Journal of the American Medical Informatics Association最新文献

{"title":"Information-blocking trends following regulatory action.","authors":"Jordan Everson, Daniel Healy","doi":"10.1093/jamia/ocaf007","DOIUrl":"10.1093/jamia/ocaf007","url":null,"abstract":"<p><strong>Objective: </strong>To describe the prevalence of and trends in practices that interfere with the exchange of patient health information (potential information blocking) 2 years after implementation of information-blocking regulations.</p><p><strong>Materials and methods: </strong>Drawing from the American Hospital Association Information Technology (IT) Supplement and a national survey of health information organizations (HIOs), we described rates and methods of potential information blocking from these organizations' perspectives in 2023 and compared them to prior years.</p><p><strong>Results: </strong>Twenty-seven percent of hospitals sometimes or often observed potential information blocking by any actor in 2023, down from 42% in 2021 and 33% in 2022. Thirty percent of HIOs routinely observed potential information blocking by health IT developers, down from 50% in 2015. 13% of HIOs routinely observed potential information blocking by hospitals and health systems, down from 25% in 2015. According to both hospitals and HIOs, the most prevalent method of potential information blocking by developers in 2023 was through price, while the most prevalent by healthcare providers/health systems was by focusing exchange on strategic affiliations. Few hospitals and HIOs that experienced potential information blocking said that they had reported it to the Department of Health and Human Services.</p><p><strong>Discussion: </strong>Hospitals and HIOs perceived lower rates of potential information blocking in 2023 than in prior years indicating some impact of regulations addressing information blocking. However, both respondent types reported that substantial potential information blocking persisted in 2023 and negatively impacted the exchange of information.</p><p><strong>Conclusion: </strong>While potential information-blocking practices have decreased, they have not been eliminated, indicating the value of continued and robust enforcement of information-blocking regulations.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"665-674"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069001","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":"Fast and interpretable mortality risk scores for critical care patients.","authors":"Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin","doi":"10.1093/jamia/ocae318","DOIUrl":"10.1093/jamia/ocae318","url":null,"abstract":"<p><strong>Objective: </strong>Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.</p><p><strong>Material and methods: </strong>We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them. For evaluation, we leveraged the largest existing public ICU monitoring datasets (MIMIC III and eICU).</p><p><strong>Results: </strong>Models produced by GroupFasterRisk outperformed OASIS and SAPS II scores and performed similarly to APACHE IV/IVa while using at most a third of the parameters. For patients with sepsis/septicemia, acute myocardial infarction, heart failure, and acute kidney failure, GroupFasterRisk models outperformed OASIS and SOFA. Finally, different mortality prediction ML approaches performed better based on variables selected by GroupFasterRisk as compared to OASIS variables.</p><p><strong>Discussion: </strong>GroupFasterRisk's models performed better than risk scores currently used in hospitals, and on par with black box ML models, while being orders of magnitude sparser. Because GroupFasterRisk produces a variety of risk scores, it allows design flexibility-the key enabler of practical model creation.</p><p><strong>Conclusion: </strong>GroupFasterRisk is a fast, accessible, and flexible procedure that allows learning a diverse set of sparse risk scores for mortality prediction.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"736-747"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054068","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":"Deciphering genomic codes using advanced natural language processing techniques: a scoping review.","authors":"Shuyan Cheng, Yishu Wei, Yiliang Zhou, Zihan Xu, Drew N Wright, Jinze Liu, Yifan Peng","doi":"10.1093/jamia/ocaf029","DOIUrl":"10.1093/jamia/ocaf029","url":null,"abstract":"<p><strong>Objectives: </strong>The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of natural language processing (NLP) techniques, particularly large language models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. The goal of this review is to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.</p><p><strong>Materials and methods: </strong>Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopus, Web of Science, Embase, and ACM Digital Library. Studies were included if they focused on NLP methodologies applied to genomic sequencing data analysis, without restrictions on publication date or article type.