Applied Clinical Informatics最新文献

{"title":"Building Bridges: Fostering Collaborative Education in Training Dental Informaticians.","authors":"Grace Gomez Felix Gomez, Jason M Mao, Thankam P Thyvalikakath, Shuning Li","doi":"10.1055/a-2446-0515","DOIUrl":"10.1055/a-2446-0515","url":null,"abstract":"<p><strong>Background: </strong> Dental informatics (DI) is an emerging discipline. Although the accreditation agency governing dental education programs asserts the importance of informatics as foundational knowledge, no well-defined DI courses currently exist within the standard predoctoral dental curriculum. There is a nationwide lack of DI academic programs. This training gap is due to a lack of qualified dental informaticians to impart knowledge on DI.</p><p><strong>Objective: </strong> This paper aims to introduce a novel conceptual framework for an interdisciplinary DI program in preparing students to become dental informaticians.</p><p><strong>Methods: </strong> In 2023, we developed a standalone graduate certificate program in DI at Indiana University (IU) School of Dentistry (IUSD) in collaboration with IU Luddy School of Informatics, Computing, and Engineering and IU Fairbanks School of Public Health. Feedback was collected through online surveys to assess course quality from students who took Introduction to Health Information in Dentistry. Feedback was analyzed qualitatively, utilizing a thematic analysis approach. Common responses relevant to DI education were grouped into themes.</p><p><strong>Results: </strong> Five major themes emerged during our analysis of the students' feedback: foundational knowledge and skills; experiential learning: learning by doing; access to resources and working on clinical information systems; health promotion through team-based learning; and retention of knowledge assessment and application. A conceptual framework was formulated through these themes as a guideline for future program improvement. This interdisciplinary educational program framework showed how students and faculty from various disciplines could collaborate, learn from each other, and bring in expertise from different domains. The collaboration happens in clinical, laboratory, and virtual settings to acquire hands-on learning through practice and research projects.</p><p><strong>Conclusion: </strong> The developed conceptual framework aligns with the interdisciplinary nature of DI. It can potentially be adopted by other interdisciplinary informatics programs in health and non-health care disciplines.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"205-214"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11882314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511166","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":"Iterative Development of a Clinical Decision Support Tool to Enhance Naloxone Coprescribing.","authors":"Richard Wu, Emily Foster, Qiyao Zhang, Tim Eynatian, Rebecca Mishuris, Nicholas Cordella","doi":"10.1055/a-2447-8463","DOIUrl":"10.1055/a-2447-8463","url":null,"abstract":"<p><strong>Background: </strong> Opioid overdoses have contributed significantly to mortality in the United States. Despite long-standing recommendations from the Centers for Disease Control and Prevention to coprescribe naloxone for patients receiving opioids who are at high risk of overdose, compliance with these guidelines has remained low.</p><p><strong>Objectives: </strong> The objective of this study was to develop and evaluate a hospital-wide electronic health record (EHR)-based clinical decision support (CDS) tool designed to promote naloxone coprescription for high-risk opioids.</p><p><strong>Methods: </strong> We employed an iterative approach to develop a point-of-order, interruptive EHR alert as the primary intervention and assessed naloxone prescription rates, EHR efficiency metrics, and barriers to adoption. Data were obtained from our EHR's clinical data warehouse and analyzed using statistical process control with odds ratios calculated to quantify statistically significant differences in prescribing rates during the intervention periods.</p><p><strong>Results: </strong> The initial implementation phase of the intervention, spanning from April 2019 to May 2022, yielded a nearly 3-fold increase in the proportion of high-risk patients receiving naloxone, rising from 13.4% (95% confidence interval [CI], 12.9-13.8%) to 36.4% (95% CI, 35.2-37.5%; <i>p</i> = 10^-38). Enhancements to the CDS design and logic during the subsequent iteration's study period, June 2022 and December 2023, reduced the number of CDS triggers by more than 30-fold while simultaneously driving an additional increase in naloxone receipt to 42.7% (95% CI, 40.6-44.8%; <i>p</i> = 2 × 10^-5). The efficiency of the CDS demonstrated marked improvement, with prescribers accepting the naloxone coprescription recommendation provided by the CDS in 41.1% of the encounters in version 2, compared with 6.2% in version 1 (<i>p</i> = 6 × 10^-9).</p><p><strong>Conclusion: </strong> This study offers a sustainable and scalable model to address low rates of naloxone coprescription and may also be used to target other opportunities for improving guideline-concordant prescribing practices.