Applied Clinical Informatics最新文献

{"title":"Special Issue on CDS Failures: The Burden of a Highly Targeted Alert.","authors":"Tatyan Clarke, Tyler Kotarski, Marc Tobias","doi":"10.1055/a-2573-8067","DOIUrl":"https://doi.org/10.1055/a-2573-8067","url":null,"abstract":"<p><strong>Background: </strong>Interruptive alerts in clinical decision support (CDS) systems are intended to guide clinicians in making informed decisions and adhering to best practices. However, these alerts can often become a source of frustration, contributing to alert fatigue and clinician burnout. Traditionally, alert burden is often assessed by evaluating total firing counts, which can overlook the true impact of highly interruptive workflows. This study demonstrates how an alert burden metric was used to uncover an ineffective alert for decommissioning.</p><p><strong>Objectives: </strong>To evaluate the effectiveness of a burden metric in identifying high-impact, low-value alerts and prioritizing improvement efforts for a CDS governance team.</p><p><strong>Methods: </strong>A clinical informatics team employed XXX to assess alert burden and identify areas requiring intervention within the alert library.</p><p><strong>Results: </strong>The team used the XXX to identify a breast cancer survivorship alert that fired 3,550 times in 2023, with an acceptance rate of just 0.00056%. Investigation identified that this alert targeted a single clinician over the span of several years and the CDS governance team promptly decommissioned the alert.</p><p><strong>Conclusion: </strong>This case highlights the value of continuous CDS monitoring, effective governance, and advanced analytics to identify and mitigate alert fatigue. Insights from this failure provide guidance for enhancing future CDS design, evaluation, and clinician engagement.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781567","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":"Primary Care Providers Acceptance of Generative AI Responses to Patient Portal Messages.","authors":"Amarpreet Kaur, Alex Budko, Katrina Liu, Bryan D Steitz, Kevin B Johnson","doi":"10.1055/a-2565-9155","DOIUrl":"https://doi.org/10.1055/a-2565-9155","url":null,"abstract":"<p><strong>Background: </strong>Patient portals bridge patient and provider communications but exacerbate physician and nursing burnout. Large language models (LLMs) can generate message responses that are viewed favorably by healthcare professionals; however, these studies have not included diverse message types or new prompt-engineering strategies. Our goal is to investigate and compare the quality and precision GPT-generated message responses versus real doctor responses across the spectrum of message types within a patient portal.</p><p><strong>Methods: </strong>We used prompt engineering techniques to craft synthetic provider responses tailored to adult primary care patients. We enrolled a sample of primary care providers in a cross-sectional study to compare authentic with synthetic patient portal message responses, generated by GPT-3.5-turbo, July 2023 version (GPT). The survey assessed each response's empathy, relevance, medical accuracy, and readability on a scale from 0 to 5. Respondents were asked to identify responses that were GPT-generated vs. provider-generated. Mean scores for all metrics were computed for subsequent analysis.</p><p><strong>Results: </strong>A total of 49 health care providers participated in the survey (59% completion rate), comprising 16 physicians and 32 advanced practice providers (APPs). In comparison to responses generated by real doctors, GPT-generated responses scored statistically significantly higher than doctors in two of the four parameters: empathy (p<0.05) and readability (p<0.05). However, no statistically significant difference was observed for relevance and accuracy (p > 0.05). Although readability scores were significantly different, the absolute difference was small, and the clinical significance of this finding remains uncertain.</p><p><strong>Conclusion: </strong>Our findings affirm the potential of GPT-generated message responses to achieve comparable levels of empathy, relevance, and readability to those found in typical responses crafted by healthcare providers. Additional studies should be done within provider workflows and with careful evaluation of patient attitudes and concerns related to the ethics as well as the quality of generated responses in all settings.