Applied Clinical Informatics最新文献

{"title":"Electronic Health Record-Embedded Automated Patient Outreach to Increase Completion of Outstanding Health Care Services.","authors":"Katherine L Liang, Eman Jammali, Yasir Tarabichi, David C Kaelber, Anna Rybinska-Campbell, Ashley M Hughes","doi":"10.1055/a-2900-1614","DOIUrl":"10.1055/a-2900-1614","url":null,"abstract":"<p><strong>Background: </strong>Automated patient outreach (APO) approaches offer promising solutions to patient engagement for outstanding health care services. Little is known about APO effectiveness.</p><p><strong>Objectives: </strong>This study evaluates the effect of an electronic health record (EHR)-embedded APO. We assessed how outreach modality, number of outreach attempts, patient sex, and patient age affected outreach success across various recommended health care services.</p><p><strong>Methods: </strong>We conducted a retrospective evaluation of an EHR-embedded APO at a large, academic safety-net health system located in Northeast Ohio. The APOs involved up to three outreaches via patient portal (PP), phone call (PC), or SMS message, based on patient preference. APO evaluation included outreach for specific outstanding recommended health care services: lead screening, laboratories, colorectal cancer screening, immunizations, CT scans, and ultrasounds.</p><p><strong>Results: </strong>Among 46,261 outreaches to 19,537 patients, success occurred in 23.4% of outreaches and 55.4% of patients. Success varied widely by campaign (3.2-35.2%). SMS (24.6%) outreach was associated with greater success than PP (21.6%, <i>p</i> < 0.001) and PC (20.4%, <i>p</i> < 0.001), though results differed between campaigns. Outreach conducted in the third phase (50.5%) was overall more likely to be successful than the first (13.4%, <i>p</i> < 0.001) or second (13.8%, <i>p</i> < 0.001). However, some campaigns showed the greatest successes at phase 2. Male and female patients showed small but significant differences in success (54.2 vs. 56.0%, <i>p</i> = 0.016). Patient age was strongly associated with campaign success (<i>p</i> < 0.001).</p><p><strong>Conclusion: </strong>EHR-embedded APOs are a viable method of patient outreach for outstanding health care services. SMS messaging and three outreach attempts were found to be associated with the greatest success overall. Notably, analysis of individual health care services campaigns shows patients responded differently to different campaigns with different message modes, message frequencies, and length between messages, indicating further need for research in this space. Patient characteristics, especially age, appear to be associated with campaign success, but additional study is needed.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 3","pages":"549-558"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13337302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148399071","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":"Spatially Integrated Electronic Health Record Prototype Improves Usability and Reduces Cognitive Load for Intensive Care Nurses: A Randomized Crossover Trial.","authors":"Lee Taylor-Vaughan, Morrison W Helena, Graciela E Silva Torres, Kimberly D Shea, Pamela G Reed, Sheila M Gephart","doi":"10.1055/a-2903-5822","DOIUrl":"10.1055/a-2903-5822","url":null,"abstract":"<p><strong>Objectives: </strong>This study evaluated the effect of a Spatial Awareness Integrated Electronic Health Record (SAI-EHR) prototype on efficiency, usability, and cognitive workload among intensive care nurses performing standardized documentation tasks, compared with a traditional linear flowsheet-style interface (LID-EHR).</p><p><strong>Methods: </strong>A randomized, two-period crossover simulation was conducted with 36 intensive care unit (ICU) nurses who completed two standardized scenarios. Primary outcomes were documentation efficiency (time to complete a fixed task), usability (System Usability Scale [SUS]), and cognitive workload (NASA Task Load Index [NASA-TLX]). Secondary outcomes included accuracy and Technology Acceptance Model (TAM) measures of Perceived Usefulness (TAM-PU) and Behavioral Intention (TAM-BI). Analyses used linear mixed-effects models. Within-participant effect sizes were calculated using Cohen's <i>d</i>.</p><p><strong>Results: </strong>SAI-EHR reduced documentation time by 177 seconds (27% faster; <i>p</i> < 0.001, <i>d</i> = 0.88) per task. NASA-TLX workload scores were 51.3% lower with SAI-EHR compared with the LID-EHR (18.44 vs. 37.82; <i>p</i> < 0.001, <i>d</i> = 0.73). Accuracy was 6.3% higher with SAI-EHR (98.70 vs. 92.86%; <i>p</i> < 0.001, <i>d</i> = 0.68). SUS and perceived usefulness scores favored SAI-EHR but did not differ significantly (SUS: 77.99 vs. 71.60; TAM-PU: 3.72 vs. 3.38). TAM-BI was substantially higher for SAI-EHR (25.5% increase; Δ = 0.96; 95% confidence interval [CI]: 0.55-1.36; <i>p</i> < 0.001, <i>d</i> = 0.72). Lower cognitive workload was associated with a stronger intention to adopt (<i>r</i> = -0.64; <i>p</i> < 0.001).