Applied Clinical Informatics最新文献

{"title":"A Survey of Clinicians' Views of the Utility of Large Language Models.","authors":"Matthew Spotnitz, Betina Idnay, Emily R Gordon, Rebecca Shyu, Gongbo Zhang, Cong Liu, James J Cimino, Chunhua Weng","doi":"10.1055/a-2281-7092","DOIUrl":"10.1055/a-2281-7092","url":null,"abstract":"<p><strong>Objectives: </strong> Large language models (LLMs) like Generative pre-trained transformer (ChatGPT) are powerful algorithms that have been shown to produce human-like text from input data. Several potential clinical applications of this technology have been proposed and evaluated by biomedical informatics experts. However, few have surveyed health care providers for their opinions about whether the technology is fit for use.</p><p><strong>Methods: </strong> We distributed a validated mixed-methods survey to gauge practicing clinicians' comfort with LLMs for a breadth of tasks in clinical practice, research, and education, which were selected from the literature.</p><p><strong>Results: </strong> A total of 30 clinicians fully completed the survey. Of the 23 tasks, 16 were rated positively by more than 50% of the respondents. Based on our qualitative analysis, health care providers considered LLMs to have excellent synthesis skills and efficiency. However, our respondents had concerns that LLMs could generate false information and propagate training data bias.Our survey respondents were most comfortable with scenarios that allow LLMs to function in an assistive role, like a physician extender or trainee.</p><p><strong>Conclusion: </strong> In a mixed-methods survey of clinicians about LLM use, health care providers were encouraging of having LLMs in health care for many tasks, and especially in assistive roles. There is a need for continued human-centered development of both LLMs and artificial intelligence in general.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11023712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140040685","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":"Pharmacoinformatics-enabled Interventions Improved Care Coordination and Identified Pharmacy-Related Safety Issues in a Multicultural Medicare Population.","authors":"Kelly J T Craig, Amanda L Zaleski, Shannon M MacKenzie, Brenda L Butler, Rebecca A Youngerman, Sherrie L McNutt, Alena M Baquet-Simpson","doi":"10.1055/a-2297-4334","DOIUrl":"10.1055/a-2297-4334","url":null,"abstract":"<p><strong>Background: </strong> Compared to White populations, multicultural older adults experience more gaps in preventive care (e.g., vaccinations, screenings, chronic condition monitoring), social determinants of health barriers (e.g., access to care, language, transportation), and disparities and inequities (e.g., comorbidities, disease burden, and health care costs).</p><p><strong>Objectives: </strong> This study aims to describe an informatics-based approach used to execute and evaluate results of a member-centric, pharmacoinformatics-informed engagement program to deliver culturally tailored microinterventions to close medication-related gaps in care utilizing multidisciplinary care coordination that leverages the expanded role of the pharmacist. The operational framework will be described, and the influence of the medication use processes will be reported in a multicultural Medicare Advantage cohort.</p><p><strong>Methods: </strong> A pharmacoinformatics framework was leveraged to conduct a retrospective, observational cohort analysis of the program. Claims data were used to evaluate the influence of medication use process microinterventions from a large Medicare Advantage cohort of members who self-identify as Black and/or Hispanic, and have type 2 diabetes mellitus and/or hypertension, and meet eligibility criteria for multidisciplinary (e.g., nursing and pharmacy) care management (CM) and received pharmacy referral from January 1, 2022, through September 30, 2023.</p><p><strong>Results: </strong> A total of 3,265 Medicare Advantage members (78.3% Black and 21.7% Hispanic) received CM and pharmacy referral. Pharmacovigilance reviews conducted during this timeframe identified 258 acute events that escalated member CM. Provider outreach (<i>n</i> = 185) informed of safety issues (drug duplication, <i>n</i> = 48; drug interactions, <i>n</i> = 21; drug-disease interactions, <i>n</i> = 5; noncompliance and/or dosing issues, <i>n</i> = 27). Outreach to members (<i>n</i> = 160) and providers (<i>n</i> = 164) informed of open quality-related measure gaps for medication adherence.</p><p><strong>Conclusion: </strong> The application of pharmacoinformatics by a payor-led multicultural clinical program demonstrated quality improvements in Medicare Advantage member identification including risk stratification, timely outreach for pharmacy-related safety issues, and improved efficiency of multidisciplinary care coordination involving medication use process workflows.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11042916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337405","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 MedWreck to MedRec: A Call to Action to Improve Medication Reconciliation.","authors":"Nitu Kashyap, Sean Jeffery, Thomas Agresta","doi":"10.1055/a-2181-1847","DOIUrl":"10.1055/a-2181-1847","url":null,"abstract":"","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10972679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41156104","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":"Factors Influencing Integration and Usability of Model-Informed Precision Dosing Software in the Intensive Care Unit.","authors":"Ming G Chai, Natasha A Roberts, Chelsea Dobbins, Jason A Roberts, Menino O Cotta","doi":"10.1055/s-0044-1786978","DOIUrl":"https://doi.org/10.1055/s-0044-1786978","url":null,"abstract":"<p><strong>Background: </strong> Antimicrobial dosing in critically ill patients is challenging and model-informed precision dosing (MIPD) software may be used to optimize dosing in these patients. However, few intensive care units (ICU) currently adopt MIPD software use.