Applied Clinical Informatics最新文献

{"title":"Teaching Informatics Competencies within Nursing Education: A Scoping Review of Teaching Strategies.","authors":"Charlene H Chu, Yana Siganevich, Simon Donato-Woodger, Michelle Nguyen, Rebecca Zavalunov, Jacquelyn Wang, Zoraida Beekhoo, Charlene E Ronquillo, Sarah Ibrahim, Don Flaming, Nadia Green, Eric Maillet","doi":"10.1055/a-2648-4914","DOIUrl":"https://doi.org/10.1055/a-2648-4914","url":null,"abstract":"<p><p>As healthcare delivery becomes increasingly digital, nursing informatics knowledge has become essential for nurses to participate effectively in digitally enabled healthcare environments. The Canadian Association of Schools of Nursing (CASN) released updated nursing informatics entry-to-practice competencies in 2025 to inform nursing education in Canada. However, the integration of informatics knowledge and content into nursing curricula and professional practice education remains inconsistent. Mapping the teaching strategies used by nursing educators to support these competencies is a necessary step toward understanding current educational practices.To identify teaching strategies currently employed to support the development of nursing informatics knowledge and competencies in undergraduate, graduate, and professional nursing education.The search strategy included six electronic databases: Scopus, Web of Science, CINAHL, ERIC, Embase, and Medline. Key search terms were synonyms and combinations of \"informatics competencies,\" \"nursing informatics,\" and \"education.\" Articles were included if they specifically described nursing informatics competencies and how they were taught to nursing students and practicing nurses. The papers were independently reviewed by two reviewers, and a thematic analysis was conducted to identify teaching strategies.A total of 120 publications were included in the scoping review. Seven strategies to teach nursing informatics were identified: (1) integration of electronic health records into laboratory simulations; (2) integration of informatics competency frameworks; (3) accessing online educational resources; (4) integration of mobile technologies; (5) informatics- competent educators; (6) integrating patient safety and data ethics; and (7) interdisciplinary collaboration.There is an urgent need to align nursing education with the rapid rise of technologies to prepare nurses for safe, competent, and person-centered digital care. This review highlights diverse, CASN-aligned teaching strategies that support informatics competency development across all levels of nursing education. The findings offer practical guidance for educators and inform cirriculum planning and professional practice education in digitally-enabled environments.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"911-929"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12390368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144975483","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":"From Steps to Mobility Levels: Validating a Consumer-Grade Activity Monitor for Automated Recording of Patient Mobility in Hospitals.","authors":"Jan Stenum, Eric Stewart, Daniel L Young, Ioannis Collector, Karli Funk, Lydia Vincent, Elizabeth Colantuoni, Erik H Hoyer","doi":"10.1055/a-2576-1505","DOIUrl":"10.1055/a-2576-1505","url":null,"abstract":"<p><p>Patient mobility during hospitalization is essential for high-quality healthcare as mobility is linked to physical function and quality of life. The Johns Hopkins Highest Level of Mobility (JH-HLM) scale is a validated method to assess mobility in hospitalized patients. Although the JH-HLM is widely utilized, it has limitations including ceiling effects, unobserved mobility events going unrecorded, and the staff time needed to observe and document.We explored the feasibility of using a consumer-grade activity monitor (Fitbit) to predict JH-HLM scores and address these limitations.JH-HLM scores and step counts were recorded simultaneously using behavioral mapping and analyzed over 1-hour periods among inpatients. We predicted JH-HLM scores based on step counts by fitting ordinal logistic regressions, according to three categorizations of JH-HLM scores reflecting increasing mobility-granularity.We collected data for 189 patient-hours in a cohort of 20 participants. Step counts increased with higher JH-HLM mobility scores. When predicting JH-HLM scores from step counts, there was a trade-off between accuracy and mobility granularity: overall accuracy was 75% when categorizing patient-hours as immobility (JH-HLM of 1 to 5) or mobility (JH-HLM of 6 to 8); accuracy was 68% when categorizing immobility, shorter walking behavior (JH-HLM of 6 to 7), and longer walking behavior (JH-HLM of 8); accuracy was 61% when categorizing immobility and three progressively higher volumes of walking (JH-HLM of 6, 7 and 8).Step counts from the activity monitor could be used to predict whether a patient was immobile or mobile but may lack the sensitivity to accurately predict specific mobility levels.