Journal of the American Medical Informatics Association最新文献

{"title":"Revisions to the Safety Assurance Factors for Electronic Health Record Resilience (SAFER) Guides to update national recommendations for safe use of electronic health records.","authors":"Dean F Sittig, Trisha Flanagan, Patricia Sengstack, Rosann T Cholankeril, Sara Ehsan, Amanda Heidemann, Daniel R Murphy, Hojjat Salmasian, Jason S Adelman, Hardeep Singh","doi":"10.1093/jamia/ocaf018","DOIUrl":"10.1093/jamia/ocaf018","url":null,"abstract":"<p><p>The Safety Assurance Factors for Electronic Health Record (EHR) Resilience (SAFER) Guides provide recommendations to healthcare organizations for conducting proactive self-assessments of the safety and effectiveness of their EHR implementation and use. Originally released in 2014, they were last updated in 2016. In 2022, the Centers for Medicare and Medicaid Services required their annual attestation by US hospitals.</p><p><strong>Objectives: </strong>This case study describes how SAFER Guide recommendations were updated to align with current evidence and clinical practice.</p><p><strong>Materials and methods: </strong>Over nine months, a multidisciplinary team updated SAFER Guides through literature reviews, iterative feedback, and online meetings.</p><p><strong>Results: </strong>We reduced the number of recommended practices across all Guides by 40% and consolidated 9 Guides into 8 to maximize ease of use, feasibility, and utility. We provide a 4-level evidence grading hierarchy for each recommendation and a new 5-point rating scale to self-assess implementation status of the recommendation. We included 429 citations of which 289 (67%) were published since the 2016 revision.</p><p><strong>Discussion: </strong>SAFER Guides were revised to offer EHR best practices, adaptable to unique organizational needs, with interactive content available at: https://www.healthit.gov/topic/safety/safer-guides.</p><p><strong>Conclusion: </strong>Revisions ensure that the 2025 SAFER Guides represent the best available current evidence for EHR developers and healthcare organizations.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"32 4","pages":"755-760"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.","authors":"Philipp Röchner, Franz Rothlauf","doi":"10.1093/jamia/ocaf011","DOIUrl":"10.1093/jamia/ocaf011","url":null,"abstract":"<p><strong>Objectives: </strong>Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalous.</p><p><strong>Materials and methods: </strong>The dataset analyzed consists of 18 587 truncated real-world cancer registry records containing 8 categorical variables describing patients diagnosed with bladder and lung tumors. We identified 800 anomalous records using an autoencoder's per-record reconstruction error, which is a common neural network-based anomaly detection approach. For each variable of a record, we determined a robust explanation score, which indicates how anomalous the variable is. A variable's robust explanation score is the autoencoder's per-variable reconstruction error measured by cross-entropy and robustly standardized across records; that is, large reconstruction errors have a small effect on standardization. To evaluate the explanation scores, medical coders identified the implausible variables of the anomalous records. We then compare the explanation scores to the medical coders' validation in a classification and ranking setting. As baselines, we identified anomalous variables using the raw autoencoder's per-variable reconstruction error, the non-robustly standardized per-variable reconstruction error, the empirical frequency of implausible variables according to the medical coders' validation, and random selection or ranking of variables.</p><p><strong>Results: </strong>When we sort the variables by their robust explanation scores, on average, the 2.37 highest-ranked variables contain all implausible variables. For the baselines, on average, the 2.84, 2.98, 3.27, and 4.91 highest-ranked variables contain all the variables that made a record implausible.</p><p><strong>Discussion: </strong>We found that explanations based on robust explanation scores were better than or as good as the baseline explanations examined in the classification and ranking settings. Due to the international standardization of cancer data coding, we expect our results to generalize to other cancer types and registries. As we anticipate different magnitudes of per-variable autoencoder reconstruction errors in data from other medical registries and domains, these may also benefit from robustly standardizing the reconstruction errors per variable. Future work could explore methods to identify subsets of anomalous variables, addressing whether individual variables or their combinations contribute to anomalies. This direction aims to improve the interpretability and utility of anomaly detection systems.