data. The secondary outcome was death within 7 days of ED discharge. Mortality data were linked to the patient records with the California Vital Statistics files for in-state death and the Social Security Death Index for out-of-state deaths. For the final week of 2010, the 7-day follow-up period included the first week of 2011. There is currently no single, consensus measure of ED crowding.13 We created and assessed a panel of 9 crowding measures at the patient transit level (3) and ED system level (6) according to the input-throughput-output model of ED crowding.14 These measures represent an individual patient’s experience (patient transit level) and surrounding exposure (ED system level) to crowding while in the ED. All measures were derived with electronic time stamps. Patient transit-level measures were straightforward to measure and included the patient’s overall or total length of stay and its subdivision of waiting time and evaluation time.14,15 Two of these measures correspond to the CMS measures of ED throughput: waiting time (corresponds to OP-20, door to diagnostic evaluation by a qualified medical professional) and median length of stay (corresponds to OP-18b, median time from ED arrival to ED departure for discharged ED patients).4 The ED system-level measures were created with a composite of individual-level data on all patients in the ED, excluding the index patient. Because these measures evaluated the ED environment during the index patient’s visit, they were obtained when the index patient checked in to the ED (termed “at entry”) and during their entire ED stay (termed “time averaged”), which describe the mean time of the crowding measure during the index patient’s entire evaluation. These measures avoid direct confounding by index patient-level characteristics and included occupancy, Volume
-,
no.
-
:
-
2015
length of stay, and boarding time. To calculate the occupancy rates, we divided the number of patients in the ED at a given time by the number of staffed beds at a given site. For example, “boarding time at entry” describes the boarding time of all patients in the ED when the index patient checks in to the ED, whereas “time averaged boarding time” describes the mean boarding time of all patients who are boarding during the duration of the index patient’s ED stay. Figure 1 describes the measures. We adjusted for the demographic variables of age, sex, and race or ethnicity. We identified 30 preexisting comorbidities from inpatient and outpatient discharge diagnosis codes for health encounters before the ED visit and organized comorbidities with the Elixhauser classification system.16 ED primary discharge diagnoses were classified into 39 categories with the Clinical Classification Software developed by the Agency for Healthcare Research and Quality. We have previously described the rationale and development of this classification system.11 We also adjusted for mode of arrival, ED triage vital signs (pulse rate, blood pressure), weekend versus weekday visit, shift of visit (midnight to 8 AM, 8 AM to 4 PM, and 4 PM to midnight), month of the visit, and the Emergency Severity Index score (categorized as 1 to 2, 3, and 4)17 in the analysis. The Emergency Severity Index triage category 1 to 5 classifies illness severity, with 1 indicating the most acutely ill patients. Because different medical centers started using electronic data at different times, we also adjusted for medical center and year, as well as the interaction term between medical center and year. Primary Data Analysis We estimated the incidence and descriptive statistics of admissions and mortality within 7 days of discharge from the ED. We compared patient characteristics with the Wilcoxon’s rank sum test and c2 test, with significant testing at P
