Applied Clinical Informatics最新文献

{"title":"Preambient Artificial Intelligence Clinical Documentation Time for Pediatric Residents: A 3-Year Baseline Observational Study.","authors":"Yahya Almodallal, Natalie Ramsy, Lindsey A Knake, Anna Schmitz","doi":"10.1055/a-2856-4821","DOIUrl":"10.1055/a-2856-4821","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of this study is to characterize pediatric resident documentation time using electronic health record (EHR) audit-log-data and to assess interindividual variability in documentation patterns.</p><p><strong>Methods: </strong>We conducted a retrospective, longitudinal study at an academic children's hospital, analyzing the EHR audit-log-data between July 1, 2021 and June 30, 2024. All clinical notes to which a pediatric resident contributed were included. Results are shown as descriptive statistics and pairwise comparisons of log-transformed continuous variables were performed using Welch's analysis of variance and Games-Howell post hoc testing.</p><p><strong>Results: </strong>Over 3 years, 79 residents contributed to the documentation of 156,898 clinical notes for an average of 2.1 hours per day. The mean (95% confidence interval) total resident time spent on one note was 12.1 (12.0-12.1) minutes. First-year residents contributed to 51.6% of all notes. More than half of resident note-editing time occurred outside scheduled shift hours (54.4%), including 56.3% of ambulatory note time and 53.0% of inpatient note time. Across the study period, monthly documentation time showed substantial month-to-month fluctuation but only small overall trends, with adjusted time-per-note declining significantly over time for most graduating classes.</p><p><strong>Conclusion: </strong>This single-center study quantified pediatric resident EHR documentation time and found that time was highest among postgraduate year-1 residents, frequently extended into nights and weekends, and varied widely between individuals. The data provide a baseline to inform residency-level workflow optimization and to evaluate interventions that aim to reduce documentation time while preserving quality and educational value.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"288-295"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786699","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":"Stewardship of Inpatient A1c Testing: An Electronic Nudge to Limit Testing after Red Cell Transfusion.","authors":"Andrew A White, Brent E Wisse","doi":"10.1055/a-2838-8292","DOIUrl":"10.1055/a-2838-8292","url":null,"abstract":"<p><p>Guidelines recommend measuring glycated hemoglobin (HgbA1c) levels on all inpatients with diabetes mellitus, if untested in the prior 3 months. In response, our health system applies default HgbA1c orders in the electronic insulin orders for qualifying patients. However, recent red blood cell transfusions may cause falsely low HgbA1c values, leading to inappropriate management. Clinicians often forget to deselect default HgbA1c orders after transfusion, leading to erroneous results. We evaluated a non-interruptive clinical decision support (CDS) intervention to discourage automatic HgbA1c testing in patients with recent transfusions.A retrospective, observational analysis of the number and percentage of HgbA1c tests performed on inpatients within 7 days of a red blood cell transfusion over a 40-month period.Preintervention, clinicians ordered an average of 827 HgbA1c tests/month on inpatients. Of these, 11.7% (97 tests/month) were on patients who received a red blood cell transfusion within the preceding 7 days. Postintervention, clinicians ordered an average of 832 HgbA1c tests per month on inpatients, of which 5.8% (48 tests/month) were performed within 7 days of red blood cell transfusions.A non-interruptive CDS intervention can significantly decrease the number of HgbA1c tests performed in hospitalized patients who received a red blood cell transfusion, a common cause of erroneous HgbA1c values. This approach reduced waste without restricting clinician autonomy or requiring interruptive alerts that generate alert fatigue.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"190-193"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13038350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147504056","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":"A Structured Framework for Electronic Health Record Optimization: The EASY Program.","authors":"Obeid Shafi, Daniel Liu, James S Magee, Ashley Antipolo, Feliciano Yu","doi":"10.1055/a-2831-5615","DOIUrl":"10.1055/a-2831-5615","url":null,"abstract":"<p><p>Despite widespread adoption, electronic health records (EHRs) continue to present persistent challenges related to usability, workflow inefficiencies, and clinician burden. Structured EHR optimization programs that address these issues in a scalable and replicable