Journal of the American Medical Informatics Association最新文献

{"title":"Increasing adherence and collecting symptom-specific biometric signals in remote monitoring of heart failure patients: a randomized controlled trial.","authors":"Sukanya Mohapatra, Mirna Issa, Vedrana Ivezic, Rose Doherty, Stephanie Marks, Esther Lan, Shawn Chen, Keith Rozett, Lauren Cullen, Wren Reynolds, Rose Rocchio, Gregg C Fonarow, Michael K Ong, William F Speier, Corey W Arnold","doi":"10.1093/jamia/ocae221","DOIUrl":"10.1093/jamia/ocae221","url":null,"abstract":"<p><strong>Objectives: </strong>Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms.</p><p><strong>Materials and methods: </strong>We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring.</p><p><strong>Results: </strong>We recruited 111 heart failure patients into the study. We found that the arm including the financial incentive led to significantly higher adherence to activity tracker (95% vs 72.2%, P = .01) and weight (87.5% vs 69.4%, P = .002) monitoring compared to the arm that included the monitoring devices alone. Furthermore, we found a significant correlation between daily steps and daily symptom severity.</p><p><strong>Discussion and conclusion: </strong>Our findings indicate that mobile apps with added engagement features can be useful tools for improving adherence over time and may thus increase the impact of mHealth-driven interventions. Additionally, activity tracker data can provide passive monitoring of disease burden that may be used to predict future events.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"181-192"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037585","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":"Real-world federated learning in radiology: hurdles to overcome and benefits to gain.","authors":"Markus Ralf Bujotzek, Ünal Akünal, Stefan Denner, Peter Neher, Maximilian Zenk, Eric Frodl, Astha Jaiswal, Moon Kim, Nicolai R Krekiehn, Manuel Nickel, Richard Ruppel, Marcus Both, Felix Döllinger, Marcel Opitz, Thorsten Persigehl, Jens Kleesiek, Tobias Penzkofer, Klaus Maier-Hein, Andreas Bucher, Rickmer Braren","doi":"10.1093/jamia/ocae259","DOIUrl":"10.1093/jamia/ocae259","url":null,"abstract":"<p><strong>Objective: </strong>Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles. To bridge this significant knowledge gap, we propose a comprehensive guide for real-world FL in radiology. Minding efforts to implement real-world FL, there is a lack of comprehensive assessments comparing FL to less complex alternatives in challenging real-world settings, which we address through extensive benchmarking.</p><p><strong>Materials and methods: </strong>We developed our own FL infrastructure within the German Radiological Cooperative Network (RACOON) and demonstrated its functionality by training FL models on lung pathology segmentation tasks across six university hospitals. Insights gained while establishing our FL initiative and running the extensive benchmark experiments were compiled and categorized into the guide.</p><p><strong>Results: </strong>The proposed guide outlines essential steps, identified hurdles, and implemented solutions for establishing successful FL initiatives conducting real-world experiments. Our experimental results prove the practical relevance of our guide and show that FL outperforms less complex alternatives in all evaluation scenarios.</p><p><strong>Discussion and conclusion: </strong>Our findings justify the efforts required to translate FL into real-world applications by demonstrating advantageous performance over alternative approaches. Additionally, they emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings. With the proposed guide, we are aiming to aid future FL researchers in circumventing pitfalls and accelerating translation of FL into radiological applications.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"193-205"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512054","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":"Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.","authors":"Jessica Sperling, Whitney Welsh, Erin Haseley, Stella Quenstedt, Perusi B Muhigaba, Adrian Brown, Patti Ephraim, Tariq Shafi, Michael Waitzkin, David Casarett, Benjamin A Goldstein","doi":"10.1093/jamia/ocae255","DOIUrl":"10.1093/jamia/ocae255","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups.</p><p><strong>Materials and methods: </strong>We collected and analyzed qualitative data from focus groups with varied end users, including: dialysis support providers (clinical providers and additional dialysis support providers such as dialysis clinic staff and social workers); patients; patients' caregivers (n = 52).</p><p><strong>Results: </strong>Participants were broadly accepting of ML-based CPMs, but with concerns on data sources, factors included in the model, and accuracy. Use was desired in conjunction with providers' views and explanations. Differences among respondent types were minimal overall but most prevalent in discussions of CPM presentation and model use.