JMIR Medical Informatics最新文献

{"title":"Large Language Model-Based Critical Care Big Data Deployment and Extraction: Descriptive Analysis.","authors":"Zhongbao Yang, Shan-Shan Xu, Xiaozhu Liu, Ningyuan Xu, Yuqing Chen, Shuya Wang, Ming-Yue Miao, Mengxue Hou, Shuai Liu, Yi-Min Zhou, Jian-Xin Zhou, Linlin Zhang","doi":"10.2196/63216","DOIUrl":"10.2196/63216","url":null,"abstract":"<p><strong>Background: </strong>Publicly accessible critical care-related databases contain enormous clinical data, but their utilization often requires advanced programming skills. The growing complexity of large databases and unstructured data presents challenges for clinicians who need programming or data analysis expertise to utilize these systems directly.</p><p><strong>Objective: </strong>This study aims to simplify critical care-related database deployment and extraction via large language models.</p><p><strong>Methods: </strong>The development of this platform was a 2-step process. First, we enabled automated database deployment using Docker container technology, with incorporated web-based analytics interfaces Metabase and Superset. Second, we developed the intensive care unit-generative pretrained transformer (ICU-GPT), a large language model fine-tuned on intensive care unit (ICU) data that integrated LangChain and Microsoft AutoGen.</p><p><strong>Results: </strong>The automated deployment platform was designed with user-friendliness in mind, enabling clinicians to deploy 1 or multiple databases in local, cloud, or remote environments without the need for manual setup. After successfully overcoming GPT's token limit and supporting multischema data, ICU-GPT could generate Structured Query Language (SQL) queries and extract insights from ICU datasets based on request input. A front-end user interface was developed for clinicians to achieve code-free SQL generation on the web-based client.</p><p><strong>Conclusions: </strong>By harnessing the power of our automated deployment platform and ICU-GPT model, clinicians are empowered to easily visualize, extract, and arrange critical care-related databases more efficiently and flexibly than manual methods. Our research could decrease the time and effort spent on complex bioinformatics methods and advance clinical research.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e63216"},"PeriodicalIF":3.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11922493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPT-3.5 Turbo and GPT-4 Turbo in Title and Abstract Screening for Systematic Reviews.","authors":"Takehiko Oami, Yohei Okada, Taka-Aki Nakada","doi":"10.2196/64682","DOIUrl":"10.2196/64682","url":null,"abstract":"<p><strong>Unlabelled: </strong>This study demonstrated that while GPT-4 Turbo had superior specificity when compared to GPT-3.5 Turbo (0.98 vs 0.51), as well as comparable sensitivity (0.85 vs 0.83), GPT-3.5 Turbo processed 100 studies faster (0.9 min vs 1.6 min) in citation screening for systematic reviews, suggesting that GPT-4 Turbo may be more suitable due to its higher specificity and highlighting the potential of large language models in optimizing literature selection.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e64682"},"PeriodicalIF":3.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11922487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Perceptions of Potential Prerequisites for Artificial Intelligence in Danish General Practice: Vignette-Based Interview Study Among General Practitioners.","authors":"Natasha Lee Jørgensen, Camilla Hoffmann Merrild, Martin Bach Jensen, Thomas B Moeslund, Kristian Kidholm, Janus Laust Thomsen","doi":"10.2196/63895","DOIUrl":"10.2196/63895","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has been deemed revolutionary in medicine; however, no AI tools have been implemented or validated in Danish general practice. General practice in Denmark has an excellent digitization system for developing and using AI. Nevertheless, there is a lack of involvement of general practitioners (GPs) in developing AI. The perspectives of GPs as end users are essential for facilitating the next stage of AI development in general practice.</p><p><strong>Objective: </strong>This study aimed to identify the essential prerequisites that GPs perceive as necessary to realize the potential of AI in Danish general practice.</p><p><strong>Methods: </strong>This study used semistructured interviews and vignettes among GPs to gain perspectives on the potential of AI in general practice. A total of 12 GPs interested in the potential of AI in general practice were interviewed in 2019 and 2021. The interviews were transcribed verbatim and thematic analysis was conducted to identify the dominant themes throughout the data.</p><p><strong>Results: </strong>In the data analysis, four main themes were identified as essential prerequisites for GPs when considering the potential of AI in general practice: (1) AI must begin with the low-hanging fruit, (2) AI must be meaningful in the GP's work, (3) the GP-patient relationship must be maintained despite AI, and (4) AI must be a free, active, and integrated option in the electronic health record (EHR). These 4 themes suggest that the development of AI should initially focus on low-complexity tasks that do not influence patient interactions but facilitate GPs' work in a meaningful manner as an integrated part of the EHR. Examples of this include routine and administrative tasks.