Journal of the American Medical Informatics Association最新文献

Objectives: We analyzed trends in adoption of advanced patient engagement and clinical data analytics functionalities among critical access hospitals (CAHs) and non-CAHs to assess how historical gaps have changed.

Materials and methods: We used 2014, 2018, and 2023 data from the American Hospital Association Annual Survey IT Supplement to measure differences in adoption rates (ie, the "adoption gap") of patient engagement and clinical data analytics functionalities across CAHs and non-CAHs. We measured changes over time in CAH and non-CAH adoption of 6 "core" clinical data analytics functionalities, 5 "core" patient engagement functionalities, 5 new patient engagement functionalities, and 3 bulk data export use cases. We constructed 2 composite measures for core functionalities and analyzed adoption for other functionalities individually.

Results: Core functionality adoption increased from 21% of CAHs in 2014 to 56% in 2023 for clinical data analytics and 18% to 49% for patient engagement. The CAH adoption gap in both domains narrowed from 2018 to 2023 (both P < .01). More than 90% of all hospitals had adopted viewing and downloading electronic data and clinical notes by 2023. The largest CAH adoption gaps in 2023 were for Fast Healthcare Interoperability Resources (FHIR) bulk export use cases (eg, analytics and reporting: 63% of CAHs, 81% of non-CAHs, P < .001).

Discussion: Adoption of advanced electronic health record functionalities has increased for CAHs and non-CAHs, and some adoption gaps have been closed since 2018. However, CAHs may continue to struggle with clinical data analytics and FHIR-based functionalities.

Conclusion: Some crucial patient engagement functionalities have reached near-universal adoption; however, policymakers should consider programs to support CAHs in closing remaining adoption gaps.

Objectives: The NIH All of Us Research Program (All of Us) is engaging a diverse community of more than 10 000 registered researchers using a robust engagement ecosystem model. We describe strategies used to build an ecosystem that attracts and supports a diverse and inclusive researcher community to use the All of Us dataset and provide metrics on All of Us researcher usage growth.

Materials and methods: Researcher audiences and diversity categories were defined to guide a strategy. A researcher engagement strategy was codeveloped with program partners to support a researcher engagement ecosystem. An adapted ecological model guided the ecosystem to address multiple levels of influence to support All of Us data use. Statistics from the All of Us Researcher Workbench demographic survey describe trends in researchers' and institutional use of the Workbench and publication numbers.

Results: From 2022 to 2024, some 13 partner organizations and their subawardees conducted outreach, built capacity, or supported researchers and institutions in using the data. Trends indicate that Workbench registrations and use have increased over time, including among researchers underrepresented in the biomedical workforce. Data Use and Registration Agreements from minority-serving institutions also increased.

Discussion: All of Us built a diverse, inclusive, and growing research community via intentional engagement with researchers and via partnerships to address systemic data access issues. Future programs will provide additional support to researchers and institutions to ameliorate All of Us data use challenges.

Conclusion: The approach described helps address structural inequities in the biomedical research field to advance health equity.

Objectives: Human monitoring of personal protective equipment (PPE) adherence among healthcare providers has several limitations, including the need for additional personnel during staff shortages and decreased vigilance during prolonged tasks. To address these challenges, we developed an automated computer vision system for monitoring PPE adherence in healthcare settings. We assessed the system performance against human observers detecting nonadherence in a video surveillance experiment.

Materials and methods: The automated system was trained to detect 15 classes of eyewear, masks, gloves, and gowns using an object detector and tracker. To assess how the system performs compared to human observers in detecting nonadherence, we designed a video surveillance experiment under 2 conditions: variations in video durations (20, 40, and 60 seconds) and the number of individuals in the videos (3 versus 6). Twelve nurses participated as human observers. Performance was assessed based on the number of detections of nonadherence.

Results: Human observers detected fewer instances of nonadherence than the system (parameter estimate -0.3, 95% CI -0.4 to -0.2, P < .001). Human observers detected more nonadherence during longer video durations (parameter estimate 0.7, 95% CI 0.4-1.0, P < .001). The system achieved a sensitivity of 0.86, specificity of 1, and Matthew's correlation coefficient of 0.82 for detecting PPE nonadherence.

Discussion: An automated system simultaneously tracks multiple objects and individuals. The system performance is also independent of observation duration, an improvement over human monitoring.

Conclusion: The automated system presents a potential solution for scalable monitoring of hospital-wide infection control practices and improving PPE usage in healthcare settings.

Objective: The American Medical Informatics Association (AMIA) Task Force on Diversity, Equity, and Inclusion (DEI) was established to address systemic racism and health disparities in biomedical and health informatics, aligning with AMIA's mission to transform healthcare. AMIA's DEI initiatives were spurred by member voices responding to police brutality and COVID-19's impact on Black/African American communities.

Materials and methods: The Task Force, consisting of 20 members across 3 groups aligned with AMIA's 2020-2025 Strategic Plan, met biweekly to develop DEI recommendations with the help of 16 additional volunteers. These recommendations were reviewed, prioritized, and presented to the AMIA Board of Directors for approval.