</p><p><strong>Conclusion: </strong>An SAI-EHR prototype mirroring ICU room layout enabled faster, more accurate documentation with lower perceived workload than a linear flowsheet interface. Stated intention to adopt was higher and inversely associated with workload; differences in SUS and TAM perceived usefulness scores favored the spatial interface but did not reach statistical significance in this sample. Findings support spatial awareness integration as a workload-reducing design principle, while formal usability and usefulness comparisons warrant a larger-sample evaluation.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 3","pages":"604-614"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563511","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":"Effectiveness of Interruptive Clinical Decision Support Alerts on Intravenous versus Oral Acetaminophen Prescribing.","authors":"Sara E Booth, David M Rubins, Ashley L O'Donoghue, Steven Horng, Amy Miller, Lawrence J Markson, Ryan Gould, Evan L Leventhal, John Marshall, Jennifer P Stevens","doi":"10.1055/a-2902-8381","DOIUrl":"10.1055/a-2902-8381","url":null,"abstract":"<p><strong>Objective: </strong>Intravenous (IV) and oral acetaminophen have equivalent bioavailability and effectiveness, but IV formulations are significantly more expensive. Clinical decision support (CDS) tools can encourage cost-effective prescribing. We evaluated the effectiveness and acceptability of different CDS strategies on prescribing patterns of acetaminophen.</p><p><strong>Methods: </strong>We conducted a two-arm randomized controlled trial with a historical comparator at our academic medical center, including 23,676 acetaminophen orders for 13,139 adult inpatients between July 2023 and November 2024. In the preintervention period, providers received a single-click interruptive alert when ordering IV acetaminophen. In the postperiod, providers were randomized to either a multiclick interruptive alert or no alert. The primary outcome was alert effectiveness; secondary outcomes included acceptability and durability.</p><p><strong>Results: </strong>The mean patient age was 57 years; 64% were female, 62% White, and 87% English-speaking. Most orders were placed for medicine (32%), primarily by residents (61%). Preintervention, 9.4% of orders were for IV acetaminophen, compared with 11.2% in the multiclick interruptive group and 12.1% in controls (<i>p</i> = 0.153). Among 624 interruptive alerts followed by orders, 24.5% switched to oral. Interrupted time series analysis showed no significant change in IV order trends.</p><p><strong>Conclusion: </strong>Approximately one-quarter of multiclick alerts prompted a switch to oral acetaminophen, but overall effectiveness was no greater than the control or single-click alert. Findings suggest that increasing alert disruptiveness may add cognitive burden without meaningful gains in effectiveness. A multiclick interruptive alert did not significantly change IV acetaminophen prescribing. Effective CDS strategies may require minimally disruptive designs integrated with education.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"568-576"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13364307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148340744","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":"Low Remote Patient Monitoring Utilization is Strongly Associated with Uncontrolled Hypertension in a Mixed-Race Sample of Urban-Dwelling Patients.","authors":"John M Meddar, Maria R Khan, Mark Schwartz, Hyung G Park, Rachel Engelberg, Devin Mann","doi":"10.1055/a-2876-7314","DOIUrl":"10.1055/a-2876-7314","url":null,"abstract":"<p><strong>Background: </strong>The coronavirus disease 2019 (COVID-19) pandemic spurred a tremendous increase in the adoption and use of remote patient monitoring (RPM) for hypertension (HTN) management. However, limited evidence exists on the associations between frequency of utilization and uncontrolled blood pressure (BP).</p><p><strong>Objectives: </strong>The present study comprehensively explores the associations between RPM use frequency and uncontrolled BP among a metropolitan-dwelling sample of hypertensive patients.</p><p><strong>Methods: </strong>Of 2,920 participants from a single urban health system, we employed a range of analytical perspectives to evaluate the RPM utilization-uncontrolled BP relationship across widely used engagement metrics: Frequency of BP transmission, digitally enabled clinician interactions, patient portal interactions, and a composite measure of utilization. Our dichotomized primary and secondary endpoints were BP >140/90 mm Hg and BP >130/80 mm Hg.