</p><p><strong>Results: </strong>A total of 26 studies published between 2021 and April 2024 were selected for review. The review highlights that tokenization and transformer models enhance the processing and understanding of genomic data, with applications in predicting regulatory annotations like transcription-factor binding sites and chromatin accessibility.</p><p><strong>Discussion: </strong>The application of NLP and LLMs to genomic sequencing data interpretation is a promising field that can help streamline the processing of large-scale genomic data while also providing a better understanding of its complex structures. It has the potential to drive advancements in personalized medicine by offering more efficient and scalable solutions for genomic analysis. Further research is also needed to discuss and overcome current limitations, enhancing model transparency and applicability.</p><p><strong>Conclusion: </strong>This review highlights the growing role of NLP, particularly LLMs, in genomic sequencing data analysis. While these models improve data processing and regulatory annotation prediction, challenges remain in accessibility and interpretability. Further research is needed to refine their application in genomics.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"761-772"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505805","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":"Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach.","authors":"Chunlong Miao, Jingjing Luo, Yan Liang, Hong Liang, Yuhui Cen, Shijie Guo, Hongliu Yu","doi":"10.1093/jamia/ocae327","DOIUrl":"10.1093/jamia/ocae327","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid demand for intelligent systems that can recommend comprehensive long-term care plans for older adults with disabilities who have stable clinical conditions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to utilize deep learning methods to recommend comprehensive care plans for the older adults with disabilities.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We model the care data of older adults with disabilities using a bipartite graph. Additionally, we employ a prediction-based graph self-supervised learning (SSL) method to mine deep representations of graph nodes. Furthermore, we propose a novel graph Transformer architecture that incorporates eigenvector centrality to augment node features and uses graph structural information as references for the self-attention mechanism. Ultimately, we present the Bipartite Graph Transformer (BiT) model to provide personalized long-term care plan recommendation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We constructed a bipartite graph comprising of 1917 nodes and 195 240 edges derived from real-world care data. The proposed model demonstrates outstanding performance, achieving an overall F1 score of 0.905 for care plan recommendations. Each care service item reached an average F1 score of 0.897, indicating that the BiT model is capable of accurately selecting services and effectively balancing the trade-off between incorrect and missed selections.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;The BiT model proposed in this paper demonstrates strong potential for improving long-term care plan recommendations by leveraging bipartite graph modeling and graph SSL. This approach addresses the challenges of manual care planning, such as inefficiency, bias, and errors, by offering personalized and data-driven recommendations. While the model excels in common care items, its performance on rare or complex services could be enhanced with further refinement. These findings highlight the model's ability to provide scalable, AI-driven solutions to optimize care planning, though future research should explore its applicability across diverse healthcare settings and service types.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Compared to previous research, the novel model proposed in this article effectively learns latent topology in bipartite graphs and achieves superior recommendation performance. Our study demonstrates the applicability of SSL and graph transformers in recommending long-term care plans for older adults with disabilitie","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"689-701"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12079649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069022","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":"Alert design in the real world: a cross-sectional analysis of interruptive alerting at 9 academic pediatric health systems.","authors":"Swaminathan Kandaswamy, Julia K W Yarahuan, Elizabeth A Dobler, Matthew J Molloy, Lindsey A Knake, Sean M Hernandez, Anne A Fallon, Lauren M Hess, Allison B McCoy, Regine M Fortunov, Eric S Kirkendall, Naveen Muthu, Evan W Orenstein, Adam C Dziorny, Juan D Chaparro","doi":"10.1093/jamia/ocaf013","DOIUrl":"10.1093/jamia/ocaf013","url":null,"abstract":"<p><strong>Objective: </strong>To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.