Rana Kabeer

All Publications

• Quantifying Emergency Medicine Residency Learning Curves Using Natural Language Processing: Retrospective Cohort Study. JMIR medical education Preiksaitis, C., Hughes, J., Kabeer, R., Dixon, W., Rose, C. 2025; 11: e82326

  Abstract

  The optimal duration of emergency medicine (EM) residency training remains a subject of national debate, with the Accreditation Council for Graduate Medical Education considering standardizing all programs to 4 years. However, empirical data on how residents accumulate clinical exposure over time are limited. Traditional measures, such as case logs and diagnostic codes, often fail to capture the breadth and depth of diagnostic reasoning. Natural language processing (NLP) of clinical documentation offers a novel approach to quantifying clinical experiences more comprehensively.This study aimed to (1) quantify how EM residents acquire clinical topic exposure over the course of training, (2) evaluate variation in exposure patterns across residents and classes, and (3) assess changes in workload and case complexity over time to inform the discussion on optimal program length.We conducted a retrospective cohort study of EM residents at Stanford Hospital, analyzing 244,255 emergency department encounters from July 1, 2016, to November 30, 2023. The sample included 62 residents across 4 graduating classes (2020-2023), representing all primary training site encounters where residents served as primary or supervisory providers. Using a retrieval-augmented generation NLP pipeline, we mapped resident clinical documentation to the 895 subcategories of the 2022 Model for Clinical Practice of Emergency Medicine (MCPEM) via intermediate mapping to the Systematized Nomenclature of Medicine, Clinical Terms, Clinical Observations, Recordings, and Encoding problem list subset. We generated cumulative topic exposure curves, quantified the diversity of topic coverage, assessed variability between residents, and analyzed the progression in clinical complexity using Emergency Severity Index (ESI) scores and admission rates.Residents encountered the largest increase in new topics during postgraduate year 1 (PGY1), averaging 376.7 (42.1%) unique topics among a total of 895 MCPEM subcategories. By PGY4, they averaged 565.9 (63.2%) topics, representing a 9.9% (51/515) increase over PGY3. Exposure plateaus generally occurred at 39 to 41 months, although substantial individual variation was observed, with some residents continuing to acquire new topics until graduation. Annual case volume more than tripled from PGY1 (mean 445.7, SD 112.7 encounters) to PGY4 (mean 1528.4, SD 112.7 encounters). Case complexity increased, as evidenced by a decrease in mean ESI score from 2.94 to 2.79, and a rise in high-acuity (ESI 1-2) cases from 16% (4374/27,340) to 30.9% (9418/30,466).NLP analysis of clinical documentation provides a scalable, detailed method for tracking EM residents' clinical exposure and progression. Many residents continue to gain new experiences into their fourth year, particularly in higher-acuity cases. These findings suggest that a 4-year training model may offer meaningful additional educational value, while also highlighting the importance of individualized assessment given the variability in learning trajectories.

  View details for DOI 10.2196/82326

  View details for PubMedID 41364786

• Value of diversity characteristics in predictive modeling: ACS screening as a case study. NPJ cardiovascular health Bunney, G., Miller, K., Ryu, K., Kabeer, R., Graber-Naidich, A., Pasao, M. A., Rizvi, M., Yiadom, M. Y. 2025; 2 (1): 52

  Abstract

  We sought to improve the subgroup performance variability of a model that identifies arriving ED patients at high risk for ACS, to receive an ECG within 10 minutes of arrival, to detect STEMI. We compared a Base Model using age, sex, and chief complaint alone to (1) an Interactions Model adding interactions among the 3 variables, and (2) a Diversity-Sensitive Model including race, ethnicity, language, as well as identity interactions. We quantified human performance and combined it with each of the 3 models simulating use as practice augmenting AI predictions. With sensitivity as our primary outcome, we found humans at 72.8% were bested by the Diversity-Sensitive Model at 82.8%, and by the human-augmented Diversity-Sensitive Model at 91.3%, improving ACS predictions in all demographic subgroups. However, there was residual variation among subgroups (range of sensitivity: 62%-98%). Given risk distribution differences, subgroup-specific ECG-testing thresholds may further equitize ACS prediction performance.

