Applied Clinical Informatics最新文献

{"title":"Pragmatic integration of user-centered design and implementation science: A new methodological approach for clinical decision support implementation in EHRs.","authors":"Anna Maw, Jason Hoppe, Nicole Wagner, James Mitchell, Meagan Bean, Katy E Trinkley","doi":"10.1055/a-2716-4479","DOIUrl":"https://doi.org/10.1055/a-2716-4479","url":null,"abstract":"<p><p>Background Clinical decision support (CDS) tools are critical for improving care delivery and guideline adherence but are associated with clinician burnout when inadequately designed and implemented. User Centered Design (UCD) and Implementation Science (IS) methods are evidence-based approaches to optimizing CDS tools, but are infrequently used in part due to limited guidance on how to apply them within the resource-constraints of health systems. Objective This paper focuses on pragmatic application of an integrated UCD-IS approach, demonstrating how it can be adapted to meet operational constraints through two real world case studies. Methods We applied an integrated UCD-IS approach guided by the Practical Robust Implementation and Sustainability Model (PRISM) to two CDS projects within a large regional health system: (1) adapting a CDS for improving prescribing of goal-directed medical therapy in patients with heart failure during virtual visits, and (2) expanding a naloxone co-prescribing CDS across outpatient settings. Each project followed iterative phases-partner engagement, design, prototyping, deployment, and evaluation tailored to time and resource constraints of the health system. Methods used included interviews, focus groups, surveys, and usability testing. Results Multilevel partner engagement surfaced critical insights that informed design adaptations. The heart failure CDS was adapted using minimal changes while the naloxone CDS underwent more extensive design iterations. Both projects balanced rigor and pragmatism, enabling timely implementation and rigorous design evaluation while supporting feasibility and sustainability. Iterative evaluations of both CDS are ongoing and structured to inform real-time refinements that support patient, clinician, and system level outcomes. Conclusions This work provides practical guidance on applying an integrated UCD-IS approach to CDS design and evaluation in time and resource-constrained health system environments. By flexibly applying this integrated approach, health systems can better address multilevel partner needs, ensure contextual relevance, and support sustained adoption.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245720","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":"Best practices to design, plan, and execute large-scale federated analyses - key learnings and suggestions from a study comprising 52 databases.","authors":"Theresa Burkard, Montse Camprubi, Daniel Prieto-Alhambra, Peter Rijnbeek, Marta Pineda Moncusi","doi":"10.1055/a-2710-4226","DOIUrl":"https://doi.org/10.1055/a-2710-4226","url":null,"abstract":"<p><strong>Background and significance: </strong>Federated network studies allow data to remain locally while the research is conducted through sharing of analytical code and aggregated results across different healthcare settings and countries. A large number of databases have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), boosting the use of analytical pipelines for standardized observational research within this open science framework. Transparency, reproducibility, and robustness of results have positioned federated analyses using the OMOP CDM within the European Health Data and Evidence Network (EHDEN) as an essential tool for generating large-scale evidence.</p><p><strong>Objectives: </strong>We conducted large-scale federated analyses involving 52 databases from 19 countries using the OMOP CDM. In this State of the Art / Best practice article, we aimed to share key lessons and strategies for conducting such complex, large multi-database analyses. Learnings and suggestions: Meticulous planning, establishing a strong community of collaborators, efficient communication channels, standardized analytics, and strategic division of responsibilities are essential. We highlight the benefits of network engagement, cross-fertilization of ideas, and shared learning. Further key elements contributing to the study's success included an inclusive, incremental implementation of the analytical code, timely engagement of data partners, and community webinars to discuss and interpret study findings. We received predominantly positive feedback from data custodians about their participation, and included input for further improvements for future large-scale federated network studies from this shared learning experience.