Applied Clinical Informatics最新文献

{"title":"Special Issue on CDS Failures: A two-phase framework leveraging user feedback and systemic validation to improve post-live Clinical Decision Support.","authors":"Wendi Zhao, Xuetao Wang, Kevin Afra","doi":"10.1055/a-2644-7250","DOIUrl":"https://doi.org/10.1055/a-2644-7250","url":null,"abstract":"<p><strong>Objectives: </strong>Despite the benefits of Clinical Decision Support (CDS), concerns of potential risks arise amidst increasing reports of CDS malfunctions. Without objective and standard methods to evaluate CDS in post-live stage, CDS performance in dynamic healthcare environment remains a black box from user perspective. In this study, we proposed a comprehensive framework to identify and evaluate post-live CDS malfunctions from the perspective of healthcare settings.</p><p><strong>Methods: </strong>We developed a 2-phase framework to identify and evaluate post-live CDS system malfunctions: (1) Real-time feedback from users in healthcare settings (2) Systematic validation through the use of databases that involves fundamental data flow validation and knowledge and rules validation. Identity, completeness, plausibility, consistency across locations and time patterns were included as measures for systematic validation. We applied this framework on a commercial CDS system in 14 acute care facilities in Canada in a 2-year period.</p><p><strong>Results: </strong>During this study, 7 types of malfunctions were identified. The general rate of malfunctions was below 2%. In addition, an increase in CDS malfunctions was found during electronic health record (EHR) upgrade and implementation periods.</p><p><strong>Conclusions: </strong>This framework can be used to comprehensively evaluate CDS performance for healthcare settings. It provides objective insights into the extent of CDS issues, with the ability to capture low prevalence malfunctions. Applying this framework to CDS evaluation can help improve CDS performance from the perspective of healthcare settings. KEY WORDS Clinical decision support, Methodologies, Error management and prevention, Quality.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530619","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":"Exploring the Relationship Between Digital Health Literacy and Patterns of Telemedicine Engagement and Appointment Attendance Within an Urban Academic Hospital.","authors":"Natali Sorajja, Julia Zheng, Sunit Jariwala","doi":"10.1055/a-2640-2742","DOIUrl":"https://doi.org/10.1055/a-2640-2742","url":null,"abstract":"<p><strong>Background: </strong>Telemedicine use has surged since the COVID-19 pandemic, offering a convenient way for patients to access healthcare. Whereas digital literacy (general comfort with and ability to use digital tools) is necessary to utilize telemedicine, digital health literacy is a subset of this, focusing on the ability to use digital tools to seek out, understand, and utilize health information. As such, barriers such as the lack of high-speed internet and limited digital health literacy can hinder telemedicine's effectiveness, particularly for historically marginalized populations with lower technological access.</p><p><strong>Objectives: </strong>This study aims to characterize the relationship between baseline digital health literacy, appointment no-shows, and telemedicine usage in a Bronx population.</p><p><strong>Methods: </strong>In a Bronx-based cohort, we assessed digital health literacy using e-HEALS and e-HeLiOS-SB, and health literacy with the Newest Vital Sign (NVS) instrument. Baseline sociodemographic characteristics (e.g. age, insurance type) were collected, and appointment no-show rates and telemedicine usage were calculated. Linear regression models were used to assess associations.</p><p><strong>Results: </strong>Higher digital health literacy, private insurance (compared to Medicaid), and older age were associated with fewer no-shows. Higher video visit usage was also associated with fewer no-shows. Individuals at high risk of housing insecurity were less likely to use video visits, and higher phone visit usage was associated with patients experiencing financial resource strain. Digital health literacy was positively associated with White race and negatively associated with Medicare usage (compared to Medicaid).</p><p><strong>Conclusion: </strong>Higher digital health literacy correlates with increased appointment attendance, indicating the need to address digital barriers in healthcare. Increasing telemedicine use may help reduce no-shows, and patient-specific strategies are needed to enhance digital health literacy and telemedicine effectiveness.