</p><p><strong>Results: </strong>Fifty-nine percent of participants were females (59%), one-third (37%) were ≥65 years old, and Hispanic patients were most represented (39%). Our primary uncontrolled BP endpoint demonstrated strong adjusted associations with suboptimal RPM use across dichotomized measures: Low BP transmission (odds ratio [OR]: 2.02, 95% confidence interval [CI]: 1.41-2.96), low clinician interactions (OR: 1.83, 95% CI: 1.43-2.36), low patient portal interactions (OR: 1.83, 95% 1.46-2.30), and low overall engagement (OR: 3.50, 95% 2.77-4.46). Our causal evaluations mirrored these findings, showing moderate causal associations after comprehensive adjustment for confounding. Assessments using other data types, such as continuous and quartiles, showed significant associations and an apparent dose-response relationship, though not at a similar magnitude.</p><p><strong>Conclusion: </strong>We observed strong associations between low RPM utilization and uncontrolled BP, with promising implications for patients with collectively high RPM use. These findings highlight the need to strengthen digital inclusion initiatives to improve RPM uptake and support existing efforts aimed at developing RPM clinical practice guidelines and expanding RPM reimbursement policies. Further research is warranted across diverse utilization components to better understand the linkages between engagement frequency and improved clinical outcomes.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 3","pages":"400-411"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13287931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148176886","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":"Characterizing Parental Use of a HIPAA-Compliant Large Language Model Chatbot in Rare Pediatric Diseases: Insights from Think-Aloud Sessions.","authors":"Anna M Kerr, Christine Bereitschaft, Stephanie Milam, James Rudloff, Melanie Bryan, Sally Cohen-Cutler, Min Zhao, Aditi Gupta, Albert M Lai, Bryan A Sisk","doi":"10.1055/a-2888-9182","DOIUrl":"10.1055/a-2888-9182","url":null,"abstract":"<p><strong>Background: </strong>Parents of children with rare and serious illnesses often have unmet information needs. Large language models (LLMs) can help parents seek medical information. However, few studies have observed parents' use of LLMs or how they would use it in conjunction with their patient portal.</p><p><strong>Objectives: </strong>We provided parents of children with cancer or vascular anomalies (VAs) access to a secure Health Insurance Portability and Accountability Act (HIPAA)-compliant chatbot. We characterized how parents used the tool while accessing their child's patient portal and evaluated the chatbot responses.</p><p><strong>Methods: </strong>Parents participated in think-aloud sessions (<i>n</i> = 48). Parents accessed a HIPAA-compliant GPT 4 Endpoint and entered queries about their child's illness. We examined query length and chatbot response length, accuracy, and readability. We also conducted content analysis on parent queries.</p><p><strong>Results: </strong>We analyzed 451 queries and 451 responses. Parents' queries ranged from 1 to 104 words. They entered primarily short well-formed questions or phrases/statements. Some entered single words or incomplete phrases. Content was related to diagnosis/etiology, treatment, symptoms/side effects, laboratory values, imaging results, clinician notes/documentation, and supportive resources, with some differences between VA and cancer contexts. Chatbot responses ranged from 9 to 883 words The mean accuracy rating was 4.9 ± 0.5 and the mean Flesch Reading Ease score was 28.4 ± 15.0 (college-graduate level).</p><p><strong>Conclusion: </strong>Parents' queries varied in length, complexity, and content, with some differences indicating unique information needs by disease context. Chatbot responses were accurate yet written at a reading level potentially challenging for some users. Future studies should consider these patterns and characteristics when designing health-related chatbot-based tools.