JAMIA Open最新文献

{"title":"Leveraging a global, federated, real-world data network to optimize investigator-initiated pediatric clinical trials: the TriNetX Pediatric Collaboratory Network.","authors":"Jurran L Wilson, Marisol Betensky, Sharda Udassi, Pavithra R Ellison, Richard Lilienthal, Lindsay R Stahl, Matvey B Palchuk, Ayesha Zia, Deborah A Town, Wes Kimble, Neil A Goldenberg, Hiroki Morizono","doi":"10.1093/jamiaopen/ooae077","DOIUrl":"10.1093/jamiaopen/ooae077","url":null,"abstract":"<p><strong>Objective: </strong>Clinical research networks facilitate collaborative research, but data sharing remains a common barrier.</p><p><strong>Materials and methods: </strong>The TriNetX platform provides real-time access to electronic health record (EHR)-derived, anonymized data from 173 healthcare organizations (HCOs) and tools for queries and analysis. In 2022, 4 pediatric HCOs worked with TriNetX leadership to found the Pediatric Collaboratory Network (PCN), facilitated via a multi-institutional data-use agreement (DUA). The DUA enables collaborative study design and execution, with institutional review board-approved transfer of complete datasets for further analyses on a per-protocol basis.</p><p><strong>Results and discussion: </strong>Of the 41.2 million children with TriNetX records, the PCN represents nearly 10%. The PCN assisted several early-career investigators to bring study concepts from conception to an international scientific meeting presentation and journal submission.</p><p><strong>Conclusion: </strong>The PCN facilitates EHR vendor-agnostic multicenter pediatric research on the global TriNetX platform. Continued growth of the PCN will advance knowledge in pediatric health.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae077"},"PeriodicalIF":2.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120789","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":"Leveraging multi-site electronic health data for characterization of subtypes: a pilot study of dementia in the N3C Clinical Tenant.","authors":"Suchetha Sharma, Jiebei Liu, Amy Caroline Abramowitz, Carol Reynolds Geary, Karen C Johnston, Carol Manning, John Darrell Van Horn, Andrea Zhou, Alfred J Anzalone, Johanna Loomba, Emily Pfaff, Don Brown","doi":"10.1093/jamiaopen/ooae076","DOIUrl":"10.1093/jamiaopen/ooae076","url":null,"abstract":"<p><strong>Objectives: </strong>To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset.</p><p><strong>Materials and methods: </strong>Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns.</p><p><strong>Results: </strong>The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification.</p><p><strong>Conclusion: </strong>Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions.</p><p><strong>Discussion: </strong>This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae076"},"PeriodicalIF":2.5,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917596","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":"Development and application of Breadth-Depth-Context (BDC), a conceptual framework for measuring technology engagement with a qualified clinical data registry.","authors":"Emma Kersey, Jing Li, Julia Kay, Julia Adler-Milstein, Jinoos Yazdany, Gabriela Schmajuk","doi":"10.1093/jamiaopen/ooae061","DOIUrl":"10.1093/jamiaopen/ooae061","url":null,"abstract":"<p><strong>Objectives: </strong>Despite the proliferation of dashboards that display performance data derived from Qualified Clinical Data Registries (QCDR), the degree to which clinicians and practices engage with such dashboards has not been well described. We aimed to develop a conceptual framework for assessing user engagement with dashboard technology and to demonstrate its application to a rheumatology QCDR.</p><p><strong>Materials and methods: </strong>We developed the BDC (Breadth-Depth-Context) framework, which included concepts of breadth (derived from dashboard sessions), depth (derived from dashboard actions), and context (derived from practice characteristics). We demonstrated its application via user log data from the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry to define engagement profiles and characterize practice-level factors associated with different profiles.</p><p><strong>Results: </strong>We applied the BDC framework to 213 ambulatory practices from the RISE registry in 2020-2021, and classified practices into 4 engagement profiles: not engaged (8%), minimally engaged (39%), moderately engaged (34%), and most engaged (19%). Practices with more patients and with specific electronic health record vendors (eClinicalWorks and eMDs) had a higher likelihood of being in the most engaged group, even after adjusting for other factors.