Faculty Bibliography

IMPORTANCE/UNASSIGNED:Patients frequently forgo filling prescriptions due to high out-of-pocket costs. Real-time prescription benefit (RTPB) tools that recommend available lower-cost, clinically equivalent medications to prescribing clinicians may increase the likelihood that patients fill their prescriptions. OBJECTIVE/UNASSIGNED:To determine whether availability of an RTPB tool increases prescription fill rates. DESIGN AND SETTING/UNASSIGNED:This post hoc analysis of a cluster randomized clinical trial included medical practices within an urban ambulatory clinical network randomized to the RTPB tool between January and December 2021. Outpatient prescriptions that were eligible for an RTPB recommendation during the study period were analyzed. Data analyses were performed from October 18, 2022, to August 9, 2024. INTERVENTION/UNASSIGNED:An electronic health record-integrated RTPB tool that displays available lower-cost and clinically equivalent alternatives to the initiated prescription at the point of prescribing. MAIN OUTCOME AND MEASURE/UNASSIGNED:The primary outcome measured whether a prescription was filled. RESULTS/UNASSIGNED:Of 1 386 577 outpatient prescriptions at randomized practices during the trial period, 38 289 (2.8%) were included in the analytic sample. Across all orders, the availability of the RTPB tool did not impact the proportion of orders filled (54% and 55% in the control and RTPB groups, respectively; adjusted difference: 1.2 percentage points [pp]; 95% CI, -1.3 to 3.7 pp). However, in the quartile of drug classes with the highest out-of-pocket costs (average out-of-pocket cost for a 30-day fill of >$120.83), fill rates increased from 33% in the control group to 49% in the RTPB group (adjusted difference: 14.5 pp; 95% CI, 8.4-20.6 pp). Similar increases were not detected in lower-cost drug classes. Increases in fill rates within the highest out-of-pocket cost drug classes were largest for patients in the lowest-income communities served by the health system (30.3 pp; 95% CI, 19.5-41.1 pp) but not substantial in the highest-income communities (1.0 pp; 95% CI, -10.2 to 12.2 pp). CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this post hoc analysis of a cluster randomized clinical trial, there was no change in overall prescription fill rates, but among high-cost drugs, the RTPB tool increased fill rates, particularly among patients from low-income communities. However, RTPB recommendations were made for a small proportion of orders, limiting the applicability of the findings to a narrow segment of the randomized population. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT04940988.

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.

IMPORTANCE/OBJECTIVE:Patients with poor glycaemic control have a high risk for major cardiovascular events. Improving glycaemic monitoring in patients with diabetes can improve morbidity and mortality. OBJECTIVE:To assess the effectiveness of a patient portal message in prompting patients with poorly controlled diabetes without a recent glycated haemoglobin (HbA1c) result to have their HbA1c repeated. DESIGN/METHODS:A pragmatic, randomised clinical trial. SETTING/METHODS:A large academic health system consisting of over 350 ambulatory practices. PARTICIPANTS/METHODS:Patients who had an HbA1c greater than 10% who had not had a repeat HbA1c in the prior 6 months. EXPOSURES/METHODS:A single electronic health record (EHR)-based patient portal message to prompt patients to have a repeat HbA1c test versus usual care. MAIN OUTCOMES/RESULTS:The primary outcome was a follow-up HbA1c test result within 90 days of randomisation. RESULTS:The study included 2573 patients with a mean (SD) HbA1c of 11.2%. Among 1317 patients in the intervention group, 24.2% had follow-up HbA1c tests completed within 90 days, versus 21.1% of 1256 patients in the control group (p=0.07). Patients in the intervention group were more likely to log into the patient portal within 60 days as compared with the control group (61.2% vs 52.3%, p<0.001). CONCLUSIONS:Among patients with poorly controlled diabetes and no recent HbA1c result, a brief patient portal message did not significantly increase follow-up testing but did increase patient engagement with the patient portal. Automated patient messages could be considered as a part of multipronged efforts to involve patients in their diabetes care.

The integration of generative AI (GenAI) in patient communication presents benefits and challenges. This retrospective observational study analyzed EHR audit logs to assess how 75 healthcare professionals (HCPs) utilized AI-generated drafts for patient messages from October 2023 to August 2024 at a large health system in New York City. Overall utilization was low (19.4%), though prompt refinements improved usage (from 12% to 20%), particularly among physicians. GenAI drafts were generated for all messages, including 80% that received no response, adding to the review burden and potentially undermining efficiency. Text analysis showed HCPs preferred concise, information-rich drafts, with role-based differences-physicians favored shorter drafts, while clinical support staff preferred more empathetic responses. AI-generated drafts reduced message turnaround time by 6.76% despite a marginal increase in required steps (InBasket actions). These findings highlight the need for targeted GenAI deployment strategies, better aligned with clinician workflows and optimized draft generation for improved efficiency.

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.

