About

Irit Rachel Rasooly, MD, MSCE, is an Assistant Professor of Pediatrics (General Pediatrics) at the Children's Hospital of Philadelphia. She holds multiple roles including Attending Physician in the Section of Hospital Medicine within the Division of General Pediatrics, and is a faculty member in the Department of Biomedical & Health Informatics at the Children's Hospital of Philadelphia. Dr. Rasooly is also involved with the Clinical Futures: A Research Institute Center of Emphasis and the PolicyLab Research Institute at the same institution. Her educational background includes an undergraduate degree from Princeton University obtained in 2007, an MD from George Washington University School of Medicine & Health Sciences completed in 2014, and a Master of Science in Clinical Epidemiology (MSCE) from the University of Pennsylvania School of Medicine completed in 2019. Her research focuses on healthcare quality, diagnostic safety, alarm management in pediatric units, and improving communication about diagnosis in pediatric primary care. She has contributed to developing methods to identify resilience in healthcare systems and has been involved in projects aimed at enhancing diagnostic safety and healthcare delivery in pediatric settings.

Research topics

• Medicine
• Emergency medicine
• Medical emergency
• Pediatrics
• Psychology

Selected publications

• Health System Scale Semantic Search Across Unstructured Clinical Notes

  arXiv (Cornell University) · 2026-04-28

  preprintOpen access

  Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keyword matching, offers substantial advantages for retrieval of clinical information. However, deploying semantic search across entire health systems, comprising hundreds of millions of clinical notes, presents formidable engineering, cost, and governance challenges that have prevented adoption. Methods: We deployed a semantic search system at a large children's hospital indexing 166 million clinical notes (484 million vectors) from 1.68 million patients. The system uses instruction-tuned qwen3-embedding-0.6B embeddings, stores vectors in a managed database with storage-optimized indexing, maintains full-text metadata in a low-latency key-value store, and operates within a HIPAA-compliant governance framework. We evaluated the system through three experiments: optimization of embedding model and chunking strategy using a physician-authored benchmark dataset, characterization of full-scale performance (cost, latency, retrieval quality), and clinical utility assessment via comparison of chart abstraction efficiency across three tasks. Results: The system delivers sub-second query latency (median 237 ms single-user, 451 ms 20-user concurrency) with monthly costs of approximately USD 4,000. Qwen3 embeddings with 300-token chunk size achieved 94.6% accuracy on a clinical question-answering benchmark. In clinical utility evaluation across three abstraction tasks, semantic search reduced time-to-completion by 24 to 89% compared to clinician-performed chart review while maintaining comparable inter-rater agreement. Conclusion: Health-system-scale semantic search is both technically and operationally feasible. The system provides infrastructure supporting interactive search, cohort generation, and downstream LLM-powered clinical applications without requiring specialized informatics expertise.

  PublisherDOI

• Rheum for earlier recognition: patient reported time to diagnosis of juvenile idiopathic arthritis

  Diagnosis · 2026-05-04

  articleSenior author

  OBJECTIVES: Early diagnosis of juvenile idiopathic arthritis (JIA) improves long-term outcomes. The study aims to assess patient reported time to diagnosis with JIA and signs of disease-related damage at the time of diagnosis. METHODS: Retrospective cohort study of patients with an incident JIA diagnosis at an academic center over a 2-year period. Patient reported time to diagnosis and signs of disease-related damage were extracted from the electronic medical record. Factors associated with time to diagnosis were evaluated with regression modeling. RESULTS: Of the 237 children diagnosed during the study period, the median patient reported time to diagnosis was 19 weeks (IQR: 8-40, range: 1-311). Time to diagnosis was >1 year in 23.5 % of patients, and >2 years in 11.7 %. In the linear regression model, older age was associated with longer time to diagnosis. Many patients (40.9 %) had at least one sign of damage. Damage was most common in younger children and children with oligoarticular disease. CONCLUSIONS: It is common for patients with JIA to have a prolonged time to diagnosis, and many have damage at diagnosis. A regression model fails to explain most of the variance in the time to diagnosis, suggesting there is much to learn about the drivers of diagnostic delay in JIA.

