Emergency Department Rotational Patient Assignment

Matching patients with therapists in culturally diverse rehabilitation services during civil unrest

A primary consideration in rehabilitation is the compatibility between clinicians and patients, where cultural diversity is a defining feature for both. The intricacies of cultural considerations in patient-clinician matching are heightened in areas of conflict and civil unrest. This paper presents three perspectives of the significance of cultural considerations in such assignments: patient-centred approach - prioritizing patients' preferences; professional-centred approach - clinicians' safety, social-emotional, and training needs; and utilitarian approach - what is best for the majority. A case study from an Israeli rehabilitation clinic is presented to exhibit the multifaceted considerations in patient-clinician matching within areas of conflict and civil unrest. The reconciliation of these three approaches in the context of cultural diversity is discussed, suggesting the benefit of a case-by-case strategy involving combinations of the three. Further research could examine how this might feasibly and beneficially optimize outcomes for all in culturally diverse societies in times of unrest.

Who should see the patient? on deviations from preferred patient-provider assignments in hospitals

In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common. This is due to the conventional wisdom that professionals should be given the flexibility to deviate from preferred assignments as needed. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in our partner hospital (a children's hospital), and generate insights into whether and when hospital administrators should disallow such flexibility. We do so by identifying 73 top medical diagnoses and using detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a survey of medical experts and utilized it to identify the preferred provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of deviations from preferred provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmissions and adverse events), and cost (measured by total charges). We find that deviating from preferred assignments is beneficial for task types (patients' diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we observe that deviations are either detrimental or yield no tangible benefits, and thus, hospitals should try to eliminate them (e.g., by developing and enforcing assignment guidelines). To understand the causal mechanism behind our results, we make use of mediation analysis and find that utilizing advanced imaging (e.g., MRIs, CT scans, or nuclear radiology) plays an important role in how deviations impact performance outcomes. Our findings also provide evidence for a "no free lunch" theorem: while for some task types, deviations are beneficial for certain performance outcomes, they can simultaneously degrade performance in terms of other dimensions. To provide clear recommendations for hospital administrators, we also consider counterfactual scenarios corresponding to imposing the preferred assignments fully or partially, and perform cost-effectiveness analyses. Our results indicate that enforcing the preferred assignments either for all tasks or only for resource-intensive tasks is cost-effective, with the latter being the superior policy. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which deviations occur more in practice.

Acuity-based rotational patient-to-physician assignment in an emergency department using electronic health records in triage

Emergency department (ED) operational metrics generated by a new acuity-based rotational patient-to-physician assignment (ARPA) algorithm are compared with those obtained with a simple rotational patient assignment (SRPA) system aimed only at an equitable patient distribution. The new ARPA method theoretically guarantees that no two physicians' assigned patient loads can differ by more than one, either partially (by acuity levels) or in total; whereas SRPA guarantees only the latter. The performance of the ARPA method was assessed in practice in the ED of the main public hospital (Hospital Compound of Navarra) in the region of Navarre in Spain. This ED attends over 140 000 patients every year. Data analysis was conducted on 9,063 ED patients in the SRPA cohort, and 8,892 ED patients in the ARPA cohort. The metrics of interest are related both to patient access to healthcare and physician workload distribution: patient length of stay; arrival-to-provider time; ratio of patients exceeding the APT target threshold; and range of assigned patients across physicians by priority levels. The transition from SRPA to ARPA is associated with improvements in all ED operational metrics. This research demonstrates that ARPA is a simple and useful strategy for redesigning front-end ED processes.

Machine Learning Methods for Predicting Patient-Level Emergency Department Workload

BACKGROUND

Work Relative Value Units (wRVUs) are a component of many compensation models, and a proxy for the effort required to care for a patient. Accurate prediction of wRVUs generated per patient at triage could facilitate real-time load balancing between physicians and provide many practical operational and clinical benefits.

OBJECTIVE

We examined whether deep-learning approaches could predict the wRVUs generated by a patient's visit using data commonly available at triage.

