Hodgson, Nicole R.

Tackling hospital emergency department (ED) overcrowding is a paramount challenge for healthcare systems. To combat this issue, an innovative approach is to identify patients who can be served vertically (i.e., in a seated position) and route them to a dedicated area termed the vertical processing pathway (VPP). Successfully implementing this design requires understanding which patients should be routed to the VPP and when. Currently, the decision to leverage the VPP is made in an ad hoc fashion. To assist our partner hospital and other EDs in capturing the value of the VPP, we develop a machine learning model that provides personalized risk scores predicting whether each arriving patient needs an ED bed. We use these scores as input to a stochastic patient flow model and analytically characterize the optimal VPP policy that minimizes length of stay. We then combine our results to derive an interpretable VPP patient streaming protocol suitable for implementation in practice and conduct a before-and-after experiment in which we leverage empirical analyses to evaluate the impact of integrating it into practice. The implemented protocol led to an 11-minute (4.2%) reduction in ED length of stay in our partner hospital without any adverse effect on quality-of-care outcomes. This effect was statistically significant and remained robust after controlling for various confounding factors and potential sources of endogeneity. Our VPP protocol can also be utilized in other EDs, offering operational improvements without requiring additional resources. This paper was accepted by Stefan Scholtes, healthcare management. Funding: S. Saghafian received financial support from the NSF [Grant CMMI-15 62645], “Data-Driven Management of Post-Transplant Medications,” which partially enabled this work, as well as funding and support from the Harvard Data Science Initiative and the Harvard Mossavar-Rahmani Center for Business and Government. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07517 .

Background/Objectives: Recent advances in artificial intelligence (AI) and machine learning (ML) enable targeted optimization of emergency department (ED) operations. We examine how reworking an ED’s vertical processing pathway (VPP) using AI- and ML-driven recommendations affected patient throughput. Methods: We trained a non-linear ML model using triage data from 49,350 ED encounters to generate a personalized risk score that predicted whether an incoming patient is suitable for vertical processing. This model was integrated into a stochastic patient flow framework using queueing theory to derive an optimized VPP design. The resulting protocol prioritized a vertical assessment for patients with Emergency Severity Index (ESI) scores of 4 and 5, as well as 3 when the chief complaints involved skin, urinary, or eye issues. In periods of ED saturation, our data-driven protocol suggested that any waiting room patient should become VPP eligible. We implemented this protocol during a 13-week prospective trial and evaluated its effect on ED performance using before-and-after data. Results: Implementation of the optimized VPP protocol reduced the average ED length of stay (LOS) by 10.75 min (4.15%). Adjusted analyses controlling for potential confounders during the study period estimated a LOS reduction between 7.5 and 11.9 min (2.89% and 4.60%, respectively). No adverse effects were observed in the quality metrics, including 72 h ED revisit or hospitalization rates. Conclusions: A personalized, data-driven VPP protocol, enabled by ML predictions, significantly improved the ED throughput while preserving care quality. Unlike standard fast-track systems, this approach adapts to ED saturation and patient acuity. The methodology is customizable to patient populations and ED operational characteristics, supporting personalized patient flow optimization across diverse emergency care settings.

Pregnancy induces significant physiologic changes that impact the cardiovascular system, potentially exacerbating pre-existing cardiac conditions or precipitating new illnesses. Pregnant patients with cardiac emergencies pose unique challenges, as standard interventions may pose risks to the developing fetus. This article aims to enhance emergency physicians’ confidence in managing pregnancy-related cardiac emergencies by providing a structured approach to initial evaluation and stabilization. We review eight common categories of pregnancy-associated cardiac illness: gestational hypertension and pre-eclampsia, cardiomyopathy, arrhythmias, valvular disease, aortopathies, congenital heart disease and pulmonary hypertension, coronary disease, and anticoagulation-related complications. For each condition, we summarize relevant pregnancy-specific pathophysiology and outline evidence-based, personalized emergency management strategies.

AbstractObjectivesTo examine heterogeneity in physician batch ordering practices and measure the associations between a physician's tendency to batch order imaging tests on patient outcomes and resource utilization.Study Setting and DesignIn this retrospective study, we used comprehensive EMR data from patients who visited the Mayo Clinic of Arizona Emergency Department (ED) between October 6, 2018 and December 31, 2019. Primary outcomes are patient length of stay (LOS) in the ED, number of diagnostic imaging tests ordered during a patient encounter, and patients' return with admission to the ED within 72 h. The association between outcomes and physician batch tendency was measured using a multivariable linear regression controlling for various covariates.Data Sources and Analytic SampleThe Mayo Clinic of Arizona Emergency Department recorded approximately 50,836 visits, all randomly assigned to physicians during the study period. After excluding rare complaints, we were left with an analytical sample of 43,299 patient encounters.Principal FindingsFindings show that having a physician with a batch tendency 1 standard deviation (SD) greater than the average physician was associated with a 4.5% increase in ED LOS (p < 0.001). It was also associated with a 14.8% (0.2 percentage points) decrease in the probability of a 72‐h return with admission (p < 0.001), implying that batching may lead to more comprehensive evaluations, reducing the need for short‐term revisits. A batch tendency 1SD greater than that of the average physician was also associated with an additional 8 imaging tests ordered per 100 patient encounters (p < 0.001), suggesting that batch ordering may be leading to tests that would not have been otherwise ordered had the physician waited for the results from one test before placing their next order.ConclusionsThis study highlights the considerable impact of physicians' diagnostic test ordering strategies on ED efficiency and patient care. The results also highlight the need to develop guidelines to optimize ED test ordering practices.