</p><p><strong>Conclusions: </strong>The research findings outline the participating GPs' perceptions of the essential prerequisites to consider when exploring the potential applications of AI in primary care settings. We believe that these perceptions of potential prerequisites can support the initial stages of future development and assess the suitability of existing AI tools for general practice.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e63895"},"PeriodicalIF":3.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11921986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prescription Refill Adherence Before and After Patient Portal Registration in Among General Practice Patients in England Using the Clinical Practice Research Datalink: Longitudinal Observational Study.","authors":"Abrar Alturkistani, Thomas Beaney, Geva Greenfield, Ceire E Costelloe","doi":"10.2196/50294","DOIUrl":"10.2196/50294","url":null,"abstract":"<p><strong>Background: </strong>Patient portal use has been associated with improved patient health and improved adherence to medications, including statins. However, there is limited research on the association between patient portal registration and outcomes such as statin prescription refill adherence in the context of the National Health Service of England, where patient portals have been widely available since 2015.</p><p><strong>Objective: </strong>We aimed to explore statin prescription refill adherence among general practice patients in England.</p><p><strong>Methods: </strong>This study was approved by the Clinical Practice Research Datalink Independent Scientific Advisory Committee (ID: 21_000411). We used patient-level general practice data from the Clinical Practice Research Datalink in England. The data included patients with cardiovascular disease, diabetes, and chronic kidney disease, who were registered on the patient portal. The primary aim was to investigate whether statin prescription refill adherence, defined as ≥80% adherence based on the medication possession ratio, improved after patient portal registration. We used a multilevel logistic regression model to compare aggregate adherence 12 months before and 12 months after patient portal registration.</p><p><strong>Results: </strong>We included 44,141 patients in the study. The analysis revealed a 16% reduction in the odds of prescription refill adherence 12 months after patient portal registration (odds ratio [OR]: 0.84, 95% CI 0.81-0.86) compared to 12 months before registration in the fully adjusted model for patient- and practice-level variables.</p><p><strong>Conclusions: </strong>This study evaluated prescription refill adherence after patient portal registration. Registering to the portal does not fully explain the mechanisms underlying the relationship between patient portal use and health-related outcomes such as medication adherence. Although a small reduction in prescription refill adherence was observed, this reduction disappeared when the follow up time was reduced to 6 months. Given the limitations of the study, reduction in prescription refill adherence cannot be entirely attributable to patient portal registration. However, there may be potential confounding factors influencing this association which should be explored through future research.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e50294"},"PeriodicalIF":3.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11918981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Interoperability in COVID-19 Vaccine Trials: Methodological Approach in the VACCELERATE Project.","authors":"Salma Malik, Zoi Pana Dorothea, Christos D Argyropoulos, Sophia Themistocleous, Alan J Macken, Olena Valdenmaiier, Frank Scheckenbach, Elena Bardach, Andrea Pfeiffer, Katherine Loens, Jordi Cano Ochando, Oliver A Cornely, Jacques Demotes-Mainard, Sergio Contrino, Gerd Felder","doi":"10.2196/65590","DOIUrl":"10.2196/65590","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Data standards are not only key to making data processing efficient but also fundamental to ensuring data interoperability. When clinical trial data are structured according to international standards, they become significantly easier to analyze, reducing the efforts required for data cleaning, preprocessing, and secondary use. A common language and a shared set of expectations facilitate interoperability between systems and devices.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The main objectives of this study were to identify commonalities and differences in clinical trial metadata, protocols, and data collection systems/items within the VACCELERATE project.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;To assess the degree of interoperability achieved in the project and suggest methodological improvements, interoperable points were identified based on the core outcome areas-immunogenicity, safety, and efficacy (clinical/physiological). These points were emphasized in the development of the master protocol template and were manually compared in the following ways: (1) summaries, objectives, and end points in the protocols of 3 VACCELERATE clinical trials (EU-COVAT-1_AGED, EU-COVAT-2_BOOSTAVAC, and EU-COVPT-1_CoVacc) against the master protocol template; (2) metadata of all 3 clinical trials; and (3) evaluations from a questionnaire survey regarding differences in data management systems and structures that enabled data exchange within the VACCELERATE network.