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"459-465"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13286089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148212944","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":"Automating Ambulatory Central Line Data Capture and Calculations.","authors":"Elizabeth M Martinez, Christopher A Grimes, Belinda A Bordeaux, Denise M Baginski, Stephanie Langevin, Merissa E Doyle, Barbara R Stein, Mary A Smith, Kimberly M Poshar, Cristina L Herrera, Emanuel E Thomas, Allison Silva, Turaj Vazifedan, Eric Werner","doi":"10.1055/a-2890-1756","DOIUrl":"10.1055/a-2890-1756","url":null,"abstract":"<p><strong>Background: </strong>Ambulatory central line-associated bloodstream infections (A-CLABSIs) cause significant morbidity, are costly, and are a relatively new patient safety target. To calculate A-CLABSI rates, the total ambulatory line-day denominator must be known; however, few hospitals can measure this accurately. Access to this data is vital to the quality improvement process.</p><p><strong>Objectives: </strong>The project aimed to automate the collection of central line (CL) information from the electronic health record (EHR) and the calculation of ambulatory CL days to within a 5% variance rate compared with the manual process to monitor and improve A-CLABSI rates.</p><p><strong>Methods: </strong>Existing documentation processes were analyzed, and appropriate fields were determined for CL data capture. Multiple revisions were needed to hard-wire accurate EHR documentation in identified areas. An algorithm was developed to identify and electronically track CL insertions/removals and calculate days. We completed 9,533 manual chart reviews, correcting retrospective data errors in documentation (EID), and implemented a process to address EID in real time to ensure accurate data and sustainability.</p><p><strong>Results: </strong>The EHR automated process identified an adjusted average of 98% of patients with a CL compared with the manual process with a variance of 0.3% between June 2024 and May 2025. The inpatient and ambulatory variance rates were 3.50 and 5.2%, respectively. The automated process identified an adjusted average of 96% total CL days compared with the manual process, with an average variance rate of 3.2% . Inpatient and ambulatory CL day calculations had variance rates of 0.7 and 4.5% respectively, compared with the manual process. The average EID rate for all CLs was 12.8%.</p><p><strong>Conclusion: </strong>Through real-time electronic data capture, a more efficient and sustainable process for maintaining accurate CL data and calculating A-CLABSI rates was developed.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 3","pages":"476-483"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13309256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148340728","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":"Designing Granular Consent Tools for Sensitive Health Data: Challenges, User Needs, and Stakeholder Guidance.","authors":"Reid A Cooper, Mengyi Wei, Martha Kaiser, Kelly Caine, Daniel J Chavez, Jeff Coughlin, Kelly Davison, Hannah K Galvin, Steven Lane, Alec Laws, Christoph Ulrich Lehmann, Panna Lossy, Maria D Moen, S Trent Rosenbloom, Jacqueline Seitz, Marianne Sharko, Julia Skapik, Raymonde Uy, L Weaver, Maria Adela Grando","doi":"10.1055/a-2900-1473","DOIUrl":"10.1055/a-2900-1473","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to examine perspectives on challenges, design requirements, and implementation considerations for developing a granular, patient-facing consent tool that enables meaningful control over sensitive health data sharing in real-world healthcare settings.</p><p><strong>Methods: </strong>Guided by the National Science Foundation I-Corps framework and conducted in collaboration with Shift-a national consortium for patient-driven sharing of health information-we conducted semi-structured interviews with 16 expert stakeholders representing clinical, health IT, governance, and policy domains. Transcripts were analyzed using thematic analysis with combined deductive and inductive coding to identify key challenges, user needs, and system constraints.</p><p><strong>Results: </strong>Participants most frequently emphasized the need for patient-centered, trauma-informed user experiences that recognize prior experiences of stigma, discrimination, surveillance, or harm associated with disclosure of sensitive health information, and that use plain language and education to support meaningful consent (75%). Lack of trust, stigma, and workflow burden were major barriers to sensitive data sharing (63%), alongside technical limitations in EHRs that prevent reliable segmentation and enforcement of preferences (56%). Granular, computable consent-allowing patients' choices-was viewed as essential (50%), though legal and policy fragmentation complicates implementation (50%). Additional considerations included proxy and adolescent-aware configurations (44%) and interoperability standards such as Fast Healthcare Interoperability Resources (FHIR) Consent and Data Segmentation for Privacy (19%).