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"215-222"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11882316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511168","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":"Interventions to Mitigate EHR and Documentation Burden in Health Professions Trainees: A Scoping Review.","authors":"Deborah R Levy, Sarah C Rossetti, Cynthia A Brandt, Edward R Melnick, Andrew Hamilton, Seppo T Rinne, Dana Womack, Vishnu Mohan","doi":"10.1055/a-2434-5177","DOIUrl":"10.1055/a-2434-5177","url":null,"abstract":"<p><strong>Background: </strong> Health professions trainees (trainees) are unique as they learn a chosen field while working within electronic health records (EHRs). Efforts to mitigate EHR burden have been described for the experienced health professional (HP), but less is understood for trainees. EHR or documentation burden (<i>EHR burden</i>) affects trainees, although not all trainees use EHRs, and use may differ for experienced HPs.</p><p><strong>Objectives: </strong> This study aimed to develop a model of how interventions to mitigate EHR burden fit within the trainee EHR workflow: the <i>Trainee EHR Burden Model</i>. (We: 1) Examined trainee experiences of interventions aimed at mitigating EHR burden (scoping review) and (2) Adapted an existing workflow model by mapping included studies (concept clarification).</p><p><strong>Methods: </strong> We conducted a four-database scoping review applying Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extensions for Scoping Review (PRISMA-ScR) guidance, examining scholarly, peer-reviewed studies that measured trainee experience of interventions to mitigate EHR burden. We conducted a concept clarification categorizing, then mapping studies to workflow model elements. We adapted the model to intervenable points for trainee EHR burden.</p><p><strong>Results: </strong> We identified 11 studies examining interventions to mitigate EHR burden that measured trainee experience. Interventions included curriculum, training, and coaching on the existing EHR for both simulated or live tasks; evaluating scribes' impact; adding devices or technology tailored to rounds; and team communication or data presentation at end-of-shift handoffs. Interventions had varying effects on EHR burden, most commonly measured through surveys, and less commonly, direct observation. Most studies had limited sample sizes and focused on inpatient settings and physician trainees.</p><p><strong>Conclusion: </strong> Few studies measured trainee perspectives of interventions aiming to mitigate EHR burden. Many studies applied quasi-experimental designs and focused on inpatient settings. The <i>Trainee EHR Burden Model</i>, adapted from an existing workflow model, offers a starting place to situate points of intervention in trainee workflow. Further research is needed to design new interventions targeting stages of HP trainee workflow, in a range of clinical settings.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"111-127"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376197","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":"Elevating Clinical Informatics: Dynamic Resident Training to Enhance Subspecialty Appeal.","authors":"Justine Mrosak, Ryan Jelinek, Deepti Pandita","doi":"10.1055/a-2431-9669","DOIUrl":"10.1055/a-2431-9669","url":null,"abstract":"<p><strong>Objective: </strong> This study aimed to bridge the educational gap in clinical informatics (CI) at the residency level and stimulate interest in CI as a rewarding career path.</p><p><strong>Methods: </strong> We developed an innovative CI and quality improvement (QI) resident rotation. This rotation replaced traditional QI blocks for Internal Medicine and several other residency programs, offering comprehensive exposure to core informatics and QI principles. The curriculum featured prerecorded didactics, hands-on projects, department meetings, and an optional EPIC SmartUser program. Resident participation and feedback were evaluated through postrotation surveys.</p><p><strong>Results: </strong> Since its inception on July 1, 2022, 57 residents have completed the rotation, with a majority rating their experience favorably. Residents also valued the educational course content and expressed an increased likelihood of integrating informatics into their future careers.</p><p><strong>Conclusion: </strong> The rotation has successfully integrated into existing multiple residency programs, demonstrating an effective model for delivering informatics education. Initial outcomes show enhanced resident engagement and competency in CI, promising a progressive impact on the future physician workforce. Continued expansion and evaluation of this rotation are expected to further encourage formal CI training and career interest.