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"753-759"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12328031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144795839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Influence of Artificial Intelligence Scribes on Clinician Experience and Efficiency among Pediatric Subspecialists: A Rapid, Randomized Quality Improvement Trial.","authors":"H Stella Shin, Herb Williams, Nikolay Braykov, Afrin Jahan, Jeremy Meller, Evan W Orenstein","doi":"10.1055/a-2657-8087","DOIUrl":"10.1055/a-2657-8087","url":null,"abstract":"<p><p>Artificial intelligence (AI) scribes may reduce the documentation burden and improve clinician experience through generative AI automatically producing provider note sections from recordings of patient-provider encounters.We aimed to examine the impact of AI scribes on clinician experience, clinician efficiency, and business efficiency measures among pediatric subspecialty physicians.We randomized pediatric subspecialty providers with ≥0.5 clinical full-time equivalent and stable electronic health record (EHR) log metrics to use Microsoft/Nuance Digital Ambient eXperience (DAX) Copilot from May 1, 2024, to July 31, 2024 (intervention group) or controls. Using difference-in-differences, we compared quantitative measures of subjective clinician experience using the KLAS Net EHR Experience survey, objective measures of clinician efficiency from EHR logs (e.g., pajama time), and business efficiency measures. At-the-elbow support checked in with intervention providers approximately weekly, and we assessed the sentiment of qualitative comments.Twelve providers were randomized to the intervention and 11 to the control group. One intervention provider stopped using DAX due to ineffectiveness. In the intervention group, DAX was used to populate one or more characters in 53% of visit notes (range across providers: 10.6-98.2%). Nine intervention and eight control providers completed pre- and postsurveys. KLAS Net EHR Experience improved among intervention providers from 52.6 (70th percentile) to 75.2 (99th percentile) but dropped from 37.3 (38th percentile) to 30 (14th percentile) among control providers. Experiencing burnout dropped from 8 (89%) to 5 (56%) among intervention providers but remained stable at 3 (38%) in the control group. There was no significant change to pajama time (-9.4 minutes per scheduled day, 95% CI: -41.2 to +22.4), time in notes per encounter (+0.2 minutes per note, 95% CI: -6.6 to +6.9), or work Relative Value Units (wRVUs) per encounter (-0.03, 95% CI: -0.5 to +0.44). Of 48 qualitative comments, 69% had a positive sentiment, 15% neutral, and 17% negative.Among pediatric subspecialists, AI scribes improved clinician experience and burnout without changing charting time or EHR work outside work hours.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1041-1052"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660864","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":"Choosing between Patient Care Needs and Accurate Data Capture: Exploring Nurses' Experiences of Excessive Documentation Burden.","authors":"Jennifer Thate, Rachel Y Lee, Rosemary Mugoya, Courtney J Diamond, Temiloluwa Daramola, Po-Yin Yen, Sarah C Rossetti","doi":"10.1055/a-2683-5752","DOIUrl":"10.1055/a-2683-5752","url":null,"abstract":"<p><p>This study aimed to explore: (1) how nurses in the acute care setting describe their experience(s) of excessive documentation burden (ExDocBurden); (2) what factors contribute to ExDocBurden for nurses in the inpatient setting; and (3) nurses' perspectives on solutions to mitigate ExDocBurden that support documentation practices that they deem essential to providing safe, high-quality care.Semistructured interviews were conducted with 18 acute care nurses. Transcribed interviews were analyzed using the constant comparative method.All sources of ExDocBurden were categorized as issues of usability which included four themes: (1) inaccurate data resulting from EHR rules or logic that force or limit responses; (2) burdensome lengthy flowsheets-scrolling, clicking, and searching for the right place to document; (3) checking the box prevents meaningful information capture; and (4) a moving target-ongoing updates and inadequate training. Strategies to reduce ExDocBurden were categorized as \"current approaches\" and \"future innovations.\"Based on synthesis of categories and themes, alongside existing literature, we propose the following recommendations: (1) develop evidence-based consensus on essential EHR data elements, (2) minimize structured data entry interfaces and maximize forms of data entry that develop and reflect nurses' clinical reasoning, (4) leverage emerging technologies to capture and parse data into structured formats suitable for secondary uses.Addressing usability issues identified by nurses is critical to reducing ExDocBurden. Increasing required data entry in structured flowsheets not only contributes to ExDocBurden, but also leads to inaccurate data capture that has serious implications for AI tools that rely on the quality of previously documented data.