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Objectives: To evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (AI)-generated, patient-facing summaries of these findings., Materials and Methods: Radiology reports extracted from the electronic health record of a large academic medical center were manually reviewed to identify non-emergent, incidental findings with high likelihood of requiring follow-up, further sub-stratified as "definitely actionable" (DA) or "possibly actionable-clinical correlation" (PA-CC). Instruction prompts to GPT-4 were developed and iteratively optimized using a validation set of 50 reports. The optimized prompt was then applied to a test set of 430 unseen reports. GPT-4 performance was primarily graded on accuracy identifying either DA or PA-CC findings, then secondarily for DA findings alone. Outputs were reviewed for hallucinations. AI-generated patient-facing summaries were assessed for appropriateness via Likert scale., Results: For the primary outcome (DA or PA-CC), GPT-4 achieved 99.3% recall, 73.6% precision, and 84.5% F-1. For the secondary outcome (DA only), GPT-4 demonstrated 95.2% recall, 77.3% precision, and 85.3% F-1. No findings were "hallucinated" outright. However, 2.8% of cases included generated text about recommendations that were inferred without specific reference. The majority of True Positive AI-generated summaries required no or minor revision., Conclusion: GPT-4 demonstrates proficiency in detecting actionable, incidental findings after refined instruction prompting. AI-generated patient instructions were most often appropriate, but rarely included inferred recommendations. While this technology shows promise to augment diagnostics, active clinician oversight via "human-in-the-loop" workflows remains critical for clinical implementation., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The Clinical Emergency Data Registry (CEDR) is a qualified clinical data registry that collects data from participating emergency departments (EDs) in the United States for quality measurement, improvement, and reporting purposes. This article aims to provide an overview of the data collection and validation process, describe the existing data structure and elements, and explain the potential opportunities and limitations for ongoing and future research use. CEDR data are primarily collected for quality reporting purposes and are obtained from diverse sources, including electronic health records and billing data that are de-identified and stored in a secure, centralized database. The CEDR data structure is organized around clinical episodes, which contain multiple data elements that are standardized using common data elements and are mapped to established terminologies to enable interoperability and data sharing. The data elements include patient demographics, clinical characteristics, diagnostic and treatment procedures, and outcomes. Key limitations include the limited generalizability due to the selective nature of participating EDs and the limited validation and completeness of data elements not currently used for quality reporting purposes, including demographic data. Nonetheless, CEDR holds great potential for ongoing and future research in emergency medicine due to its large-volume, longitudinal, near real-time, clinical data. In 2021, the American College of Emergency Physicians authorized the transition from CEDR to the Emergency Medicine Data Institute, which will catalyze investments in improved data quality and completeness for research to advance emergency care., (Copyright © 2023 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Health information exchange (HIE) provides an essential enhancement to electronic health records (EHR), allowing information to follow patients across provider organizations. There is also an opportunity to improve public health surveillance, quality measurement, and research through secondary use of HIE data, but data quality presents potential barriers. Our objective was to validate the secondary use of HIE data for two emergency department (ED) quality measures: identification of frequent ED users and early (72-hour) ED returns. We compared concordance of various demographic and encounter data from an HIE for four hospitals to data provided by the hospitals from their EHRs over a two year period, and then compared measurement of our two quality measures using both HIE and EHR data. We found that, following data cleaning, there was no significant difference in the total counts for frequent ED users or early ED returns for any of the four hospitals (p<0.001).

The participants of the Electronic Collaboration working group of the 2010 Academic Emergency Medicine consensus conference developed recommendations and research questions for improving regional quality of care through the use of electronic collaboration. A writing group devised a working draft prior to the meeting and presented this to the breakout session at the consensus conference for input and approval. The recommendations include: 1) patient health information should be available electronically across the entire health care delivery system from the 9-1-1 call to the emergency department (ED) visit through hospitalization and outpatient care, 2) relevant patient health information should be shared electronically across the entire health care delivery system, 3) Web-based collaborative technologies should be employed to facilitate patient transfer and timely access to specialists, 4) personal health record adoption should be considered as a way to improve patient health, and 5) any comprehensive reform of regionalization in emergency care must include telemedicine. The workgroup emphasized the need for funding increases so that research in this new and exciting area can expand., (2010 by the Society for Academic Emergency Medicine.)