JAMIA Open最新文献

{"title":"The Bitemporal Lens Model—toward a holistic approach to chronic disease prevention with digital biomarkers","authors":"F. Barata, Jinjoo Shim, Fan Wu, Patrick Langer, E. Fleisch","doi":"10.1093/jamiaopen/ooae027","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae027","url":null,"abstract":"Abstract Objectives We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. Materials and Methods The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification. Results By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention. Discussion We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection. Conclusion The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140728133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A blockchain-based healthcare data marketplace: prototype and demonstration","authors":"KangHyun Kim, Sung-Min Kim, YoungMin Park, Eunsol Lee, Sungjae Jung, Jeong-Hum Kang, DongUk An, Kyung-Jin Min, Sung Ryul Shim, H. Yu, Hyun Wook Han","doi":"10.1093/jamiaopen/ooae029","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae029","url":null,"abstract":"Abstract Objectives This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces. Materials and methods We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios. Results We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario. Discussion Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data. Conclusion The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140728135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative artificial intelligence responses to patient messages in the electronic health record: early lessons learned","authors":"Sally L Baxter, Christopher Longhurst, Marlene Millen, Amy M Sitapati, Ming Tai-Seale","doi":"10.1093/jamiaopen/ooae028","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae028","url":null,"abstract":"Abstract Background Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed. Methods A retrospective sample (n = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses. Results Some LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations. Conclusion Further work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of laboratory data transmission between two healthcare institutions using a widely used point-to-point health information exchange platform: a case report","authors":"Hung S Luu, Walter S Campbell, Raja A Cholan, Mary E. Edgerton, Andrea Englund, Alana Keller, Elizabeth D. Korte, Sandra H Mitchell, Greg T Watkins, Lindsay Westervelt, Daniel Wyman, Stephen Powell","doi":"10.1093/jamiaopen/ooae032","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae032","url":null,"abstract":"Abstract Objective The objective was to identify information loss that could affect clinical care in laboratory data transmission between 2 health care institutions via a Health Information Exchange platform. Materials and Methods Data transmission results of 9 laboratory tests, including LOINC codes, were compared in the following: between sending and receiving electronic health record (EHR) systems, the individual Health Level Seven International (HL7) Version 2 messages across the instrument, laboratory information system, and sending EHR. Results Loss of information for similar tests indicated the following potential patient safety issues: (1) consistently missing specimen source; (2) lack of reporting of analytical technique or instrument platform; (3) inconsistent units and reference ranges; (4) discordant LOINC code use; and (5) increased complexity with multiple HL7 versions. Discussion and Conclusions Using an HIE with standard messaging, SHIELD (Systemic Harmonization and Interoperability Enhancement for Laboratory Data) recommendations, and enhanced EHR functionality to support necessary data elements would yield consistent test identification and result value transmission.","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140728993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient factors associated with telehealth quality and experience among adults with chronic conditions","authors":"Esther S Yoon, Scott Hur, Laura M Curtis, J. Y. Benavente, Michael S Wolf, M. Serper","doi":"10.1093/jamiaopen/ooae026","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae026","url":null,"abstract":"\u0000 \u0000 \u0000 To evaluate patient-reported experiences of telehealth and disparities in access, use, and satisfaction with telehealth during the COVID-19 pandemic.\u0000 \u0000 \u0000 \u0000 We examined data from the 5th wave of the COVID-19 & Chronic Conditions (C3) study conducted between December 2020 and March 2021.