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711826","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":"Implementation and Adoption of an Order-Based Surgical Case Request Tool Across Subspecialty Clinics.","authors":"Andrew Patrick Bain, Alyssa Low, Robert W Turer, Jonathan E Reeder, Brandon R Bruns, Derek Ngai, Christoph Ulrich Lehmann, Hongzhao Ji","doi":"10.1055/a-2564-7405","DOIUrl":"https://doi.org/10.1055/a-2564-7405","url":null,"abstract":"<p><p>Introduction While computerized provider order entry (CPOE) has become standard for medication, laboratory, referral, and imaging ordering, use in surgical case requests is not well described. Our many surgical clinics used varying workflows for case requests, leading to data duplication and data storage outside of the electronic health record (EHR). We hypothesized that a provider-entered order-based case request (OBCR) tool would improve data entry efficiency and provide a more comprehensive EHR audit trail. Methods An OBCR tool was implemented across surgical clinics at a large safety-net hospital system. The existing workflow, whereby clinic managers created operative cases within the EHR after provider communication, remained available. All cases requested via both old and new workflows for six months after the tool went live were analyzed. Results From 2022-2023, managers created 7,226 operative cases across 19 surgical clinics, 158 faculty surgeons, and 1,737 procedure combinations. 4,585 cases (63%) were created via OBCR. Clinic OBCR use ranged from 2% to 97% of cases created. With OBCR, mean time from case creation to scheduling increased significantly, 12.0 vs 0.7 days respectively (p<0.001). Concordantly, mean time from creation to completion increased from 35.4 to 54.6 days (p<0.001). Rates of \"voided cases\" decreased in the new workflow (1.9% vs 4.5%, p<0.001). Conclusions Most surgical clinics at our institution adopted the OBCR tool, facilitating earlier operative case entry with lower void rates than traditional workflows and improving preoperative planning. OBCR system also enabled data collection needed for robust reporting and identification of clinics in need of support or workflow optimization.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701873","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 on Reducing Technology Related Stress and Burnout: Digital Compassion Fatigue as an Emerging Phenomenon for Registered Nurses Experiencing Techno-stress.","authors":"Matthew Byrne","doi":"10.1055/a-2564-8809","DOIUrl":"https://doi.org/10.1055/a-2564-8809","url":null,"abstract":"<p><strong>Background: </strong>Registered nurses increasingly work in remote care and digital interaction roles, offering flexibility and expansion of their scope of practice. These roles may expose nurses to digital compassion fatigue, a phenomenon proposed to be characterized by the negative psychological and emotional impact of caring for patients remotely through the use technology.</p><p><strong>Objectives: </strong>The first objective of this work is to propose the phenomenon of \"digital compassion fatigue\" as a potentially further evolved and differentiated form of \"compassion fatigue.\" The second objective is to produce a comparative analysis of attributes, antecedents, and consequences through literature reviews.</p><p><strong>Methods: </strong>An evolutionary concept analysis approach was selected as a guide for this concept exploration and evaluation. Concept analysis has been used to identify, explore, and clarify concepts, particularly given the dynamic nature of technology and practice. The process of conducting a concept analysis includes consideration of diverse and multi-disciplinary perspectives. As a result, those in caring, educational, and/or support service roles (e.g., social work, counseling, teaching) for which distance suffering and techno-stress could feasibly be present were also included. Healthcare-specific often included nurses in the sample, but may not have differentiated their specific insights or data points in the results.</p><p><strong>Results: </strong>The concept analysis explored the attributes, antecedents, and consequences of digital compassion fatigue, differentiating it from its evolutionary parent, compassion fatigue. Key antecedents included techno-stress, distant suffering, and the unique challenges of delivering care remotely and digital interactions. A major confounding variable was the COVID-19 pandemic which may have heightened or introduced new technology related stressors or highlighted the existence of digital compassion fatigue.</p><p><strong>Conclusions: </strong>Further defining and understanding digital compassion fatigue is crucial for developing effective strategies to support nurses who may experience it or who are at risk.