Faculty Bibliography

Despite widespread adoption of electronic health records (EHRs), health systems remain heavily dependent on faxed documents for critical patient information. At New York University Langone Health, this represents nearly 20 million document-pages per year - laboratory results, consult notes, imaging prescriptions, refill requests, and prior authorizations - each requiring manual review and indexing. These workflows are time consuming, involve multiple staff touchpoints, can be prone to error, and may create delays for patients awaiting follow-up care. To provide the highest quality of care to patients and to augment staff experience, the authors developed and deployed an Intelligent Document Processing (IDP) solution leveraging existing enterprise technologies for document management, robotic process automation, data classification and extraction, and EHR-integrated indexing. This solution identifies electronically faxed documents, extracts patient and provider information, matches the EHR record, sorts the documents into clinical or administrative queues, and assigns a document type for indexing. To ensure patient safety, documents that cannot be confidently processed are routed to an exceptions folder for manual review. The IDP solution was deployed and monitored at one high-volume multispecialty practice from August to October 2025. In this time, the system processed approximately 20,000 document-pages, representing 13,700 faxes or scans. Of these, 8500 (62%) were successfully classified to one of the predefined in-scope clinical and administrative document types that the system was trained to recognize (e.g., laboratory results, pathology and radiology reports, procedure notes such as colonoscopy or endoscopy, medication- and insurance-related authorizations, and consult or therapy reports); based on the classification, they were then routed to the appropriate work queue for indexing. The remaining 38% required manual review - 32% were identified as being outside the target set of document types, and 6% were flagged as exceptions (e.g., multiple patients in one fax, document longer than 20 pages). The cost to operate was approximately 1.5 U.S. cents per page during the pilot, significantly less expensive than competitive industry offers of approximately 15 U.S. cents per page. Implementation required not only technical integration, but also operational redesign. Key hurdles included applying existing technologies to a single orchestrated solution, managing the unclassified documents workload, aligning document type taxonomies between systems, handling provider name variation, and training clinical staff. Change management was paramount, as individual practices had developed varied and entrenched fax workflows that required reengineering and preproduction dress rehearsals prior to go-live. This experience demonstrates the potential for an artificial intelligence (AI)-enabled IDP solution to meaningfully reduce administrative burden, improve timeliness and accuracy of document indexing, and unlock structured data from scanned pages. Never before had these practices been able to quantify and route faxed documents automatically. Although challenges remain in scaling across diverse workflows, this case illustrates how health systems can pragmatically deploy AI using existing infrastructure to improve efficiency, reduce staff burden, and support better care delivery.

BACKGROUND:Patient experience is a key component of care quality, linked to outcomes like safety and medication adherence. In high-acuity inpatient settings with rotating teams, opportunities exist to better integrate patients' identities and values into the hospital experience. AIM/OBJECTIVE:To design, implement, and evaluate an EHR-integrated tool that enables hospitalized patients to share personal, non-medical information to help care teams deliver more personalized care. SETTING/METHODS:Fourteen adult inpatient units across a large academic medical center (NYU Langone Health, New York, NY). PARTICIPANTS/METHODS:Hospitalized adults and their interprofessional care teams, including physicians, nurses, advanced practice providers, and allied health professionals. PROGRAM DESCRIPTION/METHODS:About Me is an Epic-integrated digital tool that allows patients or caregivers to share brief, non-clinical details-such as hobbies, values, family, and communication preferences-visible to the entire care team. Patients complete entries via the NYU Langone Health App or with nurse assistance; nurses validate content for appropriateness. PROGRAM EVALUATION/RESULTS:Over a 2-month period, 1001 of 5452 eligible patients (18.4%) submitted entries. Entries were brief but meaningful. Five major themes emerged: hobbies, family, identity, career, and animals. DISCUSSION/CONCLUSIONS:Implementation of About Me was feasible and acceptable. Even brief entries offered meaningful context that may strengthen rapport and promote more personalized, compassionate inpatient care.

BACKGROUND:Communication failures are a leading cause of sentinel events in U.S. healthcare, often due to unclear provider contact identification. The electronic health record (EHR) system offers a solution by enabling the discrete assignment of a first contact provider (FCP), who oversees and coordinates patient care. However, adoption of this practice is inconsistent across many hospital settings. This study describes the impact of continuous learning and improvement cycles to address this challenge. METHODS:Following the Plan-Do-Study-Act (PDSA) lifecycle, we completed five quality improvement cycles. Each PDSA cycle included a technological intervention accompanied by evolving operational expectations for clinical staff. We evaluated improvement after each PDSA by measuring the percent of a hospitalized patient's time with an assigned FCP. RESULTS:FCP coverage significantly improved from a baseline average of 5.1% to 59.0% after PDSA Cycle 1 (p < 0.001), 67.4% after Cycle 2 (p < 0.001), 79.7% after Cycle 3 (p < 0.001), 87.5% after Cycle 4 (p < 0.001), and 99.4% after Cycle 5 (p < 0.001). CONCLUSION/CONCLUSIONS:Having a reliable FCP at any point during a patient's hospital admission is an important safety practice. Continuous learning and improvement cycles, driven by a strong partnership between technology and operations, led to significant and sustained improvements in FCP assignments.