\u0000 \u0000 \u0000 \u0000 Of the 718 participants, 342 (47.6%) reported having a telehealth visit within the past four months. Participants who had a recent telehealth visit were younger, reported worse overall health and chronic illness burden, and living below poverty level. Among participants who had a telehealth visit, 66.7% reported telephone visits and most participants (57.6%) rated telehealth quality as better-or-equal-to in-person visits. Inadequate health literacy was associated with lower likelihood of reporting telehealth quality and usefulness. In multivariable analyses, lower patient activation (adjusted odds ratio (AOR) 0.19, 95% CI 0.05–0.59) and limited English proficiency (AOR 0.12, 95% CI 0.03–0.47) were less likely to report telehealth as being better than in-person visits; lower patient activation (AOR 0.06, 95% CI 0.003–0.41) and income below poverty level (AOR 0.36, 95% CI 0.13–0.98) were associated with difficulty remembering telehealth visit information.\u0000 \u0000 \u0000 \u0000 Most participants reported usefulness and ease of navigating telehealth. Lower socioeconomic status, limited English proficiency, inadequate health literacy, lower educational attainment, and low patient activation are risks for poorer quality telehealth.\u0000 \u0000 \u0000 \u0000 The COVID pandemic has accelerated the adoption of telehealth, however disparities in access and self-reported visit quality persist. Since telemedicine is here to stay, we identify vulnerable populations and discuss potential solutions to reduce healthcare disparities in telehealth use.\u0000","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital health technologies for high-risk pregnancy management: three case studies using Digilego framework.","authors":"Sahiti Myneni, Alexandra Zingg, Tavleen Singh, Angela Ross, Amy Franklin, Deevakar Rogith, Jerrie Refuerzo","doi":"10.1093/jamiaopen/ooae022","DOIUrl":"10.1093/jamiaopen/ooae022","url":null,"abstract":"<p><strong>Objective: </strong>High-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products leveraging advances in social computing, data science, and digital health.</p><p><strong>Methods: </strong>We describe three studies that leverage core methods from <i>Digilego</i> digital health development framework to (1) conduct large-scale social media analysis (<i>n</i> = 55 301 posts) to understand population-level patterns in women's needs, (2) architect a digital repository to enable women curate HRP related information, and (3) develop a digital platform to support PPD prevention. We applied a combination of qualitative coding, machine learning, theory-mapping, and programmatic implementation of theory-linked digital features. Further, we conducted preliminary testing of the resulting products for acceptance with sample of pregnant women for GDM/HTN information management (<i>n</i> = 10) and PPD prevention (<i>n</i> = 30).</p><p><strong>Results: </strong>Scalable social computing models using deep learning classifiers with reasonable accuracy have allowed us to capture and examine psychosociobehavioral drivers associated with HRPM. Our work resulted in two digital health solutions, MyPregnancyChart and MomMind are developed. Initial evaluation of both tools indicates positive acceptance from potential end users. Further evaluation with MomMind revealed statistically significant improvements (<i>P</i> < .05) in PPD recognition and knowledge on how to seek PPD information.</p><p><strong>Discussion: </strong>Digilego framework provides an integrative methodological lens to gain micro-macro perspective on women's needs, theory integration, engagement optimization, as well as subsequent feature and content engineering, which can be organized into core and specialized digital pathways for women engagement in disease management.</p><p><strong>Conclusion: </strong>Future works should focus on implementation and testing of digital solutions that facilitate women to capture, aggregate, preserve, and utilize, otherwise siloed, prenatal information artifacts for enhanced self-management of their high-risk conditions, ultimately leading to improved health outcomes.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10919928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140060750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieval augmented scientific claim verification.","authors":"Hao Liu, Ali Soroush, Jordan G Nestor, Elizabeth Park, Betina Idnay, Yilu Fang, Jane Pan, Stan Liao, Marguerite Bernard, Yifan Peng, Chunhua Weng","doi":"10.1093/jamiaopen/ooae021","DOIUrl":"10.1093/jamiaopen/ooae021","url":null,"abstract":"<p><strong>Objective: </strong>To automate scientific claim verification using PubMed abstracts.</p><p><strong>Materials and methods: </strong>We developed CliVER, an end-to-end scientific <b>Cl</b>a<b>i</b>m <b>VER</b>ification system that leverages retrieval-augmented techniques to automatically retrieve relevant clinical trial abstracts, extract pertinent sentences, and use the PICO framework to support or refute a scientific claim. We also created an ensemble of three state-of-the-art deep learning models to classify rationale of support, refute, and neutral. We then constructed CoVERt, a new <b>CO</b>VID <b>VER</b>ifica<b>t</b>ion dataset comprising 15 PICO-encoded drug claims accompanied by 96 manually selected and labeled clinical trial abstracts that either support or refute each claim. We used CoVERt and SciFact (a public scientific claim verification dataset) to assess CliVER's performance in predicting labels. Finally, we compared CliVER to clinicians in the verification of 19 claims from 6 disease domains, using 189 648 PubMed abstracts extracted from January 2010 to October 2021.