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Article ; Online: Exploring the impact of missingness on racial disparities in predictive performance of a machine learning model for emergency department triage.

Teeple, Stephanie / Smith, Aria / Toerper, Matthew / Levin, Scott / Halpern, Scott / Badaki-Makun, Oluwakemi / Hinson, Jeremiah

JAMIA open

2023 Volume 6, Issue 4, Page(s) ooad107

Abstract: Objective: To investigate how missing data in the patient problem list may impact racial disparities in the predictive performance of a machine learning (ML) model for emergency department (ED) triage.: Materials and methods: Racial disparities may ... ...

Abstract	Objective: To investigate how missing data in the patient problem list may impact racial disparities in the predictive performance of a machine learning (ML) model for emergency department (ED) triage. Materials and methods: Racial disparities may exist in the missingness of EHR data (eg, systematic differences in access, testing, and/or treatment) that can impact model predictions across racialized patient groups. We use an ML model that predicts patients' risk for adverse events to produce triage-level recommendations, patterned after a clinical decision support tool deployed at multiple EDs. We compared the model's predictive performance on sets of observed (problem list data at the point of triage) versus manipulated (updated to the more complete problem list at the end of the encounter) test data. These differences were compared between Black and non-Hispanic White patient groups using multiple performance measures relevant to health equity. Results: There were modest, but significant, changes in predictive performance comparing the observed to manipulated models across both Black and non-Hispanic White patient groups; c-statistic improvement ranged between 0.027 and 0.058. The manipulation produced no between-group differences in c-statistic by race. However, there were small between-group differences in other performance measures, with greater change for non-Hispanic White patients. Discussion: Problem list missingness impacted model performance for both patient groups, with marginal differences detected by race. Conclusion: Further exploration is needed to examine how missingness may contribute to racial disparities in clinical model predictions across settings. The novel manipulation method demonstrated may aid future research.
Language	English
Publishing date	2023-12-20
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooad107
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Article ; Online: Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.

Hinson, Jeremiah S / Zhao, Xihan / Klein, Eili / Badaki-Makun, Oluwakemi / Rothman, Richard / Copenhaver, Martin / Smith, Aria / Fenstermacher, Katherine / Toerper, Matthew / Pekosz, Andrew / Levin, Scott

Journal of the American College of Emergency Physicians open

2024 Volume 5, Issue 2, Page(s) e13117

Abstract: Objective: Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for ... ...

Abstract	Objective: Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged. Methods: We developed random forest machine learning (ML) models to estimate needs for critical care within 24 h and inpatient care within 72 h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score. Results: Among 8032 patients with laboratory-confirmed influenza, incidence of critical care needs was 6.3% and incidence of inpatient care needs was 19.6%. The most common reasons for ED visit were symptoms of respiratory tract infection, fever, and shortness of breath. Model AUCs were 0.89 (95% CI 0.86-0.93) for prediction of critical care and 0.90 (95% CI 0.88-0.93) for inpatient care needs; Brier scores were 0.026 and 0.042, respectively. Importantpredictors included shortness of breath, increasing respiratory rate, and a high number of comorbid diseases. Conclusions: ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision-making.
Language	English
Publishing date	2024-03-18
Publishing country	United States
Document type	Journal Article
ISSN	2688-1152
ISSN (online)	2688-1152
DOI	10.1002/emp2.13117
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Article ; Online: Improving antimicrobial prescribing for upper respiratory infections in the emergency department: Implementation of peer comparison with behavioral feedback.

Jones, George F / Fabre, Valeria / Hinson, Jeremiah / Levin, Scott / Toerper, Matthew / Townsend, Jennifer / Cosgrove, Sara E / Saheed, Mustapha / Klein, Eili Y

Antimicrobial stewardship & healthcare epidemiology : ASHE

2021 Volume 1, Issue 1, Page(s) e70

Abstract: Objective: To reduce inappropriate antibiotic prescribing for acute respiratory infections (ARIs) by employing peer comparison with behavioral feedback in the emergency department (ED).: Design: A controlled before-and-after study.: Setting: The ... ...