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"1231-1243"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208157","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":"Refining a Machine Learning Model for Predicting Infant Sepsis: A Multidisciplinary Team Supported by Human-Centered Design Methods.","authors":"Dean Karavite, Lusha Cao, Mary C Harris, Alex Fidel, Lyle Ungar, Gerald Shaeffer, Rui Xiao, Patrick Brady, Heather C Kaplan, Robert W Grundmeier","doi":"10.1055/a-2618-4470","DOIUrl":"10.1055/a-2618-4470","url":null,"abstract":"<p><p>Human-centered design (HCD) methods in machine learning generally focus on workflow, user interfaces, and data visualizations, but there is the potential to apply these methods to inform the model development and testing process.This study aimed to demonstrate the potential of HCD methods to support the design and testing of machine learning models developed for clinical decision-making.In preparing for formative user testing of clinician facing representations of a machine learning model for detecting sepsis in neonatal intensive care unit (NICU) patients, we discovered that interactive low fidelity mockups using real patient data revealed potential model anomalies. To further investigate these potential anomalies, we utilized the qualitative analysis of interviews with 31 NICU clinicians concerning their experience with neonatal sepsis. The review process was conducted by a multidisciplinary team with members having expertise in neonatology, informatics, data science, and human computer interaction (HCI). Anomalies identified via the mockups and interview analysis were further analyzed by inspections of patient charts and model features and code.The HCD-facilitated review revealed anomalies in three categories: (1) feature inclusion and exclusion, (2) feature importance, and (3) model stability over time. Data entry errors in the electronic health record and their impact on model output were also noted. The review resulted in 41 changes to the model.The discovery of over 41 opportunities to improve our prediction model was a serendipitous by-product of the HCD process. Our results suggest that HCD can be applied not only to model display design and measures of explainability, but to the development and evaluation of the model itself. This case report also demonstrates the need for a multidisciplinary team of clinicians, data scientists, and HCI experts in identifying and addressing issues involving machine learning model performance.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"1332-1340"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12513773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276326","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":"Perspectives of Spanish-Speaking Caregivers on Pediatric Patient Portal Use.","authors":"Gabriel Tse, Stephanie Squires, Katherine Hu, Michelle M Kelly, Bonnie Halpern-Felsher, Jennifer Carlson","doi":"10.1055/a-2688-3992","DOIUrl":"10.1055/a-2688-3992","url":null,"abstract":"<p><p>Disparities exist in patient portal use among non-English-speaking caregivers of pediatric patients. This study aims to evaluate the reasons behind Spanish-speaking caregivers' use of patient portals and identify facilitators and barriers, focusing on those caring for children with chronic conditions.We conducted semi-structured interviews and surveys with Spanish-speaking caregivers of pediatric patients with chronic conditions at an academic pediatric health network in California. Data were transcribed, coded, and analyzed using inductive thematic analysis.Twenty caregivers participated. Participants primarily accessed patient portals via their smartphones, and most accessed the patient portal at least weekly. Three main themes emerged: perceived benefits (managing appointments, medications, and results), facilitators that improved use (support from healthcare professionals), and barriers that negatively affected use (differences in language, health literacy, and digital health literacy).Spanish-speaking caregivers find patient portals beneficial but face significant barriers related to language discordance and differences in health literacy and digital health literacy. This study highlights the need for health systems to provide language concordance within patient portals and consider innovative solutions that promote equitable use.