</p><p><strong>Conclusion: </strong>Our learnings and suggestions aim to help other teams conduct large-scale multinational federated network studies efficiently and within a timely manner. The successful execution of such analyses, as demonstrated here, fostered positive experiences for data partners and stakeholders, encouraging future participation and contributing to sustainable large-scale evidence generation.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145179787","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 for Streamlined Medical Record Generation: Implications for Healthcare Informatics.","authors":"Yi-Ling Chiang, Kuei-Fen Yang, Pin-Chih Su, Shang-Feng Tsai, Kai-Li Liang","doi":"10.1055/a-2707-2959","DOIUrl":"https://doi.org/10.1055/a-2707-2959","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to leverage a Large Language Model (LLM) to improve the efficiency and thoroughness of medical record documentation. This study focused on aiding clinical staff in creating structured summaries with the help of an LLM and assessing the quality of these AI-proposed records in comparison to those produced by doctors.</p><p><strong>Methods: </strong>This strategy involved assembling a team of specialists, including data engineers, physicians, and medical information experts, to develop guidelines for medical summaries produced by an LLM (Llama 3.1), all under the direction of policymakers at the study hospital. The LLM proposes admission, weekly summaries, and discharge notes for physicians to review and edit. A validated Physician Documentation Quality Instrument (PDQI-9) was used to compare the quality of physician-authored and LLM-generated medical records.</p><p><strong>Results: </strong>The results showed no significant difference was observed in the total PDQI-9 scores between the physician-drafted and AI-created weekly summaries and discharge notes (P = 0.129 and 0.873, respectively). However, there was a significant difference in the total PDQI-9 scores between the physician and AI admission notes (P = 0.004). Furthermore, there were significant differences in item levels between physicians' and AI notes. After deploying the note-assisted function in our hospital, it gradually gained popularity.</p><p><strong>Conclusions: </strong>LLM shows considerable promise for enhancing the efficiency and quality of medical record summaries. For the successful integration of LLM-assisted documentation, regular quality assessments, continuous support, and training are essential. Implementing LLMs can allow clinical staff to concentrate on more valuable tasks, potentially enhancing overall healthcare delivery.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151547","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":"Changes in Pediatric Portal Use Among Caregivers Before, During, and After the COVID-19 Pandemic: A Longitudinal Study.","authors":"Philipp Haessner, Jessica M Ray, Megan E Gregory","doi":"10.1055/a-2703-3735","DOIUrl":"https://doi.org/10.1055/a-2703-3735","url":null,"abstract":"<p><strong>Background: </strong>Patient portals are increasingly used to support digital health engagement, but little is known about how caregivers used patient portals before, during, and after the COVID-19 pandemic.</p><p><strong>Objectives: </strong>To examine longitudinal changes in caregiver engagement with pediatric patient portals, focusing on logins, session duration, messaging behaviors, and provider response times across pre-pandemic, pandemic, and post-pandemic periods.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study using de-identified MyChart data from caregivers of children aged 0 through 11 who received care at four pediatric primary care clinics in the Southeastern U.S. between March 2018 and March 2023. Generalized linear models were used to compare portal engagement across pre-pandemic, pandemic, and post-pandemic periods. Outcomes included login frequency, session duration, message volume, message types and recipients, and provider response times, all normalized per user per year.</p><p><strong>Results: </strong>Among 478 caregivers, portal logins and session duration increased significantly during and post-pandemic, with 16-fold increases post-pandemic compared to pre-pandemic (p < 0.001). Message volume declined substantially during the pandemic (p < 0.001) but returned to baseline levels. Provider response times shortened during the pandemic and remained lower than pre-pandemic levels (p = 0.032). Messaging to primary care declined and did not recover fully, while specialty care messaging increased across all periods. Appointment and medical advice messages declined during the pandemic, with only the latter rebounding. Customer service inquiries rose significantly and remained elevated, and medication renewal messages increased markedly post-pandemic.</p><p><strong>Conclusions: </strong>The COVID-19 pandemic initiated lasting changes in caregivers' engagement with pediatric patient portals, including deeper engagement, quicker provider responses, and shifts in messaging patterns. Findings can be used to guide and optimize caregiver-centered digital health strategies in pediatrics. Future work should explore potential provider burnout from increased portal workload, incorporate multicenter studies, and link portal use to clinical characteristics to better inform digital health interventions.