BACKGROUND:Decisions involving the purchase of procedural equipment at the health system level require balancing efficacy, safety, physician preference, and cost. The application of efficient and low-cost pragmatic study designs has the potential to rapidly generate data to inform health system operations. METHODS:The aim of the pragmatic RAISE-EMR study is to determine physician preference between two commercially available radial artery introducer sheaths, one of which has a higher acquisition cost, to guide inventory selection in the hospital system's catheterization laboratories. Patients undergoing coronary angiography using 6-French radial artery access were prospectively identified and randomized through the health system's electronic medical record (EMR). Among 1696 eligible unique patients, 554 patients (32.7%) were randomized over 37 days across three hospitals. Randomization took place through the EMR after the attending interventional cardiologist signed a mandated pre-procedure note. The study was deemed non-human subject research and approved by the NYU Langone Health Quality Improvement Oversight Committee. The primary endpoint, a physician satisfaction score, will be ascertained by a mandated semi-quantitative survey within the electronic procedure note. All data, including co-variables and clinical outcomes, will be ascertained using structured data within the EMR. CONCLUSIONS:The RAISE-EMR study is designed to determine physician preference of two commercially available radial artery introducer sheaths and potentially reduce supply costs using an entirely EMR-based randomized study design. Pragmatic study designs leveraging structured data within an EMR can be used to rapidly provide data to inform operational decision-making and have implications for the future of evidence generation.

BACKGROUND:Poor documentation quality can significantly affect healthcare operations, but the feedback process for clinicians to improve clinical notes is time-consuming and often insufficient. Large language models (LLMs) such as Generative Pre-trained Transformer 4 (GPT-4) have the potential to streamline this process. OBJECTIVES/OBJECTIVE:To determine whether an LLM can generate feedback to improve the medical contingency and discharge planning (MCDP) component of clinical documentation that is non-inferior to feedback by physicians. METHODS:A cross-sectional study of GPT-4 feedback and physician feedback on inpatient progress notes was conducted. A random sample of 64 inpatient progress notes identified by the validated AI Audit Tool as having a low likelihood of containing MCDP was included from adult general medicine patients hospitalized at New York University Langone Health (NYULH) in December 2023. Both GPT-4 model and attending physicians generated feedback on these inpatient progress notes. A/B testing was then conducted on the measures of understandability, usefulness, acceptability, and impartiality. Evaluations employed 5-point Likert scales that were converted to 10-point bidirectional interval scales for interpretability, ranging from -10 (human suggestions significantly better) to +10 (GPT-4 suggestions significantly better), with a non-inferiority threshold set to -1 for the primary endpoint. RESULTS:64 inpatient progress notes were included, representing 55% female patients with a median age of 73. GPT-4 feedback was non-inferior to physician feedback in all measures: understandability (mean 1.27, 95% CI 0.73 to 1.8, P < 0.001), usefulness (mean 2.09, 95% CI 1.27 to 2.91, P < 0.001), acceptability (mean 2.07, 95% CI 1.33 to 2.81, P < 0.001), and impartiality (mean -0.20, 95% CI -0.52 to 0.12, P < 0.001). CONCLUSIONS:This study shows that an LLM can be leveraged to generate note quality feedback that is non-inferior to expert clinician feedback.

BACKGROUND:Accurate and equitable prediction of trauma-related in-hospital mortality is critical for guiding clinical decisions and optimising trauma care resources. Traditional severity scoring systems like the Injury Severity Score (ISS) do not account for demographic factors, potentially limiting their fairness and generalisability across diverse populations. METHODS:We developed and externally validated an artificial intelligence (AI) model based on ISS and integrated demographic features (age and sex) to predict in-hospital mortality after trauma. Data from the Korean Trauma Data Bank were used for model development and internal validation, comprising 121,418 patients with trauma aged ≥15 years treated at 19 trauma centres in South Korea (2017-2022). External validation was performed on an independent cohort of 7458 patients from five trauma centres (four in South Korea and one in Australia, 2022-2024). The primary outcome was trauma-related in-hospital mortality. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, and balanced accuracy. Fairness was evaluated by comparing AUROC differences across age (<65 vs ≥65 years) and sex (female vs male) subgroups. FINDINGS/RESULTS:The ISS-based AI model incorporating age and sex achieved high predictive performance (internal validation AUROC, 0.934; external validation AUROC range, 0.901-0.920), outperforming conventional ISS-based methods. The model also demonstrated improved fairness, showing reduced AUROC differences across subgroups (age: 0.068 vs 0.091; sex: 0.021 vs 0.046 for AI model vs ISS, respectively). INTERPRETATION/CONCLUSIONS:Scaling an ISS-based AI model through demographic integration yielded accurate, fair, and generalisable predictions of trauma-related in-hospital mortality. This approach may enhance trauma care decision-making and enable more equitable resource allocation across diverse clinical settings. FUNDING/BACKGROUND:This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2025-RS-2024-00438239) and the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2024-00509257, Global AI Frontier Lab). In addition, this research was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS-2025-02220492).