While congenital heart disease historically was a pathology primarily restricted to specialized pediatric centers, advances in technology have dramatically increased the number of people living into adulthood, the number of complications faced by these patients, and the number of patients visiting non-specialized emergency departments for these concerns. Clinicians need to be aware of the issues specific to patients’ individual congenital defects but also have an understanding of how typical cardiac pathology may manifest in this special group of patients. This manuscript attempts to provide an overview of this diverse but increasingly common group of adult patients with congenital heart diseases, including a review of their anatomical variants, the complications they face at the highest rates, and ways that emergency physicians may need to manage these patients differently to avoid causing harm.

Introduction: Clue cells result from aberrant vaginal microflora and are associated with an increased vaginal pH, which can allow colonization of uropathogens in the vaginal introitus, increasing the risk for urinary tract infections (UTI). We sought to determine whether clue cells on vaginal wet preparation in the emergency department (ED) are associated with emergency physician diagnoses of UTIs and positive urine cultures. Methods: We conducted a retrospective analysis examining a dataset of women (≥18 years of age) who received both a genital wet preparation and urine testing in the ED. Both chi-square and multivariable regression analysis were performed. Results: We analyzed 14,952 encounters. On both univariable and multivariable analyses, emergency physicians diagnosed significantly fewer clue cell-positive women with a UTI (10.9% diagnosed with UTI vs 13.1% without UTI) (P <.001). Women with clue cells on vaginal wet preparation were not more likely to have a positive urine culture or have a urine culture growing Escherichia coli. Pregnant women with clue cells on vaginal wet preparation were not more likely to have a UTI or have a positive urine culture. Conclusion: Emergency physicians diagnosed significantly fewer women with UTIs when they found clue cells on vaginal wet preparation. Clue cells on vaginal wet preparation were not associated with an increased likelihood of a positive urine culture or having E. coli growing in the urine.

Introduction: Emergency departments (ED) are rapidly replacing conventional troponin assays with high-sensitivity troponin tests. We sought to evaluate emergency physician utilization of troponin tests before and after high-sensitivity troponin introduction in our ED. Methods: We retrospectively examined 9,477 ED encounters, identifying the percentage in which physicians ordered a serum troponin both before and after our institution adopted a high-sensitivity troponin test. Results: After introduction of high-sensitivity troponin testing, the percentage of ED encountersin which physicians ordered troponin studies decreased (28.3% before vs 22% after; P <.001),with the drop most pronounced in admitted patients (decrease of 10.9% [95% confidenceinterval [CI]: 7.3%- 14.5%] in admitted patients vs decrease of 3.6% [95% CI: 1.7%- 5.4%]in discharged patients; P<.001) Conclusion: Introduction of high-sensitivity troponin testing was associated with a decrease in troponin ordering. While the reasons for this are unclear, it is possible that physicians became more selective in their ordering behavior because of the lower specificity of high-sensitivity troponin.

Objectives: Patients admitted to a medical-surgical unit infrequently require early transfer to higher level care, although how their inpatient length of stay compares to untransferred patients, or those directly admitted to intermediate care, is unknown. We sought to compare the inpatient length of stay of these groups. Design: Single-site retrospective analysis. Setting: An academic hospital specializing in complex care. Patients: We evaluated 23,694 patients admitted to the Hospital Internal Medicine service over a 4-year period (January 1, 2013, to December 31, 2016). Interventions: None. Measurements and Main Results: Using 6- and 24-hour definitions of early transfer, we categorized patients as admitted to medical-surgical unit without early transfer (medical-surgical unit), transferred (TX) early to higher level care, or initially admitted to an intermediate care unit. We report patient characteristics and inpatient length of stay adjusted for patient demographics (age and sex) and initial acuity (measured by Emergency Severity Index). There were significant increases in both unadjusted inpatient length of stay (6 hr: medical-surgical unit = 73.4 hr, TX = 137.9 hr, intermediate care unit = 101.1 hr; 24 hr: medical-surgical unit = 72.4 hr, TX = 141.9 hr, intermediate care unit = 98.2 hr; p < 0.01 for all groups) and adjusted inpatient length of stay (6-hr definition: medical-surgical unit = 50.9 hr [95% CI, 50.3–51.6 hr], TX = 100.4 hr [90.4–112.0 hr], intermediate care unit = 72.3 hr [70.6–74.0 hr]; 24-hr definition: medical-surgical unit = 50.3 hr [49.7–50.9 hr], TX = 108.3 hr [101.5–116.0 hr], intermediate care unit = 70.7 hr [69.0–72.3 hr]; p < 0.0001 for comparison of TX to medical-surgical unit and intermediate care unit in both groups). The increases in inpatient length of stay for the TX groups were not explained by differences in demographics or acuity. Conclusions: In a single facility study, patients admitted to a medical/surgical unit who require early transfer to intermediate care unit have a significant and unexplained increase in inpatient length of stay. This unexplained increased inpatient length of stay suggests that triage to the appropriate inpatient unit significantly affects inpatient length of stay.