</p><p><strong>Discussion and conclusion: </strong>Evidence of acceptability of ML-based CPM usage provides support for ethical use, but numerous specific considerations in acceptability, model construction, and model use for shared clinical decision-making must be considered. There are specific steps that could be taken by data scientists and health systems to engender use that is accepted by end users and facilitates trust, but there are also ongoing barriers or challenges in addressing desires for use. This study contributes to emerging literature on interpretability, mechanisms for sharing complexities, including uncertainty regarding the model results, and implications for decision-making. It examines numerous stakeholder groups including providers, patients, and caregivers to provide specific considerations that can influence health system use and provide a basis for future research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"51-62"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631467","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":"Oncointerpreter.ai enables interactive, personalized summarization of cancer diagnostics data.","authors":"Arihant Tripathi, Brett Ecker, Patrick Boland, Saum Ghodoussipour, Gregory R Riedlinger, Subhajyoti De","doi":"10.1093/jamia/ocae284","DOIUrl":"10.1093/jamia/ocae284","url":null,"abstract":"<p><strong>Objectives: </strong>Cancer diagnosis comes as a shock to many patients, and many of them feel unprepared to handle the complexity of the life-changing event, understand technicalities of the diagnostic reports, and fully engage with the clinical team regarding the personalized clinical decision-making.</p><p><strong>Materials and methods: </strong>We develop Oncointerpreter.ai an interactive resource to offer personalized summarization of clinical cancer genomic and pathological data, and frame questions or address queries about therapeutic opportunities in near-real time via a graphical interface. It is built on the Mistral-7B and Llama-2 7B large language models trained on a local database trained using a large, curated corpus.</p><p><strong>Results: </strong>We showcase its utility with case studies, where Oncointerpreter.ai extracted key clinical and molecular attributes from deidentified pathology and clinical genomics reports, summarized their contextual significance and answered queries on pertinent treatment options. Oncointerpreter also provided personalized summary of currently active clinical trials that match the patients' disease status, their selection criteria, and geographic locations. Benchmarking and comparative assessment indicated that the model responses were generally consistent, and hallucination, ie, factually incorrect or nonsensical response was rare; treatment- and outcome related queries led to context-aware responses, and response time correlated with verbosity.</p><p><strong>Discussion: </strong>The choice of model and domain-specific training also affected the response quality.</p><p><strong>Conclusion: </strong>Oncointerpreter.ai can aid the existing clinical care with interactive, individualized summarization of diagnostics data to promote informed dialogs with the patients with new cancer diagnoses.</p><p><strong>Availability: </strong>https://github.com/Siris2314/Oncointerpreter.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"129-138"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631472","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":"Opportunities for the informatics community to advance learning health systems.","authors":"Melissa A Gunderson, Peter Embí, Charles P Friedman, Genevieve B Melton","doi":"10.1093/jamia/ocae281","DOIUrl":"10.1093/jamia/ocae281","url":null,"abstract":"<p><strong>Objectives: </strong>There is rapidly growing interest in learning health systems (LHSs) nationally and globally. While the critical role of informatics is recognized, the informatics community has been relatively slow to formalize LHS as a priority area.</p><p><strong>Materials and methods: </strong>We compiled results from a short survey of LHS leaders and American Medical Informatics Association (AMIA) members, discussion from an LHS reception at the AMIA annual meeting, and a follow-up survey to inform priorities at the intersection of LHS and informatics.</p><p><strong>Results: </strong>We present opportunities between informatics and LHS which fell into themes of: Understanding and Context, Shared Resources, Collaboration, Education, Data, Evaluation, and Patient Centeredness. Immediate LHS informatics priorities identified include establishing informatics LHS forum(s), case reports of LHS informatics successes and failures, LHS informatics education resources, and improved understanding of LHS principles in informatics.</p><p><strong>Conclusion: </strong>Increased informatics and LHS alignment is critical for advancing this transformative national priority.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"253-257"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696018","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":"Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.","authors":"Xiomara T Gonzalez, Karen Steger-May, Joanna Abraham","doi":"10.1093/jamia/ocae257","DOIUrl":"10.1093/jamia/ocae257","url":null,"abstract":"<p><strong>Objectives: </strong>Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care.</p><p><strong>Materials and methods: </strong>A sequential explanatory study was conducted. Stage 1 collected public opinions through a survey. Stage 2 ascertained surgical patients' experiences and attitudes via focus groups and interviews.