Pub Date : 2025-01-03DOI: 10.1016/j.ajem.2025.01.003
Rohit B Sangal, Robert Teresi, Meir Dashevsky, Andrew Ulrich, Asim Tarabar, Vivek Parwani, Reinier Van Tonder, Marissa King, Arjun K Venkatesh
<p><strong>Background: </strong>This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.</p><p><strong>Methods: </strong>A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables. A physician's patients' actual LOSs were compared to the model's predictions to calculate a measurement of that physician's speed. Linear regression models were employed to assess how physician performance changed based on the measured speed of the concurrent ED co-attendings, on outcomes including patient LOS, patients treated per hour, imaging utilization, admission rates, and 72-h ED revisits.</p><p><strong>Results: </strong>Eighty physicians and 212,902 ED visits were included. Overall, patients assigned to the fastest physicians have a 17.8 % [13.5 %, 22.0 %] shorter LOS compared to average-speed attendings. When the fastest physicians work alongside the fastest co-attendings, their LOS benefit is reduced to 14.9 %, representing a 2.9 % [0.2 %, 5.6 %] longer LOS than when working without the fastest co-attendings. Similarly, the fastest physicians see 0.21 [0.13, 0.28] more patients per hour compared to average attendings, but this benefit decreases to 0.13 [0.09, 0.17] more patients per hour when the fastest co-attendings are present, reflecting a reduction of 0.08 [0.04, 0.11] patients per hour. The fastest physicians order 0.18 [0.13, 0.23] fewer imaging tests per patient than average-speed attendings; however, this reduction diminishes by 0.05 [0.04, 0.07] imaging tests per patient when the fastest co-attendings are present. Our model found effects of similar magnitudes but in the opposite direction when the slowest co-attendings are present. The speed of co-attendings had no significant association on the attending admission rate or 72-h revisit rate. Additionally, compared to the average attending team speed, slower attending teams, over an 8 h shift, experienced increased waiting room volume by 6.4 % [4.5 %, 8.4 %] while there was no difference when staffed by the fastest attending teams (-1.2 % [-3.2 %,0.7 %]).</p><p><strong>Conclusion: </strong>In this exploratory analysis, physicians have slower throughput and order more imaging when faster co-attendings are present, and faster throughput with less imaging ordered when slower co-attendings are present. Administrators might consider these relationships and balancing attending speeds, particularly at the extremes (slowest and fastest), when designing staffing models as a potential strategy to enhance ED operational efficiency. What is already known on this topic: ED throughput is known to be dependent on multiple factors however physician behavior is commonly modeled as single attendings work
{"title":"Who is coming in? Evaluation of physician performance within multi-physician emergency departments.","authors":"Rohit B Sangal, Robert Teresi, Meir Dashevsky, Andrew Ulrich, Asim Tarabar, Vivek Parwani, Reinier Van Tonder, Marissa King, Arjun K Venkatesh","doi":"10.1016/j.ajem.2025.01.003","DOIUrl":"https://doi.org/10.1016/j.ajem.2025.01.003","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.</p><p><strong>Methods: </strong>A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables. A physician's patients' actual LOSs were compared to the model's predictions to calculate a measurement of that physician's speed. Linear regression models were employed to assess how physician performance changed based on the measured speed of the concurrent ED co-attendings, on outcomes including patient LOS, patients treated per hour, imaging utilization, admission rates, and 72-h ED revisits.</p><p><strong>Results: </strong>Eighty physicians and 212,902 ED visits were included. Overall, patients assigned to the fastest physicians have a 17.8 % [13.5 %, 22.0 %] shorter LOS compared to average-speed attendings. When the fastest physicians work alongside the fastest co-attendings, their LOS benefit is reduced to 14.9 %, representing a 2.9 % [0.2 %, 5.6 %] longer LOS than when working without the fastest co-attendings. Similarly, the fastest physicians see 0.21 [0.13, 0.28] more patients per hour compared to average attendings, but this benefit decreases to 0.13 [0.09, 0.17] more patients per hour when the fastest co-attendings are present, reflecting a reduction of 0.08 [0.04, 0.11] patients per hour. The fastest physicians order 0.18 [0.13, 0.23] fewer imaging tests per patient than average-speed attendings; however, this reduction diminishes by 0.05 [0.04, 0.07] imaging tests per patient when the fastest co-attendings are present. Our model found effects of similar magnitudes but in the opposite direction when the slowest co-attendings are present. The speed of co-attendings had no significant association on the attending admission rate or 72-h revisit rate. Additionally, compared to the average attending team speed, slower attending teams, over an 8 h shift, experienced increased waiting room volume by 6.4 % [4.5 %, 8.4 %] while there was no difference when staffed by the fastest attending teams (-1.2 % [-3.2 %,0.7 %]).</p><p><strong>Conclusion: </strong>In this exploratory analysis, physicians have slower throughput and order more imaging when faster co-attendings are present, and faster throughput with less imaging ordered when slower co-attendings are present. Administrators might consider these relationships and balancing attending speeds, particularly at the extremes (slowest and fastest), when designing staffing models as a potential strategy to enhance ED operational efficiency. What is already known on this topic: ED throughput is known to be dependent on multiple factors however physician behavior is commonly modeled as single attendings work","PeriodicalId":55536,"journal":{"name":"American Journal of Emergency Medicine","volume":"90 ","pages":"9-15"},"PeriodicalIF":2.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.ajem.2024.12.069
Alison Treichel, Remle P Crowe, Antonio R Fernandez, Scott S Bourn, Brent J Myers
Objective: To describe changes in patient and encounter characteristics among Emergency Medical Services (EMS) responses for patients ages 0-19 with firearm-related injuries.
