JAMIA Open最新文献

{"title":"Navigating the new NIH Data Management Sharing plan: the NIH HEAL Initiative Data2Action DMPTool template.","authors":"Kaitlyn N Shreeve, Robert W Hurley, Meredith C B Adams","doi":"10.1093/jamiaopen/ooaf040","DOIUrl":"10.1093/jamiaopen/ooaf040","url":null,"abstract":"<p><strong>Objectives: </strong>This work aims to provide a specialized template for integrating diverse study data types with varying sharing restrictions specific to NIH HEAL Initiative chronic pain and substance use research.</p><p><strong>Materials and methods: </strong>We adapted the NIH-GEN DMSP template from DMPTool to incorporate comprehensive guidance from the NIH HEAL Initiative Public Access and Data Sharing Policy.</p><p><strong>Results: </strong>Our template provides structured guidance on implementing common data elements, standardizing metadata, protecting sensitive information, and selecting appropriate HEAL-compliant data repositories.</p><p><strong>Discussion: </strong>Adapting to evolving data policies presents challenges for researchers, particularly when funding institutions update requirements and existing guidance primarily addresses clinical trials and basic science research rather than complex, multisource data integration.</p><p><strong>Conclusion: </strong>This streamlined planning template enables researchers to efficiently comply with NIH data sharing standards while ensuring data follows Findable, Accessible, Interoperable, and Reusable (FAIR) principles . By simplifying the process, we facilitate improved data management practices that enhance collaboration across diverse HEAL research projects.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf040"},"PeriodicalIF":2.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An approach to evaluating the impact of virtual specialty care: the Veterans Health Administration's clinical resource hub as case study.","authors":"Rebecca L Tisdale, Kedron Burnett, Matthew Rogers, Karin Nelson, Leonie Heyworth, Donna M Zulman","doi":"10.1093/jamiaopen/ooaf038","DOIUrl":"10.1093/jamiaopen/ooaf038","url":null,"abstract":"<p><strong>Objectives: </strong>Telemedicine for specialty medical care is evolving from a COVID-19 pandemic-era requirement to an option for patients and clinicians alike, requiring evidence to guide optimal use of virtual specialty care. Heterogeneity across medical specialties complicates this evidence generation. To address this gap in the literature, we present an approach to evaluation of telehealth across specialties with the potential to generate findings generalizable across specialties and health systems.</p><p><strong>Materials and methods: </strong>We describe an approach to evaluation of virtual specialty care that balances widely generalizable metrics, such as patient and clinician satisfaction and avoided travel or cost, and those that are specialty-specific. We use the Veterans Health Administration (VA)'s Clinical Resource Hub program to illustrate potential applications of this approach.</p><p><strong>Results: </strong>Clinical Resource Hub clinics leverage a hub-and-spoke model to deliver virtual care across many specialties, compensating for staffing shortages and expanding access to more specialized services not available at every VA site. Use cases for these clinics span the spectrum of short-term, episodic care to long-term substitution for a usual source of specialty care and offer opportunities to apply a range of evaluation metrics that generalize across telehealth use cases.</p><p><strong>Discussion: </strong>Clinical Resource Hub clinics provide a variety of examples for this approach, demonstrating a path forward for virtual specialty care evaluation.</p><p><strong>Conclusion: </strong>As the Clinical Resource Hub case illustrates, combining universal and specialty- or use case-specific metrics has the potential to build the evidence base for virtual specialty care.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf038"},"PeriodicalIF":2.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A phenotyping algorithm for classification of single ventricle physiology using electronic health records.","authors":"Hang Xu, Pierangelo Renella, Ramin Badiyan, Ziad R Hindosh, Francisco X Elisarraras, Bing Zhu, Gary M Satou, Majid Husain, J Paul Finn, William Hsu, Kim-Lien Nguyen","doi":"10.1093/jamiaopen/ooaf035","DOIUrl":"10.1093/jamiaopen/ooaf035","url":null,"abstract":"<p><strong>Objectives: </strong>Congenital heart disease (CHD) patients with single ventricle physiology (SVP) have heterogeneous characteristics that challenge cohort classification. We aim to develop a phenotyping algorithm that accurately identifies SVP patients using electronic health record (EHR) data.