</p><p><strong>Materials and methods: </strong>We conducted a 3-phase mixed-methods cross-sectional study. Phase 1 involved developing a codebook for alert content classification. Phase 2 identified the most frequently interruptive alerts at participating sites. Phase 3 applied the codebook to classify alerts. Inter-rater reliability (IRR) for the codebook and descriptive statistics for alert design contents were reported.</p><p><strong>Results: </strong>We classified alert content on design elements such as the rationale for the alert's appearance, the hazard of ignoring it, directive versus informational content, administrative purpose, and whether it aligned with one of the Institute of Medicine's (IOM) domains of healthcare quality. Most design elements achieved an IRR above 0.7, with the exceptions for identifying directive content outside of an alert (IRR 0.58) and whether an alert was for administrative purposes only (IRR 0.36). IRR was poor for all IOM domains except equity. Institutions varied widely in the number of unique alerts and their designs. 78% of alerts stated their purpose, over half were directive, and 13% were informational. Only 2%-20% of alerts explained the consequences of inaction.</p><p><strong>Discussion: </strong>This study raises important questions about the optimal balance of alert functions and desirable features of alert representation.</p><p><strong>Conclusion: </strong>Our study provides the first multi-center analysis of EHR alert design elements in pediatric care settings, revealing substantial variation in content and design. These findings underline the need for future research to experimentally explore EHR alert design best practices to improve efficiency and effectiveness.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"682-688"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191202","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":"Using dataflow diagrams to support research informed consent data management communications: participant perspectives.","authors":"Brian J McInnis, Ramona Pindus, Daniah H Kareem, Julie Cakici, Daniela G Vital, Eric Hekler, Camille Nebeker","doi":"10.1093/jamia/ocaf004","DOIUrl":"10.1093/jamia/ocaf004","url":null,"abstract":"<p><strong>Objectives: </strong>Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.</p><p><strong>Materials and methods: </strong>We conducted eight semi-structured focus groups to explore whether dataflow diagrams (DFD) can complement informed consent and improve participants' understanding of data management and associated risks (N = 34 participants).</p><p><strong>Results: </strong>Our analysis found that DFDs could supplement text-based information about data management and sharing practices, such as by helping raise new questions that prompt conversation between prospective participants and members of a research team. Participants in the study emphasized the need for clear, simple, and accessible diagrams that are participant centered. Third-party access to data and sharing of sensitive health data were identified as high-risk areas requiring thorough explanation. Participants generally agreed that the design process should be led by the research team, but it should incorporate many diverse perspectives to ensure the diagram was meaningful to potential participants who are likely unfamiliar with data management. Nearly all participants rejected the idea that artificial intelligence could identify risks during the design process, but most were comfortable with it being used as a tool to format and simplify the diagram. In short, DFDs may complement standard text-based informed consent documents, but they are not a replacement.</p><p><strong>Discussion: </strong>Prospective research participants value diverse ways of learning about study risks and benefits. Our study highlights the value of incorporating information visualizations, such as DFDs, into the informed consent procedures to participate in research.</p><p><strong>Conclusion: </strong>Future research should explore other ways of visualizing consent information in ways that help people to overcome digital and data literacy barriers to participating in research. However, creating a DFD requires significant time and effort from research teams. To alleviate these costs, research sponsors can support the creation of shared infrastructure, communities of practice, and incentivize researchers to develop better consent procedures.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"712-723"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191205","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":"Building an allergy reconciliation module to eliminate allergy discrepancies in electronic health records.","authors":"Suzanne V Blackley, Ying-Chih Lo, Sheril Varghese, Frank Y Chang, Oliver D James, Diane L Seger, Kimberly G Blumenthal, Foster R Goss, Li Zhou","doi":"10.1093/jamia/ocaf022","DOIUrl":"10.1093/jamia/ocaf022","url":null,"abstract":"<p><strong>Objective: </strong>Accurate, complete allergy histories are critical for decision-making and medication prescription. However, allergy information is often spread across the electronic health record (EHR); thus, allergy lists are often inaccurate or incomplete. Discrepant allergy information can lead to suboptimal or unsafe clinical care and contribute to alert fatigue. We developed an allergy reconciliation module within Mass General Brigham (MGB)'s EHR to support accurate and intuitive reconciliation of discrepancies in the allergy list, thereby enhancing patient safety.