Applied Clinical Informatics最新文献

{"title":"Robotic Process Automation as an EHR Reliability Layer for Inpatient Care Gaps: Automating Virtual Huddles Using Secure Chat.","authors":"Jared Silberlust, Tiffany Rose Martinez, Katerina Andreadis, Christopher Sonne, Ulka Kothari, Thomas Roncoli, Ajay Mansukhani, Hardev Randhawa, John Will, Jonathan Saul Austrian, Paul Testa, Jonah Feldman","doi":"10.1055/a-2976-8076","DOIUrl":"https://doi.org/10.1055/a-2976-8076","url":null,"abstract":"<p><strong>Objectives: </strong>This case report describes the implementation and outcomes of robotic process automation (RPA) interventions used as an electronic health record (EHR)-based reliability layer for 2 inpatient quality initiatives: First Contact Provider (FCP) assignment and admission medication reconciliation (AMR) within 24 hours.</p><p><strong>Methods: </strong>RPA bots were created to support 2 inpatient quality initiatives by monitoring EHR patient lists for predefined care gaps: missing FCP assignment and incomplete AMR. When a gap was identified, the bot opened the patient chart, initiated a secure-message conversation with frontline clinicians and quality improvement champions, and generated operational reports to support follow-up. Chi-square tests compared FCP before (September 1, 2022-February 28, 2023) versus after implementation (March 1-August 31, 2023), and AMR before (December 1, 2022-May 31, 2023) versus after implementation (June 1-November 30, 2023).</p><p><strong>Results: </strong>For FCP, the bot identified 9,361 encounters without an assignment across 118 hospital units and generated 14,984 secure-message conversations linking 17 quality improvement champions with 2,336 providers. FCP assignment improved from 81.2% (5,096,966/6,269,754 patient-hours) to 88.5% (5,858,442/6,619,047; p < 0.001). For AMR, the bot identified 10,680 incomplete reconciliations across 131 hospital units and generated 21,502 secure-message conversations linking 208 quality improvement champions with 3,234 unique providers. Timely completion improved from 86% (33,886/39,489 encounters) to 96% (39,790/41,624; p < 0.001).</p><p><strong>Conclusions: </strong>Implementation of RPA was associated with substantial improvement in FCP assignment and timely AMR completion. Initiating a bot-enabled secure-chat \"virtual safety huddle\" may provide a practical EHR reliability layer for rule-based inpatient care gaps.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185341","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":"Patient portal messaging patterns among Spanish-speaking patients in North Texas.","authors":"Robin Higashi, Bhaskar Thakur, Emily Repasky, Ferdinand Velasco, Brett Moran, Bryan D Steitz, Samuel McDonald, Christoph Ulrich Lehmann, Eric D Peterson, Ann Marie Navar, Robert W Turer","doi":"10.1055/a-2969-2331","DOIUrl":"https://doi.org/10.1055/a-2969-2331","url":null,"abstract":"<p><strong>Background: </strong>Patient portal messaging connects patients with their healthcare teams but is underused by Spanish-speaking patients.</p><p><strong>Objective: </strong>Evaluate content and use patterns of portal messaging among Spanish-speaking patients to inform future implementation strategies to increase portal messaging.</p><p><strong>Methods: </strong>We evaluated messaging use patterns among a random sample of adults with an EHR-designated Spanish language preference across three diverse healthcare systems in Texas: an academic medical center, a public safety-net institution, and a regional, integrated community health system. Eligible participants initiated ≥1 medical advice request conversation via the portal between April 5, 2021-April 4, 2022. We thematically coded messages to capture characteristics including message sender and receiver role, language, topic, conversation length, and concordance.</p><p><strong>Results: </strong>We analyzed 883 patient-initiated conversations from 297 patients including n=1,557 messages sent by patients and n=1,123 sent to patients. Most (77.3%, 1,204/1,557) messages from patient accounts were initiated in English, and 26.7% (414/1,557) were written by a self-identified care partner. Messages were most frequently about prescriptions (40.0%, 353/883), appointments (26.6%, 235/883), and symptoms (21.5%, 190/883). Most conversations were brief, with 56.4% consisting of one (21.4%) or two (35.0%) messages. Nearly all messages sent by patient accounts received language concordant responses (94.9%, 976/1,028), although some patients and care partners used automated translation tools.