manner remain limited in the literature.This study aimed to describe the development, implementation, and outcomes of the EASY (Eliminate, Automate, Standardize, and Simplify, Y'all) EHR Optimization program-designed to enhance EHR usability, provider satisfaction, and workflow efficiency through a multidisciplinary, data-informed approach.EASY employs a structured three-phase model (people, process, technology), integrating user-centered design principles and institutional tools such as clinician observations, GROSS (Getting Rid of Stupid Stuff) surveys, Epic EHR analytics (Signal and Tune-Up reports), and SOAARR (Subjective, Objective, Artifacts, Assessment, Recommendations, Results) documentation. The program evolved to adopt a sprint-based implementation cycle (4-6 weeks) to maintain engagement, reduce scope creep, and support iterative improvements.EASY yielded both user-reported and analytic benefits. Providers described improved satisfaction, greater alignment with clinical workflows, and enhanced transparency through structured communication. Signal data demonstrated measurable improvements in documentation efficiency, ordering practices, and reduced time spent in specific EHR functions. Targeted usability enhancements, ranging from quick wins to complex builds, were data-informed and guided by clinician feedback. The EASY program reflects best practices in EHR optimization, aligning with literature that emphasizes provider engagement, data-informed design and decision-making, and multidisciplinary collaboration. Its modular design and analytic rigor support adaptability across varied clinical settings, though implementation success is contingent on sustained provider involvement and informatics infrastructure.EASY offers a practical, scalable model for structured EHR optimization. It provides a replicable framework and actionable strategies for informatics teams seeking to improve EHR usability, reduce burden, and foster provider-centered innovation.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"153-161"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147476021","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":"Object-Centric Process Mining of Laparoscopic Cholecystectomy Workflows: Identifying Bottlenecks and Optimization Opportunities.","authors":"Ufuk Celik","doi":"10.1055/a-2832-9369","DOIUrl":"10.1055/a-2832-9369","url":null,"abstract":"<p><p>This study applies object-centric process mining (OCPM) techniques to analyze laparoscopic cholecystectomy (LC) procedures. Traditional process mining techniques are limited in analyzing workflows involving multiple interacting entities (patients, surgeries, anesthesia physical status, etc.).This study aimed to construct an Object-Centric Event Log (OCEL) from LC procedures, discover multientity process patterns, identify workflow bottlenecks, and analyze how patient complexity affects perioperative dynamics.An OCEL representing 1,186 LC cases (1,108 performed, 78 cancelled) from the University of California, Irvine Medical Center (UCIMC) was analyzed, using PM4Py to obtain object-centric directly-follows graphs (OC-DFG) and variant explorer. Statistical comparisons examined intensive care unit (ICU) admission and the American Society of Anesthesiologists (ASA) classification effects on perioperative duration using Mann-Whitney U and Kruskal-Wallis tests.Process discovery revealed a 92% adherence to the reference clinical pathway. ICU-admitted patients (<i>n</i> = 353, 31.6%) demonstrated significantly longer perioperative durations than non-ICU patients (<i>n</i> = 765, median 13.83 vs. 8.85 hours, <i>p</i> < 0.001, Cohen's <i>d</i> = 0.81). The ASA rating showed no significant effect on total perioperative time (<i>p</i> = 0.824). Major bottlenecks included booking-to-operating room (OR) transfer (median 7.2 hours), preanesthesia preparation delays (28 minutes in 5.6% of cases), and postanesthesia care unit (PACU) discharge (median 19.5 hours, interquartile range [IQR]: 6.3-42.1 hours). Pathway completeness was 99.1% with minimal documentation errors. Handoff efficiency varied substantially, with OR-to-PACU transfers occurring rapidly (median: 6 minutes) but PACU-to-discharge transitions exhibiting extreme variability.OCPM enables multiperspective insights invisible to traditional case-centric approaches. While intraoperative phases function efficiently, preoperative scheduling and postoperative discharge represent primary bottlenecks. The high ICU admission rate (31.6%) likely reflects institutional case mix and data classification practices rather than true critical care needs. Targeted interventions addressing preoperative scheduling optimization, discharge bottlenecks, and real-time monitoring could substantially improve surgical throughput.