Abstract	Objective: To reduce inappropriate antibiotic prescribing for acute respiratory infections (ARIs) by employing peer comparison with behavioral feedback in the emergency department (ED). Design: A controlled before-and-after study. Setting: The study was conducted in 5 adult EDs at teaching and community hospitals in a health system. Patients: Adults presenting to the ED with a respiratory condition diagnosis code. Hospitalized patients and those with a diagnosis code for a non-respiratory condition for which antibiotics are or may be warranted were excluded. Interventions: After a baseline period from January 2016 to March 2018, 3 EDs implemented a feedback intervention with peer comparison between April 2018 and December 2019 for attending physicians. Also, 2 EDs in the health system served as controls. Using interrupted time series analysis, the inappropriate ARI prescribing rate was calculated as the proportion of antibiotic-inappropriate ARI encounters with a prescription. Prescribing rates were also evaluated for all ARIs. Attending physicians at intervention sites received biannual e-mails with their inappropriate prescribing rate and had access to a dashboard that was updated daily showing their performance relative to their peers. Results: Among 28,544 ARI encounters, the inappropriate prescribing rate remained stable at the control EDs between the 2 periods (23.0% and 23.8%). At the intervention sites, the inappropriate prescribing rate decreased significantly from 22.0% to 15.2%. Between periods, the overall ARI prescribing rate was 38.1% and 40.6% in the control group and 35.9% and 30.6% in the intervention group. Conclusions: Behavioral feedback with peer comparison can be implemented effectively in the ED to reduce inappropriate prescribing for ARIs.
Language	English
Publishing date	2021-12-23
Publishing country	England
Document type	Journal Article
ISSN	2732-494X
ISSN (online)	2732-494X
DOI	10.1017/ash.2021.240
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Article ; Online: Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data.

Durojaiye, Ashimiyu B / Levin, Scott / Toerper, Matthew / Kharrazi, Hadi / Lehmann, Harold P / Gurses, Ayse P

Journal of the American Medical Informatics Association : JAMIA

2019 Volume 26, Issue 6, Page(s) 506–515

Abstract: Objectives: The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes.: Materials and methods: A process mining-based ... ...

Abstract	Objectives: The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes. Materials and methods: A process mining-based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared. Results: Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9-246.0] minutes vs 295.2 [95% confidence, 289.2-297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists. Discussion: The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay. Conclusions: Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.
MeSH term(s)	Child ; Cooperative Behavior ; Data Mining ; Electronic Health Records ; Emergency Service, Hospital/organization & administration ; Humans ; Interprofessional Relations ; Length of Stay ; Metadata ; Patient Care Team ; Pediatrics/organization & administration ; Social Network Analysis ; Traumatology/organization & administration
Language	English
Publishing date	2019-03-18
Publishing country	England
Document type	Evaluation Study ; Journal Article ; Research Support, U.S. Gov't, P.H.S.
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocy184
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Article ; Online: Monocyte distribution width as a pragmatic screen for SARS-CoV-2 or influenza infection.

Badaki-Makun, Oluwakemi / Levin, Scott / Debraine, Arnaud / Hernried, Benjamin / Malinovska, Alexandra / Smith, Aria / Toerper, Matthew / Fenstermacher, Katherine Z J / Cottle, Taylor / Latallo, Malgorzata / Rothman, Richard E / Hinson, Jeremiah S

Scientific reports

2022 Volume 12, Issue 1, Page(s) 21528

Abstract: Monocyte distribution width (MDW) is a novel marker of monocyte activation, which is known to occur in the immune response to viral pathogens. Our objective was to determine the performance of MDW and other leukocyte parameters as screening tests for ... ...