  PublisherDOI

• High Health Care Utilization Preceding Diagnosis With Juvenile Idiopathic Arthritis

  Arthritis Care & Research · 2026-03-09

  articleOpen access

  OBJECTIVE: Although early diagnosis improves long-term outcomes, patients with juvenile idiopathic arthritis (JIA) often experience prolonged, circuitous paths to diagnosis. To inform diagnostic improvement, we sought to characterize health care utilization in the year preceding diagnosis. METHODS: We identified 10,021 patients with an incident diagnosis of JIA and 20,042 age- and sex-matched healthy controls in the Merative MarketScan administrative datasets (2014-2022). Using negative binomial or hurdle models, we calculated incidence rate ratios (IRRs) comparing outpatient, inpatient, and emergency department or urgent care (ED/UC) utilization between patients with JIA and controls. RESULTS: In the year before diagnosis, patients with JIA had significantly increased health care utilization compared to controls (IRR 2.55 [95% confidence interval (CI) 2.49-2.62], P < 0.001). Accounting for sex, age, and insurance, utilization was increased across care settings: outpatient (IRR 2.56 [95% CI 2.49-2.62], P < 0.001), inpatient (IRR 2.00 [95% CI 1.48-2.71], P < 0.001), and ED/UC encounters (IRR 1.76 [95% CI 1.67-1.86], P < 0.001). The most common visits by patients with JIA preceding diagnosis were to a general practitioner (91.8%), ED/UC (47.0%), and orthopedist (22.9%). Health care utilization increased as the index date approached. In patients insured by Medicaid, ED/UC care was more frequent (odds ratio 2.05 [95% CI 1.89-2.23]) and orthopedic care less frequent (odds ratio 0.27 [95% CI 0.24-0.34]) than in patients with commercial insurance. CONCLUSION: In the year before diagnosis, children with JIA have significantly higher health care utilization compared to healthy peers. There are differences in the patterns of utilization in patients with Medicaid versus commercial insurance. There may be opportunities for earlier identification of JIA in primary care, orthopedics, and ED/UC.

  PublisherDOI

• Health System Scale Semantic Search Across Unstructured Clinical Notes

  ArXiv.org · 2026-04-28

  articleOpen access

  Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keyword matching, offers substantial advantages for retrieval of clinical information. However, deploying semantic search across entire health systems, comprising hundreds of millions of clinical notes, presents formidable engineering, cost, and governance challenges that have prevented adoption. Methods: We deployed a semantic search system at a large children's hospital indexing 166 million clinical notes (484 million vectors) from 1.68 million patients. The system uses instruction-tuned qwen3-embedding-0.6B embeddings, stores vectors in a managed database with storage-optimized indexing, maintains full-text metadata in a low-latency key-value store, and operates within a HIPAA-compliant governance framework. We evaluated the system through three experiments: optimization of embedding model and chunking strategy using a physician-authored benchmark dataset, characterization of full-scale performance (cost, latency, retrieval quality), and clinical utility assessment via comparison of chart abstraction efficiency across three tasks. Results: The system delivers sub-second query latency (median 237 ms single-user, 451 ms 20-user concurrency) with monthly costs of approximately USD 4,000. Qwen3 embeddings with 300-token chunk size achieved 94.6% accuracy on a clinical question-answering benchmark. In clinical utility evaluation across three abstraction tasks, semantic search reduced time-to-completion by 24 to 89% compared to clinician-performed chart review while maintaining comparable inter-rater agreement. Conclusion: Health-system-scale semantic search is both technically and operationally feasible. The system provides infrastructure supporting interactive search, cohort generation, and downstream LLM-powered clinical applications without requiring specialized informatics expertise.