</p><p><strong>Conclusions: </strong>Robust explanation scores can improve explanations for identifying implausible variables in cancer data.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"724-735"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative large language models for automated data extraction in living systematic reviews.","authors":"Muhammad Ali Khan, Umair Ayub, Syed Arsalan Ahmed Naqvi, Kaneez Zahra Rubab Khakwani, Zaryab Bin Riaz Sipra, Ammad Raina, Sihan Zhou, Huan He, Amir Saeidi, Bashar Hasan, Robert Bryan Rumble, Danielle S Bitterman, Jeremy L Warner, Jia Zou, Amye J Tevaarwerk, Konstantinos Leventakos, Kenneth L Kehl, Jeanne M Palmer, Mohammad Hassan Murad, Chitta Baral, Irbaz Bin Riaz","doi":"10.1093/jamia/ocae325","DOIUrl":"10.1093/jamia/ocae325","url":null,"abstract":"<p><strong>Objective: </strong>Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world 2-reviewer process.</p><p><strong>Materials and methods: </strong>A dataset of 10 trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data. The dataset was split into prompt development (n = 5) and held-out test sets (n = 17). GPT-4-turbo and Claude-3-Opus were used for data extraction. Responses from the 2 LLMs were considered concordant if they were the same for a given variable. The discordant responses from each LLM were provided to the other LLM for cross-critique. Accuracy, ie, the total number of correct responses divided by the total number of responses, was computed to assess performance.</p><p><strong>Results: </strong>In the prompt development set, 110 (96%) responses were concordant, achieving an accuracy of 0.99 against the gold standard. In the test set, 342 (87%) responses were concordant. The accuracy of the concordant responses was 0.94. The accuracy of the discordant responses was 0.41 for GPT-4-turbo and 0.50 for Claude-3-Opus. Of the 49 discordant responses, 25 (51%) became concordant after cross-critique, increasing accuracy to 0.76.</p><p><strong>Discussion: </strong>Concordant responses by the LLMs are likely to be accurate. In instances of discordant responses, cross-critique can further increase the accuracy.</p><p><strong>Conclusion: </strong>Large language models, when simulated in a collaborative, 2-reviewer workflow, can extract data with reasonable performance, enabling truly \"living\" systematic reviews.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"638-647"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.","authors":"Siru Liu, Allison B McCoy, Adam Wright","doi":"10.1093/jamia/ocaf008","DOIUrl":"10.1093/jamia/ocaf008","url":null,"abstract":"<p><strong>Objective: </strong>The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.</p><p><strong>Materials and methods: </strong>We conducted a systematic literature review and a meta-analysis. The report was created in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 analysis. Searches were performed in 3 databases (PubMed, Embase, PsycINFO) using terms related to \"retrieval augmented generation\" and \"large language model,\" for articles published in 2023 and 2024. We selected studies that compared baseline LLM performance with RAG performance. We developed a random-effect meta-analysis model, using odds ratio as the effect size.</p><p><strong>Results: </strong>Among 335 studies, 20 were included in this literature review. The pooled effect size was 1.35, with a 95% confidence interval of 1.19-1.53, indicating a statistically significant effect (P = .001). We reported clinical tasks, baseline LLMs, retrieval sources and strategies, as well as evaluation methods.</p><p><strong>Discussion: </strong>Building on our literature review, we developed Guidelines for Unified Implementation and Development of Enhanced LLM Applications with RAG in Clinical Settings to inform clinical applications using RAG.</p><p><strong>Conclusion: </strong>Overall, RAG implementation showed a 1.35 odds ratio increase in performance compared to baseline LLMs. Future research should focus on (1) system-level enhancement: the combination of RAG and agent, (2) knowledge-level enhancement: deep integration of knowledge into LLM, and (3) integration-level enhancement: integrating RAG systems within electronic health records.