JMIR Medical Informatics最新文献

{"title":"Optimizing an Electronic Health Record System Used to Help Health Care Professionals Comply With a Standardized Care Pathway for Heart Failure During the Transition From Hospital To Chronic Care: Qualitative Semistructured Interview Study.","authors":"Marta Font, Nadia Davoody","doi":"10.2196/63665","DOIUrl":"https://doi.org/10.2196/63665","url":null,"abstract":"<p><strong>Background: </strong>In Spain, the prevalence of heart failure is twice the European average, partly due to inadequate patient management. To address this issue, a standardized care model, the Care Model for Patients with Heart Failure (Modelos Asistenciales de Atención al Paciente con Insuficiencia Cardíaca), was developed. This model emphasizes the importance of sequential visits from hospital discharge until the patient transitions to chronic care to prevent rehospitalization. The standardized care pathway has been implemented in certain areas of the Andalusia Health Service. However, there is uncertainty about whether the region's electronic health record system, Diraya, can effectively support this model. If not properly integrated, it could lead to data inaccuracies and noncompliance with the standardized care pathway.</p><p><strong>Objective: </strong>This study aimed to explore how to improve Diraya to better support health care professionals in adhering to the transition standardized care model for patients with heart failure as they move from hospital care to chronic care.</p><p><strong>Methods: </strong>In total, 16 semistructured interviews were conducted with nurses and physicians from both hospital and primary care settings. Thematic analysis was used to analyze the data and recommendations for improvements that were developed based on the findings. These recommendations were further supported by existing literature and validated through additional interviews.</p><p><strong>Results: </strong>In total, 65 codes, 23 subthemes, and 8 themes were identified. The main themes included optimizing medical data management for enhanced clinical workflow, agreement on standardization and enhancement of the discharge report, enhancing clinical decision support through updated guidelines and automated tools, optimizing interoperability as a solution for better management of patients with heart failure, and encouraging communication based on digital tools and personal connection. In total, 15 improvements were proposed, such as standardizing technology across Andalusia Health Service facilities and offering targeted training programs. These measures aim to enhance interoperability, streamline communication between different health care settings, and reduce the administrative burden for health care professionals.</p><p><strong>Conclusions: </strong>Diraya currently does not adequately support the transition standardized care model, placing a significant administrative burden on health care professionals, often with ethically concerning implications. To ensure effective implementation of the standardized care model, major updates are necessary for Diraya's clinical information management, system functionality, and organizational structure within the Andalusia Health Service.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e63665"},"PeriodicalIF":3.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12041825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029473","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":"Summarizing Online Patient Conversations Using Generative Language Models: Experimental and Comparative Study.","authors":"Rakhi Asokkumar Subjagouri Nair, Matthias Hartung, Philipp Heinisch, Janik Jaskolski, Cornelius Starke-Knäusel, Susana Veríssimo, David Maria Schmidt, Philipp Cimiano","doi":"10.2196/62909","DOIUrl":"https://doi.org/10.2196/62909","url":null,"abstract":"<p><strong>Background: </strong>Social media is acknowledged by regulatory bodies (eg, the Food and Drug Administration) as an important source of patient experience data to learn about patients' unmet needs, priorities, and preferences. However, current methods rely either on manual analysis and do not scale, or on automatic processing, yielding mainly quantitative insights. Methods that can automatically summarize texts and yield qualitative insights at scale are missing.</p><p><strong>Objective: </strong>The objective of this study was to evaluate to what extent state-of-the-art large language models can appropriately summarize posts shared by patients in web-based forums and health communities. Specifically, the goal was to compare the performance of different language models and prompting strategies on the task of summarizing documents reflecting the experiences of individual patients.</p><p><strong>Methods: </strong>In our experimental and comparative study, we applied 3 different language models (Flan-T5, Generative Pretrained Transformer [GPT], GPT-3, and GPT-3.5) in combination with various prompting strategies to the task of summarizing posts from patients in online communities. The generated summaries were evaluated with respect to 124 manually created summaries as a ground-truth reference. As evaluation metrics, we used 2 standard metrics from the field of text generation, namely, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and BERTScore, to compare the automatically generated summaries to the manually created reference summaries.