</p><p><strong>Materials and methods: </strong>We used ICD-9 and ICD-10 codes for initial classification, then enhanced the algorithm with domain expertise, imaging reports, and progress notes. The algorithm was developed using a cohort of 1020 patients who underwent magnetic resonance imaging scans and tested in a separate cohort of 2500 CHD patients with adjudication. Validation was performed in a holdout group of 22 500 CHD patients. We evaluated performance using accuracy, sensitivity, precision, and F1 score, and compared it to a published algorithm for SVP using the same dataset.</p><p><strong>Results: </strong>In the 2500-testing cohort, our algorithm based on specialty-defined features and International Classification of Diseases (ICD) codes achieved 99.24% accuracy, 94.12% precision, 85.11% sensitivity, and 89.39% F1 score. In contrast, the published method achieved 95.20% accuracy, 43.23% precision, 88.30% sensitivity, and 58.04% F1 score. In the 22 500-validation cohort, our algorithm achieved 93.82% precision, while the published method achieved 43.00%.</p><p><strong>Discussion and conclusions: </strong>Our automated phenotype algorithm, combined with physician adjudication, outperforms a published method for SVP classification. It effectively identifies false positives by cross-referencing clinical notes and detects missed SVP cases that were due to absent or erroneous ICD codes. Our integrated phenotyping algorithm showed excellent performance and has the potential to improve research and clinical care of SVP patients through the automated development of an electronic cohort for prognostication, monitoring, and management.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf035"},"PeriodicalIF":2.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical document corpora-real ones, translated and synthetic substitutes, and assorted domain proxies: a survey of diversity in corpus design, with focus on German text data.","authors":"Udo Hahn","doi":"10.1093/jamiaopen/ooaf024","DOIUrl":"10.1093/jamiaopen/ooaf024","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We survey clinical document corpora, with a focus on German textual data. Due to rigid data privacy legislation in Germany, these resources, with only few exceptions, are stored in protected clinical data spaces and locked against clinic-external researchers. This situation stands in stark contrast with established workflows in the field of natural language processing, where easy accessibility and reuse of (textual) data collections are common practice. Hence, alternative corpus designs have been examined to escape from data poverty. Besides machine translation of English clinical datasets and the generation of synthetic corpora with fictitious clinical contents, several types of domain proxies have come up as substitutes for real clinical documents. Common instances of close proxies are medical journal publications, therapy guidelines, drug labels, etc., more distant proxies include medical contents from social media channels or online encyclopedic medical articles.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We follow the PRISM (Preferred Reporting Items for Systematic reviews and Meta-analyses) guidelines for surveying the field of German-language clinical/medical corpora. Four bibliographic databases were searched: PubMed, ACL Anthology, Google Scholar, and the author's personal literature database.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;After PRISM-conformant identification of 362 hits from the 4 bibliographic systems, the screening process yielded 78 relevant documents for inclusion in this review. They contained overall 92 different published versions of corpora, from which 71 were truly unique in terms of their underlying document sets. Out of these, the majority were clinical corpora-46 real ones from which 32 were unique, 5 translated ones (3 unique), and 6 synthetic ones (3 unique). As to domain proxies, we identified 18 close ones (16 unique) and 17 distant ones (all of them unique).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;There is a clear divide between the large number of non-accessible real clinical German-language corpora and their publicly accessible substitutes: translated or synthetic datasets, close or more distant proxies. So, at first sight, the data bottleneck seems broken. Intuitively, yet, differences in genre-specific writing style, lexical or terminological diction, and required medical background expertise in this typological space are also obvious. This raises the question how valid alternative corpus designs really are. A systematic, empirically grounded yardstick for comparing real clinical corpora with those suggested substitutes and proxies is missing until now.