Abstract	Monocyte distribution width (MDW) is a novel marker of monocyte activation, which is known to occur in the immune response to viral pathogens. Our objective was to determine the performance of MDW and other leukocyte parameters as screening tests for SARS-CoV-2 and influenza infection. This was a prospective cohort analysis of adult patients who underwent complete blood count (CBC) and SARS-CoV-2 or influenza testing in an Emergency Department (ED) between January 2020 and July 2021. The primary outcome was SARS-CoV-2 or influenza infection. Secondary outcomes were measures of severity of illness including inpatient hospitalization, critical care admission, hospital lengths of stay and mortality. Descriptive statistics and test performance measures were evaluated for monocyte percentage, MDW, white blood cell (WBC) count, and neutrophil to lymphocyte ratio (NLR). 3,425 ED patient visits were included. SARS-CoV-2 testing was performed during 1,922 visits with a positivity rate of 5.4%; influenza testing was performed during 2,090 with a positivity rate of 2.3%. MDW was elevated in patients with SARS-Cov-2 (median 23.0U; IQR 20.5-25.1) or influenza (median 24.1U; IQR 22.0-26.9) infection, as compared to those without (18.9U; IQR 17.4-20.7 and 19.1U; 17.4-21, respectively, P < 0.001). Monocyte percentage, WBC and NLR values were within normal range in patients testing positive for either virus. MDW identified SARS-CoV-2 and influenza positive patients with an area under the curve (AUC) of 0.83 (95% CI 0.79-0.86) and 0.83 (95% CI 0.77-0.88), respectively. At the accepted cut-off value of 20U for MDW, sensitivities were 83.7% (95% CI 76.5-90.8%) for SARS-CoV-2 and 89.6% (95% CI 80.9-98.2%) for influenza, compared to sensitivities below 45% for monocyte percentage, WBC and NLR. MDW negative predictive values were 98.6% (95% CI 98.0-99.3%) and 99.6% (95% CI 99.3-100.0%) respectively for SARS-CoV-2 and influenza. Monocyte Distribution Width (MDW), available as part of a routine complete blood count (CBC) with differential, may be a useful indicator of SARS-CoV-2 or influenza infection.
MeSH term(s)	Adult ; Humans ; SARS-CoV-2 ; COVID-19 Testing ; Influenza, Human/diagnosis ; Monocytes ; Prospective Studies ; COVID-19/diagnosis
Language	English
Publishing date	2022-12-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-24978-w
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Article ; Online: Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.

Hinson, Jeremiah S / Klein, Eili / Smith, Aria / Toerper, Matthew / Dungarani, Trushar / Hager, David / Hill, Peter / Kelen, Gabor / Niforatos, Joshua D / Stephens, R Scott / Strauss, Alexandra T / Levin, Scott

NPJ digital medicine

2022 Volume 5, Issue 1, Page(s) 94

Abstract: Demand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. ...

Abstract	Demand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. The novelty and high variability of COVID-19 have made these determinations challenging. In this study, we developed, implemented and evaluated an electronic health record (EHR) embedded clinical decision support (CDS) system that leverages machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 h and inpatient care needs within 72 h into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. ML models were derived in a retrospective cohort of 21,452 ED patients who visited one of five ED study sites and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation; model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. Incidence of critical care needs within 24 h and inpatient care needs within 72 h were 10.7% and 22.5%, respectively and were similar across study periods. ML model performance was excellent under all conditions, with AUC ranging from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after CDS implementation.
Language	English
Publishing date	2022-07-16
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-022-00646-1
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Article ; Online: Monocyte distribution width as part of a broad pragmatic sepsis screen in the emergency department.

Malinovska, Alexandra / Hinson, Jeremiah S / Badaki-Makun, Oluwakemi / Hernried, Benjamin / Smith, Aria / Debraine, Arnaud / Toerper, Matthew / Rothman, Richard E / Kickler, Thomas / Levin, Scott

Journal of the American College of Emergency Physicians open

2022 Volume 3, Issue 2, Page(s) e12679

Abstract: Study objective: Enhancement of a routine complete blood count (CBC) for detection of sepsis in the emergency department (ED) has pragmatic utility for early management. This study evaluated the performance of monocyte distribution width (MDW) alone and ...