</p><p><strong>Objectives: </strong> To determine the usability of MIPD software perceived by ICU clinicians and identify implementation barriers and enablers of software in the ICU.</p><p><strong>Methods: </strong> Clinicians (pharmacists and medical staff) who participated in a wider multicenter study using MIPD software were invited to participate in this mixed-method study. Participants scored the industry validated Post-study System Usability Questionnaire (PSSUQ, assessing software usability) and Technology Acceptance Model 2 (TAM2, assessing factors impacting software acceptance) survey. Semistructured interviews were used to explore survey responses. The framework approach was used to identify factors influencing software usability and integration into the ICU from the survey and interview data.</p><p><strong>Results: </strong> Seven of the eight eligible clinicians agreed to participate in the study. The PSSUQ usability scores ranked poorer than the reference norms (2.95 vs. 2.62). The TAM2 survey favorably ranked acceptance in all domains, except image. Qualitatively, key enablers to workflow integration included clear and accessible data entry, visual representation of recommendations, involvement of specialist clinicians, and local governance of software use. Barriers included rigid data entry systems and nonconformity of recommendations to local practices.</p><p><strong>Conclusion: </strong> Participants scored the MIPD software below the threshold that implies good usability. Factors such as availability of software support by specialist clinicians was important to participants while rigid data entry was found to be a deterrent.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958867","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":"Explaining Variability in Electronic Health Record Effort in Primary Care Ambulatory Encounters.","authors":"J Marc Overhage, Fares Qeadan, Eun Ho Eunice Choi, Duncan Vos, Philip J Kroth","doi":"10.1055/s-0044-1782228","DOIUrl":"10.1055/s-0044-1782228","url":null,"abstract":"<p><strong>Background: </strong> Electronic health record (EHR) user interface event logs are fast providing another perspective on the value and efficiency EHR technology brings to health care. Analysis of these detailed usage data has demonstrated their potential to identify EHR and clinical process design factors related to user efficiency, satisfaction, and burnout.</p><p><strong>Objective: </strong> This study aimed to analyze the event log data across 26 different health systems to determine the variability of use of a single vendor's EHR based on four event log metrics, at the individual, practice group, and health system levels.</p><p><strong>Methods: </strong> We obtained de-identified event log data recorded from June 1, 2018, to May 31, 2019, from 26 health systems' primary care physicians. We estimated the variability in total Active EHR Time, Documentation Time, Chart Review Time, and Ordering Time across health systems, practice groups, and individual physicians.</p><p><strong>Results: </strong> In total, 5,444 physicians (Family Medicine: 3,042 and Internal Medicine: 2,422) provided care in a total of 2,285 different practices nested in 26 health systems. Health systems explain 1.29, 3.55, 3.45, and 3.30% of the total variability in Active Time, Documentation Time, Chart Review Time, and Ordering Time, respectively. Practice-level variability was estimated to be 7.96, 13.52, 8.39, and 5.57%, respectively, and individual physicians explained the largest proportion of the variability for those same outcomes 17.09, 27.49, 17.51, and 19.75%, respectively.</p><p><strong>Conclusion: </strong> The most variable physician EHR usage patterns occurs at the individual physician level and decreases as you move up to the practice and health system levels. This suggests that interventions to improve individual users' EHR usage efficiency may have the most potential impact compared with those directed at health system or practice levels.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10954376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177357","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":"HistoriView: Implementation and Evaluation of a Novel Approach to Review a Patient Using a Scalable Space-Efficient Timeline without Zoom Interactions.","authors":"Heekyong Park, Taowei David Wang, Nich Wattanasin, Victor M Castro, Vivian Gainer, Shawn Murphy","doi":"10.1055/a-2269-0995","DOIUrl":"10.1055/a-2269-0995","url":null,"abstract":"<p><strong>Background: </strong> Timelines have been used for patient review. While maintaining a compact overview is important, merged event representations caused by the intricate and voluminous patient data bring event recognition, access ambiguity, and inefficient interaction problems. Handling large patient data efficiently is another challenge.</p><p><strong>Objective: </strong> This study aims to develop a scalable, efficient timeline to enhance patient review for research purposes. The focus is on addressing the challenges presented by the intricate and voluminous patient data.