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The extreme sparsity of real clinical corpora in almost all non-Anglo-American countries worldwide, Germany in particular, has triggered an active search for alternative, publicly accessible data resources laid out in this survey. However, the utility of these substitutes compared with real clinical corpora and the","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf024"},"PeriodicalIF":2.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12077144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports.","authors":"Jordan Tschida, Mayanka Chandrashekar, Alina Peluso, Zachary Fox, Patrycja Krawczuk, Dakota Murdock, Xiao-Cheng Wu, John Gounley, Heidi A Hanson","doi":"10.1093/jamiaopen/ooaf033","DOIUrl":"10.1093/jamiaopen/ooaf033","url":null,"abstract":"<p><strong>Objectives: </strong>This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status.</p><p><strong>Materials and methods: </strong>We utilized 594 875 electronic pathology reports from 178 121 tumors diagnosed in Kentucky, Louisiana, New Jersey, New Mexico, Seattle, and Utah to train 2 deep-learning algorithms to classify breast cancer patients using their biomarkers test results. We used balanced error rate (BER), demographic parity (DP), equalized odds (EOD), and equal opportunity (EOP) to assess bias.</p><p><strong>Results: </strong>We found differences in predictive accuracy between registries, with the highest accuracy in the registry that contributed the most data (Seattle Registry, BER ratios for all registries >1.25). BER showed no significant algorithmic bias in extracting biomarkers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2) for race, Hispanic ethnicity, age at diagnosis, or socioeconomic subgroups (BER ratio <1.25). DP, EOD, and EOP all showed insignificant results.</p><p><strong>Discussion: </strong>We observed significant differences in BER by registry, but no significant bias using the DP, EOD, and EOP metrics for socio-demographic or racial categories. This highlights the importance of employing a diverse set of metrics for a comprehensive evaluation of model fairness.</p><p><strong>Conclusion: </strong>A thorough evaluation of algorithmic biases that may affect equality in clinical care is a critical step before deploying algorithms in the real world. We found little evidence of algorithmic bias in our biomarker classification tool. Artificial intelligence tools to expedite information extraction from clinical records could accelerate clinical trial matching and improve care.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf033"},"PeriodicalIF":2.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a score for identifying hospital stays that trigger a pharmacist intervention: integration into a clinical decision support system.","authors":"Laurine Robert, Nathalie Vidoni, Erwin Gérard, Emmanuel Chazard, Pascal Odou, Chloé Rousselière, Bertrand Décaudin","doi":"10.1093/jamiaopen/ooaf030","DOIUrl":"10.1093/jamiaopen/ooaf030","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of the study was to determine, after medication review, the patient risk score threshold that would distinguish between stays with prescriptions triggering pharmacist intervention (PI) and stays with prescriptions not triggering PI.</p><p><strong>Materials and methods: </strong>The study was retrospective and observational, conducted in the clinical pharmacy team. The patient risk score was adapted from a Canadian score and was integrated in the clinical decision support system (CDSS). For each hospital stay, the score was calculated at the beginning of hospitalization and we retrospectively showed if a medication review and a PI were conducted. Then, the optimal patient risk score threshold was determined to help pharmacist in optimizing medication review.</p><p><strong>Results: </strong>During the study, 973 (56.7%) medication reviews were performed and 248 (25.5%) led to a PI. After analyzing sensitivity, specificity, and positive predictive value of different thresholds, the threshold of 4 was deemed discriminating to identify hospital stays likely to lead to a PI following a medication review. At this threshold, 600 hospital stays would have been detected (33.3% of the latter led to a PI), and 5.0% of stays with a medication review would not have been detected even though they were hospital stays that had triggered a PI.</p><p><strong>Discussion and conclusion: </strong>Integration of a patient risk score in a CDSS can help clinical pharmacist to target hospital stays likely to trigger a PI. However, an optimal threshold is difficult to determine. Constructing and using a score in practice should be organized with the local clinical pharmacy team, in order to understand the tool's limitations and maximize its use in detecting at-risk drug prescriptions.