Faculty Bibliography

ABSTRACT/UNASSIGNED:OBJECTIVES: This case report describes the implementation and outcomes of robotic process automation (RPA) interventions used as an electronic health record (EHR)-based reliability layer for two inpatient quality initiatives: First Contact Provider (FCP) assignment and Admission Medication Reconciliation (AMR) within 24 hours. ABSTRACT/UNASSIGNED:METHODS: RPA bots were created to support two inpatient quality initiatives by monitoring EHR patient lists for predefined care gaps: missing FCP assignment and incomplete AMR. When a gap was identified, the bot opened the patient chart, initiated a secure-message conversation with frontline clinicians and quality improvement (QI) champions, and generated operational reports to support follow-up. Chi-square tests compared FCP before (September 1, 2022-February 28, 2023) versus after implementation (March 1-August 31, 2023), and AMR before (December 1, 2022-May 31, 2023) versus after implementation (June 1-November 30, 2023). ABSTRACT/UNASSIGNED:< 0.001). ABSTRACT/UNASSIGNED:CONCLUSION: Implementation of RPA was associated with substantial improvement in FCP assignment and timely AMR completion. Initiating a bot-enabled secure-chat "virtual safety huddle" may provide a practical EHR reliability layer for rule-based inpatient care gaps.

OBJECTIVE/UNASSIGNED:To test whether AI-generated, clinician-edited after-visit summaries (AVS) are more patient-friendly than clinician-generated AVS, without compromising safety, when implemented in a live inpatient setting. PATIENTS AND METHODS/UNASSIGNED:grade reading levels, and presence of a "simplified" description of both the diagnosis and treatment/interventions. Our composite outcome was positive if all 3 of these outcomes were positive. We also surveyed patients, nurses, and clinicians on perceptions of safety, empathy, and understandability. RESULTS/UNASSIGNED:grade than the control group (8.5 vs 10.6, P <.001). The AI-generated, clinician-edited AVS were also more likely to contain simplified explanations of diagnoses (96.7% vs 12.9%, P <.001) and hospital interventions (86.7% vs 12.9%, P <.001). Our composite outcome favored AI-generated, clinician-edited summaries (13.3% vs 6.5%, p = 0.637). CONCLUSION/UNASSIGNED:In this small, randomized trial, exploratory and component measures suggest improved patient-friendliness in AI-generated, clinician-edited AVS, although no significant difference was observed in the primary outcome. CLINICALTRIALSGOV NUMBER/UNASSIGNED:NCT06711458.

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: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.

IMPORTANCE/UNASSIGNED:Hospital course (HC) summarization represents an increasingly onerous discharge summary component for physicians. Literature supports large language models (LLMs) for HC summarization, but whether physicians can effectively partner with electronic health record-embedded LLMs to draft HCs is unknown. OBJECTIVES/UNASSIGNED:To compare the editing effort required by time-constrained resident physicians to improve LLM- vs physician-generated HCs toward a novel 4Cs (complete, concise, cohesive, and confabulation-free) HC. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Quality improvement study using a convenience sample of 10 internal medicine resident editors, 8 hospitalist evaluators, and randomly selected general medicine admissions in December 2023 lasting 4 to 8 days at New York University Langone Health. EXPOSURES/UNASSIGNED:Residents and hospitalists reviewed randomly assigned patient medical records for 10 minutes. Residents blinded to author type who edited each HC pair (physician and LLM) for quality in 3 minutes, followed by comparative ratings by attending hospitalists. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Editing effort was quantified by analyzing the edits that occurred on the HC pairs after controlling for length (percentage edited) and the degree to which the original HCs' meaning was altered (semantic change). Hospitalists compared edited HC pairs with A/B testing on the 4Cs (5-point Likert scales converted to 10-point bidirectional scales). RESULTS/UNASSIGNED:Among 100 admissions, compared with physician HCs, residents edited a smaller percentage of LLM HCs (LLM mean [SD], 31.5% [16.6%] vs physicians, 44.8% [20.0%]; P < .001). Additionally, LLM HCs required less semantic change (LLM mean [SD], 2.4% [1.6%] vs physicians, 4.9% [3.5%]; P < .001). Attending physicians deemed LLM HCs to be more complete (mean [SD] difference LLM vs physicians on 10-point bidirectional scale, 3.00 [5.28]; P < .001), similarly concise (mean [SD], -1.02 [6.08]; P = .20), and cohesive (mean [SD], 0.70 [6.14]; P = .60), but with more confabulations (mean [SD], -0.98 [3.53]; P = .002). The composite scores were similar (mean [SD] difference LLM vs physician on 40-point bidirectional scale, 1.70 [14.24]; P = .46). CONCLUSIONS AND RELEVANCE/UNASSIGNED:Electronic health record-embedded LLM HCs required less editing than physician-generated HCs to approach a quality standard, resulting in HCs that were comparably or more complete, concise, and cohesive, but contained more confabulations. Despite the potential influence of artificial time constraints, this study supports the feasibility of a physician-LLM partnership for writing HCs and provides a basis for monitoring LLM HCs in clinical practice.