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The noncommonalities identified in the protocols and metadata were attributed to differences in populations, variations in protocol design, and vaccination patterns. The detailed metadata released for all 3 vaccine trials were clearly structured using internal standards, terminology, and the general approach of Clinical Data Acquisition Standards Harmonisation (CDASH) for data collection (eg, on electronic case report forms). VACCELERATE benefited significantly from the selection of the Clinical Trials Centre Cologne as the sole data management provider. With system database development coordinated by a single individual and no need for coordination among different trial units, a high degree of uniformity was achieved automatically. The harmonized transfer of data to all sites, using well-established methods, enabled quick exchanges and provided a relatively secure means of data transfer.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrated that using master protocols can significantly enhance trial operational efficiency and data interoperability, provided that similar infrastructure and data management procedures are adopted across multiple trials. To further improve data interoperability and facilitate interpretation and analysis, shared data should be structured, described, formatted, and stored using widely recognized data and metadata standards.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial registration: &lt;/strong&gt;EudraCT 2021-004526-29; https://www.clinicaltrialsr","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e65590"},"PeriodicalIF":3.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143582409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying Robotic Process Automation to Monitor Business Processes in Hospital Information Systems: Mixed Method Approach.","authors":"Adam Park, Se Young Jung, Ilha Yune, Ho-Young Lee","doi":"10.2196/59801","DOIUrl":"10.2196/59801","url":null,"abstract":"<p><strong>Background: </strong>Electronic medical records (EMRs) have undergone significant changes due to advancements in technology, including artificial intelligence, the Internet of Things, and cloud services. The increasing complexity within health care systems necessitates enhanced process reengineering and system monitoring approaches. Robotic process automation (RPA) provides a user-centric approach to monitoring system complexity by mimicking end user interactions, thus presenting potential improvements in system performance and monitoring.</p><p><strong>Objective: </strong>This study aimed to explore the application of RPA in monitoring the complexities of EMR systems within a hospital environment, focusing on RPA's ability to perform end-to-end performance monitoring that closely reflects real-time user experiences.</p><p><strong>Methods: </strong>The research was conducted at Seoul National University Bundang Hospital using a mixed methods approach. It included the iterative development and integration of RPA bots programmed to simulate and monitor typical user interactions with the hospital's EMR system. Quantitative data from RPA process outputs and qualitative insights from interviews with system engineers and managers were used to evaluate the effectiveness of RPA in system monitoring.</p><p><strong>Results: </strong>RPA bots effectively identified and reported system inefficiencies and failures, providing a bridge between end user experiences and engineering assessments. The bots were particularly useful in detecting delays and errors immediately following system updates or interactions with external services. Over 3 years, RPA monitoring highlighted discrepancies between user-reported experiences and traditional engineering metrics, with the bots frequently identifying critical system issues that were not evident from standard component-level monitoring.</p><p><strong>Conclusions: </strong>RPA enhances system monitoring by providing insights that reflect true end user experiences, which are often overlooked by traditional monitoring methods. The study confirms the potential of RPA to act as a comprehensive monitoring tool within complex health care systems, suggesting that RPA can significantly contribute to the maintenance and improvement of EMR systems by providing a more accurate and timely reflection of system performance and user satisfaction.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e59801"},"PeriodicalIF":3.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review.","authors":"Luis B Elvas, Ana Almeida, Joao C Ferreira","doi":"10.2196/64349","DOIUrl":"10.2196/64349","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including health care. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to be applied across different health care settings with varying patient demographics and practices. This issue is critical for ensuring effective and equitable AI deployment. Cardiovascular diseases (CVDs), the leading cause of global mortality with 17.9 million annual deaths, encompass conditions like coronary heart disease and hypertension. The increasing availability of medical data, coupled with AI advancements, offers new opportunities for early detection and intervention in cardiovascular events, leveraging AI's capacity to analyze complex datasets and uncover critical patterns.