</p><p><strong>Results: </strong>For Stage 1, a total of 281 respondents' (140 males [49.8%]) data were considered. Among participants without ML awareness, males were almost three times more likely than females to report more acceptance (OR = 2.97; 95% CI, 1.36-6.49) and embrace (OR = 2.74; 95% CI, 1.23-6.09) of ML-CDSS use by perioperative teams. Males were almost twice as likely as females to report more acceptance across all perioperative phases with ORs ranging from 1.71 to 2.07. In Stage 2, insights from 10 surgical patients revealed unanimous agreement that ML-CDSS should primarily serve a supportive function. The pre- and post-operative phases were identified explicitly as forums where ML-CDSS can enhance care delivery. Patients requested for education on ML-CDSS's role in their care to be disseminated by surgeons across multiple platforms.</p><p><strong>Discussion and conclusion: </strong>The general public and surgical patients are receptive to ML-CDSS use throughout their perioperative care provided its role is auxiliary to perioperative teams. However, the integration of ML-CDSS into perioperative workflows presents unique challenges for healthcare settings. Insights from this study can inform strategies to support large-scale implementation and adoption of ML-CDSS by patients in all perioperative phases. Key strategies to promote the feasibility and acceptability of ML-CDSS include clinician-led discussions about ML-CDSS's role in perioperative care, established metrics to evaluate the clinical utility of ML-CDSS, and patient education.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"150-162"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479226","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":"Reducing diagnostic delays in acute hepatic porphyria using health records data and machine learning.","authors":"Balu Bhasuran, Katharina Schmolly, Yuvraaj Kapoor, Nanditha Lakshmi Jayakumar, Raymond Doan, Jigar Amin, Stephen Meninger, Nathan Cheng, Robert Deering, Karl Anderson, Simon W Beaven, Bruce Wang, Vivek A Rudrapatna","doi":"10.1093/jamia/ocae141","DOIUrl":"10.1093/jamia/ocae141","url":null,"abstract":"<p><strong>Background: </strong>Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recognition of rare diseases like AHP. However, prediction models can be difficult to train given the limited case numbers, unstructured EHR data, and selection biases intrinsic to healthcare delivery. We sought to train and characterize models for identifying patients with AHP.</p><p><strong>Methods: </strong>This diagnostic study used structured and notes-based EHR data from 2 centers at the University of California, UCSF (2012-2022) and UCLA (2019-2022). The data were split into 2 cohorts (referral and diagnosis) and used to develop models that predict (1) who will be referred for testing of acute porphyria, among those who presented with abdominal pain (a cardinal symptom of AHP), and (2) who will test positive, among those referred. The referral cohort consisted of 747 patients referred for testing and 99 849 contemporaneous patients who were not. The diagnosis cohort consisted of 72 confirmed AHP cases and 347 patients who tested negative. The case cohort was 81% female and 6-75 years old at the time of diagnosis. Candidate models used a range of architectures. Feature selection was semi-automated and incorporated publicly available data from knowledge graphs. Our primary outcome was the F-score on an outcome-stratified test set.</p><p><strong>Results: </strong>The best center-specific referral models achieved an F-score of 86%-91%. The best diagnosis model achieved an F-score of 92%. To further test our model, we contacted 372 current patients who lack an AHP diagnosis but were predicted by our models as potentially having it (≥10% probability of referral, ≥50% of testing positive). However, we were only able to recruit 10 of these patients for biochemical testing, all of whom were negative. Nonetheless, post hoc evaluations suggested that these models could identify 71% of cases earlier than their diagnosis date, saving 1.2 years.</p><p><strong>Conclusions: </strong>ML can reduce diagnostic delays in AHP and other rare diseases. Robust recruitment strategies and multicenter coordination will be needed to validate these models before they can be deployed.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"63-70"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472084","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":"Barriers to obtaining and using interoperable information among non-federal acute care hospitals.","authors":"Jordan Everson, Chelsea Richwine","doi":"10.1093/jamia/ocae263","DOIUrl":"10.1093/jamia/ocae263","url":null,"abstract":"<p><strong>Objective: </strong>To understand barriers to obtaining and using interoperable information at US hospitals.</p><p><strong>Materials and methods: </strong>Using 2023 nationally representative survey data on US hospitals (N = 2420), we examined major and minor barriers to exchanging information with other organizations, and how barriers vary by hospital characteristics and methods used to obtain information. Using a series of regression models, we examined how hospital experiences with barriers relate to routine use of information at responding hospitals.