OBJECTIVES/OBJECTIVE:This case series aims to evaluate several applications of inline disappearing text (DT) clinical decision support (CDS) tools within clinician documentation. METHODS:DT blocks were created to prompt documentation for perioperative anticoagulation planning (Scenario 1), pre-discharge intravenous antibiotic planning (Scenario 2), and advanced care planning (Scenario 3). In Scenario 1, DT was the only intervention. In Scenario 2, DT was paired with a documentation SmartList. In Scenario 3, DT was paired with a documentation SmartList and an OurPractice Advisory. The number of documented perioperative anticoagulation plans, pre-discharge intravenous antibiotic plans, and Advanced Care Planning notes were measured pre- and post-intervention and compared using Chi-square analyses. RESULTS:In Scenario 1, there was no statistically significant change in the percentage of perioperative anticoagulation plans documented at 0-24 and 24-48 hours before surgery. In Scenario 2, documentation of antibiotic contingency planning in patients expected to be discharged within 24 hours increased from 60% (54 of 90 notes) to 93% (1,850 of 1,994 notes) X2 (1, N=2,084) = 113.1, p < 0.001. In Scenario 3, ACP note documentation by discharge in patients with a positive mandatory surprise question increased from 43% (821 of 1,909 encounters) to 52% (975 of 1,874 encounters) X2 (1, N=3,783) = 30.5, p < 0.001. CONCLUSIONS:Utilizing DT in conjunction with other forms of CDS was associated with an improvement of documentation quality in pre-discharge IV antibiotics and advanced care planning. A sociotechnical analysis explores how interactions between technology, people, workflow, and culture could contextualize how utilizing DT with other forms of CDS was more effective than DT alone.

BACKGROUND:Online accessible patient education materials (PEMs) are essential for patient empowerment. However, studies have shown that these materials often exceed the recommended sixth-grade reading level, making them difficult for many patients to understand. Large language models (LLMs) have the potential to simplify PEMs into more readable educational content. OBJECTIVE:We sought to evaluate whether 3 LLMs (ChatGPT [OpenAI], Gemini [Google], and Claude [Anthropic PBC]) can optimize the readability of PEMs to the recommended reading level without compromising accuracy. METHODS:This cross-sectional study used 60 randomly selected PEMs available online from 3 websites. We prompted LLMs to simplify the reading level of online PEMs. The primary outcome was the readability of the original online PEMs compared with the LLM-simplified versions. Readability scores were calculated using 4 validated indices Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, and Simple Measure of Gobbledygook Index. Accuracy and understandability were also assessed as balancing measures, with understandability measured using the Patient Education Materials Assessment Tool-Understandability (PEMAT-U). RESULTS:The original readability scores for the American Heart Association (AHA), American Cancer Society (ACS), and American Stroke Association (ASA) websites were above the recommended sixth-grade level, with mean grade level scores of 10.7,10.0, and 9.6, respectively. After optimization by the LLMs, readability scores significantly improved across all 3 websites when compared with the original text. Compared with the original website, Wilcoxon signed rank test showed ChatGPT improved the readability to 7.6 from 10.1 (P<.001); Gemini, to 6.6 (P<.001); and Claude, to 5.6 (P<.001). Word counts were significantly reduced by all LLMs, with a decrease from a mean range of 410.9-953.9 words to a mean range of 201.9-248.1 words. None of the ChatGPT LLM-simplified PEMs were inaccurate, while 3.3% of Gemini and Claude LLM-simplified PEMs were inaccurate. Baseline understandability scores, as measured by PEMAT-U, were preserved across all LLM-simplified versions. CONCLUSIONS:This cross-sectional study demonstrates that LLMs have the potential to significantly enhance the readability of online PEMs while maintaining accuracy and understandability, making them more accessible to a broader audience. However, variability in model performance and demonstrated inaccuracies underscore the need for human review of LLM output. Further study is needed to explore advanced LLM techniques and models trained for medical content.