  PublisherOA PDF

• Efficient Build of EHR Simulation for Research: A Template to Collect Clinician Cognitive Data

  Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care · 2025-08-19

  articleOpen access

  Electronic Health Record simulation is a research method that offers valuable data on clinician cognitive processes like problem solving, information foraging, and in particular, decision making. Despite this, established paths to develop optimal EHR simulation to explore these processes only exist for education. No guide to develop efficient EHR simulation for research exists. We propose a six-part methodology to efficiently develop EHR simulations used for research and patient safety inquiry. Our approach produces high-fidelity simulations for challenging clinical scenarios, ideal for data collection of this type. This reproducible template addresses a critical gap, enabling EHR simulation as a research method to be stood up in any institution.

  PublisherOA PDFDOI

• Telehealth triage in pediatric rheumatology: a diagnostically accurate tool to improve access to care

  Diagnosis · 2025-05-22

  articleOpen access

  OBJECTIVES: During the SARS-CoV-2 pandemic, new patient evaluations in pediatric rheumatology were performed using telehealth. Given the pediatric rheumatology workforce shortage, telehealth may be a way to efficiently triage referrals. The objective was to assess the utility of telehealth visits as a diagnostic tool to accurately assess the need for in-person evaluation. METHODS: This was a retrospective cohort study of patients evaluated by telehealth for a new patient visit from March 1 to June 30, 2020 at a tertiary center. Electronic health record documentation from subsequent rheumatology, specialty, and primary care encounters over the subsequent 4 years were reviewed. The primary outcome was diagnostic concordance, defined as consistency in the documented diagnostic reasoning, between the initial telehealth video visit and in-person follow-up visits. RESULTS: During the study period, there were 111 telehealth visits, 80 (72 %) of which had follow-up data. 55/80 had in-person rheumatology evaluations. Only 9 % patients had discordant diagnoses, all of whom had initial concern for inflammatory arthritis during the telehealth visit but a diagnosis of a non-inflammatory condition after in-person evaluation. Nine patients with a significant rheumatic disease were identified via telehealth. There were no unplanned ED visits or hospital admissions following telehealth visits. 33 % of patients were found to not warrant rheumatologic follow-up after the telehealth visit. CONCLUSIONS: For pediatric rheumatology new patient evaluations, diagnostic accuracy via telehealth evaluation was high. Providers triaged patients with chronic rheumatologic conditions for in-person evaluations and were able to accurately identify benign conditions that did not require in-person follow-up.

  PublisherOA PDFDOI

• Diagnostic excellence: turning to diagnostic performance improvement

  Diagnosis · 2025-09-16

  articleOpen access

  The field of diagnostic excellence has advanced considerably in the past decade, reframing diagnosis as a patient safety priority and highlighting the prevalence and harms of diagnostic error. Foundational evidence now supports the development of Diagnostic Excellence Programs; organizational initiatives designed to reduce diagnostic errors and improve system-level and individual performance. While early studies established the epidemiology of diagnostic error across inpatient, emergency, and ambulatory care, newer approaches emphasize continuous, systematic surveillance to inform targeted improvements. Emerging frameworks, such as the DEER Taxonomy and root cause or success cause analyses, help classify drivers of both failures and successes in diagnostic processes. Effective programs must address system factors, including electronic health record design, workload, team structures, and communication, while also enhancing individual clinician performance through feedback, diagnostic reflection, cross-checks, and coaching. Patient engagement represents a critical but underdeveloped dimension; strategies such as structured communication frameworks, patient-family advisory councils, and electronic tools co-designed with patients aim to foster shared diagnostic decision-making and improve transparency. Artificial intelligence (AI) holds promise to accelerate measurement, streamline clinical workflows, reduce cognitive load, and support communication, though careful implementation and oversight are required to ensure safety. Ultimately, Diagnostic Excellence Programs will succeed by embedding diagnostic safety into institutional standards of care, providing clinicians with ongoing, psychologically safe opportunities for recalibration, and leveraging AI to scale surveillance and improvement activities.