</p><p><strong>Objective: </strong>This review aims to examine AI methodologies combined with medical data to advance the intelligent monitoring and detection of CVDs, identifying areas for further research to enhance patient outcomes and support early interventions.</p><p><strong>Methods: </strong>This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure a rigorous and transparent literature review process. This structured approach facilitated a comprehensive overview of the current state of research in this field.</p><p><strong>Results: </strong>Through the methodology used, 64 documents were retrieved, of which 40 documents met the inclusion criteria. The reviewed papers demonstrate advancements in AI and ML for CVD detection, classification, prediction, diagnosis, and patient monitoring. Techniques such as ensemble learning, deep neural networks, and feature selection improve prediction accuracy over traditional methods. ML models predict cardiovascular events and risks, with applications in monitoring via wearable technology. The integration of AI in health care supports early detection, personalized treatment, and risk assessment, possibly improving the management of CVDs.</p><p><strong>Conclusions: </strong>The study concludes that AI and ML techniques can improve the accuracy of CVD classification, prediction, diagnosis, and monitoring. The integration of multiple data sources and noninvasive methods supports continuous monitoring and early detection. These advancements help enhance CVD management and patient outcomes, indicating the potential for AI to offer more precise and cost-effective solutions in health care.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e64349"},"PeriodicalIF":3.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143568881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study.","authors":"Eui Geum Oh, Sunyoung Oh, Seunghyeon Cho, Mir Moon","doi":"10.2196/56671","DOIUrl":"10.2196/56671","url":null,"abstract":"<p><strong>Background: </strong>Unplanned readmissions increase unnecessary health care costs and reduce the quality of care. It is essential to plan the discharge care from the beginning of hospitalization to reduce the risk of readmission. Machine learning-based readmission prediction models can support patients' preemptive discharge care services with improved predictive power.</p><p><strong>Objective: </strong>This study aimed to develop a readmission early prediction model utilizing nursing data for high-risk discharge patients.</p><p><strong>Methods: </strong>This retrospective study included the electronic medical records of 12,977 patients with 1 of the top 6 high-risk readmission diseases at a tertiary hospital in Seoul from January 2018 to January 2020. We used demographic, clinical, and nursing data to construct a prediction model. We constructed unplanned readmission prediction models by dividing them into Model 1 and Model 2. Model 1 used early hospitalization data (up to 1 day after admission), and Model 2 used all the data. To improve the performance of the machine learning method, we performed 5-fold cross-validation and utilized adaptive synthetic sampling to address data imbalance. The 6 algorithms of logistic regression, random forest, decision tree, XGBoost, CatBoost, and multiperceptron layer were employed to develop predictive models. The analysis was conducted using Python Language Reference, version 3.11.3. (Python Software Foundation).</p><p><strong>Results: </strong>In Model 1, among the 6 prediction model algorithms, the random forest model had the best result, with an area under the receiver operating characteristic (AUROC) curve of 0.62. In Model 2, the CatBoost model had the best result, with an AUROC of 0.64. BMI, systolic blood pressure, and age consistently emerged as the most significant predictors of readmission risk across Models 1 and 2. Model 1, which enabled early readmission prediction, showed a higher proportion of nursing data variables among its important predictors compared to Model 2.</p><p><strong>Conclusions: </strong>Machine learning-based readmission prediction models utilizing nursing data provide basic data for evidence-based clinical decision support for high-risk discharge patients with complex conditions and facilitate early intervention. By integrating nursing data containing diverse patient information, these models can provide more comprehensive risk assessment and improve patient outcomes.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e56671"},"PeriodicalIF":3.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11921987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation Between Diagnosis-Related Group Weights and Nursing Time in the Cardiology Department: Cross-Sectional Study.","authors":"Chen Lv, Yi-Hong Gong, Xiu-Hua Wang, Jun An, Qian Wang, Jing Han, Xiao-Feng Chen","doi":"10.2196/65549","DOIUrl":"10.2196/65549","url":null,"abstract":"<p><strong>Background: </strong>Diagnosis-related group (DRG) payment has become the main form of medical expense settlements, and its application is becoming increasingly extensive.</p><p><strong>Objective: </strong>This study aimed to explore the correlation between DRG weights and nursing time and to develop a predictive model for nursing time in the cardiology department based on DRG weights and other factors.