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"605-615"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An examination of ambulatory care code specificity utilization in ICD-10-CM compared to ICD-9-CM: implications for ICD-11 implementation.","authors":"Susan H Fenton, Cassandra Ciminello, Vickie M Mays, Mary H Stanfill, Valerie Watzlaf","doi":"10.1093/jamia/ocaf003","DOIUrl":"10.1093/jamia/ocaf003","url":null,"abstract":"<p><strong>Objective: </strong>The ICD-10-CM classification system contains more specificity than its predecessor ICD-9-CM. A stated reason for transitioning to ICD-10-CM was to increase the availability of detailed data. This study aims to determine whether the increased specificity contained in ICD-10-CM is utilized in the ambulatory care setting and inform an evidence-based approach to evaluate ICD-11 content for implementation planning in the United States.</p><p><strong>Materials and methods: </strong>Diagnosis codes and text descriptions were extracted from a 25% random sample of the IQVIA Ambulatory EMR-US database for 2014 (ICD-9-CM, n = 14 327 155) and 2019 (ICD-10-CM, n = 13 062 900). Code utilization data was analyzed for the total and unique number of codes. Frequencies and tests of significance determined the percentage of available codes utilized and the unspecified code rates for both code sets in each year.</p><p><strong>Results: </strong>Only 44.6% of available ICD-10-CM codes were used compared to 91.5% of available ICD-9-CM codes. Of the total codes used, 14.5% ICD-9-CM codes were unspecified, while 33.3% ICD-10-CM codes were unspecified.</p><p><strong>Discussion: </strong>Even though greater detail is available, a 108.5% increase in using unspecified codes with ICD-10-CM was found. The utilization data analyzed in this study does not support a rationale for the large increase in the number of codes in ICD-10-CM. New technologies and methods are likely needed to fully utilize detailed classification systems.</p><p><strong>Conclusion: </strong>These results help evaluate the content needed in the United States national ICD standard. This analysis of codes in the current ICD standard is important for ICD-11 evaluation, implementation, and use.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"675-681"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns of willingness to share health data with key stakeholders in US consumers: a latent class analysis.","authors":"Ashwini Nagappan, Xi Zhu","doi":"10.1093/jamia/ocaf014","DOIUrl":"10.1093/jamia/ocaf014","url":null,"abstract":"<p><strong>Objective: </strong>To identify distinct patterns in consumer willingness to share health data with various stakeholders and analyze characteristics across consumer groups.</p><p><strong>Materials and methods: </strong>Data from the Rock Health Digital Health Consumer Adoption Survey from 2018, 2019, 2020, and 2022 were analyzed. This study comprised a Census-matched representative sample of U.S. adults. Latent class analysis (LCA) identified groups of respondents with similar data-sharing attitudes. Groups were compared by sociodemographics, health status, and digital health utilization.</p><p><strong>Results: </strong>We identified three distinct LCA groups: (1) Wary (36.8%), (2) Discerning (47.9%), and (3) Permissive (15.3%). The Wary subgroup exhibited reluctance to share health data with any stakeholder, with predicted probabilities of willingness to share ranging from 0.07 for pharmaceutical companies to 0.34 for doctors/clinicians. The Permissive group showed a high willingness, with predicted probabilities greater than 0.75 for most stakeholders except technology companies and government organizations. The Discerning group was selective, willing to share with healthcare-related entities and family (predicted probabilities >0.62), but reluctant to share with other stakeholders (predicted probabilities <0.29). Individual characteristics were associated with LCA group membership.</p><p><strong>Discussion: </strong>Findings highlight a persistent trust in traditional healthcare providers. However, the varying willingness to share with non-traditional stakeholders suggests that while some consumers are open to sharing, others remain hesitant and selective. Data privacy policies and practices need to recognize and respond to multifaceted and stakeholder-specific attitudes.