</p><p><strong>Conclusion: </strong>Findings suggest that effective consent management requires alignment between patient-centered design, technical computability, and governance. Enforceable consent integrated into routine workflows and paired with transparency may reduce stigma-driven nondisclosure while supporting clinical safety. Granular, patient-directed consent can translate privacy expectations into practice. Embedding computable consent within interoperable standards and trauma-informed design offers a path toward more trustworthy, equitable, and safe health data sharing.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 3","pages":"577-586"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13368399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148450164","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":"Generating Patient Documents from Electronic Health Records Using Generative Artificial Intelligence: A Feasibility Study in a Japanese Cancer Center.","authors":"Nobuaki Michihata, Hiroshi Ishii, Hideki Tsujimura, Nobuya Takano, Takuji Nomura, Yoshihisa Takeuchi, Shingo Kagawa, Shunichi Asai, Emiri Kita, Atsushi Komaru, Hiroshi Suito, Kunishige Koga, Yoshifumi Miura, Aya Washio, Yoshitaka Hippo","doi":"10.1055/a-2917-6695","DOIUrl":"10.1055/a-2917-6695","url":null,"abstract":"<p><strong>Objectives: </strong>Clinical documentation consumes substantial clinician time, potentially detracting from patient care. Generative artificial intelligence (AI) may support drafting discharge summaries and patient referral documents, but feasibility in non-Western-language oncology settings using real-world electronic health record (EHR) data remains insufficiently evaluated. This study assessed feasibility in a Japanese cancer hospital using an enterprise AI system.</p><p><strong>Methods: </strong>Medical records from 61 consenting adult patients at Chiba Cancer Center were analyzed. Although the plan aimed at comprehensive EHR data, actual input was limited to extractable text (physician notes, nursing records); structured laboratory data and imaging, endoscopy, and pathology reports were not directly used, and existing summaries and external referrals were excluded to avoid information leakage. Data were converted to JavaScript Object Notation; GaiXer generated 31 discharge summaries and 30 referral documents. Four evaluators scored them; ≥80/100 was an exploratory threshold for draft-level practical utility. Feedback drove one refinement cycle.</p><p><strong>Results: </strong>Generated documents scored approximately 60 to 70. A score ≥80 was reached by 9 of 31 discharge summaries in each evaluation; for referrals, none reached the threshold initially, whereas 5 of 30 did after refinement. Discharge summary scores did not substantially improve; referral scores did. Raw percent agreement among three nonphysician evaluators was high, although chance-corrected agreement varied. Wilcoxon signed-rank tests showed no significant change for discharge summaries (<i>p</i> = 0.866) but significant improvement for referrals (<i>p</i> = 0.006).</p><p><strong>Conclusion: </strong>This feasibility study suggests AI may support drafting these documents in a secure environment using real-world Japanese EHR data, although the generated documents did not consistently reach the predefined threshold for draft-level utility. Findings should not be interpreted as demonstrating workload reduction or maximum performance under ideal data conditions. Future studies should evaluate larger datasets, multiple institutions and models, blinded evaluations, actual editing time, clinician acceptance, and workflow impact.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"652-658"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13412938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537578","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":"Risk-Tiered Governance for Hospital Artificial Intelligence: A Framework Synthesis and Implementation Pathway.","authors":"Mengying Zhu, Bingjie Luo, Xiaodong Wu, Ying Zhao, Shoujing Wang, Yang Hu, Ran Liu, Congyin Zhang, Ji Li, Hongpan Wang, Dijia Wang, Liwen Liu, Yuehua Li","doi":"10.1055/a-2921-5990","DOIUrl":"10.1055/a-2921-5990","url":null,"abstract":"<p><strong>Objectives: </strong>Hospital artificial intelligence (AI) is increasingly embedded in electronic health record workflows, cloud inference pipelines, imaging, medication review, triage, early warning, documentation, and operational management. This paper proposes a risk-tiered governance framework and implementation pathway for hospital AI applications that are integrated into clinical and operational workflows.