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486749","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":"Special Issue on CDS Failures: The Costs and Benefits of Clinical Decision Support for Radiology Appropriate Use Criteria: A Retrospective Observational Study.","authors":"Andrew Fischer Lees, Andrew White, Michael G Leu, Jeff Robinson, M Kennedy Hall, Robert Doerning","doi":"10.1055/a-2635-3820","DOIUrl":"https://doi.org/10.1055/a-2635-3820","url":null,"abstract":"<p><strong>Background: </strong>Appropriate Use Criteria Clinical Decision Support (AUC CDS) was legislatively mandated in the United States in 2014, and multiple CDS vendors were designated as qualified Clinical Decision Support Mechanisms by the Centers for Medicare and Medicaid Services. Little is known about the costs and benefits of these systems in real-world settings.</p><p><strong>Objectives: </strong>We evaluated the effectiveness of an AUC CDS system and the time costs it imposes on clinicians at a US academic medical center.</p><p><strong>Methods: </strong>Our academic medical center's enterprise data warehouse was queried for AUC CDS alert events and timestamps occurring between July 1, 2021 and June 30, 2022. We calculated percent of altered orders and alert-related timespans, and used these to calculate CDS positive predictive value (PPV), time costs, and the cost-benefit ratio of minutes of provider time per altered order. Based on the medical literature and expert opinion on well-performing CDS, we hypothesized a CDS PPV of 8%.</p><p><strong>Results: </strong>Overall PPV was 1%, leading us to reject our hypothesis that our AUC CDS was well performing (p < 0.001). Median time costs per alert were high (12 seconds load time, 2 seconds dwell time), yielding a CDS cost/benefit ratio of 38 provider minutes per altered order.</p><p><strong>Conclusions: </strong>Despite using one of three market-leading AUC CDS tools, our CDS demonstrated long load times, short dwell times, and low PPV. Provider attention is not free - policymakers should consider both CDS effectiveness and costs (including time costs) when designing AUC policy.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310651","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":"Artificial Intelligence-Based Hospital Malnutrition Screening: Validation of a Novel Machine Learning Model.","authors":"Adam M Bernstein, Pierre Janeke, Richard V Riggs, Emily Burke, Jemima Meyer, Meagan F Moyer, Keiy Murofushi, Ray A Botha, Josiah E M Meyer","doi":"10.1055/a-2635-3158","DOIUrl":"https://doi.org/10.1055/a-2635-3158","url":null,"abstract":"<p><strong>Background: </strong>Despite its morbidity, mortality, and financial burden, in-hospital malnutrition remains underdiagnosed and undertreated. Artificial intelligence offers a promising clinical informatics solution for identifying malnutrition risk and one that can be coupled with clinician-delivered patient care.</p><p><strong>Objective: </strong>The objective of the study was to evaluate an artificial intelligence-based hospital malnutrition screening model in a large and diverse inpatient population and to compare it to the currently used clinician-delivered malnutrition screening tool.</p><p><strong>Methods: </strong>We studied the performance of a gradient-boosted decision tree model incorporating a large language model (LLM) for feature extraction using the electronic medical record data of 106,449 patients over 3.75 years.</p><p><strong>Results: </strong>The model's area under the receiver operating curve was 0.92 (95% CI: 0.91-0.92) on the first day of hospitalization and rose to 0.95 (95% CI: 0.95-0.96) using the maximum risk predicted for each patient throughout hospitalization, indexed against discharge-coded malnutrition. Similar results were observed when indexed against dietitian-recorded malnutrition. The model outperformed a nurse-administered, modified version of the Malnutrition Screening Tool (MST) and patients identified by the model had higher likelihoods of readmission and death compared to patients identified by the nurse-administered screener.</p><p><strong>Conclusion: </strong>Our study findings provide validation for a novel model's use in the prediction of in-hospital malnutrition.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310650","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":"Comparing the performances of a fifty-four-year-old computer-based consultation to ChatGPT-4o.","authors":"Elvan Burak Verdi, Oguz Akbilgic","doi":"10.1055/a-2628-8408","DOIUrl":"https://doi.org/10.1055/a-2628-8408","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate and compare the diagnostic responses generated by two artificial intelligence models developed 54 years apart and to encourage physicians to explore the use of large language models (LLMs) like GPT-4o in clinical practice.