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092628","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":"Nursing Performance Using Clinical Prediction Rules for Acute Respiratory Infection Management: A Case-Based Simulation.","authors":"Victoria L Tiase, Patrice Hicks, Haddy Bah, Ainsley Snow, Devin Mann, David A Feldstein, Wendy Halm, Paul D Smith, Rachel Hess","doi":"10.1055/a-2700-7036","DOIUrl":"https://doi.org/10.1055/a-2700-7036","url":null,"abstract":"<p><p>Background Overuse and misuse of antibiotics is an urgent healthcare problem and one of the key factors in antibiotic resistance. Validated clinical prediction rules have shown effectiveness in guiding providers to an appropriate diagnosis and identifying when antibiotics are the recommended choice for treatment. Objective We aimed to study the relative ability of registered nurses using clinical prediction rules to guide the management of acute respiratory infections in a simulated environment compared to practicing primary care physicians. Design We evaluated a case-based simulation of the diagnosis and treatment for acute respiratory infections using clinical prediction rules. As a secondary outcome, we examined nursing self-efficacy by administering a survey before and after case evaluations. Participants Participants included 40 registered nurses from three academic medical centers and five primary care physicians as comparators. Participants evaluated six simulated case studies, three for patients presenting with cough symptoms and three for sore throat. Key Results Compared to physicians, nurses determined risk and treatment for simulated sore throat cases using clinical prediction rules with nurses having 100% accuracy in low-risk sore throat cases versus 80% for physicians. We found great variability in the accuracy of the risk level and appropriate treatment for cough cases. Nurses reported slight increases in self-efficacy from baseline to post-case evaluation suggesting further information is needed to understand correlation. Conclusions Clinical prediction rules used by nurses in sore throat management workflows can guide accurate diagnosis and treatment in simulated cases, while cough management requires further exploration. Our results support the future implementation of automated prediction rules in a clinical decision support tool and a thorough examination of their effect on clinical practice and patient outcomes.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070972","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":"Identifying Electronic Health Record Tasks and Activity Using Computer Vision.","authors":"Liem Manh Nguyen, Amrita Sinha, Adam Dziorny, Daniel Tawfik","doi":"10.1055/a-2698-0841","DOIUrl":"https://doi.org/10.1055/a-2698-0841","url":null,"abstract":"<p><strong>Background: </strong>Time spent in the electronic health record (EHR) is an important measure of clinical activity. Vendor-derived EHR use metrics may not correspond to actual EHR experience. Raw EHR audit logs enable customized EHR use metrics, but translating discrete timestamps to time intervals is challenging. There are insufficient data available to quantify inactivity between audit log timestamps.</p><p><strong>Methods: </strong>We propose a computer vision-based model that can 1) classify EHR tasks being performed, and identify when task changes occur, and 2) quantify active-use time using session screen recordings of EHR use. We generated 111 minutes of simulated workflow in an Epic sandbox environment for development and training and collected 86 minutes of real-world clinician session recordings for validation. The model used YOLOv8, Tesseract OCR, and a predefined dictionary to perform task classification and task change detection. We developed a frame comparison algorithm to delineate activity from inactivity and thus quantify active time. We compared the model's output of task classification, task change identification, and active time quantification against clinician annotations. We then performed a post-hoc sensitivity analysis to identify the model's accuracy when using optimal parameters.</p><p><strong>Results: </strong>Our model classified time spent in various high-level tasks with 94% accuracy. It detected task changes with 90.6% sensitivity. Active-use quantification varied by task, with lower MAPE for tasks with clear visual changes (e.g., Results Review) and higher MAPE for tasks with subtle interactions (e.g., Note Entry). A post-hoc sensitivity analysis revealed improvement in active-use quantification with a lower threshold of inactivity than initially used.