  PublisherDOI

• A systems engineering approach to alarm management on pediatric medical–surgical units

  Journal of Hospital Medicine · 2024-10-17 · 1 citations

  articleOpen access

  The authors declare no conflicts of interest. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

  PublisherOA PDFDOI

• Developing methods to identify resilience and improve communication about diagnosis in pediatric primary care

  Frontiers in Medicine · 2024-09-30

  articleOpen access1st authorCorresponding

  Communication underlies every stage of the diagnostic process. The Dialog Study aims to characterize the pediatric diagnostic journey, focusing on communication as a source of resilience, in order to ultimately develop and test the efficacy of a structured patient-centered communication intervention in improving outpatient diagnostic safety. In this manuscript, we will describe protocols, data collection instruments, methods, analytic approaches, and theoretical frameworks to be used in to characterize the patient journey in the Dialog Study. Our approach to characterization of the patient journey will attend to patient and structural factors, like race and racism, and language and language access, before developing interventions. Our mixed-methods approach is informed by the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework (which describes the sociotechnical system underpinning diagnoses within the broader context of multiple interactions with different care settings over time) and the Safety II framework (which seeks to understand successful and unsuccessful adaptations to ongoing changes in demand and capacity within the healthcare system). We will assess the validity of different methods to detect diagnostic errors along the diagnostic journey. In doing so, we will emphasize the importance of viewing the diagnostic process as the product of communications situated in systems-of-work that are constantly adapting to everyday challenges.

  PublisherOA PDFDOI

• Development and evaluation of a writing retreat program to build community and promote productivity in academic hospital medicine

  Journal of Hospital Medicine · 2024-04-10 · 5 citations

  articleOpen access

  BACKGROUND: Scientific writing is a core component of academic hospital medicine, and yet finding time to engage in deeply focused writing is difficult in part due to the highly clinical, 24/7 nature of the specialty that can limit opportunities for writing-focused collaboration and mentorship. OBJECTIVE: Our objective was to develop and evaluate an academic writing retreat program. METHODS: We drafted a set of key retreat features to guide implementation of a 3-day, 2-night retreat program held within a 2 h radius of our hospital. Agendas included writing blocks ranging from 45 to 90 min interspersed with breaks and opportunities for feedback, exercise, and preparing meals together. After each retreat, we distributed an evaluation with multiple choice and free text response options to characterize retreat helpfulness and later gathered data on the status of each paper and grant worked on. RESULTS: We held 4 retreats between September 2022 and October 2023, engaging 18 faculty and fellows at a cost of $296 per attendee per retreat. In evaluations, nearly 80% reported that the retreat was extremely helpful, and comments praised the highly mentored environment, enriching community of colleagues, and release from commitments that get in the way of writing. Of the 24 papers attendees worked on, 12 have been accepted and 6 are under review. Of the 4 grant proposals, 2 are under review. CONCLUSIONS: We implemented a low-cost, productive writing retreat program that attendees reported was helpful in supporting deep work and represented a meaningful step toward building a community centered around academic writing.

  PublisherOA PDFDOI

Frequent coauthors

• Naveen Muthu

  Emory University

  64 shared

• Debra T. Choi

  Michael E. DeBakey VA Medical Center

  49 shared

• Toby Maurer

  Indiana University School of Medicine

  49 shared

• Shakila Khan

  Children's Hospital of Philadelphia

  49 shared

• Carmel Crock

  The Royal Victorian Eye & Ear Hospital

  49 shared

• Akshar V. Patel

  The Ohio State University

  49 shared

• Christopher P. Bonafide

  Children's Hospital of Philadelphia

  48 shared

• Caitlin B. Clancy

  Chapman University

  37 shared

Labs

• Irit Rachel Rasooly LabPI