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"204-212"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13065364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460671","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":"A Novel Workflow for Artificial Intelligence-Enhanced Patient Messaging Services.","authors":"Matthew R Allen, Vijay M Tiyyala, Karthik Ramesh, Nimit Desai, Job Shiach, Mark Dredze, John W Ayers","doi":"10.1055/a-2852-9026","DOIUrl":"https://doi.org/10.1055/a-2852-9026","url":null,"abstract":"<p><strong>Background: </strong>Current artificial intelligence (AI) integration in patient messaging often relies on generating AI drafts for clinician review, yet this workflow has achieved limited effect.</p><p><strong>Objective: </strong>This study aimed to describe and evaluate a novel, clinician-first workflow for patient messaging where AI enhances a clinician-generated draft.</p><p><strong>Methods: </strong>Using 268 patient questions from public data, we compared physician-only responses, AI-only responses, and AI-enhanced responses.</p><p><strong>Results: </strong>Responses were ranked on overall preference and the CREATE TRUST framework. AI-enhanced responses were significantly preferred overall, ranking first in 38.8% of evaluations (average rank 1.69; <i>p</i> < 0.01), outperforming both AI-only (27.6%; 2.29) and physician-only (25.5%; 2.11) responses. AI-enhanced responses ranked highest for Understandable (44.5%) and Tailored (39.4%). AI-only responses ranked highest for Thorough (71.0%) and Empathic (69.8%), while physician-only responses ranked highest in Authentic (90.9%; all <i>p</i> < 0.01). Safety analysis identified consequential additions in 3.36% and omissions in 1.12% of AI-enhanced messages.</p><p><strong>Conclusion: </strong>A novel workflow-based on the Clinical Action Support framework-where AI enhances a clinician's draft may offer an improved approach to AI implementation in patient messaging services.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 2","pages":"269-274"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786676","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":"Leveraging a Large Language Model to Generate Quality Improvement Feedback for Clinical Notes.","authors":"Christopher J Kim, Joseph Gelfinbein, Nihan Gencerliler, Nusrat Jahan, Jahnavi Udaikumar, Lauren M Heery, Adam Goodman, Sarah Ng, Joel Attard, Sharmin Asha, Jesse Burk-Rafel, Benedict V Guzman, Katherine A Hochman, Paul A Testa, Jonah Feldman","doi":"10.1055/a-2851-0739","DOIUrl":"10.1055/a-2851-0739","url":null,"abstract":"<p><strong>Background: </strong>Poor documentation quality can significantly affect health care operations, but the feedback process for clinicians to improve clinical notes is time-consuming and often insufficient. Large language models (LLMs) such as Generative Pre-trained Transformer 4 (GPT-4) have the potential to streamline this process.</p><p><strong>Objectives: </strong>This study aimed to determine whether an LLM can generate feedback to improve the medical contingency and discharge planning (MCDP) component of clinical documentation that is non-inferior to feedback by physicians.</p><p><strong>Methods: </strong>A cross-sectional study of GPT-4 feedback and physician feedback on inpatient progress notes was conducted. A random sample of 64 inpatient progress notes identified by the validated artificial intelligence (AI) Audit Tool as having a low likelihood of containing MCDP was included from adult general medicine patients hospitalized at New York University Langone Health (NYULH) in December 2023. Both the GPT-4 model and attending physicians generated feedback on these inpatient progress notes. A/B testing was then conducted on the measures of understandability, usefulness, acceptability, and impartiality. Evaluations employed 5-point Likert scales that were converted to 10-point bidirectional interval scales for interpretability, ranging from -10 (human suggestions significantly better) to +10 (GPT-4 suggestions significantly better), with a non-inferiority threshold set to -1 for the primary endpoint.</p><p><strong>Results: </strong>Sixty-four inpatient progress notes were included, representing 55% female patients with a median age of 73 years. GPT-4 feedback was non-inferior to physician feedback in all measures: Understandability (mean, 1.27, 95% CI: 0.73-1.8, <i>p</i> < 0.001), usefulness (mean, 2.09, 95% CI: 1.27-2.91, <i>p</i> < 0.001), acceptability (mean, 2.07, 95% CI: 1.33-2.81, <i>p</i> < 0.001), and impartiality (mean, -0.20, 95% CI: -0.52 to 0.12, <i>p</i> < 0.001).</p><p><strong>Conclusion: </strong>This study shows that an LLM can be leveraged to generate note quality feedback that is non-inferior to expert clinician feedback.