</p><p><strong>Results: </strong>Among the zero-shot prompting-based large language models investigated, GPT-3.5 performed better than the other models with respect to the ROUGE metrics, as well as with respect to BERTScore. While zero-shot prompting seems to be a good prompting strategy, overall GPT-3.5 in combination with directional stimulus prompting in a 3-shot setting had the best results with respect to the aforementioned metrics. A manual investigation of the summarization of the best-performing method showed that the generated summaries were accurate and plausible compared to the manual summaries.</p><p><strong>Conclusions: </strong>Taken together, our results suggest that state-of-the-art pretrained language models are a valuable tool to provide qualitative insights about the patient experience to better understand unmet needs, patient priorities, and how a disease impacts daily functioning and quality of life to inform processes aimed at improving health care delivery and ensure that drug development focuses more on the actual priorities and unmet needs of patients. The key limitations of our work are the small data sample as well as the fact that the manual summaries were created by 1 annotator only. Furthermore, the results hold only for the examined models and prompting strategies, potentially not generalizing to other models and strategies.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e62909"},"PeriodicalIF":3.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12038288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144021468","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":"Implementation of an Integrated, Clinical Decision Support Tool at the Point of Antihypertensive Medication Refill Request to Improve Hypertension Management: Controlled Pre-Post Study.","authors":"John Charles Matulis Rd, Jason Greenwood, Michele Eberle, Benjamin Anderson, David Blair, Rajeev Chaudhry","doi":"10.2196/70752","DOIUrl":"https://doi.org/10.2196/70752","url":null,"abstract":"<p><strong>Background: </strong>Improving processes regarding the management of electronic health record (EHR) requests for chronic antihypertensive medication renewals may represent an opportunity to enhance blood pressure (BP) management at the individual and population level.</p><p><strong>Objective: </strong>This study aimed to evaluate the effectiveness of the eRx HTN Chart Check, an integrated clinical decision support tool available at the point of antihypertensive medication refill request, in facilitating enhanced provider management of chronic hypertension.</p><p><strong>Methods: </strong>The study was conducted at two Mayo Clinic sites-Northwest Wisconsin Family Medicine and Rochester Community Internal Medicine practices-with control groups in comparable Mayo Clinic practices. The intervention integrated structured clinical data, including recent BP readings, laboratory results, and visit dates, into the electronic prescription renewal interface to facilitate prescriber decision-making regarding hypertension management. A difference-in-differences (DID) design compared pre- and postintervention hypertension control rates between the intervention and control groups. Data were collected from the Epic EHR system and analyzed using linear regression models.</p><p><strong>Results: </strong>The baseline BP control rates were slightly higher in intervention clinics. Postimplementation, no significant improvement in population-level hypertension control was observed (DID estimate: 0.07%, 95% CI -4.0% to 4.1%; P=.97). Of the 19,968 refill requests processed, 46% met all monitoring criteria. However, clinician approval rates remained high (90%), indicating minimal impact on prescribing behavior.</p><p><strong>Conclusions: </strong>Despite successful implementation, the tool did not significantly improve hypertension control, possibly due to competing quality initiatives and high in-basket volumes. Future iterations should focus on enhanced integration with other decision support tools and strategies to improve clinician engagement and patient outcomes. Further research is needed to optimize chronic disease management through EHR-integrated decision support systems.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e70752"},"PeriodicalIF":3.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014122","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":"Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study.","authors":"Daniel Sumsion, Elijah Davis, Marta Fernandes, Ruoqi Wei, Rebecca Milde, Jet Malou Veltink, Wan-Yee Kong, Yiwen Xiong, Samvrit Rao, Tara Westover, Lydia Petersen, Niels Turley, Arjun Singh, Stephanie Buss, Shibani Mukerji, Sahar Zafar, Sudeshna Das, Valdery Moura Junior, Manohar Ghanta, Aditya Gupta, Jennifer Kim, Katie Stone, Emmanuel Mignot, Dennis Hwang, Lynn Marie Trotti, Gari D Clifford, Umakanth Katwa, Robert Thomas, M Brandon Westover, Haoqi Sun","doi":"10.2196/64113","DOIUrl":"https://doi.org/10.2196/64113","url":null,"abstract":"<p><strong>Background: </strong>Congestive heart failure (CHF) is a common cause of hospital admissions. Medical records contain valuable information about CHF, but manual chart review is time-consuming. Claims databases (using International Classification of Diseases [ICD] codes) provide a scalable alternative but are less accurate. Automated analysis of medical records through natural language processing (NLP) enables more efficient adjudication but has not yet been validated across multiple sites.</p><p><strong>Objective: </strong>We seek to accurately classify the diagnosis of CHF based on structured and unstructured data from each patient, including medications, ICD codes, and information extracted through NLP of notes left by providers, by comparing the effectiveness of several machine learning models.