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 1","pages":"77-83"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11753862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025256","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":"FHIR Granular Sensitive Data Segmentation.","authors":"Preston Lee, Daniel Mendoza, Martha Kaiser, Eric Lott, Gagandeep Singh, Adela Grando","doi":"10.1055/a-2466-4371","DOIUrl":"10.1055/a-2466-4371","url":null,"abstract":"<p><strong>Background: </strong> Due to fear of stigma, patients want more control over the sharing of sensitive medical records. The Substance Abuse and Mental Health Administration (SAMHSA) and the Office of the National Coordinator (ONC) supported the development of standards-compliant, consent-respecting medical record exchange technology using metadata labeling (e.g., substance use information). Existing technologies must be updated with newer standards and support more than binary-sensitive categorizations to better align with how physicians categorize sensitive medical records.</p><p><strong>Objectives: </strong> Our goal was to deploy, pilot test, and share open-source Fast Healthcare Interoperability Resources (FHIR)-based data segmentation technologies. We pilot-tested the technologies using real-world patient electronic health record data in the context of substance use information. We involved physicians in designing a novel decision engine that supports various confidence levels.</p><p><strong>Results: </strong> We deployed a web-based Patient Portal and Clinical Decision Support (CDS) granular data segmentation Engine to allow patients to make consent-based granular data choices (e.g., not sharing substance use medical records). Compared with previous solutions, the Engine innovates by using the latest Health Level 7 (HL7) standards to support data sensitivity labeling and redaction: FHIR R5 and its Consent resource type and CDS Hooks. It also supports configurable floating point confidence threshold cutoffs as opposed to binary medical record categorizations. Multiple engineering choices were made to simplify software development and maintenance and to improve technology adaptability, reusability, and scalability.</p><p><strong>Conclusion: </strong> The resulting data segmentation technologies update SAMHSA and ONC software with the newest HL7 standards and better mimic how physicians categorize sensitive medical information with various confidence levels. To support reusability, we shared the resulting open-source code through the HL7 FHIR Foundry.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 1","pages":"156-166"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460211","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":"Vanderbilt Clinical Informatics Center Education Strategy: To Infinity and Beyond!","authors":"Elise Russo, Allison McCoy, Dara Mize, Travis Osterman, Scott Nelson, Jonathan P Wanderer, Adam Wright","doi":"10.1055/a-2443-8318","DOIUrl":"10.1055/a-2443-8318","url":null,"abstract":"<p><strong>Background: </strong> The Vanderbilt Clinical Informatics Center (VCLIC) is based in the Department of Biomedical Informatics (DBMI) and operates across Vanderbilt University Medical Center (VUMC) and Vanderbilt University (VU) with a goal of enabling and supporting clinical informatics research and practice. VCLIC supports several types of applied clinical informatics teaching, including teaching of students in courses, professional education for staff and faculty throughout VUMC, and workshops and conferences that are open to the public.</p><p><strong>Objectives: </strong> In this paper, we provide a detailed accounting of our center and institution's methods of educating and training faculty, staff, students, and trainees from across the academic institution and health system on clinical informatics topics, including formal training programs and informal applied learning sessions.</p><p><strong>Methods: </strong> Through a host of informal learning events, such as workshops, seminars, conference-style events, bite-size instructive videos, and hackathons, as well as several formal education programs, such as the Clinical Informatics Graduate Course, Master's in Applied Clinical Informatics, Medical Student Integrated Science Course, Graduate Medical Education Elective, and Fellowship in Clinical Informatics, VCLIC, and VUMC provide opportunities for faculty, students, trainees, and even staff to engage with Clinical Informatics topics and learn related skills.</p><p><strong>Results: </strong> The described programs have trained hundreds of participants from across the academic and clinical enterprises. Of the VCLIC-held events, the majority of attendees indicated through surveys that they were satisfied, with the average satisfaction score being 4.63/5, and all events averaging a satisfaction score of greater than 4. Across the 20 events VCLIC has held, our largest audiences are DBMI, HealthIT operational staff, and students from the medical and nursing schools.</p><p><strong>Conclusion: </strong> VCLIC has created and delivered a successful suite of formal and informal educational events and programs to disseminate clinical informatics knowledge and skills to learners across the academic institution and health care system.