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf030"},"PeriodicalIF":2.5,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12051848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the acceptability of using patient portals to recruit pregnant women and new mothers for maternal-child health research.","authors":"Sean N Halpin, Rebecca Wright, Angela Gwaltney, Annabelle Frantz, Holly Peay, Emily Olsson, Melissa Raspa, Lisa Gehtland, Sara M Andrews","doi":"10.1093/jamiaopen/ooaf027","DOIUrl":"10.1093/jamiaopen/ooaf027","url":null,"abstract":"<p><strong>Objective: </strong>Electronic patient portals (PP) allow for targeted and efficient research recruitment. We assessed pre- and postnatal women's recruitment methods preferences, focusing on PP.</p><p><strong>Materials and methods: </strong>We conducted 4 in-person focus groups with new and expecting mothers. Participants reported demographics, health status, and comfort with technology including PP. We used descriptive statistics to characterize quantitative data and a quasi-deductive approach to analyze qualitative data.</p><p><strong>Results: </strong>Participants (<i>n</i> = 32) were an average age of 31.9 years, mostly White (65.6%), married (90.6%), and had a 4-year degree or higher (71.9%). Although they preferred PP for research recruitment over other methods (eg, in-person, physical mail), participants suggested potential barriers, including high message frequency, messages feeling like spam, and concerns about confidentiality. Participants suggested solutions, including enhancing autonomy through opt-in methods; integrating their healthcare provider's feedback; sending personal and relevant messages; and assuring their PP data are confidential.</p><p><strong>Discussion: </strong>PPs are a promising recruitment method for pre- and postnatal women including for maternal-child health studies. To ensure engagement with the method, researchers must respond to known patient concerns and incorporate their feedback into future efforts.</p><p><strong>Conclusion: </strong>Although PP were generally viewed as an acceptable recruitment method, researchers should be mindful of barriers that may limit its reach and effectiveness.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf027"},"PeriodicalIF":2.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12047077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating public preferences to overcome racial disparities in research: findings from a US survey on enhancing trust in research data-sharing practices.","authors":"Stephanie Niño de Rivera, Yihong Zhao, Shalom Omollo, Sarah Eslami, Natalie Benda, Yashika Sharma, Meghan Reading Turchioe, Marianne Sharko, Lydia S Dugdale, Ruth Masterson Creber","doi":"10.1093/jamiaopen/ooaf031","DOIUrl":"10.1093/jamiaopen/ooaf031","url":null,"abstract":"<p><strong>Objectives: </strong>Data-sharing policies are rapidly evolving toward increased data sharing. However, participants' perspectives are not well understood and could have an adverse impact on participation in research. We evaluated participants' preferences for sharing specific types of data with specific groups, and strategies to enhance trust in data-sharing practices.</p><p><strong>Materials and methods: </strong>In March 2023, we conducted a nationally representative online survey with 610 US adults and used logistic regression models to assess sociodemographic differences in their willingness to share different types of data.</p><p><strong>Results: </strong>Our findings highlight notable racial disparities in willingness to share research data with external entities, especially health policy and public health organizations. Black participants were significantly less likely to share most health data with public health organizations, including mental health (odds ratio [OR]: 0.543, 95% CI, 0.323-0.895) and sexual health/fertility information (OR: 0.404, 95% CI, 0.228-0.691), compared to White participants. Moreover, 63% of participants expressed that their trust in researchers would improve if given control over the data recipients.</p><p><strong>Discussion: </strong>Participants exhibit reluctance to share specific types of personal research data, emphasizing strong preferences regarding external data access. This highlights the need for a critical reassessment of current data-sharing policies to align with participant concerns.</p><p><strong>Conclusion: </strong>It is imperative for data-sharing policies to integrate diverse patient viewpoints to mitigate risk of distrust and a potential unintended consequence of lower participation among racial and ethnic minority participants in research.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf031"},"PeriodicalIF":2.