</p><p><strong>Methods: </strong>We developed an NLP model to identify CHF from medical records using electronic health records (EHRs) from two hospitals (Mass General Hospital and Beth Israel Deaconess Medical Center; from 2010 to 2023), with 2800 clinical visit notes from 1821 patients. We trained and compared the performance of logistic regression, random forests, and RoBERTa models. We measured model performance using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). These models were also externally validated by training the data on one hospital sample and testing on the other, and an overall estimated error was calculated using a completely random sample from both hospitals.</p><p><strong>Results: </strong>The average age of the patients was 66.7 (SD 17.2) years; 978 (54.3%) out of 1821 patients were female. The logistic regression model achieved the best performance using a combination of ICD codes, medications, and notes, with an AUROC of 0.968 (95% CI 0.940-0.982) and an AUPRC of 0.921 (95% CI 0.835-0.969). The models that only used ICD codes or medications had lower performance. The estimated overall error rate in a random EHR sample was 1.6%. The model also showed high external validity from training on Mass General Hospital data and testing on Beth Israel Deaconess Medical Center data (AUROC 0.927, 95% CI 0.908-0.944) and vice versa (AUROC 0.968, 95% CI 0.957-0.976).</p><p><strong>Conclusions: </strong>The proposed EHR-based phenotyping model for CHF achieved excellent performance, external validity, and generalization across two institutions. The model enables multiple downstream uses, paving the way for large-scale studies of CHF treatment effectiveness, comorbidities, outcomes, and mechanisms.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e64113"},"PeriodicalIF":3.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12022513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144011588","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":"Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study.","authors":"Mohammed Mahyoub, Kacie Dougherty, Ajit Shukla","doi":"10.2196/67706","DOIUrl":"https://doi.org/10.2196/67706","url":null,"abstract":"<p><strong>Background: </strong>Pulmonary embolism (PE) is a critical condition requiring rapid diagnosis to reduce mortality. Extracting PE diagnoses from radiology reports manually is time-consuming, highlighting the need for automated solutions. Advances in natural language processing, especially transformer models like GPT-4o, offer promising tools to improve diagnostic accuracy and workflow efficiency in clinical settings.</p><p><strong>Objective: </strong>This study aimed to develop an automatic extraction system using GPT-4o to extract PE diagnoses from radiology report impressions, enhancing clinical decision-making and workflow efficiency.</p><p><strong>Methods: </strong>In total, 2 approaches were developed and evaluated: a fine-tuned Clinical Longformer as a baseline model and a GPT-4o-based extractor. Clinical Longformer, an encoder-only model, was chosen for its robustness in text classification tasks, particularly on smaller scales. GPT-4o, a decoder-only instruction-following LLM, was selected for its advanced language understanding capabilities. The study aimed to evaluate GPT-4o's ability to perform text classification compared to the baseline Clinical Longformer. The Clinical Longformer was trained on a dataset of 1000 radiology report impressions and validated on a separate set of 200 samples, while the GPT-4o extractor was validated using the same 200-sample set. Postdeployment performance was further assessed on an additional 200 operational records to evaluate model efficacy in a real-world setting.</p><p><strong>Results: </strong>GPT-4o outperformed the Clinical Longformer in 2 of the metrics, achieving a sensitivity of 1.0 (95% CI 1.0-1.0; Wilcoxon test, P<.001) and an F₁-score of 0.975 (95% CI 0.9495-0.9947; Wilcoxon test, P<.001) across the validation dataset. Postdeployment evaluations also showed strong performance of the deployed GPT-4o model with a sensitivity of 1.0 (95% CI 1.0-1.0), a specificity of 0.94 (95% CI 0.8913-0.9804), and an F₁-score of 0.97 (95% CI 0.9479-0.9908). This high level of accuracy supports a reduction in manual review, streamlining clinical workflows and improving diagnostic precision.</p><p><strong>Conclusions: </strong>The GPT-4o model provides an effective solution for the automatic extraction of PE diagnoses from radiology reports, offering a reliable tool that aids timely and accurate clinical decision-making. This approach has the potential to significantly improve patient outcomes by expediting diagnosis and treatment pathways for critical conditions like PE.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e67706"},"PeriodicalIF":3.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041964","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":"Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study.","authors":"Adam Remaki, Jacques Ung, Pierre Pages, Perceval Wajsburt, Elise Liu, Guillaume Faure, Thomas Petit-Jean, Xavier Tannier, Christel Gérardin","doi":"10.2196/68704","DOIUrl":"https://doi.org/10.2196/68704","url":null,"abstract":"<p><strong>Background: </strong>Valuable insights gathered by clinicians during their inquiries and documented in textual reports are often unavailable in the structured data recorded in electronic health records (EHRs).</p><p><strong>Objective: </strong>This study aimed to highlight that mining unstructured textual data with natural language processing techniques complements the available structured data and enables more comprehensive patient phenotyping. A proof-of-concept for patients diagnosed with specific autoimmune diseases is presented, in which the extraction of information on laboratory tests and drug treatments is performed.