PURPOSE/UNASSIGNED:We aimed to determine whether implementation of clinical decision support (CDS) tool integrated into the electronic health record of a multisite academic medical center increased the proportion of patients with AUA "high-risk" microscopic hematuria (MH) who receive guideline concordant evaluations. MATERIALS AND METHODS/UNASSIGNED:We conducted a two-arm cluster randomized quality improvement project in which 202 ambulatory sites from a large health system were randomized to either have their physicians receive at time of test results an automated CDS alert for patients with "high-risk" MH with associated recommendations for imaging and cystoscopy (intervention) or usual care (control). Primary outcome was met if a patient underwent both imaging and cystoscopy within 180 days from MH result. Secondary outcomes assessed individual completion of imaging, cystoscopy, or placement of imaging orders. RESULTS/UNASSIGNED:= .09). CONCLUSIONS/UNASSIGNED:Implementing an electronic health record-integrated CDS tool to promote evaluation of patients with high-risk MH did not lead to improvements in patient completion of a full guideline-concordant evaluation. The development of an algorithm to trigger a CDS alert was demonstrated to be feasible and effective. Further multilevel assessment of barriers to evaluation is necessary to continue to improve the approach to evaluating high-risk patients with MH.

INTRODUCTION/BACKGROUND:Failure to document colonoscopy follow-up needs postpolypectomy can lead to delayed detection of colorectal cancer (CRC). Automating the update of a unified follow-up date in the electronic health record (EHR) may increase the number of patients with guideline-concordant CRC follow-up screening. METHODS:Prospective pre-post design study of an automated rules engine-based tool using colonoscopy pathology results to automate updates to documented CRC screening due dates was performed as an operational initiative, deployed enterprise-wide May 2023. Participants were aged 45-75 years who received a colonoscopy November 2022 to November 2023. Primary outcome measure is rate of updates to screening due dates and proportion with recommended follow-up < 10 years. Multivariable log-binomial regression was performed (relative risk, 95% confidence intervals). RESULTS:Study population included 9,824 standard care and 19,340 intervention patients. Patients had a mean age of 58.6 ± 8.6 years and were 53.4% female, 69.6% non-Hispanic White, 13.5% non-Hispanic Black, 6.5% Asian, and 4.6% Hispanic. Postintervention, 46.7% of follow-up recommendations were updated by the rules engine. The proportion of patients with a 10-year default follow-up frequency significantly decreased (88.7%-42.8%, P < 0.001). The mean follow-up frequency decreased by 1.9 years (9.3-7.4 years, P < 0.001). Overall likelihood of an updated follow-up date significantly increased (relative risk 5.62, 95% confidence intervals: 5.30-5.95, P < 0.001). DISCUSSION/CONCLUSIONS:An automated rules engine-based tool has the potential to increase the accuracy of colonoscopy follow-up dates recorded in patient EHR. The results emphasize the opportunity for more automated and integrated solutions for updating and maintaining EHR health maintenance activities.

BACKGROUND:Regular physical activity (PA) is a component of cardiovascular health and is associated with a lower risk of cardiovascular disease (CVD). However, only about half of US adults achieved the current PA recommendations. OBJECTIVE:The study purpose was to implement PA counseling using a clinical decision support tool in a preventive cardiology clinic and to assess changes in CVD risk factors in a sample of patients enrolled over 12 weeks of PA monitoring. METHODS:This intervention, piloted for 1 year, had 3 components embedded in the electronic health record: assessment of patients' PA, an electronic prompt for providers to counsel patients reporting low PA, and patient monitoring using a Fitbit. Cardiovascular disease risk factors included PA (self-report and Fitbit), body mass index, blood pressure, lipids, and cardiorespiratory fitness assessed with the 6-minute walk test. Depression and quality of life were also assessed. Paired t tests assessed changes in CVD risk. RESULTS:The sample who enrolled in the remote patient monitoring (n = 59) were primarily female (51%), White adults (76%) with a mean age of 61.13 ± 11.6 years. Self-reported PA significantly improved over 12 weeks ( P = .005), but not Fitbit steps ( P = .07). There was a significant improvement in cardiorespiratory fitness (469 ± 108 vs 494 ± 132 m, P = .0034), and 23 participants (42%) improved at least 25 m, signifying a clinically meaningful improvement. Only 4 participants were lost to follow-up over 12 weeks of monitoring. CONCLUSIONS:Patients may need more frequent reminders to be active after an initial counseling session, perhaps getting automated messages based on their step counts syncing to their electronic health record.

Electronic health record (EHR)-embedded tools are known to improve prescribing of guideline-directed medical therapy (GDMT) for patients with heart failure. However, physicians may perceive EHR tools to be unhelpful, and may be therefore hesitant to implement these in their practice. We surveyed cardiologists about two effective EHR-tools to improve heart failure care, and they perceived the EHR tools to be easy to use, helpful, and improve the overall management of their patients with heart failure.