</p><p><strong>Methods: </strong>A clinical case of metabolic acidosis was presented to GPT-4o, and the model's diagnostic reasoning, data interpretation, and management recommendations were recorded. These outputs were then compared to the responses from Schwartz's 1970 AI model built with a decision-tree algorithm using Conversational Algebraic Language (CAL). Both models were given the same patient data to ensure a fair comparison.</p><p><strong>Results: </strong>GPT-4o generated an advanced analysis of the patient's acid-base disturbance, correctly identifying likely causes and suggesting relevant diagnostic tests and treatments. It provided a detailed, narrative explanation of the metabolic acidosis. The 1970 CAL model, while correctly recognizing the metabolic acidosis and flagging implausible inputs, was constrained by its rule-based design. CAL offered only basic stepwise guidance and required sequential prompts for each data point, reflecting a limited capacity to handle complex or unanticipated information. GPT-4o, by contrast, integrated the data more holistically, although it occasionally ventured beyond the provided information.</p><p><strong>Conclusion: </strong>This comparison illustrates substantial advances in AI capabilities over five decades. GPT-4o's performance demonstrates the transformative potential of modern LLMs in clinical decision-making, showcasing abilities to synthesize complex data and assist diagnosis without specialized training, yet necessitating further validation, rigorous clinical trials, and adaptation to clinical contexts. Although innovative for its era and offering certain advantages over GPT-4o, the rule-based CAL system had technical limitations. Rather than viewing one as simply \"better,\" this study provides perspective on how far AI in medicine has progressed while acknowledging that current AI tools remain supplements to-not replacements for-physician judgment.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250521","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":"Special Issue on CDS Failures: Finding the Right Level of Interruption to Improve Suicide Screening Compliance in the Emergency Department.","authors":"Katrina Ann-Marie Lee, Christopher S Evans, Misty Skinner","doi":"10.1055/a-2627-2493","DOIUrl":"https://doi.org/10.1055/a-2627-2493","url":null,"abstract":"<p><strong>Background: </strong>The use of real-time Clinical Decision Support (CDS), such as Our Practice Advisory (OPAs), augments clinical decisions while helping to reduce errors and ensuring compliance with organizational best practices1. In complex large health systems, processes for standardization and adherence to emergency department (ED) based suicide screening practices are challenging and may benefit from the use of CDS-based tools adhering to the five rights of CDS2.</p><p><strong>Objectives: </strong>To improve suicide screening compliance for the ED to 95% by implementing a contextually appropriate CDS-based tool within the electronic health record (EHR).</p><p><strong>Methods: </strong>A multidisciplinary group of Quality and ED nursing leadership aimed to develop a chief complaint driven OPA that improved adherence to and completion of suicide screening in the ED. Using an iterative design process over 3 months, a series of two distinct suicide screening OPAs were developed with varying levels of interruption, but both relied on rule-based logic to identify if an ED patient met one of the 57 pre-defined \"Reasons for Visit\" or chief complaints requiring suicide screening. Use of chief complaint driving CDS removed the need for manually remembering complex criteria while contributing to meeting regulatory and organizational standards.</p><p><strong>Results: </strong>The ED suicide screening compliance improved from 64.96% to 77.66% with the initial implementation of the non-interruptive OPA. Subsequently, an interruptive OPA (pop-up window based on a defined trigger that stops the clinician and requires a response), was introduced which further increased screenings being completed to 91.69%. The use of CDS interruptive OPAs significantly improved compliance with suicide screening by including the Columbia Suicide Severity Rating Scale (C-SSRS) tool directly in the OPA.</p><p><strong>Conclusion: </strong>Use of contextually relevant information, such as reason for visit or chief complaint, and interruptive CDS tools embedded into EHR workflows may improve ED based suicide screening.