OBJECTIVES/UNASSIGNED:To propose a structured framework for evaluating and comparing clinical informatics fellowship programs using the People, Process, and Technology (PPT) model. MATERIALS AND METHODS/UNASSIGNED:We adapted Leavitt's organizational theory to create a three-pillar framework operationalized with features relevant to fellowship applicants and directors. We then applied this framework to a random sample of 18 program websites. RESULTS/UNASSIGNED:The PPT framework categorizes key fellowship characteristics into People (eg, mentorship, co-fellows, diversity), Process (eg, clinical duties, research emphasis, education), and Technology (eg, EHR systems, technical training, remote work). A visual grid illustrates variation in operational versus research focus and levels of mentorship. Website analysis revealed inconsistent transparency and detail. DISCUSSION/UNASSIGNED:The PPT framework provides a systematic, accessible approach for applicants to assess fellowship fit and for programs to communicate their strengths. CONCLUSION/UNASSIGNED:Standardizing fellowship descriptions using the PPT model may improve alignment between applicant goals and program offerings, enhancing both the application process and training experience.

Early identification of high-risk trauma patients in the prehospital setting is crucial for optimizing resource allocation and improving survival. We developed and externally validated a real-time AI model predicting emergency room mortality using 21 prehospital variables. Model development and internal validation utilized the Korean Trauma Data Bank (KTDB; 204,189 patients), and external validation included four South Korean trauma centers (8,358 patients) and one Australian Level 1 center (3,578 patients). Our Prehospital-AI model, an ensemble of XGBoost, LightGBM, and random forest, achieved an AUROC of 0.923 (sensitivity: 0.780, specificity: 0.880) on the test set, outperforming the shock index (AUROC: 0.712). External validation yielded AUROCs of 0.925-0.956 across South Korean centers and 0.895 in the Australian center. Here we show that the Prehospital-AI model enables accurate, real-time risk assessment in the prehospital setting, outperforming traditional triage tools and improving trauma system efficiency. Nonetheless, additional multinational studies are warranted to further evaluate its generalizability across diverse trauma care systems.

BACKGROUND/UNASSIGNED:With rising patient volumes and a focus on quality, our health system had the objective to create a more efficient way to ensure accurate documentation of colorectal cancer (CRC) screening intervals from inbound colonoscopy reports to ensure timely follow-up. We developed an integrated end-to-end workflow solution using machine learning (ML) and robotic process automation (RPA) to extract and update electronic health record (EHR) follow-up dates from unstructured data. OBJECTIVE/UNASSIGNED:This study aimed to automate data extraction from external, free-text colonoscopy reports to identify and document recommended follow-up dates for CRC screening in structured EHR fields. METHODS/UNASSIGNED:As proof of concept, we outline the process development, validity, and implementation of an approach that integrates available tools to automate data retrieval and entry within the EHR of a large academic health system. The health system uses Epic Systems as its EHR platform, and the ML model used was trained on health system patient colonoscopy reports. This proof-of-concept process study consisted of six stages: (1) identification of gaps in documenting recommendations for follow-up CRC screening from external colonoscopy reports, (2) defining process objectives, (3) identification of technologies, (4) creation of process architecture, (5) process validation, and (6) health system-wide implementation. A chart review was performed to validate process outcomes and estimate impact. RESULTS/UNASSIGNED:We developed an automated process with 3 primary steps leveraging ML and RPA to create a fully orchestrated workflow to update CRC screening recall dates based on colonoscopy reports received from external sources. Process validity was assessed with 690 scanned colonoscopy reports. During process validation, the overall automated process achieved an accuracy of 80.7% (557/690, 95% CI 77.8%-83.7%) for correctly identifying the presence or absence of a valid follow-up date and a follow-up date false negative identification rate of 32.9% (130/395, 95% CI 29.4%-36.4%). From the organization-wide implementation to go-live until December 31, 2024, the system processed 16,563 external colonoscopy reports. Of these, 35.3% (5841/16,563) had a follow-up date meeting the relevant ML model threshold and thus were identified as ready for RPA processing. CONCLUSIONS/UNASSIGNED:Implementation of an automated workflow to extract and update CRC screening follow-up dates from colonoscopy reports is feasible and has the potential to improve accuracy in patient recall while reducing documentation burden. By standardizing data ingestion, extending this approach to various unstructured data types can address deficiencies in structured EHR documentation and solve for a lack of data integration and reporting for quality measures. Automated workflows leveraging ML and RPA offer practical solutions to overcome interoperability challenges and the use of unstructured data within health care systems.