</p><p><strong>Conclusion: </strong>LCA reveals significant heterogeneity in health data-sharing attitudes among U.S. consumers, providing insights to inform the development of data privacy policies.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"702-711"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing systematic literature reviews with generative artificial intelligence: development, applications, and performance evaluation.","authors":"Ying Li, Surabhi Datta, Majid Rastegar-Mojarad, Kyeryoung Lee, Hunki Paek, Julie Glasgow, Chris Liston, Long He, Xiaoyan Wang, Yingxin Xu","doi":"10.1093/jamia/ocaf030","DOIUrl":"10.1093/jamia/ocaf030","url":null,"abstract":"<p><strong>Objectives: </strong>We developed and validated a large language model (LLM)-assisted system for conducting systematic literature reviews in health technology assessment (HTA) submissions.</p><p><strong>Materials and methods: </strong>We developed a five-module system using abstracts acquired from PubMed: (1) literature search query setup; (2) study protocol setup using population, intervention/comparison, outcome, and study type (PICOs) criteria; (3) LLM-assisted abstract screening; (4) LLM-assisted data extraction; and (5) data summarization. The system incorporates a human-in-the-loop design, allowing real-time PICOs criteria adjustment. This is achieved by collecting information on disagreements between the LLM and human reviewers regarding inclusion/exclusion decisions and their rationales, enabling informed PICOs refinement. We generated four evaluation sets including relapsed and refractory multiple myeloma (RRMM) and advanced melanoma to evaluate the LLM's performance in three key areas: (1) recommending inclusion/exclusion decisions during abstract screening, (2) providing valid rationales for abstract exclusion, and (3) extracting relevant information from included abstracts.</p><p><strong>Results: </strong>The system demonstrated relatively high performance across all evaluation sets. For abstract screening, it achieved an average sensitivity of 90%, F1 score of 82, accuracy of 89%, and Cohen's κ of 0.71, indicating substantial agreement between human reviewers and LLM-based results. In identifying specific exclusion rationales, the system attained accuracies of 97% and 84%, and F1 scores of 98 and 89 for RRMM and advanced melanoma, respectively. For data extraction, the system achieved an F1 score of 93.</p><p><strong>Discussion: </strong>Results showed high sensitivity, Cohen's κ, and PABAK for abstract screening, and high F1 scores for data extraction. This human-in-the-loop AI-assisted SLR system demonstrates the potential of GPT-4's in context learning capabilities by eliminating the need for manually annotated training data. In addition, this LLM-based system offers subject matter experts greater control through prompt adjustment and real-time feedback, enabling iterative refinement of PICOs criteria based on performance metrics.</p><p><strong>Conclusion: </strong>The system demonstrates potential to streamline systematic literature reviews, potentially reducing time, cost, and human errors while enhancing evidence generation for HTA submissions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"616-625"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Techniques Loss-Based Algorithm for Severity Classification (ATLAS): a novel approach for continuous quantification of exertional symptoms during incremental exercise testing.","authors":"Abed A Hijleh, Sophia Wang, Danilo C Berton, Igor Neder-Serafini, Sandra Vincent, Matthew James, Nicolle Domnik, Devin Phillips, Luiz E Nery, Denis E O'Donnell, J Alberto Neder","doi":"10.1093/jamia/ocaf051","DOIUrl":"https://doi.org/10.1093/jamia/ocaf051","url":null,"abstract":"<p><strong>Objective: </strong>Heightened muscular effort and breathlessness (dyspnea) are disabling sensory experiences. We sought to improve the current approach of assessing these symptoms only at the maximal effort to new paradigms based on their continuous quantification throughout cardiopulmonary exercise testing (CPET).</p><p><strong>Materials and methods: </strong>After establishing sex- and age-adjusted reference centiles (0-10 Borg scale), we developed a novel algorithm (AI-Techniques Loss-Based Algorithm for Severity Classification [ATLAS]) based on reciprocal exponential loss for CPET data from patients with chronic obstructive lung disease of varied severity.