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144975641","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":"Special Topic Burnout: Examining Health Professional Trainee Burnout: Lessons Learned Using Qualitative Inquiry to Elicit Rich Data.","authors":"Ellen Ahlness, Deborah R Levy","doi":"10.1055/a-2624-5482","DOIUrl":"https://doi.org/10.1055/a-2624-5482","url":null,"abstract":"<p><strong>Background: </strong>Health professional (HP) trainee burnout is hard to capture. There are many validated quantitative tools to assess trainee burnout, but fewer qualitative methodological tools that can elicit rich and trustworthy qualitative data on HP trainee burnout.</p><p><strong>Objective: </strong>Report the process, results, and lessons learned while developing and pilot testing a qualitative tool to assess HP trainee experiences of burnout to complement quantitative tools.</p><p><strong>Methods: </strong>We developed a set of semi-structured interview questions to probe burnout for HP trainees which were refined using a Modified Delphi approach (n= 10 subject matter experts). We conducted pilot testing (n=43 interviews with n=14 trainees).</p><p><strong>Results: </strong>We present the results of pilot testing a novel qualitative tool to assess HP trainee experiences of burnout, consisting of 3 core questions and 3 follow-up probes that elicit data on key dimensions of HP trainee burnout for integration into a structured or semi-structured interview guide.</p><p><strong>Conclusion: </strong>We present results as lessons learned, which can support the further development of tools to articulate HP trainee perspectives in studying burnout, especially during health system transitions. Developing qualitative measurement tools designed to be used with well-validated established quantitative tools may be a complex process but is critical in efforts to mitigate HP trainee burnout.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210013","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":"Introduction of a Healthcare System Lens-of-Equity Measurement Strategy to Optimize Breast Cancer Screening.","authors":"Danielle Jungst, Anthony Solomonides, Chad Konchak","doi":"10.1055/a-2621-0110","DOIUrl":"https://doi.org/10.1055/a-2621-0110","url":null,"abstract":"<p><strong>Objective: </strong>Health equity is greatly impacted by the systems and processes with which health systems deliver care. Given the minimal guidance on measurement and reporting of health inequities specific to key population health outcomes, a solution for measurement of health equity is proposed.</p><p><strong>Materials and methods: </strong>The concept of a lens of equity was adopted to disaggregate common measures such as breast cancer screening rates in order to expose inequities across neighborhoods and races in populations served. Two lenses of equity were introduced into the corporate measurement systems, race/ethnicity as measured in the electronic health record, and a surrogate measure of family income.</p><p><strong>Results: </strong>An Equity category wasadded to our measurement systems and counted towards our corporate goals (\"system scorecards\") along with data insights and discovery tools to support the efforts of the breast cancer screening improvement teams. Over a one-year timeframe, Endeavor Health not only met but exceeded its breast cancer screening equity goal, increasing mammography adherence from 73% to 82.6% among residents in the lowest income neighborhoods served.</p><p><strong>Discussion: </strong>The analytics and data systems that support complex healthcare measurement tools require diligent and thoughtful design to meet external reporting requirements and support the internal teams who aim to improve the care of populations served.</p><p><strong>Conclusions: </strong>The analytic approach presented may be readily extended to populations with other potentially impactful differences in social determinants and health status. A \"lens-of-equity\" tool may be established along similar lines, allowing policy and strategy initiatives to be appropriately targeted and successfully implemented.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210011","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":"Special Topic Burnout: Ambient Artificial Intelligence Scribes in Pediatric Primary Care: A Mixed Methods Study.","authors":"Naveed Rabbani, Mondira Ray, Eleanor Verhagen, Jonathan Hatoun, Laura Patane, Louis Vernacchio","doi":"10.1055/a-2625-0750","DOIUrl":"https://doi.org/10.1055/a-2625-0750","url":null,"abstract":"<p><strong>Objective: </strong>Quantify the effect of ambient artificial intelligence (AI) scribe technology on work experience, clinical operations, and patient experience in pediatric primary care.