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701876","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: Analyzing Physician In Basket Burden and Efficiency Using K-Means Clustering.","authors":"Vincent Lattanze, Xinyue Lan, Drew Vander Leest, Jasper Sim, Melissa Fazzari, Xianhong Xie, Sunit Jariwala","doi":"10.1055/a-2562-1100","DOIUrl":"10.1055/a-2562-1100","url":null,"abstract":"<p><strong>Background: </strong>Electronic health record (EHR) systems are essential for modern healthcare but contribute to significant documentation burden, affecting physician workflow and well-being. While previous studies have identified differences in EHR usage across demographics, systematic methods for identifying high-burden physician groups remain limited. This study applies cluster analysis to uncover distinct EHR usage profiles and provide a framework to inform the development of targeted interventions.</p><p><strong>Objectives: </strong>This study investigated two research questions: (1) Can cluster analysis effectively identify distinct physician EHR usage profiles? (2) How do these profiles vary across physician demographics and practice characteristics? We hypothesized that (1) EHR usage clusters would emerge based on workload intensity, after-hours documentation, and In Basket management patterns, and (2) would be significantly associated with physician experience, sex, and specialty.</p><p><strong>Methods: </strong>We analyzed outpatient EHR usage data from 323 physicians at an academic health system using Epic Signal, an analytical tool for Epic EHR. Using k-means clustering, we examined six metrics representing EHR workload (after-hours and extended-day activities) and In Basket efficiency (message handling and management patterns). We assessed cluster differences and conducted subgroup analyses by physician sex and specialty.</p><p><strong>Results: </strong>Two distinct physician clusters emerged: one high-burden cluster, predominantly comprising experienced primary care physicians, and another lower-burden cluster, consisting mostly of younger specialists. Physicians in the high-burden cluster spent nearly three times as much time on after-hours documentation and In Basket management. While message response times remained similar, subgroup analyses revealed significant sex and specialty-based differences, particularly in the lower-burden cluster.</p><p><strong>Conclusions: </strong>Cluster analysis effectively identified distinct EHR usage patterns, highlighting disparities in workload by experience, sex, and specialty. This approach provides a scalable, data-driven method for health systems to identify at-risk groups and design targeted interventions to mitigate documentation burden and enhance EHR efficiency.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664961","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":"Relationship between additional required nursing documentation and patient outcomes: A Scoping Review.","authors":"Rachel Lee, Jennifer A Thate, Jennifer Withall, Po-Yin Yen, Kenrick Cato, Sarah Collins Rossetti","doi":"10.1055/a-2561-3960","DOIUrl":"10.1055/a-2561-3960","url":null,"abstract":"<p><strong>Background: </strong>While many aspects of nursing documentation are considered an essential part of clinical communication and care coordination, other types of nursing documentation have been implemented to meet compliance and other secondary use needs. Adding required documentation without carefully assessing its association with patient outcomes adds excessive documentation burden on nurses. There is a gap in the evidence of the association between additional required nursing documentation and improvements in patient outcomes.</p><p><strong>Objectives: </strong>To synthesize and describe the state of the evidence on the relationship between adding required electronic nursing documentation and improved patient outcomes in inpatient hospital settings.</p><p><strong>Methods: </strong>Databases were searched using relevant terms for original studies examining the effects of additional required nursing documentation. Two authors screened titles, abstracts and full texts for eligibility criteria.</p><p><strong>Data sources: </strong>PubMed, CINAHL (EBSCO), Web of Science, and Embase from January 2011 to May 2023.</p><p><strong>Results: </strong>A total of 47 studies were included. Of the studies reviewed, 57.4% (n=27) focused only on process measures, primarily measuring documentation compliance and 42.6% (n=20) studies included patient outcome measures such as infection rates, length of stay, and falls. Of these studies 45% (n=9) reported statistically significant relationship between required nursing documentation and improved patient outcomes. Overall quality of evidence was generally low, with 72% (n=34) being quality improvement studies and only one study being a randomized controlled trial.</p><p><strong>Conclusion: </strong>The findings of this scoping review suggest an assumed, yet unverified, connection between added required nursing documentation and improved patient outcomes that is not substantiated by high quality empirical evidence. The paucity of studies with significant findings-and the methodological weaknesses of those that report them-suggest the need for critical examination of documentation practices that are truly beneficial to patient outcomes versus those documentation practices that are excessively burdensome.