</p><p><strong>Methods: </strong>A convenience sampling method was used to select patients who were hospitalized in the cardiology ward of Beijing Chest Hospital between April 2023 and April 2024. Nursing time was measured by direct and indirect nursing time. To determine the distributions of nursing time based on different demographics, a Pearson correlation was used to analyze the relationship between DRG weight and nursing time, and a multiple linear regression was used to determine the influencing factors of total nursing time.</p><p><strong>Results: </strong>A total of 103 subjects were included in this study. The DRG weights were positively correlated with direct nursing time (r=0.480; P<.001), indirect nursing time (r=0.394; P<.001), and total nursing time (r=0.448; P<.001). Moreover, age was positively correlated with the 3 nursing times (direct: r=0.235; indirect: r=0.192; total: r=0.235; all P<.001). The activities of daily living (ADL) score on admission was negatively correlated with the 3 nursing times (direct: r=-0.316; indirect: r=-0.252; total: r=-0.301; all P<.001). In addition, the nursing level on the first day of admission was positively correlated with the 3 nursing times (direct: r=0.333; indirect: r=0.332; total: r=0.352; all P<.001). Furthermore, the multivariate analysis found that the nursing level on the first day of admission, complications or comorbidities, DRG weight, and ADL score on admission were the influencing factors of nursing time (R2=0.328; F5,97=69.58; P<.001).</p><p><strong>Conclusions: </strong>DRG weight showed a strong correlation with nursing time and could be used to predict nursing time, which may assist in nursing resource allocation in cardiology departments.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e65549"},"PeriodicalIF":3.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11896087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking Electronic Health Record Prescribing Data and Pharmacy Dispensing Records to Identify Patient-Level Factors Associated With Psychotropic Medication Receipt: Retrospective Study.","authors":"Peng Wu, Jillian H Hurst, Alexis French, Michael Chrestensen, Benjamin A Goldstein","doi":"10.2196/63740","DOIUrl":"10.2196/63740","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Pharmacoepidemiology studies using electronic health record (EHR) data typically rely on medication prescriptions to determine which patients have received a medication. However, such data do not affirmatively indicate whether these prescriptions have been filled. External dispensing databases can bridge this information gap; however, few established methods exist for linking EHR data and pharmacy dispensing records.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We described a process for linking EHR prescribing data with pharmacy dispensing records from Surescripts. As a use case, we considered the prescriptions and resulting fills for psychotropic medications among pediatric patients. We evaluated how dispensing information affects identifying patients receiving prescribed medications and assessing the association between filling prescriptions and subsequent health behaviors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective study identified all new psychotropic prescriptions to patients younger than 18 years of age at Duke University Health System in 2021. We linked dispensing to prescribing data using proximate dates and matching codes between RxNorm concept unique identifiers and National Drug Codes. We described demographic, clinical, and service use characteristics to assess differences between patients who did versus did not fill prescriptions. We fit a least absolute shrinkage and selection operator (LASSO) regression model to evaluate the predictability of a fill. We then fit time-to-event models to assess the association between whether a patient filled a prescription and a future provider visit.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 1254 pediatric patients with a new psychotropic prescription. In total, 976 (77.8%) patients filled their prescriptions within 30 days of their prescribing encounters. Thus, we set 30 days as a cut point for defining a valid prescription fill. Patients who filled prescriptions differed from those who did not in several key factors. Those who did not fill had slightly higher BMIs, lived in more disadvantaged neighborhoods, were more likely to have public insurance or self-pay, and included a higher proportion of male patients. Patients with prior well-child visits or prescriptions from primary care providers were more likely to fill. Additionally, patients with anxiety diagnoses and those prescribed selective serotonin reuptake inhibitors were more likely to fill prescriptions. The LASSO model achieved an area under the receiver operator characteristic curve of 0.816. The time to the follow-up visit with the same provider was censored at 90 days after the initial encounter. Patients who filled prescriptions showed higher levels of follow-up visits. The marginal hazard ratio of a follow-up visit with the same provider was 1.673 (95% CI 1.463-1.913) for patients who filled their prescriptions. Using the LASSO model as a propensity-based weight, we calculated the weighted hazard ra","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e63740"},"PeriodicalIF":3.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}