</p><p><strong>Discussion: </strong>Spanish-speaking patients who use the patient portal usually message in English, and more than a quarter of messages are written by care partners. Vendors and health systems could increase portal messaging use among Spanish speaking patients via Spanish language portal configuration and educational resources, especially for those without access to an English-speaking care partner. The most common messaging topics (appointment scheduling and medication refill requests) have existing, but underused, discrete portal functions. Generative artificial intelligence (AI) solutions in Spanish could facilitate portal messaging triage and language concordant responses to Spanish speakers.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Ambient Artificial Intelligence Documentation on Clinician Workload, Efficiency, and Patient Experience in a Multisite Emergency Department Network.","authors":"Markos G Kashiouris, Andrew Miner, Sameh Saleh, Matthrew Scripps, Lindsey Stanton, Golda Samuel, Brent Dibble","doi":"10.1055/a-2939-3038","DOIUrl":"10.1055/a-2939-3038","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: Ambient artificial intelligence (AI) documentation systems have emerged as a potential solution to clerical burden in medicine, yet evidence in emergency settings remains limited.</p><p><strong>Abstract: </strong>OBJECTIVES: The objective of this study is to evaluate the ambient AI on patient experience, documentation efficiency, clinician workload, and clinical throughput across a large emergency department (ED) network.</p><p><strong>Abstract: </strong>METHODS: This is a retrospective, observational cohort study of ambient AI rollout across 14 EDs (May 2024-June 2025). Clinicians applied the tool on a voluntary basis and contributed both AI-assisted and conventional notes. Outcomes included patient-reported experience, active editing time, disposition-to-completion time, copied-forward content, and on-time note completion. Clinician workload was assessed with pre- and postimplementation surveys including the NASA Task Load Index (TLX).</p><p><strong>Abstract: </strong>RESULTS: Ambient AI was used in 8.6% of 315,242 ED clinical notes. AI use was associated with higher top-box ratings for clinician listening (82.0 vs. 76.6%; odds ratio [OR]: 1.39; <i>p</i> = 0.003) but not likelihood to recommend (77.6 vs. 75.0%; OR: 1.15; <i>p</i> = 0.185). Within-clinician paired analysis showed no difference in active editing time between AI and conventional notes (median difference: +0.17 min; 95% confidence interval [CI]: -1.00 to +1.55; <i>p</i> = 0.349), and clinicians typed 722 fewer characters per AI-assisted note. Documentation finalized 3.4 h earlier for admitted and 6.0 h earlier for discharged patients; the disposition-to-completion difference was +5.06 h (<i>p</i> = 0.337), reflecting attenuation of early-adopter's advantage. AI-assisted notes were half as likely to include copied content (9.4 vs. 18.3%; OR: 0.46; <i>p</i> < 0.001). NASA-TLX workload decreased by 40.2 points (95% CI: 30.4-50.1).</p><p><strong>Abstract: </strong>CONCLUSION: Ambient AI documentation was associated with improved patient-reported listening, earlier note completion, reduced copied-forward content, and lower clinician-perceived cognitive burden, without altering active editing time per note. These findings suggest a scalable tool for reducing clerical burden in high-throughput emergency care, while introducing new responsibilities for clinicians to review, edit, and sign machine-generated notes.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"767-780"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13574497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800353","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":"Systemic Challenges in EHR Alert Design: A Thematic Analysis of Physician Discourse on X (Formerly Twitter).","authors":"Guy Vishnevsky, Jacob N Ablin","doi":"10.1055/a-2945-3280","DOIUrl":"10.1055/a-2945-3280","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: Electronic health record (EHR) clinical decision support (CDS) systems generate excessive, non-contextual alerts, causing clinician desensitization with override rates exceeding 90%. This alert fatigue disrupts workflows, contributes to burnout, and creates patient safety risks. Traditional survey-based methods fail to capture real-time end-user feedback at scale.</p><p><strong>Abstract: </strong>OBJECTIVES: This study aimed to evaluate real-world CDS usability signals by analyzing physician discourse on X (formerly Twitter) and identify physician-perceived sociotechnical pain points to inform future, hypothesis-driven EHR optimization.