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"177-184"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11864845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478547","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 Information, Right Care, Right Patient, Right Time: Community Preferences to Inform a Self-Management Support Tool for Upper Respiratory Symptoms.","authors":"Damara Gutnick, Carlo Lutz, Kyle A Mani, Christine B Weldon, Julia R Trosman, Bruce Rapkin, Kimberly Jinnett, Judes Fleurimont, Savneet Kaur, Sunit P Jariwala","doi":"10.1055/a-2441-6016","DOIUrl":"10.1055/a-2441-6016","url":null,"abstract":"<p><strong>Objectives: </strong> During and since the coronavirus disease 2019 (COVID-19) pandemic, communities have needed to cope with several conditions that cause similar upper respiratory symptoms but are managed differently. We describe community reactions to a self-management toolkit for patients with upper respiratory symptoms to inform mobile e-health app development. The toolkit is based on the \"4R\" (Right Information, Right Care, Right Patient, Right Time) care planning and management model.</p><p><strong>Methods: </strong> The 4R Cold, Flu, and COVID-19 Information Tool (4R-Toolkit) along with a brief evaluation survey were distributed in three ways: through a Bronx NY Allergy/Asthma clinic, through the Bronx Borough President's Office listserv, and through peer recruitment. The survey assessed respondents' perceptions of the 4R-Toolkit's accessibility, preferences for sharing symptoms with clinicians, social media use, and e-health literacy.</p><p><strong>Results: </strong> We obtained a diverse sample of 106 Bronx residents, with 83% reporting personal or a social contact with symptoms suggestive of COVID-19. Respondents varied in the information sources they preferred: computer (39%), smartphone (28%), paper (11%), and no preference (22%). Most (67%) reported that social media had at least some impact on their health care decisions. Regardless of media preferences, respondents were positive about the 4R-Toolkit. Out of 106 respondents, 91% believed the 4R-Toolkit would help people self-manage upper respiratory symptoms and 85% found it easy to understand. Respondents strongly endorsed retention of all 4R-Toolkit content domains with 81% indicating that they would be willing to share symptoms with providers using a 4R-Toolkit smartphone app.</p><p><strong>Conclusion: </strong> The 4R-Toolkit can offer patients and community members accurate and up-to-date information on COVID-19, the common cold, and the flu. The user-friendly tool is accessible to diverse individuals, including those with limited e-health literacy. It has potential to support self-management of upper respiratory symptoms and promote patient engagement with providers.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"145-155"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548499","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":"A Transformer-Based Pipeline for German Clinical Document De-Identification.","authors":"Kamyar Arzideh, Giulia Baldini, Philipp Winnekens, Christoph M Friedrich, Felix Nensa, Ahmad Idrissi-Yaghir, René Hosch","doi":"10.1055/a-2424-1989","DOIUrl":"10.1055/a-2424-1989","url":null,"abstract":"<p><strong>Objective: </strong> Commercially available large language models such as Chat Generative Pre-Trained Transformer (ChatGPT) cannot be applied to real patient data for data protection reasons. At the same time, de-identification of clinical unstructured data is a tedious and time-consuming task when done manually. Since transformer models can efficiently process and analyze large amounts of text data, our study aims to explore the impact of a large training dataset on the performance of this task.</p><p><strong>Methods: </strong> We utilized a substantial dataset of 10,240 German hospital documents from 1,130 patients, created as part of the investigating hospital's routine documentation, as training data. Our approach involved fine-tuning and training an ensemble of two transformer-based language models simultaneously to identify sensitive data within our documents. Annotation Guidelines with specific annotation categories and types were created for annotator training.</p><p><strong>Results: </strong> Performance evaluation on a test dataset of 100 manually annotated documents revealed that our fine-tuned German ELECTRA (gELECTRA) model achieved an F1 macro average score of 0.95, surpassing human annotators who scored 0.93.</p><p><strong>Conclusion: </strong> We trained and evaluated transformer models to detect sensitive information in German real-world pathology reports and progress notes. By defining an annotation scheme tailored to the documents of the investigating hospital and creating annotation guidelines for staff training, a further experimental study was conducted to compare the models with humans. These results showed that the best-performing model achieved better overall results than two experienced annotators who manually labeled 100 clinical documents.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 1","pages":"31-43"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11710903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957113","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":"A Comprehensive Multifunctional Approach for Measuring Parkinson's Disease Severity.","authors":"Morteza Rahimi, Zeina Al Masry, John Michael Templeton, Sandra Schneider, Christian Poellabauer","doi":"10.1055/a-2420-0413","DOIUrl":"10.1055/a-2420-0413","url":null,"abstract":"<p><strong>Objectives: </strong> This research study aims to advance the staging of Parkinson's disease (PD) by incorporating machine learning to assess and include a broader multifunctional spectrum of neurocognitive symptoms in the staging schemes beyond motor-centric assessments. Specifically, we provide a novel framework to modernize and personalize PD staging more objectively by proposing a hybrid feature scoring approach.