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12047078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning.","authors":"Junghwan Lee, Simin Ma, Nicoleta Serban, Shihao Yang","doi":"10.1093/jamiaopen/ooaf032","DOIUrl":"10.1093/jamiaopen/ooaf032","url":null,"abstract":"<p><strong>Objectives: </strong>Observational data have been actively used to estimate treatment effect, driven by the growing availability of electronic health records (EHRs). However, EHRs typically consist of longitudinal records, often introducing time-dependent confounding that hinder the unbiased estimation of treatment effect. Inverse probability of treatment weighting (IPTW) is a widely used propensity score method since it provides unbiased treatment effect estimation and its derivation is straightforward. In this study, we aim to utilize IPTW to estimate treatment effect in the presence of time-dependent confounding using claims records.</p><p><strong>Materials and methods: </strong>Previous studies have utilized propensity score methods with features derived from claims records through feature processing, which generally requires domain knowledge and additional resources to extract information to accurately estimate propensity scores. Deep learning, particularly using deep sequence models such as recurrent neural networks and Transformer, has demonstrated good performance in modeling EHRs for various downstream tasks. We propose that these deep sequence models can provide accurate IPTW estimation of treatment effect by directly estimating the propensity scores from claims records without the need for feature processing.</p><p><strong>Results: </strong>Comprehensive evaluations on synthetic and semi-synthetic datasets demonstrate that IPTW treatment effect estimation using deep sequence models consistently outperforms baseline approaches, including logistic regression and multilayer perceptrons, combined with feature processing.</p><p><strong>Discussion: </strong>Our findings demonstrate that deep sequence models consistently outperform traditional approaches in estimating treatment effects, particularly under time-dependent confounding. Moreover, Transformer-based models offer interpretability by assigning higher attention weights to relevant confounders, even when prior domain knowledge is limited.</p><p><strong>Conclusion: </strong>Deep sequence models enable accurate treatment effect estimation through IPTW without the need for feature processing.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 2","pages":"ooaf032"},"PeriodicalIF":2.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"Everything is electronic health record-driven\": the role of the electronic health record in the emergency department diagnostic process.","authors":"Tyler G James, Courtney W Mangus, Sarah J Parker, P Paul Chandanabhumma, C M Cassady, Fernanda Bellolio, Kalyan Pasupathy, Milisa Manojlovich, Hardeep Singh, Prashant Mahajan","doi":"10.1093/jamiaopen/ooaf029","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooaf029","url":null,"abstract":"<p><strong>Objectives: </strong>There is limited knowledge on how providers and patients in the emergency department (ED) use electronic health records (EHRs) to facilitate the diagnostic process. While EHRs can support diagnostic decision-making, EHR features that are not user-centered may increase the likelihood of diagnostic error. We aimed to identify how EHRs facilitate or impede the diagnostic process in the ED and to identify opportunities to reduce diagnostic errors and improve care quality.</p><p><strong>Materials and methods: </strong>We conducted semistructured interviews with 10 physicians, 15 nurses, and 8 patients across 4 EDs. Data were analyzed using a hybrid thematic analysis approach, which blends deductive (ie, using multiple conceptual frameworks) and inductive coding strategies. A team of 4 coders performed coding.</p><p><strong>Results: </strong>We identified 4 themes, 3 at the care team level and 1 at the patient level. At the care team level, the benefits of the EHR in the diagnostic process included (1) customizing features to facilitate diagnostic workup and (2) aiding in communication. However, (3) EHR-driven protocols were found to potentially burden the care process and reliance on asynchronous communication could impede team dynamics. At the patient-level, we found that (4) patient portals facilitated meaningful patient engagement through timely delivery of results.</p><p><strong>Discussion: </strong>While EHRs can improve the diagnostic process, they can also impair communication and increase workload. Electronic health record design should leverage provider-created tools to improve usability and enhance diagnostic safety.</p><p><strong>Conclusions: </strong>Our findings have important implications for health information technology design and policy. Further work should assess optimal ways to release patient results via the EHR portal.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 2","pages":"ooaf029"},"PeriodicalIF":2.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12015938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}