</p><p><strong>Abstract: </strong>METHODS: X was systematically searched in August 2025 using Grok 4 (2025-08 build) via an internal API (Application Programming Interface). The AI temperature was set to 0.7 for search generation to ensure a broad retrieval of relevant discourse, utilizing keywords including \"EHR alert fatigue,\" \"clinical decision support usability,\" \"EMR burnout,\" \"override EHR,\" and \"alert fatigue doctors,\" yielding 117 posts from 106 self-identified physicians after manual curation (spanning from 2016 to 2025). Only the first post per author was included in the final analysis of 106 posts. We applied Braun and Clarke's reflexive thematic analysis, with artificial intelligence (AI)-assisted coding verification. Sentiment classification was conducted as a secondary assessment.</p><p><strong>Abstract: </strong>RESULTS: Six CDS usability failure domains were identified: Alert Overwhelm (38.7%, 95% confidence interval [CI]: 30.0-48.2%), Burnout and Fatigue (43.4%, 95% CI: 34.4-52.9%), Patient Safety Threats (9.4%, 95% CI: 5.2-16.5%), Regulatory and Billing Pressures (17.0%, 95% CI: 11.0-25.3%), Mitigation Strategies, including AI prioritization (36.8%, 95% CI: 28.2-46.3%), and Lack of Customization (7.5%, 95% CI: 3.9-14.2%). Sentiment was 76.4% (95% CI: 67.5-83.5%) negative. Co-occurrence analysis showed 50.0% of safety posts co-occurred with alert overwhelm, and 19.6% of burnout posts co-occurred with regulatory and billing pressures.</p><p><strong>Abstract: </strong>CONCLUSION: Physician discourse on X provides valuable, exploratory usability signals. The prevalence of alert overwhelm and burnout themes highlights recurring pain points in current EHR architectures, generating hypotheses for future user-centered CDS redesign such as tiered alerting, role-based routing, and alert deferral mechanisms.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"781-788"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13569921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957234","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":"Clinical Implementation of Pharmacogenetics-Based Machine Learning Clinical Decision Support Systems: A Scoping Review.","authors":"April D Taylor, Robert J Goodloe, Marc B Rosenman, Cathy R Fulton","doi":"10.1055/a-2927-0698","DOIUrl":"10.1055/a-2927-0698","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: Pharmacogenetics is increasingly recognized as essential for optimizing drug therapy and reducing preventable adverse drug events. However, applying pharmacogenetic data in real-world clinical decisions remains challenging. Traditional rule-based clinical decision support systems often fall short. Machine learning-based clinical decision support systems offer a more dynamic solution, combining pharmacogenetic variants, patient-specific clinical data, medication history, and guidelines to generate personalized treatment recommendations.</p><p><strong>Abstract: </strong>OBJECTIVE: This scoping review examined the extent and nature of evidence on integrating machine learning into clinical decision support systems for pharmacogenetics, emphasizing clinical implementation. By focusing on articles where tools have been integrated into clinical workflows, this review highlights progress and persistent gaps in translating pharmacogenetics-informed machine learning models into everyday clinical practice.</p><p><strong>Abstract: </strong>METHODS: A comprehensive search across multiple databases identified studies published from January 2015 to September 2025. Eligible studies included any design reporting on machine learning-based clinical decision support systems incorporating pharmacogenetic data to support therapeutic decisions in clinical settings.</p><p><strong>Abstract: </strong>RESULTS: Of 1262 records screened, 7 studies met inclusion criteria. These studies implemented machine learning-based clinical decision support systems integrating pharmacogenetic data to guide drug therapy in clinical environments. While varying in design, setting, and implementation maturity, most systems demonstrated potential benefits, such as reducing preventable adverse drug events, improving prescribing accuracy, or enhancing workflow integration. Two of the included studies evaluated PGx ML-CDSS tools in live clinical or trial workflows.