</p><p><strong>Methods: </strong> We propose a high-throughput, space-efficient HistoriView timeline for an individual patient. For a compact overview, it uses nonstacking event representation. An overlay detection algorithm, y-shift visualization, and popup-based interaction facilitate comprehensive analysis of overlapping datasets. An i2b2 HistoriView plugin was deployed, using split query and event reduction approaches, delivering the entire history efficiently without losing information. For evaluation, 11 participants completed a usability survey and a preference survey, followed by qualitative feedback. To evaluate scalability, 100 randomly selected patients over 60 years old were tested on the plugin and were compared with a baseline visualization.</p><p><strong>Results: </strong> Most participants found that HistoriView was easy to use and learn and delivered information clearly without zooming. All preferred HistoriView over a stacked timeline. They expressed satisfaction on display, ease of learning and use, and efficiency. However, challenges and suggestions for improvement were also identified. In the performance test, the largest patient had 32,630 records, which exceeds the baseline limit. HistoriView reduced it to 2,019 visual artifacts. All patients were pulled and visualized within 45.40 seconds. Visualization took less than 3 seconds for all.</p><p><strong>Discussion and conclusion: </strong> HistoriView allows complete data exploration without exhaustive interactions in a compact overview. It is useful for dense data or iterative comparisons. However, issues in exploring subconcept records were reported. HistoriView handles large patient data preserving original information in a reasonable time.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10990596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139742378","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":"PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service.","authors":"Robert H Dolin, Edna Shenvi, Carla Alvarez, Randolph C Barrows, Aziz Boxwala, Benson Lee, Brian H Nathanson, Yelena Kleyner, Rachel Hagemann, Tonya Hongsermeier, Joan Kapusnik-Uner, Adnan Lakdawala, James Shalaby","doi":"10.1055/a-2274-6763","DOIUrl":"10.1055/a-2274-6763","url":null,"abstract":"<p><strong>Objectives: </strong> Pharmacogenetics (PGx) is increasingly important in individualizing therapeutic management plans, but is often implemented apart from other types of medication clinical decision support (CDS). The lack of integration of PGx into existing CDS may result in incomplete interaction information, which may pose patient safety concerns. We sought to develop a cloud-based orchestrated medication CDS service that integrates PGx with a broad set of drug screening alerts and evaluate it through a clinician utility study.</p><p><strong>Methods: </strong> We developed the PillHarmonics service for implementation per the CDS Hooks protocol, algorithmically integrating a wide range of drug interaction knowledge using cloud-based screening services from First Databank (drug-drug/allergy/condition), PharmGKB (drug-gene), and locally curated content (drug-renal/hepatic/race). We performed a user study, presenting 13 clinicians and pharmacists with a prototype of the system's usage in synthetic patient scenarios. We collected feedback via a standard questionnaire and structured interview.</p><p><strong>Results: </strong> Clinician assessment of PillHarmonics via the Technology Acceptance Model questionnaire shows significant evidence of perceived utility. Thematic analysis of structured interviews revealed that aggregated knowledge, concise actionable summaries, and information accessibility were highly valued, and that clinicians would use the service in their practice.</p><p><strong>Conclusion: </strong> Medication safety and optimizing efficacy of therapy regimens remain significant issues. A comprehensive medication CDS system that leverages patient clinical and genomic data to perform a wide range of interaction checking and presents a concise and holistic view of medication knowledge back to the clinician is feasible and perceived as highly valuable for more informed decision-making. Such a system can potentially address many of the challenges identified with current medication-related CDS.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139933748","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 Electronic Health Record Voice Assistant Supports Clinicians.","authors":"Yaa A Kumah-Crystal, Christoph U Lehmann, Dan Albert, Tim Coffman, Hala Alaw, Sydney Roth, Alexandra Manoni, Peter Shave, Kevin B Johnson","doi":"10.1055/a-2177-4420","DOIUrl":"10.1055/a-2177-4420","url":null,"abstract":"<p><strong>Background: </strong> Electronic health records (EHRs) present navigation challenges due to time-consuming searches across segmented data. Voice assistants can improve clinical workflows by allowing natural language queries and contextually aware navigation of the EHR.</p><p><strong>Objectives: </strong> To develop a voice-mediated EHR assistant and interview providers to inform its future refinement.</p><p><strong>Methods: </strong> The Vanderbilt EHR Voice Assistant (VEVA) was developed as a responsive web application and designed to accept voice inputs and execute the appropriate EHR commands. Fourteen providers from Vanderbilt Medical Center were recruited to participate in interactions with VEVA and to share their experience with the technology. The purpose was to evaluate VEVA's overall usability, gather qualitative feedback, and detail suggestions for enhancing its performance.