Methods: This retrospective national analysis used data from the 2018-2022 ESO Data Collaborative and included all 9-1-1 records for patients ages 0-19 years with documentation of firearm-related injuries. Percent changes are reported; annual changes were evaluated using a non-parametric test of trend.
Results: Among 7913 total EMS encounters from 586 agencies, median age was 16 years and 82 % were male. 9-1-1 call volume increased 8 % over the study period while firearm injuries increased by 79 % (p-trend<0.01), peaking in 2021 (n = 2036). Assault was the most documented intent category across all years (96 % total increase). Black and Hispanic patients experienced the largest percent increase (86 %, 110 %). Most encounters occurred in communities of the greatest socioeconomic vulnerability (57 %). Overall, 10 % of patients died on-scene; among those transported with outcome data (n = 1514), 9 % died.
Conclusions: We observed a continued upward trend in EMS encounters for firearm injuries among children and adolescents between 2018 and 2022. Injury burden disproportionately impacted minorities and socioeconomically vulnerable communities.
{"title":"Trends in EMS encounters and outcomes among children and adolescents with firearm injuries from 2018 to 2022.","authors":"Alison Treichel, Remle P Crowe, Antonio R Fernandez, Scott S Bourn, Brent J Myers","doi":"10.1016/j.ajem.2024.12.069","DOIUrl":"https://doi.org/10.1016/j.ajem.2024.12.069","url":null,"abstract":"<p><strong>Objective: </strong>To describe changes in patient and encounter characteristics among Emergency Medical Services (EMS) responses for patients ages 0-19 with firearm-related injuries.</p><p><strong>Methods: </strong>This retrospective national analysis used data from the 2018-2022 ESO Data Collaborative and included all 9-1-1 records for patients ages 0-19 years with documentation of firearm-related injuries. Percent changes are reported; annual changes were evaluated using a non-parametric test of trend.</p><p><strong>Results: </strong>Among 7913 total EMS encounters from 586 agencies, median age was 16 years and 82 % were male. 9-1-1 call volume increased 8 % over the study period while firearm injuries increased by 79 % (p-trend<0.01), peaking in 2021 (n = 2036). Assault was the most documented intent category across all years (96 % total increase). Black and Hispanic patients experienced the largest percent increase (86 %, 110 %). Most encounters occurred in communities of the greatest socioeconomic vulnerability (57 %). Overall, 10 % of patients died on-scene; among those transported with outcome data (n = 1514), 9 % died.</p><p><strong>Conclusions: </strong>We observed a continued upward trend in EMS encounters for firearm injuries among children and adolescents between 2018 and 2022. Injury burden disproportionately impacted minorities and socioeconomically vulnerable communities.</p>","PeriodicalId":55536,"journal":{"name":"American Journal of Emergency Medicine","volume":"90 ","pages":"55-60"},"PeriodicalIF":2.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.ajem.2025.01.002
Mustafa Comoglu
{"title":"Comparison of McMahon and dialysis scores in earthquake survivors: A response to the letter.","authors":"Mustafa Comoglu","doi":"10.1016/j.ajem.2025.01.002","DOIUrl":"https://doi.org/10.1016/j.ajem.2025.01.002","url":null,"abstract":"","PeriodicalId":55536,"journal":{"name":"American Journal of Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.ajem.2025.01.001
Akif Bayyigit
{"title":"Comments on \"A new score predicting renal replacement therapy in patients with crush injuries: Analysis of a major earthquake\".","authors":"Akif Bayyigit","doi":"10.1016/j.ajem.2025.01.001","DOIUrl":"https://doi.org/10.1016/j.ajem.2025.01.001","url":null,"abstract":"","PeriodicalId":55536,"journal":{"name":"American Journal of Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-09-19DOI: 10.1016/j.ajem.2024.09.045
Adhish Gautam, Alexei A Birkun
{"title":"Optimizing emergency dispatcher pre-arrival guidance on first aid for snakebites.","authors":"Adhish Gautam, Alexei A Birkun","doi":"10.1016/j.ajem.2024.09.045","DOIUrl":"10.1016/j.ajem.2024.09.045","url":null,"abstract":"","PeriodicalId":55536,"journal":{"name":"American Journal of Emergency Medicine","volume":" ","pages":"201-203"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-10-28DOI: 10.1016/j.ajem.2024.10.039
Ziyi He, Chenxi Wang, Huichuan Tian
{"title":"Regarding the measurement of left atrial size as a predictor of severity of illness in sickle cell disease.","authors":"Ziyi He, Chenxi Wang, Huichuan Tian","doi":"10.1016/j.ajem.2024.10.039","DOIUrl":"10.1016/j.ajem.2024.10.039","url":null,"abstract":"","PeriodicalId":55536,"journal":{"name":"American Journal of Emergency Medicine","volume":" ","pages":"213"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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