METHODS

Adult patients presenting to an urban, academic emergency department from July 1, 2016-March 1, 2020 were included. Deidentified triage information included structured data (age, sex, vital signs, Emergency Severity Index score, language, race, standardized chief complaint) and unstructured data (free-text chief complaint) with wRVUs as outcome. Five models were examined: average wRVUs per chief complaint, linear regression, neural network and gradient-boosted tree on structured data, and neural network on unstructured textual data. Models were evaluated using mean absolute error.

RESULTS

We analyzed 204,064 visits between July 1, 2016 and March 1, 2020. The median wRVUs were 3.80 (interquartile range 2.56-4.21), with significant effects of age, gender, and race. Models demonstrated lower error as complexity increased. Predictions using averages from chief complaints alone demonstrated a mean error of 2.17 predicted wRVUs per visit (95% confidence interval [CI] 2.07-2.27), the linear regression model: 1.00 wRVUs (95% CI 0.97-1.04), gradient-boosted tree: 0.85 wRVUs (95% CI 0.84-0.86), neural network with structured data: 0.86 wRVUs (95% CI 0.85-0.87), and neural network with unstructured data: 0.78 wRVUs (95% CI 0.76-0.80).

CONCLUSIONS

Chief complaints are a poor predictor of the effort needed to evaluate a patient; however, deep-learning techniques show promise. These algorithms have the potential to provide many practical applications, including balancing workloads and compensation between emergency physicians, quantify crowding and mobilizing resources, and reducing bias in the triage process.

The effect of batched patient–physician assignment on patient length of stay in the emergency department

Objectives

Queuing theory suggests that signing up for multiple patients at once (batching) can negatively affect patients’ length of stay (LOS). At academic centers, resident assignment adds a second layer to this effect. In this study, we measured the rate of batched patient assignment by resident physicians, examined the effect on patient in‐room LOS, and surveyed residents on underlying drivers and perceptions of batching.

Methods

This was a retrospective study of discharged patients from August 1, 2020 to October 27, 2020, supplemented with survey data conducted at a large, urban, academic hospital with an emergency medicine training program in which residents self‐assign to patients. Time stamps were extracted from the electronic health record and a definition of batching was set based on findings of a published time and motion study.

Results

A total of 3794 patients were seen by 28 residents and ultimately discharged during the study period. Overall, residents batched 23.7% of patients, with a greater rate of batching associated with increasing resident seniority and during the first hour of resident shifts. In‐room LOS for batched assignment patients was 15.9 minutes longer than single assignment patients (P value < 0.01). Residents’ predictions of their rates of batching closely approximated actual rates; however, they underestimated the effect of batching on LOS.

Conclusions

Emergency residents often batch patients during signup with negative consequences to LOS. Moreover, residents significantly underestimate this negative effect.

Not All Testers are Admitters: An Analysis of Emergency Physician Resource Utilization and Consultation Rates

BACKGROUND

Variability exists in emergency physician (EP) resource utilization as measured by ordering practices, rate of consultation, and propensity to admit patients.

OBJECTIVE

To validate and expand upon previous data showing that resource utilization as measured by EP ordering patterns is positively correlated with admission rates.

METHODS

This is a retrospective study of routinely gathered operational data from the ED of an urban academic tertiary care hospital. We collected individual EP data on advanced imaging, consultation, and admission rates per patient encounter. To investigate whether there might be distinct groups of practice patterns relating these 3 resources, we used a Gaussian mixture model, a classification method used to determine the likelihood of distinct subgroups within a larger population.

RESULTS

Our Gaussian mixture model revealed 3 distinct groups of EPs based on their ordering practices. The largest group is characterized by a homogenous pattern of neither high or low resource utilization (n = 37, 27% female, median years' experience: 6 [interquartile ratio {IQR} 3-18]; rates of advanced imaging, 38.9%; consultation, 45.1%; and admission 39.3%), with a modest group of low-resource users (n = 15, 60% female, median years' experience: 6 [IQR 5-14]; rates of advanced imaging, 37%; consultation, 42.6%; and admission 37.3%), and far fewer members of a high-resource use group (n = 6, 0% female, median years' experience: 6 [IQR 4-16]; rates of advanced imaging, 42.2%; consultation, 45.8%; and admission 40.6%). This variation suggests that not "all testers are admitters," but that there exist wider practice variations among EPs.