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664960","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":"Provider adoption of an online ADHD eHealth care application.","authors":"Jeff Epstein, Bill Brinkman, Froehlich Tanya, Constance A Mara, John Simon, Andrew Beck, Suzanne Emmer","doi":"10.1055/a-2562-1161","DOIUrl":"10.1055/a-2562-1161","url":null,"abstract":"<p><strong>Objectives: </strong>To assess what practice-, provider-, and patient population-level predictors predict adoption of an ADHD ehealth technology in community pediatric settings, pediatric providers nationwide were recruited and offered free use of an evidence-based mental-health-focused ehealth quality improvement intervention (mehealth for ADHD). Practice-, provider-, and patient population-level factors predicting provider's adoption of the intervention were studied. We hypothesized that providers who were younger, nearing re-credentialing, having more patients with ADHD, working at larger practices, serving socioeconomically deprived patient populations, and using an electronic health record (EHR) with mehealth integration would predict higher rates of adoption.</p><p><strong>Methods: </strong>A variety of recruitment strategies were attempted. Providers completed a baseline survey, were given free access to mehealth, and then had their software adoption recorded (i.e., account activation, rate of patients registered, completion of Plan-Do-Study-Act cycles). Multiple regressions examined what practice-, provider-, and patient population-level variables predicted provider's adoption of the software.</p><p><strong>Results: </strong>A total of 1,612 providers at 813 practices across 48 states and the District of Columbia consented to the study. The most common ways that providers heard about the research study was through word-of-mouth (37%), advertising (23%), and through professional affiliation (11%). 1,210 (75.1%) providers activated their mehealth provider account and 446 (36.8%) registered at least 1 patient. Over 4.5 years, 21,804 patients were registered on the platform. Being able to access mehealth within their EHR predicted provider account activation, provider rate of patients registered, and the practice's completion of Plan-Do-Study-Act cycles. In addition, having a lower proportion of Medicaid patients predicted higher rates of patients being registered on the software.</p><p><strong>Conclusions: </strong>Getting providers to consider, try, and adopt new evidence-based assistive technologies is challenging. Making ehealth software easier for providers' to access through EHR integration appears critical to adoption.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665030","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":"User Actions Within a Clinical Decision Support Alert for the Management of Hypertension in Chronic Kidney Disease.","authors":"Lipika Samal, Sarah W Chen, Stuart Lipsitz, Heather J Baer, John L Kilgallon, Michael P Gannon, Ryan Dunk, Weng Ian Chay, Richard Fay, Michael Sainlaire, Chenxi Gao, Matthew Wien, Pamela Garabedian, Edward Wu, Hojjat Salmasian, David W Bates, Patricia Dykes, Adam Wright, Allison B McCoy","doi":"10.1055/a-2554-3969","DOIUrl":"10.1055/a-2554-3969","url":null,"abstract":"<p><strong>Objective: </strong>To examine user actions within a clinical decision support (CDS) alert addressing hypertension (HTN) in chronic kidney disease (CKD).</p><p><strong>Methods: </strong>A pragmatic randomized controlled trial of a CDS alert for primary care patients with CKD and uncontrolled blood pressure included pre-checked default orders for medication initiation or titration, basic metabolic panel (BMP), and nephrology electronic consult. We examined each type of action and calculated percentages of placed and signed orders for subgroups of firings.</p><p><strong>Results: </strong>There were firings for medication initiation (813) and medication titration (430), and every firing also included orders for nephrology electronic consult (1243) and BMP (1243). High rates of override (59.6%) and deferral (14.6%) were observed, and CDS-recommended orders were only signed about one-third of the time from within the alert. The percentage of orders that were signed after being placed within the alert was higher for medication initiation than for medication titration (33% vs 12.0% for angiotensin-converting enzyme inhibitors (ACEi), 38.8% vs 14% for angiotensin II receptor blockers (ARB).</p><p><strong>Discussion: </strong>Findings suggest that users are hesitant to commit to immediate action within the alert.</p><p><strong>Conclusion: </strong>Evaluating user interaction within alerts reveals nuances in physician preferences and workflow that should inform CDS alert design.