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"252-258"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693173","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":"Industry Electives in Clinical Informatics Fellowship: Early Experiences from a Multi-Institution Survey.","authors":"Nicholas Genes, Priyanka Solanki, Joseph Kannry, Raman Khanna, Dara Mize, Veena Lingam, Robert W Turer, Michael G Leu","doi":"10.1055/a-2820-3029","DOIUrl":"10.1055/a-2820-3029","url":null,"abstract":"<p><p>Clinical informatics (CI) fellowship training equips physicians to design, implement, and evaluate health information systems in support of patient care. While core curricula emphasize academic health system experiences, fellows may benefit from exposure to industry settings where much health technology innovation originates.This study aimed to characterize the structure, perceived value, and logistical challenges of industry electives among CI fellowship programs and to synthesize best practices for integrating these experiences into training.We surveyed current and former CI fellows and their program directors from two Accreditation Council for Graduate Medical Education (ACGME)-accredited programs between September 2024 and March 2025. To be included, fellows were required to complete at least 4 weeks of an industry elective. Free-text responses were analyzed using inductive thematic analysis. A consensus-driven process was used to generate practical considerations for program design.Eight fellows reported on industry electives at non-health-center sites such as startups, vendors, and standards bodies. Their responses revealed four themes: (1) Enhanced skill development and exposure to technologies and workflows not available in academic settings; (2) logistical barriers, including limited institutional support, short duration, and complex legal agreements; (3) tangible deliverables such as dashboards, analytic tools, abstracts, and grants; and (4) professional networking that often shaped career trajectories, with some fellows receiving job offers. Practical considerations included identifying partner sites, designating supervisors, negotiating agreements early, defining objectives and deliverables, and addressing financial and logistical support.Industry electives provide career-shaping experiences for CI fellows, expanding exposure to innovation and fostering collaboration between academia and industry. With clear objectives, aligned competencies, and institutional support, these rotations can strengthen training and prepare fellows for diverse roles across health care and technology.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"138-143"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12991848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318805","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":"Patient Portal Usability Gaps: A Heuristic Evaluation of Two Major Electronic Health Record Systems.","authors":"Arianna P Milicia, Patricia A Spaar, Lucy S Bocknek, Seth A Krevat, Jeffrey A Gold, Raj M Ratwani","doi":"10.1055/a-2852-8767","DOIUrl":"10.1055/a-2852-8767","url":null,"abstract":"<p><strong>Background: </strong>Patient portals, as extensions of electronic health records (EHRs), play a critical role in providing patients with access to diagnoses, test results, and clinical communication. Despite their potential benefits, portals often inherit usability issues from EHRs, which can make information difficult to navigate or interpret, and errors can compromise patient safety and affect clinical decision-making.</p><p><strong>Objective: </strong>This study aims to evaluate usability shortcomings in patient portals by identifying heuristic violations in portal functions from two leading EHR vendors, Oracle Cerner, and Epic and examine how these issues may limit patient understanding and engagement.</p><p><strong>Methods: </strong>Three human factors researchers independently conducted heuristic evaluations on 23 patient portal functions, focusing on ten key diagnostic-related areas (e.g., display of diagnoses, test results, etc.). Usability flaws were mapped to Nielsen-Schneiderman heuristics, assigned a four-level severity score, and reconciled through consensus review.</p><p><strong>Results: </strong>A total of 80 heuristic violations were identified, 76% from Oracle Cerner (<i>n</i> = 61) and 24% from Epic (<i>n</i> = 19). The most frequent violations involved system feedback, inconsistencies, and excess information. The portal functions with the highest number of violations were display of diagnoses, test results, messaging, and clinical notes. Most violations were minor (65%), followed by moderate (20%), with no critical issues observed.</p><p><strong>Conclusion: </strong>Both portals displayed usability gaps, particularly in critical functions like diagnosis display and provider messaging, that may compromise patient understanding of diagnostic information and engagement in care. Although most issues were minor, cumulative effects in high-risk functions could compromise diagnostic safety. Addressing these gaps through human-centered design, standardized usability assessments, and collaboration among vendors, institutions, and policymakers is essential to improve patient understanding and reduce diagnostic error risk.