</p><p><strong>Abstract: </strong>CONCLUSION: Despite growing interest and model development, clinical implementation of machine learning-based pharmacogenetic clinical decision support tools remains limited. Most systems remain rule-based, while a few integrate machine learning with pharmacogenetic data for more personalized and adaptive decision-making. This review underscores the gap between theoretical model development and real-world application. To advance the field, implementation science research is needed to evaluate usability, clinical workflow integration, and patient outcomes. Real-world evidence is essential to unlock the full potential of machine learning-based clinical decision support in pharmacogenetics.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"727-735"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13526494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860611","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":"Qualitative Study of Inpatient Hospital Medicine Interruptive Alerts and Clinical Decision Support.","authors":"Afifah Khan, Jeffrey Schnipper","doi":"10.1055/a-2955-9555","DOIUrl":"10.1055/a-2955-9555","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: Although interruptive alerts, or pop-ups, are one of the most common forms of clinical decision support (CDS) tools within the electronic health record (EHR), they are often ignored. Interruptive alerts have the potential to enhance patient care and reduce clinician burden, but end-users frequently experience alert fatigue and override alerts, defeating their purpose. Few studies have elicited the perspectives of all stakeholder groups involved in the development, experience, and review of inpatient interruptive alerts in the modern vendor EHR era.</p><p><strong>Abstract: </strong>OBJECTIVES: This study aimed to understand the perspectives of multiple stakeholders involved in development, governance, and use of inpatient interruptive alerts.</p><p><strong>Abstract: </strong>METHODS: We conducted a qualitative study, interviewing 22 participants, including 15 end-users, 4 governance group members, 2 clinical leads, and 1 information systems staff member. The semi-structured interview guides were designed to explore different participant perspectives on interruptive alerts.</p><p><strong>Abstract: </strong>RESULTS: We found three domains across end-users related to interruptive alert helpfulness, proportionality, and alarm fatigue. We found three domains across the governance, clinical lead, and information system groups about the causes of high-volume and high-sensitivity interruptive alerts, differing definitions of interruptive alert success from end-users, and reluctance to add new interruptive alerts but limited changes or retirement of existing interruptive alerts. Barriers to successful CDS could be grouped into end-user, technical, and governance-level barriers. We found one governance-level facilitator and gathered several recommendations for CDS improvement from all stakeholder groups, including changing alert \"interruptiveness,\" customizability, design, responsiveness, and involving end-users in alert analysis.</p><p><strong>Abstract: </strong>CONCLUSION: This study highlights the need for robust, user-centered alerts and CDS development and evaluation to improve clinician experiences and potentially patient outcomes.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"889-898"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065522","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":"Validity and Reliability Study of the Nursing Informatics Competency Assessment Tool.","authors":"Yasemin Adıgüzel Yaşar, Zeynep Çetinkaya, Ayşe Dost, Media Subaşı Baybuğa, Alphonsa A Rahman","doi":"10.1055/a-2943-3447","DOIUrl":"10.1055/a-2943-3447","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: The rapid development of information and communication technologies has rendered informatics competencies of health care professionals critical in the digital transformation process. Nurses, who constitute a substantial proportion of the health care workforce, are expected to develop nursing informatics competencies. These competencies are crucial for effective adaptation to digital health care environments and for ensuring the delivery of safe care. Therefore, it is important that informatics competencies are assessed with practical and valid tools. The results of such assessments can provide valuable insights into nurses' educational needs.</p><p><strong>Abstract: </strong>OBJECTIVES: This study aimed to evaluate the validity and reliability of the \"Nursing Informatics Competency Assessment Tool\" (NICAT), originally developed by Rahman (2015), and to adapt it for use in Turkish.