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1244-1251"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144975625","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":"Information Prioritization and Reading Patterns in Electronic Health Record Nursing Summaries: An Eye-Tracking Case Study.","authors":"Suhyun Park, Jenna L Marquard, Robin R Austin, Christie L Martin, David S Pieczkiewicz, Connie W Delaney","doi":"10.1055/a-2688-4056","DOIUrl":"10.1055/a-2688-4056","url":null,"abstract":"<p><p>While nursing summaries in electronic health records are used for initial orientation to a patient's status, research on nurses' use of these summaries remains scarce. This case study conducted an eye-tracking simulation to identify (1) key information types (orders, vital signs, etc.), (2) frequently paired information types, and (3) common sequential patterns of information types within nursing summaries as nurses review simulated patient cases.We recruited 33 medical-surgical nurses from a university hospital. As part of an eye-tracking simulation, they reviewed three simulated patients' nursing summaries. A screen-based eye-tracker was used to capture participants' gaze fixation on different information types. For analysis, we used discrete-time Markov chains and sequential pattern mining.The average total gaze fixation time was 1.77 minutes from 26 analyzed participants' eye gaze data. Most of this time was spent shifting between information types or making notes. \"Orders\" and \"Sidebar\" (mini summary of demographics and health status) were the information types that consistently emerged as key areas of focus. Participants tended to read specific information types in pairs and followed a top-to-bottom order of reading on the screen.When reviewing unfamiliar patient cases, nurses prefer to construct a comprehensive patient narrative. Nursing summaries can be redesigned by prioritizing key information types, grouping relevant information pairs, and arranging information in a top-to-bottom manner based on relevance. We recommend that hospitals and EHR vendors prioritize the customization of nursing summaries to align with nurses' information needs and workflows. Tailored summary layout improvements beyond a one-size-fits-all design, informed by interdisciplinary collaboration, can enhance information reading efficiency.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"1252-1262"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208212","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":"Development of a Patient-Facing Clinical Decision Support Application for Hypertension.","authors":"Michelle Bobo, Shannon M Canfield, Victoria Shaffer, Matt Storer, LeAnn Michaels, Amy Yates, Abigail J Rolbiecki, Richelle Koopman, David Dorr","doi":"10.1055/a-2697-2107","DOIUrl":"10.1055/a-2697-2107","url":null,"abstract":"<p><p>Hypertension is a chronic condition defined by persistent high blood pressure (BP) that contribute to significant morbidity and mortality. Evidence-based clinical guidelines provide recommendations for the diagnosis and management of hypertension. These recommendations are frequently incorporated into clinical decision support (CDS) tools used by clinicians. CDS tools can also be oriented toward patients but careful attention to the development process is required to make a useful, usable, and engaging digital health intervention.We sought to design, develop, and optimize a patient-facing CDS application for hypertension, which emphasizes home-based monitoring and collaboration with the health care team around treatment goals.We conducted an iterative, user-centered design process to develop the application. First, we identified user needs, key components, and the technological platform. Then, we developed the integrated application and performed extensive testing to validate and optimize performance and usefulness. After identifying issues in the testing processes, we performed an additional round of optimization development.We have completed development of the COACH (Collaborative Approach to Controlling High Blood Pressure) web application using JAVA and SMART on FHIR technologies with a focus on interoperability. The COACH application supports home-based BP monitoring and provides evidence-based, patient-centered CDS incorporating education, counseling, and treatment recommendations. Early results showed that we were able to increase usability, address data quality concerns, and demonstrate improved BP control in a pilot study.Extensive preparatory research and user-centered design processes enabled the successful development of a novel tool for enabling management of high BP. The tool uses data from the patient's medical record and ambulatory BP monitoring to provide patient-centered CDS recommendations. We are now evaluating the tool through a multisite clinical trial.