</p><p><strong>Methods: </strong>We collected EHRs available in the clinical data warehouse of the Greater Paris University Hospitals from 2012 to 2021 for patients hospitalized and diagnosed with 1 of 4 immune-mediated inflammatory diseases: systemic lupus erythematosus, systemic sclerosis, antiphospholipid syndrome, and Takayasu arteritis. Then, we built, trained, and validated natural language processing algorithms on 103 discharge summaries selected from the cohort and annotated by a clinician. Finally, all discharge summaries in the cohort were processed with the algorithms, and the extracted data on laboratory tests and drug treatments were compared with the structured data.</p><p><strong>Results: </strong>Named entity recognition followed by normalization yielded F₁-scores of 71.1 (95% CI 63.6-77.8) for the laboratory tests and 89.3 (95% CI 85.9-91.6) for the drugs. Application of the algorithms to 18,604 EHRs increased the detection of antibody results and drug treatments. For instance, among patients in the systemic lupus erythematosus cohort with positive antinuclear antibodies, the rate increased from 18.34% (752/4102) to 71.87% (2949/4102), making the results more consistent with the literature.</p><p><strong>Conclusions: </strong>While challenges remain in standardizing laboratory tests, particularly with abbreviations, this work, based on secondary use of clinical data, demonstrates that automated processing of discharge summaries enriched the information available in structured data and facilitated more comprehensive patient profiling.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e68704"},"PeriodicalIF":3.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144063310","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":"Construction and Application of an Information Closed-Loop Management System for Maternal and Neonatal Access and Exit Rooms: Non Randomized Controlled Trial.","authors":"Shafeng Jia, Naifeng Zhu, Jia Liu, Niankai Cheng, Ling Jiang, Jing Yang","doi":"10.2196/66451","DOIUrl":"10.2196/66451","url":null,"abstract":"<p><strong>Background: </strong>Traditional management methods can no longer meet the demand for efficient and accurate neonatal care. There is a need for an information-based and intelligent management system.</p><p><strong>Objective: </strong>This study aimed to construct an information closed-loop management system to improve the accuracy of identification in mother-infant rooming-in care units and enhance the efficiency of infant admission and discharge management.</p><p><strong>Methods: </strong>Mothers who delivered between January 2023 and June 2023 were assigned to the control group (n=200), while those who delivered between July 2023 and May 2024 were assigned to the research group (n=200). The control group adopted traditional management methods, whereas the research group implemented closed-loop management. Barcode technology, a wireless network, mobile terminals, and other information technology equipments were used to complete the closed loop of newborn exit and entry management. Data on the satisfaction of mothers and their families, the monthly average qualification rate of infant identity verification, and the qualification rate of infant consultation time were collected and statistically analyzed before and after the closed-loop process was implemented.</p><p><strong>Results: </strong>After the closed-loop process was implemented, the monthly average qualification rate of infant identity verification increased to 99.45 (SD 1.34), significantly higher than the control group before implementation 83.58 (SD 1.92) (P=.02). The satisfaction of mothers and their families was 96.45 (SD 3.32), higher than that of the control group before the closed-loop process was implemented 92.82 (SD 4.73) (P=.01). Additionally, the separation time between infants and mothers was restricted to under 1 hour.</p><p><strong>Conclusions: </strong>The construction and application of the information closed-loop management system significantly improved the accuracy and efficiency of maternal and infant identity verification, enhancing the safety of newborns.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e66451"},"PeriodicalIF":3.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804842","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":"Expression of Concern: A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study.","authors":"","doi":"10.2196/75352","DOIUrl":"10.2196/75352","url":null,"abstract":"","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e75352"},"PeriodicalIF":3.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12118361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789394","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":"Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis.","authors":"Brian C Coleman, Kelsey L Corcoran, Cynthia A Brandt, Joseph L Goulet, Stephen L Luther, Anthony J Lisi","doi":"10.2196/66466","DOIUrl":"10.2196/66466","url":null,"abstract":"<p><strong>Background: </strong>The use of patient-reported outcome measures (PROMs) is an expected component of high-quality, measurement-based chiropractic care. The largest health care system offering integrated chiropractic care is the Veterans Health Administration (VHA). Challenges limit monitoring PROM use as a care quality metric at a national scale in the VHA. Structured data are unavailable, with PROMs often embedded within clinic text notes as unstructured data requiring time-intensive, peer-conducted chart review for evaluation. Natural language processing (NLP) of clinic text notes is one promising solution to extracting care quality data from unstructured text.