CONCLUSIONS

At our academic tertiary center, 3 distinct subgroups of EP ordering practices exist based on consultation rates, advanced imaging use, and propensity to admit a patient. These data validate previous work showing that resource utilization and admission rates are related, while demonstrating that more nuanced patterns of EP ordering practices exist. Further investigation is needed to understand the impact of EP characteristics and behavior on throughput and quality of care. © 2022 Elsevier Inc.

Improving Emergency Care Through a Dedicated Redesigned Obstetrics and Gynecology Emergency Unit at the Women's Hospital, Doha, Qatar

BACKGROUND

OBJECTIVE

STUDY DESIGN

RESULTS

CONCLUSION

Modeling patient-related workload in the emergency department using electronic health record data

INTRODUCTION

Understanding and managing clinician workload is important for clinician (nurses, physicians and advanced practice providers) occupational health as well as patient safety. Efforts have been made to develop strategies for managing clinician workload by improving patient assignment. The goal of the current study is to use electronic health record (EHR) data to predict the amount of work that individual patients contribute to clinician workload (patient-related workload).

METHODS

One month of EHR data was retrieved from an emergency department (ED). A list of workload indicators and five potential workload proxies were extracted from the data. Linear regression and four machine learning classification algorithms were utilized to model the relationship between the indicators and the proxies.

RESULTS

Linear regression proved that the indicators explained a substantial amount of variance of the proxies (four out of five proxies were modeled with R² > 0.80). Classification algorithms also showed success in classifying a patient as having high or low task demand based on data from early in the ED visit (e.g. 80 % accurate binary classification with data from the first hour).

CONCLUSION

The main contribution of this study is demonstrating the potential of using EHR data to predict patient-related workload automatically in the ED. The predicted workload can potentially help in managing clinician workload by supporting decisions around the assignment of new patients to providers. Future work should focus on identifying the relationship between workload proxies and actual workload, as well as improving prediction performance of regression and multi-class classification.

Association of resident characteristics with patterns of patient self-assignment

OBJECTIVE

We hypothesized that resident characteristics impact patterns of patient self-assignment in the emergency department (ED). Our goal was to determine if male residents would be less likely than their female colleagues to see patients with sensitive (e.g. breast-related or gynecologic) chief complaints (CCs). We also investigated whether resident specialty was associated with preferentially choosing patients with more familiar chief complaints.

METHODS

We performed a retrospective cross-sectional study at a tertiary academic medical center using data from all adult patients presenting to the ED between 2010 and 2019 with one of six CC categories (vaginal bleeding, breast-related concerns, male genitourinary [GU] concerns, gastrointestinal bleeding, epistaxis, and laceration). These CCs were chosen as they each require either an invasive medical exam or procedure, and cannot easily be evaluated with an exam in a hallway bed. We used logistic regression to assess the likelihood of being treated by a male resident compared to a female resident for each CC, adjusting for candidate variables of patient age, race, primary language, ESI score, bed location, time of day, day of week, calendar month, and resident specialty. We also similarly analyzed patterns of patient self-assignment according to resident specialty.

RESULTS

Male residents were significantly less likely than female residents to treat patients with breast-related CCs (adjusted OR 0.67, 95% CI 0.54-0.83, p < 0.001) or vaginal bleeding (adjusted OR 0.73, 95% CI 0.63-0.84, p < 0.001, reference group: epistaxis). Off-service residents were more likely to assign themselves to familiar chief complaints, for example surgery residents were more likely to see patients with lacerations (adjusted OR 2.11, 95% CI 1.71-2.61, p < 0.001) and OB/GYN residents were less likely to see patients with male GU concerns (adjusted OR 0.21, 95% CI 0.05-0.85, p = 0.029), compared to emergency medicine residents.

CONCLUSION

In a single facility, resident characteristics were associated with preferential patient self-assignment. Further work is necessary to determine the underlying reasons for patient avoidance, and to create work environments in which preferentially choosing patients is discouraged.