</p><p><strong>Abstract: </strong>METHODS: Construct validity was examined using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Internal consistency coefficients were calculated to assess reliability.</p><p><strong>Abstract: </strong>RESULTS: EFA revealed a three-factor structure accounting for 71.79% of the total variance. CFA results indicated an acceptable model fit (Comparative Fit Index [CFI] = 0.925; Root Mean Square Error of Approximation [RMSEA]= 0.082). Cronbach's α coefficients ranged from 0.764 to 0.968, demonstrating high internal consistency. While the original NICAT study assessed only content validity, this study provides the first empirical testing of its factor structure.</p><p><strong>Abstract: </strong>CONCLUSION: The 26-item Turkish version of the tool has demonstrated satisfactory validity and reliability. It can be considered a valid, reliable, and practical instrument for assessing nursing informatics competencies.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"736-746"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13526493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800375","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":"Measuring Expert Inter-Rater Agreement with a Semi-Automated Clinical Decision Support System: Who Agrees with What?","authors":"Yasmin Benhalim, Matthew W Hodgman, John R Charpie, Gabe E Owens, Ranjit Aiyagari, Daniel E Ehrmann","doi":"10.1055/a-2956-4559","DOIUrl":"10.1055/a-2956-4559","url":null,"abstract":"<p><strong>Abstract: </strong>OBJECTIVE: Assess inter-rater agreement on clinician- and clinical decision support (CDS)-driven diuretic titrations and risk classifications to benchmark a semi-autonomous CDS (OTTO-FM).</p><p><strong>Abstract: </strong>METHODS: Secondary analysis of prospectively collected porcine data modeling postoperative fluid overload. Three pediatric cardiac intensivists rated items (reasonable/unsure/unreasonable) for clinician-driven (human phase; 29 items) and CDS-driven titrations with risk classification (CDS phase; 44 items). Inter-rater agreement was estimated with ordinal-weighted Gwet's AC2.</p><p><strong>Abstract: </strong>RESULTS: Dosing inter-rater agreement was similar between phases (human AC2: 0.79 [0.61-0.92]; CDS: 0.83 [0.68-0.92]), though unanimity was reached on only 19 of 29 (65.5%) human-phase and 31 of 44 (70.5%) CDS-phase items. Risk-label inter-rater agreement stratified sharply by label: high-risk, 0.92 (0.74-0.95) versus low-risk, 0.54 (0.28-0.75). Disagreement also differed in structure between tasks: 19 of 20 nonunanimous risk items were minority dissent (one rater against a two-rater majority), including 16 of 17 among low-risk items, whereas 7 of 13 nonunanimous CDS dosing items were three-way splits.</p><p><strong>Abstract: </strong>CONCLUSION: Inter-rater agreement on dosing was substantial and similar across phases, but not unanimous, suggesting that perfect reasonableness consensus with CDS may be unrealistic. Agreement on risk classification was lower, and the structure of disagreement differed between CDS dose and risk. Empirical inter-rater agreement baselines may contextualize CDS evaluation, and these differences may have implications for the design of semi-autonomous CDS if confirmed in larger studies.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"817-824"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026501","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":"Digital Services in Pandemic Response: Lessons Learned from Interviews with COVID-19 Patients and Health Care Providers.","authors":"Mayes Barak, Ruby Biezen, Annie Lau","doi":"10.1055/a-2950-6345","DOIUrl":"10.1055/a-2950-6345","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: The COVID-19 pandemic accelerated the adoption of digital health technologies, including telehealth, electronic medical records, and patient-facing applications. These tools maintained access to care during periods of restriction but exposed gaps in workforce readiness, patient trust, digital literacy, and equity. Despite widespread implementation, limited evidence exists on how patients and health care providers (HCPs) experienced this transition and how these experiences influenced digital health adoption.</p><p><strong>Abstract: </strong>OBJECTIVE: This study aimed to compare the experiences of patients and HCPs in Australia during the pandemic, identify key barriers and enablers of digital health adoption, and inform sustainable models of care for future disruptions.