</p><p><strong>Objective: </strong>This study aims to test NLP approaches to identify PROMs documented in VHA chiropractic text notes.</p><p><strong>Methods: </strong>VHA chiropractic notes from October 1, 2017, to September 30, 2020, were obtained from the VHA Musculoskeletal Diagnosis/Complementary and Integrative Health Cohort. A rule-based NLP model built using medspaCy and spaCy was evaluated on text matching and note categorization tasks. SpaCy was used to build bag-of-words, convoluted neural networks, and ensemble models for note categorization. Performance metrics for each model and task included precision, recall, and F-measure. Cross-validation was used to validate performance metric estimates for the statistical and machine-learning models.</p><p><strong>Results: </strong>Our sample included 377,213 visit notes from 56,628 patients. The rule-based model performance was good for soft-boundary text-matching (precision=81.1%, recall=96.7%, and F-measure=88.2%) and excellent for note categorization (precision=90.3%, recall=99.5%, and F-measure=94.7%). Cross-validation performance of the statistical and machine learning models for the note categorization task was very good overall, but lower than rule-based model performance. The overall prevalence of PROM documentation was low (17.0%).</p><p><strong>Conclusions: </strong>We evaluated multiple NLP methods across a series of tasks, with optimal performance achieved using a rule-based method. By leveraging NLP approaches, we can overcome the challenges posed by unstructured clinical text notes to track documented PROM use. Overall documented use of PROMs in chiropractic notes was low and highlights a potential for quality improvement. This work represents a methodological advancement in the identification and monitoring of documented use of PROMs to ensure consistent, high-quality chiropractic care for veterans.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e66466"},"PeriodicalIF":3.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12038758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775023","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":"Large-Scale Evaluation and Liver Disease Risk Prediction in Finland's National Electronic Health Record System: Feasibility Study Using Real-World Data.","authors":"Viljami Männikkö, Janne Tommola, Emmi Tikkanen, Olli-Pekka Hätinen, Fredrik Åberg","doi":"10.2196/62978","DOIUrl":"10.2196/62978","url":null,"abstract":"<p><strong>Background: </strong>Globally, the incidence and mortality of chronic liver disease are escalating. Early detection of liver disease remains a challenge, often occurring at symptomatic stages when preventative measures are less effective. The Chronic Liver Disease score (CLivD) is a predictive risk model developed using Finnish health care data, aiming to forecast an individual's risk of developing chronic liver disease in subsequent years. The Kanta Service is a national electronic health record system in Finland that stores comprehensive health care data including patient medical histories, prescriptions, and laboratory results, to facilitate health care delivery and research.</p><p><strong>Objective: </strong>This study aimed to evaluate the feasibility of implementing an automatic CLivD score with the current Kanta platform and identify and suggest improvements for Kanta that would enable accurate automatic risk detection.</p><p><strong>Methods: </strong>In this study, a real-world data repository (Kanta) was used as a data source for \"The ClivD score\" risk calculation model. Our dataset consisted of 96,200 individuals' whole medical history from Kanta. For real-world data use, we designed processes to handle missing input in the calculation process.</p><p><strong>Results: </strong>We found that Kanta currently lacks many CLivD risk model input parameters in the structured format required to calculate precise risk scores. However, the risk scores can be improved by using the unstructured text in patient reports and by approximating variables by using other health data-like diagnosis information. Using structured data, we were able to identify only 33 out of 51,275 individuals in the \"low risk\" category and 308 out of 51,275 individuals (<1%) in the \"moderate risk\" category. By adding diagnosis information approximation and free text use, we were able to identify 18,895 out of 51,275 (37%) individuals in the \"low risk\" category and 2125 out of 51,275 (4%) individuals in the \"moderate risk\" category. In both cases, we were not able to identify any individuals in the \"high-risk\" category because of the missing waist-hip ratio measurement. We evaluated 3 scenarios to improve the coverage of waist-hip ratio data in Kanta and these yielded the most substantial improvement in prediction accuracy.</p><p><strong>Conclusions: </strong>We conclude that the current structured Kanta data is not enough for precise risk calculation for CLivD or other diseases where obesity, smoking, and alcohol use are important risk factors. Our simulations show up to 14% improvement in risk detection when adding support for missing input variables. Kanta shows the potential for implementing nationwide automated risk detection models that could result in improved disease prevention and public health.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e62978"},"PeriodicalIF":3.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765901","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}