Results: In 9 months, the Task Force (1) created a logic model to support workforce diversity and raise AMIA's DEI awareness, (2) conducted an environmental scan of other associations' DEI activities, (3) developed a DEI framework for AMIA meetings, (4) gathered member feedback, (5) cultivated DEI educational resources, (6) created a Board nominations and diversity session, (7) reviewed the Board's Strategic Planning for DEI alignment, (8) led a program to increase diversity at the 2020 AMIA Virtual Annual Symposium, and (9) standardized socially-assigned race and ethnicity data collection.

Discussion: The Task Force proposed actionable recommendations that focused on AMIA's role in addressing systemic racism and health equity, helping the organization understand its member diversity.

Conclusion: This work supported marginalized groups, broadened the research agenda, and positioned AMIA as a DEI leader while reinforcing the need for ongoing transformation within informatics.

Objectives: To understand how health-related social needs (HRSN) data are collected at US hospitals and implications for use.

Materials and methods: Using 2023 nationally representative survey data on US hospitals (N = 2775), we described hospitals' routine and structured collection and use of HRSN data and examined the relationship between methods of data collection and specific uses. Multivariate logistic regression was used to identify characteristics associated with data collection and use and understand how methods of data collection relate to use.

Results: In 2023, 88% of hospitals collected HRSN data (64% routinely, 72% structured). While hospitals commonly used data for internal purposes (eg, discharge planning, 79%), those that collected data routinely and in a structured format (58%) used data for purposes involving coordination or exchange with other organizations (eg, making referrals, 74%) at higher rates than hospitals that collected data but not routinely or in a non-structured format (eg, 93% vs 67% for referrals, P< .05). In multivariate regression, routine and structured data collection was positively associated with all uses of data examined. Hospital location, ownership, system-affiliation, value-based care participation, and critical access designation were associated with HRSN data collection, but only system-affiliation was consistently (positively) associated with use.

Discussion: While most hospitals screen for social needs, fewer collect data routinely and in a structured format that would facilitate downstream use. Routine and structured data collection was associated with greater use, particularly for secondary purposes.

Conclusion: Routine and structured screening may result in more actionable data that facilitates use for various purposes that support patient care and improve community and population health, indicating the importance of continuing efforts to increase routine screening and standardize HRSN data collection.

Objectives: This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models.

Materials and methods: PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks. Data extraction followed the CHARMS checklist. Quality appraisal followed the PROBAST tool. Data synthesis was guided by the socio-ecological conceptual framework.

Results: Thirteen studies were included, mainly focusing on respiratory infections and nursing homes. Most used regression models with structured electronic health record data. Since 2020, there has been a shift toward advanced ML algorithms and multimodal data, biosensors, and clinical notes being significant sources of unstructured data. Despite these advances, there is insufficient evidence to support performance improvements over traditional models. Individual-level risk predictors, like impaired cognition, declined function, and tachycardia, were commonly used, while contextual-level predictors were barely utilized, consequently limiting model fairness. Major sources of bias included lack of external validation, inadequate model calibration, and insufficient consideration of data complexity.

Discussion and conclusion: Despite the growth of advanced modeling approaches in infection-related models in PAC settings, evidence supporting their superiority remains limited. Future research should leverage a socio-ecological lens for predictor selection and model construction, exploring optimal data modalities and ML model usage in PAC, while ensuring rigorous methodologies and fairness considerations.

Objective: Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel preprocessed dataset, the MIMIC-IV-BHC, encapsulating clinical note and BHC pairs to adapt LLMs for BHC synthesis. Furthermore, we introduce a benchmark of the summarization performance of 2 general-purpose LLMs and 3 healthcare-adapted LLMs.

Materials and methods: Using clinical notes as input, we apply prompting-based (using in-context learning) and fine-tuning-based adaptation strategies to 3 open-source LLMs (Clinical-T5-Large, Llama2-13B, and FLAN-UL2) and 2 proprietary LLMs (Generative Pre-trained Transformer [GPT]-3.5 and GPT-4). We evaluate these LLMs across multiple context-length inputs using natural language similarity metrics. We further conduct a clinical study with 5 clinicians, comparing clinician-written and LLM-generated BHCs across 30 samples, focusing on their potential to enhance clinical decision-making through improved summary quality. We compare reader preferences for the original and LLM-generated summary using Wilcoxon signed-rank tests. We further request optional qualitative feedback from clinicians to gain deeper insights into their preferences, and we present the frequency of common themes arising from these comments.

Results: The Llama2-13B fine-tuned LLM outperforms other domain-adapted models given quantitative evaluation metrics of Bilingual Evaluation Understudy (BLEU) and Bidirectional Encoder Representations from Transformers (BERT)-Score. GPT-4 with in-context learning shows more robustness to increasing context lengths of clinical note inputs than fine-tuned Llama2-13B. Despite comparable quantitative metrics, the reader study depicts a significant preference for summaries generated by GPT-4 with in-context learning compared to both Llama2-13B fine-tuned summaries and the original summaries (P<.001), highlighting the need for qualitative clinical evaluation.

Discussion and conclusion: We release a foundational clinically relevant dataset, the MIMIC-IV-BHC, and present an open-source benchmark of LLM performance in BHC synthesis from clinical notes. We observe high-quality summarization performance for both in-context proprietary and fine-tuned open-source LLMs using both quantitative metrics and a qualitative clinical reader study. Our research effectively integrates elements from the data assimilation pipeline: our methods use (1) clinical data sources to integrate, (2) data translation, and (3) knowledge creation, while our evaluation strategy paves the way for (4) deployment.