</p><p><strong>Abstract: </strong>METHODS: A qualitative study was conducted between March 2023 and September 2024. Semi-structured Zoom interviews were undertaken with 20 patients diagnosed with COVID-19, and 21 HCPs involved in pandemic care in New South Wales and Victoria. Data were analyzed using reflexive thematic analysis and interpreted through the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) framework to examine technology adoption, communication, and system preparedness.</p><p><strong>Abstract: </strong>RESULTS: Four themes captured patient and provider experiences. First, rapid digitization shifted health care-seeking behavior, with many patients delaying in-person care due to safety concerns. Second, providers reported limited readiness, increased digital workload, and workflow disruption. Third, differences in digital literacy and misaligned expectations undermined trust, communication, and diagnostic confidence in virtual settings. Finally, long-term sustainability requires hybrid models of care, interoperable infrastructure, and supportive governance. Overall, the findings highlight the dual nature of crisis-driven digital transformation: While innovation accelerated rapidly, systemic and relational vulnerabilities in care delivery were also exposed.</p><p><strong>Abstract: </strong>CONCLUSION: Digital care was essential for maintaining access during the pandemic; however, rapid deployment alone cannot ensure safety, equity, or quality of care. Sustainable integration requires coordinated investment in interoperable infrastructure, workforce capability, and patient-centered design to support hybrid delivery models and strengthen preparedness for future health care disruptions.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"802-816"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889110","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":"Technology-Related Stress and Burnout in Nursing Practice: A Bibliometric and Knowledge Mapping Analysis.","authors":"İsmail Keleş","doi":"10.1055/a-2973-0104","DOIUrl":"10.1055/a-2973-0104","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: The rapid expansion of digital health care technologies has substantially transformed nursing practice and has been accompanied by growing concerns regarding technology-related stress and burnout among nurses. Despite the growing body of literature, the intellectual structure and thematic evolution of this field remain insufficiently explored from a bibliometric perspective.</p><p><strong>Abstract: </strong>OBJECTIVES: This study aimed to conduct a comprehensive bibliometric and knowledge mapping analysis of the literature on technology-related stress and burnout in nursing practice.</p><p><strong>Abstract: </strong>METHODS: Bibliographic records were retrieved from the Web of Science Core Collection database. A total of 345 publications published between 2000 and 2026 were analyzed using the Bibliometrix package and Biblioshiny interface in R software. Bibliometric indicators, keyword co-occurrence analysis, thematic mapping, trend topic analysis, and thematic evolution analyses were performed to evaluate the conceptual structure and development in the field.</p><p><strong>Abstract: </strong>RESULTS: Scientific production increased markedly after 2020, with an annual growth rate of 15.77%. \"Electronic health records,\" \"digital health,\" \"nursing,\" \"nursing informatics,\" and \"burnout\" emerged as the dominant research themes. The United States was the leading contributor in publication output, whereas <i>CIN: Computers, Informatics, Nursing</i> was identified as the most productive journal. Keyword co-occurrence analyses revealed strong relationships among workload, stress, usability, and documentation burden. Furthermore, thematic analyses demonstrated increasing attention toward artificial intelligence, ChatGPT, and generative AI within nursing informatics and digital health care research.</p><p><strong>Abstract: </strong>CONCLUSION: The literature has evolved from traditional documentation and workload concerns toward broader digital health and artificial intelligence-related themes. As intelligent health care technologies continue to expand, future research should investigate their relationships with nurses' cognitive workload, perceived usability, and occupational well-being.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"922-932"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168611","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}