Association Between Emergency Physician Length of Stay Rankings and Patient Characteristics

OBJECTIVE

Emergency physicians are commonly compared by their patients' length of stay (LOS). We test the hypothesis that LOS is associated with patient characteristics and that accounting for these features impacts physician LOS rankings.

METHODS

This was a retrospective observational study of all encounters at an emergency department in 2010 to 2015. We compared the characteristics of patients seen by physicians in different quartiles of LOS. Primary outcome was variation in patient characteristics at time of physician assignment (age, sex, comorbidities, Emergency Severity Index [ESI], and chief complaint) across LOS quartiles. We also quantified the change in LOS rankings after accounting for difference in characteristics of patients seen by different physicians.

RESULTS

A total of 264,776 encounters seen by 62 attending physicians met inclusion criteria. Physicians in the longest LOS quartile saw patients who were older (age = 49.1 vs 48.6 years, difference = +0.5 years, 95% confidence interval [CI] = 0.3 to 0.7) with more comorbidities (Gagne score = 1.3 vs. 0.9, difference = +0.4, 95% CI = 0.4 to 0.4) and higher acuity (ESI = 2.8 vs. 2.9, difference = -0.1, 95% CI = 0.1 to 0.1) than physicians in the shortest LOS quartile. The odds ratio (OR) of physicians in the longest LOS quartile seeing patients over age 50 compared to the shortest LOS quartile was 1.1 (95% CI = 1.0 to 1.1); the OR of physicians in the longest LOS quartile seeing patients with ESI of 1 or 2 was also 1.1 (95% CI = 1.0 to 1.1). Accounting for variation in patient characteristics seen by different physicians resulted in substantial reordering of physician LOS rankings: 62.9% (39/62) of physicians reclassified into a different quartile with mean absolute percentile change of 25.8 (95% CI = 20.3 to 31.3). A total of 62.5% (10/16) of physicians in the shortest LOS quartile and 56.3% (9/16) in the longest LOS quartile moved into a different quartile after accounting for variation in patient characteristics.

CONCLUSIONS

Length of stay was significantly associated with patient characteristics, and accounting for variation in patient characteristics resulted in substantial reordering of relative physician rankings by LOS. Comparisons of emergency physicians by LOS that do not account for patient characteristics should be reconsidered.

Patient Assignment Models in the Emergency Department

Early assignment of patients to specific treatment teams improves length of stay, rate of patients leaving without being seen, patient satisfaction, and resident education. Multiple variations of patient assignment systems exist, including provider-in-triage/team triage, fast-tracks/vertical pathways, and rotational patient assignment. The authors discuss the theory behind patient assignment systems and review potential benefits of specific models of patient assignment found in the current literature.

Association of Vital Signs and Process Outcomes in Emergency Department Patients

Introduction

We sought to determine the association of abnormal vital signs with emergency department (ED) process outcomes in both discharged and admitted patients.

Methods

We performed a retrospective review of five years of operational data at a single site. We identified all visits for patients 18 and older who were discharged home without ancillary services, and separately identified all visits for patients admitted to a floor (ward) bed. We assessed two process outcomes for discharged visits (returns to the ED within 72 hours and returns to the ED within 72 hours resulting in admission) and two process outcomes for admitted patients (transfer to a higher level of care [intermediate care or intensive care] within either six hours or 24 hours of arrival to floor). Last-recorded ED vital signs were obtained for all patients. We report rates of abnormal vital signs in each group, as well as the relative risk of meeting a process outcome for each individual vital sign abnormality.

Results

Patients with tachycardia, tachypnea, or fever more commonly experienced all measured process outcomes compared to patients without these abnormal vitals; admitted hypotensive patients more frequently required transfer to a higher level of care within 24 hours.

Conclusion

In a single facility, patients with abnormal last-recorded ED vital signs experienced more undesirable process outcomes than patients with normal vitals. Vital sign abnormalities may serve as a useful signal in outcome forecasting.