</p><p><strong>Discussion: </strong>We developed the BDC framework to characterize user engagement with a registry dashboard and demonstrated its use in a specialty QCDR. The application of the BDC framework revealed a wide range of breadth and depth of use and that specific contextual factors were associated with nature of engagement.</p><p><strong>Conclusion: </strong>Going forward, the BDC framework can be used to study engagement with similar dashboards.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae061"},"PeriodicalIF":2.5,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11278873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789317","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":"Feasibility of a contraceptive-specific electronic health record system to promote the adoption of pharmacist-prescribed contraceptive services in community pharmacies in the United States.","authors":"Devin J Bustin, Rebecca Simmons, Jake Galdo, Mary E Kucek, Lissette Logan, Rich Cohn, Heather Smith","doi":"10.1093/jamiaopen/ooae071","DOIUrl":"10.1093/jamiaopen/ooae071","url":null,"abstract":"<p><strong>Objectives: </strong>Pharmacists in over half of the United States can prescribe contraceptives; however, low pharmacist adoption has impeded the full realization of potential public health benefits. Many barriers to adoption may be addressed by leveraging an electronic health records (EHR) system with clinical decision support tools and workflow automation. We conducted a feasibility study to determine if utilizing a contraceptive-specific EHR could improve potential barriers to the implementation of pharmacist-prescribed contraceptive services.</p><p><strong>Materials and methods: </strong>20 pharmacists each performed two standardized patient encounter simulations: one on the EHR and one on the current standard of care paper-based workflow. A crossover study design was utilized, with each pharmacist performing encounters on both standardized patients with the modality order randomized. Encounters were timed, contraceptive outputs were recorded, and the pharmacists completed externally validated workload and usability surveys after each encounter, and a Perception, Attitude, and Satisfaction survey created by the research team after the final encounter.</p><p><strong>Results: </strong>Pharmacists were more likely to identify contraceptive ineligibility using the EHR-based workflow compared to the paper workflow (<i>P</i> = .003). Contraceptive encounter time was not significantly different between the 2 modalities (<i>P</i> = .280). Pharmacists reported lower mental demand (<i>P</i> = .003) and greater perceived usefulness (<i>P</i> = .029) with the EHR-based workflow compared to the paper modality.</p><p><strong>Discussion and conclusion: </strong>Pharmacist performance and acceptance of contraceptive services delivery were improved with the EHR workflow. Pharmacist-specific contraceptive EHR workflows show potential to improve pharmacist adoption and provision of appropriate contraceptive care.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae071"},"PeriodicalIF":2.5,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11262636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749213","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":"Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models.","authors":"Jeya Balaji Balasubramanian, Parichoy Pal Choudhury, Srijon Mukhopadhyay, Thomas Ahearn, Nilanjan Chatterjee, Montserrat García-Closas, Jonas S Almeida","doi":"10.1093/jamiaopen/ooae055","DOIUrl":"10.1093/jamiaopen/ooae055","url":null,"abstract":"<p><strong>Objectives: </strong>Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face limitations in portability and privacy due to their need for circulating user data in remote servers for operation. We overcome this by porting iCARE to the web platform.</p><p><strong>Materials and methods: </strong>We refactored R-iCARE into a Python package (Py-iCARE) and then compiled it to WebAssembly (Wasm-iCARE)-a portable web module, which operates within the privacy of the user's device.</p><p><strong>Results: </strong>We showcase the portability and privacy of Wasm-iCARE through 2 applications: for researchers to statistically validate risk models and to deliver them to end-users. Both applications run entirely on the client side, requiring no downloads or installations, and keep user data on-device during risk calculation.