</p><p><strong>Methods: </strong> We recruited 37 individuals diagnosed with PD, each of whom completed a series of tablet-based neurocognitive tests assessing motor, memory, speech, executive functions, and tasks ranging in complexity from single to multifunctional. Then, the collected data were used to develop a hybrid feature scoring system to calculate a weighted vector for each function. We evaluated the current PD staging schemes and developed a new approach based on the features selected and extracted using random forest and principal component analysis.</p><p><strong>Results: </strong> Our findings indicate a substantial bias in current PD staging systems toward fine motor skills, that is, other neurological functions (memory, speech, executive function, etc.) do not map into current PD stages as well as fine motor skills do. The results demonstrate that a more accurate and personalized assessment of PD severity could be achieved by including a more exhaustive range of neurocognitive functions in the staging systems either by involving multiple functions in a unified staging score or by designing a function-specific staging system.</p><p><strong>Conclusion: </strong> The proposed hybrid feature score approach provides a comprehensive understanding of PD by highlighting the need for a staging system that covers various neurocognitive functions. This approach could potentially lead to more effective, objective, and personalized treatment strategies. Further, this proposed methodology could be adapted to other neurodegenerative conditions such as Alzheimer's disease or amyotrophic lateral sclerosis.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"11-23"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11693400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308859","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":"Effect of Tiered Implementation of Clinical Decision Support System for Acute Kidney Injury and Nephrotoxin Exposure in Cardiac Surgery Patients.","authors":"Christopher M Justice, Connor Nevin, Rebecca L Neely, Brian Dilcher, Nicole Kovacic-Scherrer, Heather Carter-Templeton, Aaron Ostrowski, Jacob Krafcheck, Gordon Smith, Paul McCarthy, Jami Pincavitch, Sandra Kane-Gill, Robert Freeman, John A Kellum, Roopa Kohli-Seth, Girish N Nadkarni, Khaled Shawwa, Ankit Sakhuja","doi":"10.1055/s-0044-1791822","DOIUrl":"10.1055/s-0044-1791822","url":null,"abstract":"<p><strong>Background: </strong> Nephrotoxin exposure may worsen kidney injury and impair kidney recovery if continued in patients with acute kidney injury (AKI).</p><p><strong>Objectives: </strong> This study aimed to determine if tiered implementation of a clinical decision support system (CDSS) would reduce nephrotoxin use in cardiac surgery patients with AKI.</p><p><strong>Methods: </strong> We assessed patients admitted to the cardiac surgery intensive care unit at a tertiary care center from January 2020 to December 2021, and August 2022 to September 2023. A passive electronic AKI alert was activated in July 2020, followed by an electronic nephrotoxin alert in March 2023. In this alert, active nephrotoxic medication orders resulted in a passive alert, whereas new orders were met with an interruptive alert. Primary outcome was discontinuation of nephrotoxic medications within 30 hours after AKI. Secondary outcomes included AKI-specific clinical actions, determined through modified Delphi process and patient-centered outcomes. We compared all outcomes across five separate eras, divided based on the tiered implementation of these alerts.</p><p><strong>Results: </strong> A total of 503 patients met inclusion criteria. Of 114 patients who received nephrotoxins before AKI, nephrotoxins were discontinued after AKI in 6 (25%) patients in pre AKI-alert era, 8 (33%) patients in post AKI-alert era, 7 (35%) patients in AKI-alert long-term follow up era, 7 (35%) patients in pre nephrotoxin-alert era, and 14 (54%) patients in post nephrotoxin-alert era (<i>p</i> = 0.047 for trend). Among AKI-specific consensus actions, we noted a decreased use of intravenous fluids, increased documentation of goal mean arterial pressure of 65 mm Hg or higher, and increased use of bedside point of care echocardiogram over time. Among exploratory clinical outcomes we found a decrease in proportion of stage III AKI, need for dialysis, and length of hospital stay over time.</p><p><strong>Conclusion: </strong> Tiered implementation of CDSS for recognition of AKI and nephrotoxin exposure resulted in a progressive improvement in the discontinuation of nephrotoxins.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 1","pages":"1-10"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11693401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916021","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}