Are testers also admitters? Comparing emergency physician resource utilization and admitting practices

OBJECTIVE

To describe the relationship between emergency department resource utilization and admission rate at the level of the individual physician.

METHODS

Retrospective observational study of physician resource utilization and admitting data at two emergency departments. We calculated observed to expected (O/E) ratios for four measures of resource utilization (intravenous medications and fluids, laboratory testing, plain radiographs, and advanced imaging studies) as well as for admission rate. Expected values reflect adjustment for patient- and time-based variables. We compared O/E ratios for each type of resource utilization to the O/E ratio for admission for each provider. We report degree of correlation (slope of the trendline) and strength of correlation (adjusted R² value) for each association, as well as categorical results after clustering physicians based on the relationship of resource utilization to admission rate.

RESULTS

There were statistically significant positive correlations between resource utilization and physician admission rate. Physicians with lower resource utilization rates were more likely to have lower admission rates, and those with higher resource utilization rates were more likely to have higher admission rates.

CONCLUSIONS

In a two-facility study, emergency physician resource utilization and admission rate were positively correlated: those who used more ED resources also tended to admit more patients. These results add to a growing understanding of emergency physician variability.

Interphysician Differences in Emergency Department Length of Stay

BACKGROUND

Emergency physicians differ in many ways with respect to practice. One area in which interphysician practice differences are not well characterized is emergency department (ED) length of stay (LOS).

OBJECTIVE

To describe how ED LOS differs among physicians.

METHODS

We performed a 3-year, five-ED retrospective study of non-fast-track visits evaluated primarily by physicians. We report each provider's observed LOS, as well as each provider's ratio of observed LOS/expected LOS (LOS_O/E); we determined expected LOS based on site average adjusted for the patient characteristics of age, gender, acuity, and disposition status, as well as the time characteristics of shift, day of week, season, and calendar year.

RESULTS

Three hundred twenty-seven thousand, seven hundred fifty-three visits seen by 92 physicians were eligible for analysis. For the five sites, the average shortest observed LOS was 151 min (range 106-184 min), and the average longest observed LOS was 232 min (range 196-270 min); the average difference was 81 min (range 69-90 min). For LOS_O/E, the average lowest LOS_O/E was 0.801 (range 0.702-0.887), and the average highest LOS_O/E was 1.210 (range 1.186-1.275); the average difference between the lowest LOS_O/E and the highest LOS_O/E was 0.409 (range 0.305-0.493).

CONCLUSION

There are significant differences in ED LOS at the level of the individual physician, even after accounting for multiple confounders. We found that the LOS_O/E for physicians with the lowest LOS_O/E at each site averaged approximately 20% less than predicted, and that the LOS_O/E for physicians with the highest LOS_O/E at each site averaged approximately 20% more than predicted.

The durability of operational improvements with rotational patient assignment

INTRODUCTION

Previous work has suggested that Emergency Department rotational patient assignment (a system in which patients are algorithmically assigned to physicians) is associated with immediate (first-year) improvements in operational metrics. We sought to determine if these improvements persisted over a longer follow-up period.

METHODS

Single-site, retrospective analysis focused on years 2-4 post-implementation (follow-up) of a rotational patient assignment system. We compared operational data for these years with previously published data from the last year of physician self-assignment and the first year of rotational patient assignment. We report data for patient characteristics, departmental characteristics and facility characteristics, as well as outcomes of length of stay (LOS), arrival to provider time (APT), and rate of patients who left before being seen (LBBS).

RESULTS

There were 140,673 patient visits during the five year period; 138,501 (98.7%) were eligible for analysis. LOS, APT, and LBBS during follow-up remained improved vs. physician self-assignment, with improvements similar to those noted in the first year of implementation. Compared with the last year of physician self-assignment, approximate yearly average improvements during follow-up were a decrease in median LOS of 18min (8% improvement), a decrease in median APT of 21min (54% improvement), and a decrease in LBBS of 0.69% (72% improvement).

CONCLUSION

In a single facility study, rotational patient assignment was associated with sustained operational improvements several years after implementation. These findings provide further evidence that rotational patient assignment is a viable strategy in front-end process redesign.