</p><p><strong>Conclusion: </strong>A computer vision approach to identifying tasks performed and measuring time spent in the EHR is feasible. Future work should refine task-specific thresholds and validate across diverse settings. This approach enables defining optimal context-sensitive thresholds for quantifying clinically relevant active EHR time using raw audit log data.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034135","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":"A Mixed-Method Case Study to Evaluate Adoption of Clinical Decision Support for Cancer Symptom Management.","authors":"Jennifer L Ridgeway, Deirdre R Pachman, Lila J Finney Rutten, Joan M Griffin, Sarah A Minteer, Jessica D Austin, Linda L Chlan, Cindy Tofthagen, Kyle A Tobin, Veronica Grzegorcyzk, Parvez Rahman, Kathryn J Ruddy, Andrea L Cheville","doi":"10.1055/a-2587-6081","DOIUrl":"https://doi.org/10.1055/a-2587-6081","url":null,"abstract":"<p><p>Electronic patient-reported outcome measures (ePROMs) can improve care for people with cancer, but effectiveness hinges on well-supported integration in clinical settings.We evaluated clinician use of specific clinical decision support (CDS) tools in the electronic health record (EHR) designed to facilitate timely, clinically appropriate responses to ePROM scores for six symptoms commonly experienced by cancer patients.The parent pragmatic trial, which took place at Mayo Clinic (Rochester, Minnesota, United States) and its affiliated community health care system between March 2019 and January 2023, evaluated the population-level effectiveness and implementation of an ePROM surveillance and EHR-facilitated collaborative care symptom management intervention. The present evaluation used a case study approach with four data sources: (1) clinician interactions with CDS tools abstracted from the EHR; (2) clinician notes identified with an institution-specific textual search tool; (3) qualitative interviews and group discussions with care teams; and (4) administrative records reviewed to identify training and outreach to care teams.EHR metrics showed very low adoption of CDS tools including alerts and symptom-specific order sets, despite educational outreach and information technology support provided to clinical care teams. Qualitative findings revealed that CDS use was not easy to integrate into busy clinical workflows and highlighted clinician perceptions that the collaborative care intervention provided additional patient support that reduced clinicians' need to utilize CDS tools. They also highlight the importance of contextual factors, including institutional priorities and EHR changes.This pragmatic clinical trial case study found limited adoption of EHR CDS tools that had been developed to increase clinicians' awareness of and responses to ePROM data. Findings suggest the need to align clinician and organizational implementation strategies, simplify CDS tools to fit practice expectations, and identify and address contextual factors that could undercut strategies like education and peer support. This may be especially important for teams who aim to iteratively evaluate and refine CDS and implementation strategies for multicomponent interventions or introduce new strategies that are responsive to barriers while maintaining scalability.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"804-814"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144975005","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":"Modeling Patients' Progression through Health-Related Social Needs.","authors":"Haleigh Kampman, Ofir Ben-Assuli, Joshua Vest","doi":"10.1055/a-2600-9192","DOIUrl":"10.1055/a-2600-9192","url":null,"abstract":"<p><p>This study sought to characterize how a population experienced health-related social needs (HRSNs) over time.We employed hidden Markov modeling using data extracted from a natural language processing state machine from 2018 to 2020 to examine whether a patient experienced any food, legal, transportation, employment, financial, or housing needs. Characteristics of patients transitioning into low/high-risk states were compared. We also identified the frequency at which patients transitioned according to their risk state.Our results identified that five hidden states best represented how patients are experiencing HRSNs longitudinally. Of 48,055 patients, 80% were categorized in states 1 and 2, labeled as low risk. Nine percent, 8%, and 3% of the study population were labeled as medium, high, and very high risk, respectively. Results also showed that low and high-risk patients (states 1, 2, and 5) only transition states once every year and a half, while patients in medium and high-risk states transition approximately once per year.Low and very high-risk patients tend to remain in the same state over time, suggesting that low-risk patients may have the means to maintain a healthy state while very high-risk patients have a difficult time resolving multiple HRSNs. Early screening and immediate interventions may be beneficial in mitigating the persistent harm of unaddressed HRSNs.