  View details for DOI 10.1038/s44325-025-00088-0

  View details for PubMedID 41133128

  View details for PubMedCentralID PMC12540186

• Cultivating Health Care Innovation: Beyond the Disruption Paradigm NEJM CATALYST INNOVATIONS IN CARE DELIVERY Rose, C., Kabeer, R., Bunney, G., Preiksaitis, C. 2025; 6 (10)

  View details for DOI 10.1056/CAT.25.0060

  View details for Web of Science ID 001585847400001

• In vitro to in vivo translation of artificial intelligence for clinical use: screening for acute coronary syndrome to identify ST-elevation myocardial infarction. Journal of the American Medical Informatics Association : JAMIA Bunney, G., Miller, K., Graber-Naidich, A., Kabeer, R., Bloos, S. M., Wessels, A. J., Pasao, M. A., Rizvi, M., Brown, I. P., Yiadom, M. Y. 2025

  Abstract

  The integration of predictive models into live clinical care requires scientific testing before implementation to ensure patient safety. We built and technically implemented a model that predicts which patients require an electrocardiogram (ECG) to screen for heart attacks within 10 minutes of their arrival to the Emergency Department. We developed a structured framework for the in vitro to in vivo translation of the model through implementation as clinical decision support (CDS).The CDS ran as a silent pilot for 2 months. We conducted (1) a Technical Component Analysis to ensure each part of the CDS coding functioned as planned, and (2) a Technical Fidelity Analysis to ensure agreement between the CDS's in vivo and the model's in vitro screening decisions.The Technical Component Analysis indicated several small coding errors in CDS components that were addressed. During this period, the CDS processed 18 335 patient encounters. CDS fidelity to the model reflected raw agreement of 95.5% (CI, 95.2%-95.9%) and kappa of 87.6% (CI, 86.7%-88.6%). Additional coding errors were identified and were corrected.Our structured framework for the in vitro to in vivo translation of our predictive model uncovered ways to improve performance in vivo and the validity of risk assessment decisions. Testing predictive models on live care data and accompanying analyses is necessary to safely implement a predictive model for clinical use.We developed a method for the translation of our model from in vitro to in vivo that can be utilized with other applications of predictive modeling in healthcare.

  View details for DOI 10.1093/jamia/ocaf101

  View details for PubMedID 40576204

• The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR medical informatics Preiksaitis, C., Ashenburg, N., Bunney, G., Chu, A., Kabeer, R., Riley, F., Ribeira, R., Rose, C. 2024; 12: e53787

  Abstract

  Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM.Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field.Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data.A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills.LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

  View details for DOI 10.2196/53787

  View details for PubMedID 38728687

• Shorter Door-to-ECG Time Is Associated with Improved Mortality in STEMI Patients. Journal of clinical medicine Yiadom, M. Y., Gong, W., Bloos, S. M., Bunney, G., Kabeer, R., Pasao, M. A., Rodriguez, F., Baugh, C. W., Mills, A. M., Gavin, N., Podolsky, S. R., Salazar, G. A., Patterson, B., Mumma, B. E., Tanski, M. E., Liu, D. 2024; 13 (9)

  Abstract

  Background: Delayed intervention for ST-segment elevation myocardial infarction (STEMI) is associated with higher mortality. The association of door-to-ECG (D2E) with clinical outcomes has not been directly explored in a contemporary US-based population. Methods: This was a three-year, 10-center, retrospective cohort study of ED-diagnosed patients with STEMI comparing mortality between those who received timely (<10 min) vs. untimely (>10 min) diagnostic ECG. Among survivors, we explored left ventricular ejection fraction (LVEF) dysfunction during the STEMI encounter and recovery upon post-discharge follow-up. Results: Mortality was lower among those who received a timely ECG where one-week mortality was 5% (21/420) vs. 10.2% (26/256) among those with untimely ECGs (p = 0.016), and in-hospital mortality was 6.0% (25/420) vs. 10.9% (28/256) (p = 0.028). Data to compare change in LVEF metrics were available in only 24% of patients during the STEMI encounter and 46.5% on discharge follow-up. Conclusions: D2E within 10 min may be associated with a 50% reduction in mortality among ED STEMI patients. LVEF dysfunction is the primary resultant morbidity among STEMI survivors but was infrequently assessed despite low LVEF being an indication for survival-improving therapy. It will be difficult to assess the impact of STEMI care interventions without more consistent LVEF assessment.