</p><p><strong>Methods: </strong>We conducted a 12-week study of 39 clinicians within a large pediatric primary care network. Clinician experience was measured using a custom survey instrument which included a combination of discrete and free-text responses. Qualitative analysis of free-text responses provided additional context and identified key facilitators and barriers to optimal usage. Proprietary EHR efficiency measures and utilization data were used to further quantify clinician experience, adoption, and operational effects. Patient experience was measured using a vendor-supplied survey instrument.</p><p><strong>Results: </strong>AI scribe technology was used in 32% of eligible encounters (6,249 of 19,264). Survey responses demonstrated significant heterogeneity in clinician experience. The most commonly reported benefits were reduction in self-perceived cognitive burden (21/39), ability to finish work sooner (18/39), and ability to enjoy clinical work more (18/39). No significant change in EHR efficiency measures around documentation time, afterhours EHR time, total EHR time, or visit closure rates were observed. Clinicians reported AI scribes were most helpful for urgent care visits and for summarizing the history of present illness. Areas of improvement specific to pediatric primary care include suboptimal performance in summarizing and organizing content relating to preventive and behavioral health visits. Patient survey responses showed no difference in Net Promoter Score and related patient experience questions between ambient and non-ambient encounters.</p><p><strong>Discussion: </strong>A subset of clinicians reported self-perceived improvements in work experience despite unchanged EHR efficiency measures. Heterogeneity in clinician experience suggests that benefit from ambient technology likely depends on personal and contextual factors. Enhancements to note organization and facility with pediatric well visit and behavioral health content could improve the utility of this tool for pediatric primary care.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210012","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":"Special Issue on CDS Failures: Unintended Delays in Pediatric Post-Operative Antibiotic Administration from Overly Complex CDS Instructions.","authors":"Swaminathan Kandaswamy, Sarah A Thompson, Edwin Ray, Tracy Ruska, Evan Orenstein","doi":"10.1055/a-2621-7717","DOIUrl":"https://doi.org/10.1055/a-2621-7717","url":null,"abstract":"<p><strong>Background: </strong>The timely administration of post-operative antibiotics is crucial for preventing surgical site infections. Despite surgical ordering workflows designed to facilitate care across settings, delays in antibiotic administration post-transfer to the Pediatric Intensive Care Unit (PICU) were identified. We aimed to develop a clinical decision support (CDS) system to enhance timely order activation in a large pediatric health system. We hypothesized that the time to release signed and held orders by PICU nurses would decrease after implementation of an electronic health record alert, ultimately reducing time to antibiotic administration.</p><p><strong>Objectives: </strong>To describe the CDS design for timely release of post-operative orders, evaluate its effectiveness, and share lessons learned from its implementation.</p><p><strong>Methods: </strong>Stakeholder interviews and a staged implementation approach were employed to develop and implement the CDS in one of the two PICUs. An interruptive alert was designed to prompt nurses to release specific signed and held orders. The study period spanned from January 2019 to August 2024, with pre- and post-intervention comparisons of the mean time to release medication orders.</p><p><strong>Results: </strong>The alert was used from May to December 2021 but was associated with increased time to release orders. Post-intervention usability testing revealed confusion among nurses, leading to the alert's discontinuation. A post-hoc analysis suggested that the observed delays might align with seasonal trends rather than the CDS intervention.</p><p><strong>Discussion and conclusion: </strong>The CDS implementation had unintended adverse effects on order release times, emphasizing the importance of monitoring and evaluating such systems post-implementation. Usability testing highlighted the complexity of the alert messaging and the importance of including end-users in the design phase. Extended evaluation periods are recommended to discern CDS impact accurately. The study also underscores the necessity of assessing whether a technological or workflow/process change is needed in response to safety reports.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974990","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}