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"1157-1164"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092644","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":"Evolution of an Electronic Health Record-Based Alert to Optimize Venous Thromboembolism Prophylaxis.","authors":"Mikhail Y Akbashev, Alyssa Utz, Phillip Anjum, Stacey Watkins, Michael Mattaliano, Palak Patel, Debbie Vigliotti, Mara L Schenker, Bhavin B Adhyaru","doi":"10.1055/a-2672-8358","DOIUrl":"10.1055/a-2672-8358","url":null,"abstract":"<p><p>Venous thromboembolism (VTE) prophylaxis in hospitalized patients must balance risks of bleeding and thrombosis. Clinical changes such as bleeding or renal injury can also trigger changes or delays in thromboprophylaxis. Electronic health record alerts (EHRAs) can allow for targeted notification to providers to improve venous thromboembolism prophylaxis and improve patient outcomes at the risk of alert fatigue if not carefully designed and implemented.This study aimed to develop and refine an EHRA that minimizes nuisance alerts while facilitating appropriate ordering of VTE prophylaxis for medical patients.A multidisciplinary group at a single large safety-net academic medical center developed an EHRA to identify patients at increased thrombosis risk, but without orders for VTE prophylaxis. This was refined over four phases: development and validation, initial monitoring and exclusion criteria adjustment, COVID-19-related modifications, and delayed surveillance and modification. Data analysis evaluated criteria including alert frequency, alert action/utilization, and alert duration.The EHRA fired an average of 33.3 times per day across all phases of the study. Phase 1 of EHRA implementation showed significantly increased alerts per patient (6.4 to 43.3 alerts per day, <i>p</i> < 0.01) as well as the percentage of patients with >5 alerts (2.8 to 60.0%, <i>p</i> < 0.01). Modifications in phase 2 and phase 3 increased alert rates without any significant effect on subsequent action taken by a provider. Phase 4 modifications led to a significant reduction in alert frequency (44.1 to 14.9 alerts per day, <i>p</i> < 0.01) coupled with a notable increase in provider action (0.24 to 7.73%, <i>p</i> < 0.01).This multidisciplinary, provider-centered, intervention improved alert appearance, and information needed to guide providers increased provider engagement 32-fold, with a 3-fold decrease in alert frequency. Despite improvements, ongoing monitoring and maintenance of this alert is important.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1060-1066"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762066","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":"Right Idea, Wrong Time: Focusing on Alert Timing for Effective Decision Support.","authors":"Averi E Wilson, Andrew P Bain, Janet Webb, Christoph U Lehmann, Brett Moran, Nainesh Shah, Ellen O'Connell","doi":"10.1055/a-2605-4510","DOIUrl":"10.1055/a-2605-4510","url":null,"abstract":"<p><p>Effective clinical decision support (CDS) interventions improve adherence to care guidelines, reduce prescribing errors, and, in some settings, decrease patient mortality. However, misalignment with the \"Five Rights\" framework, particularly regarding CDS timing in clinical workflows, can lead to implementation failures, alert fatigue, and physician burnout.This case series aimed to evaluate and redesign three interruptive CDS alerts at a large safety net health system to better align with clinician workflows, reduce interruptions, and improve compliance with care guidelines.We analyzed three interruptive alerts using data from Epic's SlicerDicer tool, focusing on alert frequency, contributors to alert triggering, and user responses before and after intervention. Alerts were modified to improve their timing and relevance within the workflow.Modifications included retiming a human immunodeficiency virus screening alert to trigger during laboratory test orders, reducing alert firings by 87% while increasing monthly screening orders from 3,561 to 4,547 (<i>p</i> < 0.001). An administrative alert's firing frequency decreased by 86% through the introduction of a 4-hour lockout period, maintaining compliance rates. Finally, restricting a pediatric head circumference discrepancy alert to in-person visits only eliminated interruptions during telehealth encounters, addressing a major source of clinician frustration.Aligning CDS tools with clinical workflows through adherence to the \"Five Rights\" framework reduces interruptions and improves outcomes. Iterative review, user feedback, and proactive redesign are essential to ensure CDS effectiveness, particularly as health care evolves to include novel care delivery models like telehealth.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1200-1207"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041901","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}