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1298-1309"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12507491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024597","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":"Exploring Provider Perceptions and Attitudes toward Copy-Paste and Copy-Forward in Clinical Documentation.","authors":"Matthew Hudkins, Jeffrey A Gold, Sky Corby, Joan Ash, Vishnu Mohan","doi":"10.1055/a-2574-1348","DOIUrl":"10.1055/a-2574-1348","url":null,"abstract":"<p><p>Copy-paste (CP) and copy-forward (CF) are common electronic health record (EHR) documentation tools that purportedly improve provider efficiency, but they can also contribute to documentation burden while increasing note bloat and errors. Our understanding of provider perceptions of these tools remains limited.This study aimed to increase understanding of provider perceptions and self-reported usage patterns of CP and CF across different clinical environments and provider roles, including the impact of these tools on clinical documentation quality and efficiency.A survey was developed and administered at a large academic medical center from December 2022 to March 2023. The survey was distributed to medical students, trainees, and faculty. Questions addressed documentation practices, perceived benefits and risks of CP/CF, and attitudes toward future use. Data were analyzed both quantitatively and qualitatively.Among 913 respondents (22-28% response rate across levels of training), 82% reported using CP, and 52% used CF in clinical documentation. Usage varied significantly by environment, with the highest utilization in inpatient primary services (91% CP, 68% CF) and the lowest in emergency departments (70% CP, 14% CF). Eighty-six percent of providers believed that CP/CF improved efficiency. A majority felt that CP (59-70%) and CF (69-76%) worsened several types of documentation errors. Providers showed stronger acceptance of copying from their own notes (90% CP, 82% CF) compared with others' notes (61% CP, 47% CF).Self-reported use of CP and CF is high by providers, driven by perception of improved efficiency despite recognition that these tools contribute to documentation errors and note bloat. Use varies by practice environment. CP is viewed more favorably compared with CF, as is copying one's own documentation compared with that of another provider. This suggests that solutions should be nuanced and workflow-specific. Future interventions must balance documentation quality with efficiency and take the practice environment and provider role into account.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"736-746"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12328030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144795838","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":"Assessing Medication CDS Usability: Pilot Results from 10 Outpatient Clinics.","authors":"Zoe Co, David W Bates, Jessica M Cole, Raj Ratwani, David C Classen","doi":"10.1055/a-2647-1069","DOIUrl":"https://doi.org/10.1055/a-2647-1069","url":null,"abstract":"<p><p>This study aimed to develop a human factors assessment for medication-related clinical decision support (CDS) based on a previously validated tool that assessed the integration of human factors principles in CDS, the instrument for evaluating human factors principles in medication-related decision support alerts (I-MeDeSA), and pilot it with 10 outpatient clinics across the United States.The human factors assessment was developed based on past validations of I-MeDeSA. Examples included changing the wording of questions and reformatting answer choices to check-box options, allowing for multiple answer choices. We also added a section about how clinicians resolved alerts. Clinics received a percentage score based on how well their CDS adhered to human factors principles. To take the assessment, testing teams at each clinic triggered a high-severity drug-drug interaction (DDI) alert, and then took the human factors assessment. This assessment was piloted in 10 outpatient clinics, each of which used a different commercial electronic health record (EHR) system.The final assessment included five sections and twelve questions related to aspects like the timing, visual aspect, severity, content, and actions within the DDI alert. The mean overall percentage score was 62%. The sections regarding the timing and visual aspects of the alert were ones where clinics' EHRs performed the best. However, in the \"actions\" section, 40% of the clinics could bypass high severity alerts without any safeguards in place.We found substantial variability in the integration of human factors principles in the design and delivery of DDI alerts among the outpatient clinics, and some lacked important medication safeguards. This assessment can be used by outpatient clinics for safety improvement initiatives.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"879-891"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12367376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144975417","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}