  View details for DOI 10.3390/jcm13092650

  View details for PubMedID 38731180

  View details for PubMedCentralID PMC11084706

• Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients. Diagnostics (Basel, Switzerland) Bunney, G., Bloos, S. M., Graber-Naidich, A., Pasao, M. A., Kabeer, R., Kim, D., Miller, K., Yiadom, M. Y. 2023; 13 (12)

  Abstract

  We compared four methods to screen emergency department (ED) patients for an early electrocardiogram (ECG) to diagnose ST-elevation myocardial infarction (STEMI) in a 5-year retrospective cohort through observed practice, objective application of screening protocol criteria, a predictive model, and a model augmenting human practice. We measured screening performance by sensitivity, missed acute coronary syndrome (ACS) and STEMI, and the number of ECGs required. Our cohort of 279,132 ED visits included 1397 patients who had a diagnosis of ACS. We found that screening by observed practice augmented with the model delivered the highest sensitivity for detecting ACS (92.9%, 95%CI: 91.4-94.2%) and showed little variation across sex, race, ethnicity, language, and age, demonstrating equity. Although it missed a few cases of ACS (7.6%) and STEMI (4.4%), it did require ECGs on an additional 11.1% of patients compared to current practice. Screening by protocol performed the worst, underdiagnosing young, Black, Native American, Alaskan or Hawaiian/Pacific Islander, and Hispanic patients. Thus, adding a predictive model to augment human practice improved the detection of ACS and STEMI and did so most equitably across the groups. Hence, combining human and model screening--rather than relying on either alone--may maximize ACS screening performance and equity.

  View details for DOI 10.3390/diagnostics13122053

  View details for PubMedID 37370948

  View details for PubMedCentralID PMC10297640

• Teaching Principles of Medical Innovation and Entrepreneurship Through Hackathons: Case Study and Qualitative Analysis. JMIR medical education Preiksaitis, C., Dayton, J. R., Kabeer, R., Bunney, G., Boukhman, M. 2023; 9: e43916

  Abstract

  Innovation and entrepreneurship training are increasingly recognized as being important in medical education. However, the lack of faculty comfort with the instruction of these concepts as well as limited scholarly recognition for this work has limited the implementation of curricula focused on these skills. Furthermore, this lack of familiarity limits the inclusion of practicing physicians in health care innovation, where their experience is valuable. Hackathons are intense innovation competitions that use gamification principles to increase comfort with creative thinking, problem-solving, and interpersonal collaboration, but they require further exploration in medical innovation.To address this, we aimed to design, implement, and evaluate a health care hackathon with 2 main goals: to improve emergency physician familiarity with the principles of health care innovation and entrepreneurship and to develop innovative solutions to 3 discrete problems facing emergency medicine physicians and patients.We used previously described practices for conducting hackathons to develop and implement our hackathon (HackED!). We partnered with the American College of Emergency Physicians, the Stanford School of Biodesign, and the Institute of Design at Stanford (d.school) to lend institutional support and expertise in health care innovation to our event. We determined a location, time frame, and logistics for the competition and settled on 3 use cases for teams to work on. We planned to explore the learning experience of participants within a pragmatic paradigm and complete an abductive thematic analysis using data from a variety of sources.HackED! took place from October 1-3, 2022. In all, 3 teams developed novel solutions to each of the use cases. Our investigation into the educational experience of participants suggested that the event was valuable and uncovered themes suggesting that the learning experience could be understood within a framework from entrepreneurship education not previously described in relation to hackathons.Health care hackathons appear to be a viable method of increasing physician experience with innovation and entrepreneurship principles and addressing complex problems in health care. Hackathons should be considered as part of educational programs that focus on these concepts.