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The STREAMLINE Pilot - Study on Time Reduction and Efficiency in AI-Mediated Logging for Improved Note-Taking Experience.","authors":"Roheet Kakaday, Elizabeth Zoe Herrera, Olivia Coskey, Andrew W Hertel, Paulina Kaiser","doi":"10.1055/a-2559-5791","DOIUrl":"10.1055/a-2559-5791","url":null,"abstract":"<p><strong>Objectives: </strong>This pilot study aimed to evaluate the impact of an ambient listening AI tool, DAX CoPilot (DAX), on clinical documentation efficiency among primary care providers in a community-based setting.</p><p><strong>Methods: </strong>We conducted a randomized controlled trial among volunteer clinicians (physicians, nurse practitioners, and physician assistants in family medicine, internal medicine, pediatrics, and urgent care), who were asked to use DAX with a standardized note template (N = 25) or to continue with traditional documentation methods (N = 20) over a three-month intervention period. We evaluated documentation efficiency with both standard and custom Epic metrics to evaluate impact on all visit types as well as specifically problem-focused visits.</p><p><strong>Results: </strong>Because of heterogeneity in DAX usage, we created post-hoc categories of Low (< 45% of all visits, N=12), Moderate (45-69.9% of all visits, N=6) and High Frequency (≥ 70% of all visits, N=7) DAX users. We observed the largest differences among High Frequency DAX users. For problem-focused visits with clinicians in this group, a median of 50% of note characters were written by DAX, and we observed a 1.4-minute decrease in time spent on notes per visit (p-value: 0.38) and a 35% decrease in the median number of characters per note (p-value: 0.38) from baseline to the end of the study period. The control group metrics were largely uncharged throughout the study.</p><p><strong>Conclusions: </strong>Our findings suggest that DAX can improve documentation efficiency, particularly among clinicians that use it frequently. Healthcare systems might benefit by using AL-AI tools like DAX but should consider implementation scope and note template features. Future investigations are needed to further explore these trends and their additional implications for outcomes such as burnout.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651504","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: A Case Study: Optimizing CDS for Pediatric Oncology Trials by Transitioning from Interruptive to Passive Alerts.","authors":"Renee Potashner, Karim Jessa, Natalie Meyer, Erica Patterson, Adam Paul Yan","doi":"10.1055/a-2555-2441","DOIUrl":"https://doi.org/10.1055/a-2555-2441","url":null,"abstract":"<p><strong>Background: </strong>Many children with cancer are treated as part of interventional clinical trials. Ensuring that the correct chemotherapy treatment plan is used is paramount.</p><p><strong>Objectives: </strong>The objectives of this report were to: (1) highlight the initial design of a clinical decision support (CDS) tool that was intended to help ensure the correct matching of research studies to research chemotherapy medications, (2) discuss the issues identified with the CDS tool, and (3) review the redesign of the tool that was done to overcome the issues identified.</p><p><strong>Methods: </strong>We previously utilized an interruptive alert developed by Epic Systems ® to identify mismatches between a patient's chemotherapy plan and research study. We identified an issue with the logic of the alert resulting in the alert firing inappropriately.</p><p><strong>Results: </strong>We estimate that the chemotherapy-research plan alert fired when 93.4% of treatment plans were applied (17.3 alerts/provider/year). A high number of misfiring alerts were identified due to the inclusion of our institution name as both (1) a \"tag\" in the research protocol, and (2) as an unallowed tag in the research study record. Since the tag was included in all protocols, but also unallowed in all research records the alert fired with the application of almost all treatment plans. We developed a new mechanism to provide CDS that did not involve an interruptive alert. Within the research study record we manually associate compatible treatment plans to that study record, then when an oncologist goes to order chemotherapy the system prioritizes display of compatible treatment plans to the oncologist. The goal of the redesigned CDS approach is to eliminate interruptive alerts, while ensuring the correct chemotherapy plan is selected.</p><p><strong>Conclusion: </strong>With end user engagement and creative approaches to CDS design, interruptive alerts can be transitioned into passive and effective CDS tools.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617625","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}