</p><p><strong>Results: </strong> VEVA's mean system usability scale score was 81 based on the 14 providers' evaluations, which was above the standard 50th percentile score of 68. For all five summaries evaluated (overview summary, A1C results, blood pressure, weight, and health maintenance), most providers offered a positive review of VEVA. Several providers suggested modifications to make the technology more useful in their practice, ranging from summarizing current medications to changing VEVA's speech rate. Eight of the providers (64%) reported they would be willing to use VEVA in its current form.</p><p><strong>Conclusion: </strong> Our EHR voice assistant technology was deemed usable by most providers. With further improvements, voice assistant tools such as VEVA have the potential to improve workflows and serve as a useful adjunct tool in health care.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10937093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10308051","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 and Validation of Information Technology Scale in Nursing.","authors":"Elif Sarac, Esra Yildiz","doi":"10.1055/s-0044-1782229","DOIUrl":"10.1055/s-0044-1782229","url":null,"abstract":"<p><strong>Background: </strong> The implementation of information technology (IT) in patient care is on the rise. The nursing workforce should be prepared for using such technology to support the delivery of patient-centered care. The integration of informatics into nursing practice has been progressing at a slower rate than the development of advancements and in which areas nurses use IT is still not clear.</p><p><strong>Objective: </strong> Our objective was to develop a new instrument to determine the usage of IT in nursing practice.</p><p><strong>Methods: </strong> A methodological study was conducted with factor analyses. A total of 498 registered nurses in a university hospital (<i>n</i> = 374) and primary care centers (<i>n</i> = 124) participated in the study. A questionnaire consisting demographic characteristics and an item pool with 50 statements were used to collect data. The validity and reliability of the instrument were statistically tested by computing the Keiser-Meier-Olkin (KMO) and Bartlett tests, an exploratory factor analysis, descriptive statistics, Cronbach's α, and a confirmatory factor analysis.</p><p><strong>Results: </strong> The instrument extracted eight factors comprising 39 items that explained 55% of the variance: professional autonomy(α = 0.82), data sharing/communication(α = 0.80), data management (α = 0.79), professional development (α = 0.71), administration (α = 0.76), research (α = 0.76), informing (α = 0.68), and classification of interventions (α = 0.75). Total reliability was 0.936. KMO index and a measure of sampling adequacy were high (0.936); the Bartlett test of sphericity was significant (<i>p</i> < 0.005).</p><p><strong>Conclusion: </strong> Study provided the evidence for the factor structure, internal consistency, reliability, and responsiveness of the 39-item \"The Information Technology Scale in Nursing.\" Further testing of the developed instrument with a larger number of nurses from various backgrounds and different settings is recommended.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10954377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177356","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":"Improving Immunization Health Care Data Quality using Two-Dimensional Barcoding and Barcode Scanning Practices.","authors":"Faisal Reza, Caroline Jones, Jenica H Reed","doi":"10.1055/a-2255-9749","DOIUrl":"10.1055/a-2255-9749","url":null,"abstract":"<p><strong>Background: </strong> Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience.</p><p><strong>Objectives: </strong> Data entry practices and entered records from routine and mass vaccination settings were analyzed. Data quality improvement opportunities were identified.</p><p><strong>Methods: </strong> A sample of 50 million emergency use authorization (EUA) coronavirus disease 2019 (COVID-19) vaccine records were analyzed for accuracy and completeness across three data fields: lot number, expiration date, and National Drug Code (NDC). The EUA COVID-19 vaccines lacked a 2D barcode containing these data fields, which necessitated manual data entry at administration. A CDC pilot at clinic compared scanned and manually entered data for routine vaccines across these same data fields.</p><p><strong>Results: </strong> Analysis of 50 million manually entered EUA COVID-19 vaccine administration records indicated significant gaps in data accuracy and completeness across three data fields. Over half of the analyzed EUA vaccine NDCs (53%) and one-third of the expiration dates (35%) had missing or inaccurate data recorded. Pilot data also showed many errors when manually entered. However, when the pilot's routine vaccines were scanned (out of 71,969 records), nearly all entries were complete and accurate across all three data fields (ranging from 99.7% to 99.999% accurate).</p><p><strong>Conclusion: </strong> Vaccine 2D barcode scanning practices increased data accuracy and completeness (up to 99.999% accurate) across data fields assessed. When used consistently, vaccine 2D barcode scanning can resolve issues demonstrated in manually entered data. To realize these benefits, the immunization community should widely use scanning practices. To increase use, CDC developed a Vaccine 2D Barcode National Adoption Strategy and implementation resources.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10990595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139576943","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}