</p><p><strong>Results: </strong>Categories of dyspnea intensity by ATLAS-but not dyspnea at peak exercise-correctly discriminated patients in progressively higher resting and exercise impairment (P < .05).</p><p><strong>Discussion: </strong>This new AI-techniques approach will be translated to the care of disabled patients to uncover the seeds and consequences of their activity-related symptoms.</p><p><strong>Conclusions: </strong>We used innovative informatics research to change paradigms in displaying, quantifying, and analyzing effort-related symptoms in patient populations.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of a combined mHealth and community health worker intervention on HIV self-management.","authors":"Fabiana Cristina Dos Santos, D Scott Batey, Emma S Kay, Haomiao Jia, Olivia R Wood, Joseph A Abua, Susan A Olender, Rebecca Schnall","doi":"10.1093/jamia/ocae322","DOIUrl":"10.1093/jamia/ocae322","url":null,"abstract":"<p><strong>Objective: </strong>To identify demographic, social, and clinical factors associated with HIV self-management and evaluate whether the CHAMPS intervention is associated with changes in an individual's HIV self-management.</p><p><strong>Method: </strong>This study was a secondary data analysis from a randomized controlled trial evaluating the effects of the CHAMPS, a mHealth intervention with community health worker sessions, on HIV self-management in New York City (NYC) and Birmingham. Group comparisons and linear regression analyses identified demographic, social, and clinical factors associated with HIV self-management. We calculated interactions between groups (CHAMPS intervention and standard of care) over time (6 and 12 months) following the baseline observation, indicating a difference in the outcome scores from baseline to each time across groups.</p><p><strong>Results: </strong>Our findings indicate that missing medical appointments, uncertainty about accessing care, and lack of adherence to antiretroviral therapy are associated with lower HIV self-management. For the NYC site, the CHAMPS showed a statistically significant positive effect on daily HIV self-management (estimate = 0.149, SE = 0.069, 95% CI [0.018 to 0.289]). However, no significant effects were observed for social support or the chronic nature of HIV self-management. At the Birmingham site, the CHAMPS did not yield statistically significant effects on HIV self-management outcomes.</p><p><strong>Discussion: </strong>Our study suggests that CHAMPS intervention enhances daily self-management activities for people with HIV at the NYC site, indicating a promising improvement in routine HIV care.</p><p><strong>Conclusion: </strong>Further research is necessary to explore how various factors influence HIV self-management over time across different regions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"510-517"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating area-level social drivers of health in predictive algorithms using electronic health record data.","authors":"Agata Foryciarz, Nicole Gladish, David H Rehkopf, Sherri Rose","doi":"10.1093/jamia/ocaf009","DOIUrl":"10.1093/jamia/ocaf009","url":null,"abstract":"<p><strong>Objectives: </strong>The inclusion of social drivers of health (SDOH) into predictive algorithms of health outcomes has potential for improving algorithm interpretation, performance, generalizability, and transportability. However, there are limitations in the availability, understanding, and quality of SDOH variables, as well as a lack of guidance on how to incorporate them into algorithms when appropriate to do so. As such, few published algorithms include SDOH, and there is substantial methodological variability among those that do. We argue that practitioners should consider the use of social indices and factors-a class of area-level measurements-given their accessibility, transparency, and quality.</p><p><strong>Results: </strong>We illustrate the process of using such indices in predictive algorithms, which includes the selection of appropriate indices for the outcome, measurement time, and geographic level, in a demonstrative example with the Kidney Failure Risk Equation.</p><p><strong>Discussion: </strong>Identifying settings where incorporating SDOH may be beneficial and incorporating them rigorously can help validate algorithms and assess generalizability.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"595-601"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}