</p><p><strong>Results: </strong>In 2023, most hospitals experienced at least one minor (81%) or major (62%) barrier to exchange, with the most common major barriers relating to different vendors and exchange partners' capabilities. Higher-resourced hospitals and those often using network-based exchange tended to experience more minor barriers whereas lower-resourced hospitals and those often using mail/fax or direct access to outside electronic health records experienced more major barriers. In multivariate regression, hospitals indicating \"Patient matching\" and \"Costs to exchange\" were a major or minor barrier had the strongest independent negative association with the likelihood of reporting providers at their hospital frequently use information from outside organizations.</p><p><strong>Discussion: </strong>Despite progress in interoperable exchange, various barriers remain. The prevalence of barriers varied by hospital type and methods used, with barriers more often preventing exchange for lower-resourced hospitals and those using outdated exchange methods.</p><p><strong>Conclusion: </strong>While several technical and policy efforts are underway to address prevalent barriers, it will be important to monitor whether efforts are successful in ensuring information from outside organizations can be seamlessly exchanged and used to inform patient care.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"20-27"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479221","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":"Extracting social support and social isolation information from clinical psychiatry notes: comparing a rule-based natural language processing system and a large language model.","authors":"Braja Gopal Patra, Lauren A Lepow, Praneet Kasi Reddy Jagadeesh Kumar, Veer Vekaria, Mohit Manoj Sharma, Prakash Adekkanattu, Brian Fennessy, Gavin Hynes, Isotta Landi, Jorge A Sanchez-Ruiz, Euijung Ryu, Joanna M Biernacka, Girish N Nadkarni, Ardesheer Talati, Myrna Weissman, Mark Olfson, J John Mann, Yiye Zhang, Alexander W Charney, Jyotishman Pathak","doi":"10.1093/jamia/ocae260","DOIUrl":"10.1093/jamia/ocae260","url":null,"abstract":"<p><strong>Objectives: </strong>Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information.</p><p><strong>Materials and methods: </strong>Psychiatric encounter notes from Mount Sinai Health System (MSHS, n = 300) and Weill Cornell Medicine (WCM, n = 225) were annotated to create a gold-standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (eg, social network, instrumental support, and loneliness).</p><p><strong>Results: </strong>For extracting SS/SI, the RBS obtained higher macroaveraged F1-scores than the LLM at both MSHS (0.89 versus 0.65) and WCM (0.85 versus 0.82). For extracting the subcategories, the RBS also outperformed the LLM at both MSHS (0.90 versus 0.62) and WCM (0.82 versus 0.81).</p><p><strong>Discussion and conclusion: </strong>Unexpectedly, the RBS outperformed the LLMs across all metrics. An intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS was designed and refined to follow the same specific rules as the gold-standard annotations. Conversely, the LLM was more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages, although additional replication studies are warranted.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"218-226"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479224","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":"The number of patient scheduled hours resulting in a 40-hour work week by physician specialty and setting: a cross-sectional study using electronic health record event log data.","authors":"Christine A Sinsky, Lisa Rotenstein, A Jay Holmgren, Nate C Apathy","doi":"10.1093/jamia/ocae266","DOIUrl":"10.1093/jamia/ocae266","url":null,"abstract":"<p><strong>Objective: </strong>To quantify how many patient scheduled hours would result in a 40-h work week (PSH40) for ambulatory physicians and to determine how PSH40 varies by specialty and practice type.</p><p><strong>Methods: </strong>We calculated PSH40 for 186 188 ambulatory physicians across 395 organizations from November 2021 through April 2022 stratified by specialty.</p><p><strong>Results: </strong>Median PSH40 for the sample was 33.2 h (IQR: 28.7-36.5). PSH40 was lowest in infectious disease (26.2, IQR: 21.6-31.1), geriatrics (27.2, IQR: 21.5-32.0) and hematology (28.6, IQR: 23.6-32.6) and highest in plastic surgery (35.7, IQR: 32.8-37.7), pain medicine (35.8, IQR: 32.6-37.9) and sports medicine (36.0, IQR: 33.3-38.1).</p><p><strong>Discussion: </strong>Health system leaders and physicians will benefit from data driven and transparent discussions about work hour expectations. The PSH40 measure can also be used to quantify the impact of variations in the clinical care environment on the in-person ambulatory patient care time available to physicians.</p><p><strong>Conclusions: </strong>PSH40 is a novel measure that can be generated from vendor-derived metrics and used by operational leaders to inform work expectations. It can also support research into the impact of changes in the care environment on physicians' workload and capacity.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"235-240"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479237","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}