Abstract	Study objective: Enhancement of a routine complete blood count (CBC) for detection of sepsis in the emergency department (ED) has pragmatic utility for early management. This study evaluated the performance of monocyte distribution width (MDW) alone and in combination with other routine CBC parameters as a screen for sepsis and septic shock in ED patients. Methods: A prospective cohort analysis of adult patients with a CBC collected at an urban ED from January 2020 through July 2021. The performance of MDW, white blood count (WBC) count, and neutrophil-to-lymphocyte-ratio (NLR) to detect sepsis and septic shock (Sepsis-3 Criteria) was evaluated using diagnostic performance measures. Results: The cohort included 7952 ED patients, with 180 meeting criteria for sepsis; 43 with septic shock and 137 without shock. MDW was highest for patients with septic shock (median 24.8 U, interquartile range [IQR] 22.0-28.1) and trended downward for patients with sepsis without shock (23.9 U, IQR 20.2-26.8), infection (20.4 U, IQR 18.2-23.3), then controls (18.6 U, IQR 17.1-20.4). In isolation, MDW detected sepsis and septic shock with an area under the receiver operator characteristic curve (AUC) of 0.80 (95% confidence interval [CI] 0.77-0.84) and 0.85 (95% CI 0.80-0 .91), respectively. Optimal performance was achieved in combination with WBC count and NLR for detection of sepsis (AUC 0.86, 95% CI 0.83-0.89) and septic shock (0.86, 95% CI 0.80-0.92). Conclusion: A CBC differential panel that includes MDW demonstrated strong performance characteristics in a broad ED population suggesting pragmatic value as a rapid screen for sepsis and septic shock.
Language	English
Publishing date	2022-02-28
Publishing country	United States
Document type	Journal Article
ISSN	2688-1152
ISSN (online)	2688-1152
DOI	10.1002/emp2.12679
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Article ; Online: The HIV Screening Cascade: Current Emergency Department-Based Screening Strategies Leave Many Patients With HIV Undiagnosed.

Mohareb, Amir M / Patel, Anuj V / Laeyendecker, Oliver B / Toerper, Matthew F / Signer, Danielle / Clarke, William A / Kelen, Gabor D / Quinn, Thomas C / Haukoos, Jason S / Rothman, Richard E / Hsieh, Yu-Hsiang

Journal of acquired immune deficiency syndromes (1999)

2021 Volume 87, Issue 1, Page(s) e167–e169

MeSH term(s)	Emergency Service, Hospital ; HIV Infections/diagnosis ; HIV Infections/epidemiology ; HIV Seroprevalence ; HIV Testing/methods ; Humans ; Mass Screening
Language	English
Publishing date	2021-03-18
Publishing country	United States
Document type	Letter ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	645053-2
ISSN	1944-7884 ; 1077-9450 ; 0897-5965 ; 0894-9255 ; 1525-4135
ISSN (online)	1944-7884 ; 1077-9450
ISSN	0897-5965 ; 0894-9255 ; 1525-4135
DOI	10.1097/QAI.0000000000002609
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Article ; Online: Effects of fully accessible magnetic resonance imaging in the emergency department.

Redd, Vanessa / Levin, Scott / Toerper, Matthew / Creel, Amanda / Peterson, Susan

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine

2015 Volume 22, Issue 6, Page(s) 741–749

Abstract: Background: The Joint Commission Comprehensive Stroke Center certification requires that magnetic resonance imaging (MRI) be available on site, 24 hours a day, 7 days a week for evaluation of stroke in emergency department (ED) patients. Increased ... ...