  View details for DOI 10.2196/43916

  View details for PubMedID 36826988

• Impact of the MyProstateScore (MPS) Test on the Clinical Decision to Undergo Prostate Biopsy: Results From a Contemporary Academic Practice UROLOGY Lebastchi, A. H., Russell, C. M., Niknafs, Y. S., Eyrich, N. W., Chopra, Z., Botbyl, R., Kabeer, R., Osawa, T., Siddiqui, J., Siddiqui, R., Davenport, M. S., Mehra, R., Tomlins, S. A., Kunju, L. P., Chinnaiyan, A. M., Wei, J. T., Tosoian, J. J., Morgan, T. M. 2020; 145: 204-210

  Abstract

  To evaluate the association of the MyProstateScore (MPS) urine test on the decision to undergo biopsy in men referred for prostate biopsy in urology practice.MPS testing was offered as an alternative to immediate biopsy in men referred to the University of Michigan for prostate biopsy from October 2013 through October 2016. The primary endpoint was the decision to perform biopsy. The proportion of patients undergoing biopsy was compared to predicted risk scores from the Prostate Cancer Prevention Trial risk calculator (PCPTrc). Analyses were stratified by the use of multiparametric magnetic resonance imaging (mpMRI). The associations of PCPTrc, MPS, and mpMRI with the decision to undergo biopsy were explored in a multivariable logistic regression model.Of 248 patients, 134 (54%) proceeded to prostate biopsy. MPS was significantly higher in biopsied patients (median 29 vs14, P < .001). The use of biopsy was strongly associated with MPS, with biopsy rates of 26%, 38%, 58%, 90%, and 85% in the first through fifth quintiles, respectively (P < .001). MPS association with biopsy persisted upon stratification by mpMRI. On multivariable analysis, MPS was strongly associated with the decision to undergo biopsy when modeled as both a continuous (odds ratio [OR] 1.05, 95%; confidence interval [CI] 1.04-1.08; <.001) and binary (OR 7.76, 95%; CI 4.14-14.5; P < .001) variable.Many patients (46%) undergoing clinical MPS testing as an alternative to immediate prostate biopsy were able to avoid biopsy. Increasing MPS was strongly associated with biopsy rates. These findings were robust to use of mpMRI.

  View details for DOI 10.1016/j.urology.2020.07.042

  View details for Web of Science ID 000587806900049

  View details for PubMedID 32777370

• Visceral hypersensitivity in female but not in male serotonin transporter knockout rats NEUROGASTROENTEROLOGY AND MOTILITY Galligan, J. J., Patel, B. A., Schneider, S. P., Wang, H., Zhao, H., Novotny, M., Bian, X., Kabeer, R., Fried, D., Swain, G. M. 2013; 25 (6): e373-e381

  Abstract

  Visceral hypersensitivity occurs in irritable bowel syndrome (IBS), particularly in women. Serotonin signaling, including reduced serotonin transporter (SERT) expression, may be disrupted in IBS patients. We studied SERT gene knockout (KO) rats to determine if they exhibited sex-related alterations in visceral sensitivity.We measured serotonin in the colonic mucosa using HPLC and amperometric microelectrode techniques. Visceral sensitivity was assessed using the electromyographic visceromotor response (VMR) in response to colorectal balloon distention (CRD). We studied the electrophysiologic properties of colon projecting sensory neurons in vitro using whole-cell recordings.Mucosal serotonin levels were not different among male and female WT and SERT KO rats. Serotonin oxidation currents in vitro were larger (P < 0.05) in tissues from male and female SERT KO compared with WT rats. Oxidation currents in male and female WT, but not SERT KO, rats were increased (P < 0.05) by the SERT inhibitor fluoxetine (1 μmol L(-1) ). The VMR to CRD was increased in female but not in male SERT KO rats (P < 0.05); this response varied with the estrous cycle. Colon projecting sensory neurons from female SERT KO rats fired more action potentials compared with neurons from female WT rats. There were no differences in action potential firing in neurons from male WT and SERT KO rats.Increased colonic extracellular serotonin in female SERT KO rats is associated with visceral hypersensitivity and hyperexcitability of colon projecting sensory neurons. The SERT KO rat is a model for studying interactions between serotonin, sex and visceral sensation.

  View details for DOI 10.1111/nmo.12133

  View details for Web of Science ID 000318945400001

  View details for PubMedID 23594365