</p><p><strong>Methods: </strong>We conducted a narrative review and framework synthesis of peer-reviewed evidence, reporting guidelines, regulatory and policy sources, implementation studies, and applied governance case reports relevant to hospital AI. Sources were used according to their evidence type: systematic and scoping reviews identified recurring risks and barriers; empirical studies and trials illustrated bounded implementation patterns; case reports informed organizational design; and reporting or regulatory frameworks informed documentation, validation, change control, and oversight elements.</p><p><strong>Results: </strong>The proposed framework has four components. First, a use-case inventory tags AI applications by decision influence and workflow coupling. Second, a six-domain risk taxonomy addresses clinical safety, privacy and data security, ethics and fairness, transparency and explainability, system stability and resilience, and compliance and accountability. Third, a four-tier risk scheme links the level of oversight to potential harm, automation, reversibility, and operational coupling. Fourth, a governance architecture assigns responsibilities to an AI governance committee, clinical owners, risk control functions, and independent assurance. A patient safety-oriented lifecycle pathway is proposed across initiation, local validation, shadow mode, controlled go-live, monitoring, change control, retirement, and organizational learning. To support feasible adoption, we also provide barriers, first steps, and a maturity-based ramp-up model.</p><p><strong>Conclusion: </strong>Hospital AI governance is best framed as proportional lifecycle control rather than one-time go-live approval. The proposed framework is an adaptable scaffold, not a universal mandate. It is intended to help clinical informatics leaders, quality and safety teams, and hospital executives prioritize oversight according to risk, resources, and organizational maturity.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"668-679"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13423554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580882","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":"Usability Evaluation of EzList: A Digital Platform for Surgical Waitlist Management Using Standardized Metrics.","authors":"Guilherme Pimenta Roncete, Guilherme Gentile, Fabio Carvalho Vicentini, Ana Teresa C Vicentini, Elisa Antolli, Marcelo Morandini, Alexandre Danilovic, Carlos Alfredo Batagello, Giovanni Scala Marchini, Fabio C M Torricelli, Rodrigo Perrella, William Carlos Nahas, Eduardo Mazzucchi","doi":"10.1055/a-2880-6345","DOIUrl":"10.1055/a-2880-6345","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluated the usability of easy-list (EzList), a Brazilian digital platform developed to optimize elective surgical waitlist management.</p><p><strong>Methods: </strong>A summative usability assessment was conducted with ten surgeons and surgical residents, both with and without prior EzList experience, following IEC 62366:2015 and ISO 14971 standards. Participants completed seven representative clinical tasks. Usability was measured using the system usability scale (SUS) and the single usability metric (SUM), integrating scores for task completion, errors, task time, and satisfaction. Additional metrics included completion rate, error rate, time-based efficiency (TBE), assistance rate, learning rate, and workflow adherence. Observed use errors were classified by severity using a risk matrix based on ISO 14971:2019 combined with NISTIR 7804 severity levels, assessed independently by two clinical evaluators.</p><p><strong>Results: </strong>The SUM score was 70.8%, reflecting high task completion (95.7%) and satisfaction, but lower efficiency. The mean SUS score was 82.92 (SD = 15.92), indicating good to excellent perceived usability. The error rate averaged 27.9%, mainly related to comprehension and redundant steps, with tasks 2 and 6 requiring more time and assistance. TBE was 1.47%, slightly below the 1.78% benchmark. Learning curves varied, with a mean improvement of 16.3%. Posttask satisfaction was high; external assistance was needed in 14.3% of tasks.</p><p><strong>Conclusion: </strong>The digital platform demonstrated adequate overall usability for its intended clinical context, with strong task completion and positive user perception. However, moderate error rates and efficiency gaps indicate specific areas requiring interface refinement before broader deployment. Variability in learning suggests the need for a more intuitive design to facilitate broader clinical adoption.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"434-442"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13286103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148007210","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}