Abstract	Background: The Joint Commission Comprehensive Stroke Center certification requires that magnetic resonance imaging (MRI) be available on site, 24 hours a day, 7 days a week for evaluation of stroke in emergency department (ED) patients. Increased access to advanced diagnostic imaging has been shown to increase utilization, ED length of stay (LOS), and health care costs. EDs nationwide face decisions to pursue certification and increase MRI access. Understanding changes in utilization and the downstream effects may inform these decisions. Objectives: The objective was to determine changes in emergency MRI utilization following placement of a 24/7 accessible MRI in the ED and its effects on resource utilization for rule-out stroke and neurology consult patients. Methods: This was a retrospective cohort study comparing MRI use during the 32 months before and 26 months after MRI acquisition period in the ED of a Level I trauma and stroke center. An interrupted time-series design was used to account for changes in clinical practice patterns following MRI acquisition. Time-series plots and segmented regression analyses are presented to compare utilization patterns pre- and post-MRI and to understand potential confounding due to secular trends. Statistical hypothesis testing was used to determine differences in utilization, demographics, and clinical characteristics for cohorts pre- and post-MRI. Results: MRI utilization in the ED increased 38.4% for rule-out stroke and 51.4% for neurology consult patients after MRI acquisition. The proportion of rule-out stroke patients receiving MRI increased from 32.5% pre-MRI to 45.0% post-MRI (p < 0.001). The proportion of neurology consult patients increased from 32.6% pre-MRI to 49.4% post-MRI (p < 0.001). Considering baseline increases in MRI utilization rates for both cohorts over time, segmented regression models detected more substantial and significant changes in utilization after MRI acquisition for the larger neurology cohort (p < 0.001) compared to the rule-out stroke cohort (p = 0.095). However, hospital admission rates declined 16.7% for rule-out stroke patients (68.2% pre, 56.8% post; p < 0.001) and remained constant for neurology patients (56.5% pre, 57.5% post; p = 0.414). Patients who obtained MRI in the ED had increased ED LOS, but decreased hospital LOS (admitted patients), compared to those with no MRI for pre and post cohorts. Conclusions: Emergency MRI utilization increased substantially after placement of a fully accessible MRI in the ED. Patients receiving emergency MRI had increased ED LOS, decreased admission rates for some patients (rule-out stroke), and reduced hospital LOS for those admitted. Potential changes in ED patient resource utilization should be considered when determining whether to acquire an MRI for Comprehensive Stroke Center certification.
MeSH term(s)	Adult ; Aged ; Aged, 80 and over ; Emergency Service, Hospital/standards ; Emergency Service, Hospital/statistics & numerical data ; Female ; Humans ; Length of Stay/statistics & numerical data ; Magnetic Resonance Imaging/utilization ; Male ; Middle Aged ; Practice Patterns, Physicians'/statistics & numerical data ; Regression Analysis ; Retrospective Studies ; Stroke/diagnosis ; Trauma Centers
Language	English
Publishing date	2015-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	1329813-6
ISSN	1553-2712 ; 1069-6563
ISSN (online)	1553-2712
ISSN	1069-6563
DOI	10.1111/acem.12686
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Article ; Online: Real-time prediction of inpatient length of stay for discharge prioritization.

Barnes, Sean / Hamrock, Eric / Toerper, Matthew / Siddiqui, Sauleh / Levin, Scott

Journal of the American Medical Informatics Association : JAMIA

2015 Volume 23, Issue e1, Page(s) e2–e10

Abstract: Objective: Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management ( ... ...

Abstract	Objective: Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information. Materials and methods: The authors use supervised machine learning methods to predict patients' likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden's Index (i.e., sensitivity + specificity - 1), and aggregate accuracy measures. Results: The model compared to clinician predictions demonstrated significantly higher sensitivity (P < .01), lower specificity (P < .01), and a comparable Youden Index (P > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge. Conclusions: There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.
MeSH term(s)	Academic Medical Centers ; Adult ; Aged ; Female ; Hospital Administration ; Humans ; Length of Stay ; Machine Learning ; Male ; Maryland ; Middle Aged ; Patient Discharge ; Workflow ; Workload
Language	English
Publishing date	2015-08-07
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocv106
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