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"281-287"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718717","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":"Evaluating the Fitness for Purpose of Primary Care Data from Electronic Health Records for Automated Antimicrobial Prescribing Audits.","authors":"Ron Cheah, Jo-Anne Manski-Nankervis, Karin Thursky, Vlada Rozova, Christine Chidgey, Dougie Boyle, Rodney James, Ruby Biezen, Daniel Capurro","doi":"10.1055/a-2839-8787","DOIUrl":"10.1055/a-2839-8787","url":null,"abstract":"<p><p>The objective of this study is to determine whether primary care electronic health record (EHR) data are sufficiently complete and plausible to support automated audits of antimicrobial prescribing quality.Cross-sectional descriptive assessment of antimicrobial auditing-related fields in Patron, a large Australian primary care EHR dataset with 3.5 million patients from 129 consenting general practices. Data from 2018 to 2022 were evaluated using the Harmonized Data Quality Assessment Terminology and Framework, covering conformance, completeness, and plausibility.Thirty-one fields (137,776,804 rows; 1,406,364 patients across 116 practices) were assessed. Value conformance and plausibility were high for most core audit variables, including demographics, antimicrobial name, dose, allergy status, and visit date. Prescribing indication was incompletely captured (13-27% completeness), and allergy severity was recorded in 26% of allergy entries. Vendor-level heterogeneity contributed substantially to variation in field completeness.Australian primary care EHR data capture the core structured elements required for automated antimicrobial prescribing audits, enabling assessments of spectrum suitability, microbiology mismatch, and prescribing prevalence. Incomplete and inconsistent documentation of indication and allergy severity necessitates the use of proxy fields or inference for more complex evaluations. Greater standardization across EHR systems is required to enhance the scalability and clinical utility of automated audits in primary care.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"225-236"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078913/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516129","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 Clinicians' Digital and Artificial Intelligence-related Competence Within Healthcare Organizations in the United States: A Strategic Framework Proposal.","authors":"Milin Patel, Thomas Scharfenberger, Jonathan Mashieh, Shitij Arora, Sunit P Jariwala","doi":"10.1055/a-2828-0479","DOIUrl":"10.1055/a-2828-0479","url":null,"abstract":"<p><p>In recent years, there has been an emerging wave of artificial intelligence (AI) and digital tools in healthcare, thereby revolutionizing clinical practice. As health systems are increasingly utilizing these tools as a means to improve clinical and operational performance outcomes, it becomes imperative to train and professionally develop key frontline stakeholders, such as clinicians, in digital health and clinical AI to ensure seamless and responsible adoption within healthcare settings.This paper presents a multi-level framework for healthcare systems to effectively integrate digital and AI tools by enhancing clinicians' proficiency and confidence in utilizing these emerging technologies.Our strategic framework consists of three integrated elements: (1) structured training programs to enhance clinicians' understanding and utilization of digital and AI tools; (2) leadership development pathways to cultivate champions who can drive implementation within clinical departments; and (3) performance management processes to ensure sustainable adoption aligned with organizational goals. This multi-level framework addresses current gaps in clinician preparedness for the digital health ecosystem.The integration of digital and AI technologies into clinical practice requires systematic approaches to clinician development. By implementing multi-level training, fostering digital-based leadership, and establishing appropriate performance evaluation metrics, healthcare systems can better prepare their workforce for responsible technology adoption. This paper provides actionable strategies that healthcare organizations can adapt to their specific contexts to maximize the potential benefits of digital innovations in patient care.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"162-171"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147370541","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}