</p><p><strong>Materials and methods: </strong>We combined data-driven methods and knowledge from domain experts to develop 5 mechanisms to compare allergy information across the EHR and designed a user interface to display discrepancies and suggested reconciliation actions, with links to relevant data sources. Qualitative and quantitative analyses were conducted to assess the module's performance and measure user acceptance.</p><p><strong>Results: </strong>We implemented and tested the proposed allergy reconciliation mechanisms and module. A comprehensive integration workflow was developed for the module, which was piloted among 111 primary care physicians at MGB. F1 scores of the reconciliation mechanisms range from 0.86 to 1.0. Qualitative analysis showed majority positive feedback from pilot users.</p><p><strong>Discussion: </strong>Our allergy reconciliation module achieved high performance, and physicians who used it largely accepted its recommendations. However, 56% of the pilot group ultimately did not use the module. User engagement and education are likely needed to increase adoption.</p><p><strong>Conclusion: </strong>We built a module to automatically identify discrepancies within patients' allergy records and remind providers to reconcile and update the allergy list. Its high accuracy shows promise for enhancing patient safety and utility of drug allergy alerts.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"648-655"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450818","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":"Communication-efficient federated learning of temporal effects on opioid use disorder with data from distributed research networks.","authors":"C Jason Liang, Chongliang Luo, Henry R Kranzler, Jiang Bian, Yong Chen","doi":"10.1093/jamia/ocae313","DOIUrl":"10.1093/jamia/ocae313","url":null,"abstract":"<p><strong>Objective: </strong>To develop a distributed algorithm to fit multi-center Cox regression models with time-varying coefficients to facilitate privacy-preserving data integration across multiple health systems.</p><p><strong>Materials and methods: </strong>The Cox model with time-varying coefficients relaxes the proportional hazards assumption of the usual Cox model and is particularly useful to model time-to-event outcomes. We proposed a One-shot Distributed Algorithm to fit multi-center Cox regression models with Time varying coefficients (ODACT). This algorithm constructed a surrogate likelihood function to approximate the Cox partial likelihood function, using patient-level data from a lead site and aggregated data from other sites. The performance of ODACT was demonstrated by simulation and a real-world study of opioid use disorder (OUD) using decentralized data from a large clinical research network across 5 sites with 69 163 subjects.</p><p><strong>Results: </strong>The ODACT method precisely estimated the time-varying effects over time. In the simulation study, ODACT always achieved estimation close to that of the pooled analysis, while the meta-estimator showed considerable amount of bias. In the OUD study, the bias of the estimated hazard ratios by ODACT are smaller than those of the meta-estimator for all 7 risk factors at almost all of the time points from 0 to 2.5 years. The greatest bias of the meta-estimator was for the effects of age ≥65 years, and smoking.</p><p><strong>Conclusion: </strong>ODACT is a privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data which allows the covariates' effects to be time-varying. ODACT provides estimates close to the pooled estimator and substantially outperforms the meta-analysis estimator.</p><p><strong>Discussion: </strong>The proposed ODACT is a privacy-preserving distributed algorithm for fitting Cox models with time-varying coefficients. The limitations of ODACT include that privacy-preserving via aggregate data does rely on relatively large number of data at each individual site, and rigorous quantification of the risk of privacy leaks requires further investigation.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"656-664"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048666","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":"Advancing the application and evaluation of large language models in health and biomedicine.","authors":"Suzanne Bakken","doi":"10.1093/jamia/ocaf043","DOIUrl":"10.1093/jamia/ocaf043","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"32 4","pages":"603-604"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144013574","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":"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}