</p><p><strong>Results: </strong>In the evaluation of label prediction accuracy on CoVERt, CliVER achieved a notable F1 score of 0.92, highlighting the efficacy of the retrieval-augmented models. The ensemble model outperforms each individual state-of-the-art model by an absolute increase from 3% to 11% in the F1 score. Moreover, when compared with four clinicians, CliVER achieved a precision of 79.0% for abstract retrieval, 67.4% for sentence selection, and 63.2% for label prediction, respectively.</p><p><strong>Conclusion: </strong>CliVER demonstrates its early potential to automate scientific claim verification using retrieval-augmented strategies to harness the wealth of clinical trial abstracts in PubMed. Future studies are warranted to further test its clinical utility.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10919922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140060751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging information gaps in menopause status classification through natural language processing","authors":"Hannah Eyre, Patrick R. Alba, Carolyn J Gibson, E. Gatsby, Kristine E Lynch, Olga V. Patterson, S. Duvall","doi":"10.1093/jamiaopen/ooae013","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae013","url":null,"abstract":"\u0000 \u0000 \u0000 To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient’s menopausal status.\u0000 \u0000 \u0000 \u0000 A rule-based NLP system was designed to capture evidence of a patient’s menopause status including dates of a patient’s last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. nlp-derived output was used in combination with structured EHR data to classify a patient’s menopausal status. NLP processing and patient classification was performed on a cohort of 307,512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA).\u0000 \u0000 \u0000 \u0000 NLP was validated at 99.6% precision. Including the nlp-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81,173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167,804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data.\u0000 \u0000 \u0000 \u0000 By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis.\u0000 \u0000 \u0000 \u0000 NLP can be used to identify concepts relevant to a patient’s menopausal status in clinical notes. Adding nlp-derived data to an algorithm classifying a patient’s menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes.\u0000","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139847779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging information gaps in menopause status classification through natural language processing","authors":"Hannah Eyre, Patrick R. Alba, Carolyn J Gibson, E. Gatsby, Kristine E Lynch, Olga V. Patterson, S. Duvall","doi":"10.1093/jamiaopen/ooae013","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae013","url":null,"abstract":"\u0000 \u0000 \u0000 To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient’s menopausal status.\u0000 \u0000 \u0000 \u0000 A rule-based NLP system was designed to capture evidence of a patient’s menopause status including dates of a patient’s last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. nlp-derived output was used in combination with structured EHR data to classify a patient’s menopausal status. NLP processing and patient classification was performed on a cohort of 307,512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA).\u0000 \u0000 \u0000 \u0000 NLP was validated at 99.6% precision. Including the nlp-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81,173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167,804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data.\u0000 \u0000 \u0000 \u0000 By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis.\u0000 \u0000 \u0000 \u0000 NLP can be used to identify concepts relevant to a patient’s menopausal status in clinical notes. Adding nlp-derived data to an algorithm classifying a patient’s menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes.\u0000","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.","authors":"Janick Weberpals, Sudha R Raman, Pamela A Shaw, Hana Lee, Bradley G Hammill, Sengwee Toh, John G Connolly, Kimberly J Dandreo, Fang Tian, Wei Liu, Jie Li, José J Hernández-Muñoz, Robert J Glynn, Rishi J Desai","doi":"10.1093/jamiaopen/ooae008","DOIUrl":"10.1093/jamiaopen/ooae008","url":null,"abstract":"<p><strong>Objectives: </strong>Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions.</p><p><strong>Materials and methods: </strong>We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR.</p><p><strong>Results: </strong>smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data.</p><p><strong>Conclusions: </strong>The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10833461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139673099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}