</p><p><strong>Conclusions: </strong>Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 2","pages":"ooae055"},"PeriodicalIF":2.5,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141471236","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":"Workflow analysis of breast cancer treatment decision-making: challenges and opportunities for informatics to support patient-centered cancer care.","authors":"Megan E Salwei, Carrie Reale","doi":"10.1093/jamiaopen/ooae053","DOIUrl":"10.1093/jamiaopen/ooae053","url":null,"abstract":"<p><strong>Objective: </strong>Decision support can improve shared decision-making for breast cancer treatment, but workflow barriers have hindered widespread use of these tools. The goal of this study was to understand the workflow among breast cancer teams of clinicians, patients, and their family caregivers when making treatment decisions and identify design guidelines for informatics tools to better support treatment decision-making.</p><p><strong>Materials and methods: </strong>We conducted observations of breast cancer clinicians during routine clinical care from February to August 2022. Guided by the work system model, a human factors engineering model that describes the elements of work, we recorded all aspects of clinician workflow using a tablet and smart pencil. Observation notes were transcribed and uploaded into Dedoose. Two researchers inductively coded the observations. We identified themes relevant to the design of decision support that we classified into the 4 components of workflow (ie, flow of information, tasks, tools and technologies, and people).</p><p><strong>Results: </strong>We conducted 20 observations of breast cancer clinicians (total: 79 hours). We identified 10 themes related to workflow that present challenges and opportunities for decision support design. We identified approximately 48 different decisions discussed during breast cancer visits. These decisions were often interdependent and involved collaboration across the large cancer treatment team. Numerous patient-specific factors (eg, work, hobbies, family situation) were discussed when making treatment decisions as well as complex risk and clinical information. Patients were frequently asked to remember and relay information across the large cancer team.</p><p><strong>Discussion and conclusion: </strong>Based on these findings, we proposed design guidelines for informatics tools to support the complex workflows involved in breast cancer care. These guidelines should inform the design of informatics solutions to better support breast cancer decision-making and improve patient-centered cancer care.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 2","pages":"ooae053"},"PeriodicalIF":2.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11192055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443471","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":"Structuring medication signeturs as a language regression task: comparison of zero- and few-shot GPT with fine-tuned models.","authors":"Augusto Garcia-Agundez, Julia L Kay, Jing Li, Milena Gianfrancesco, Baljeet Rai, Angela Hu, Gabriela Schmajuk, Jinoos Yazdany","doi":"10.1093/jamiaopen/ooae051","DOIUrl":"10.1093/jamiaopen/ooae051","url":null,"abstract":"<p><strong>Importance: </strong>Electronic health record textual sources such as medication signeturs (sigs) contain valuable information that is not always available in structured form. Commonly processed through manual annotation, this repetitive and time-consuming task could be fully automated using large language models (LLMs). While most sigs include simple instructions, some include complex patterns.</p><p><strong>Objectives: </strong>We aimed to compare the performance of GPT-3.5 and GPT-4 with smaller fine-tuned models (ClinicalBERT, BlueBERT) in extracting the average daily dose of 2 immunomodulating medications with frequent complex sigs: hydroxychloroquine, and prednisone.</p><p><strong>Methods: </strong>Using manually annotated sigs as the gold standard, we compared the performance of these models in 702 hydroxychloroquine and 22 104 prednisone prescriptions.</p><p><strong>Results: </strong>GPT-4 vastly outperformed all other models for this task at any level of in-context learning. With 100 in-context examples, the model correctly annotates 94% of hydroxychloroquine and 95% of prednisone sigs to within 1 significant digit. Error analysis conducted by 2 additional manual annotators on annotator-model disagreements suggests that the vast majority of disagreements are model errors. Many model errors relate to ambiguous sigs on which there was also frequent annotator disagreement.</p><p><strong>Discussion: </strong>Paired with minimal manual annotation, GPT-4 achieved excellent performance for language regression of complex medication sigs and vastly outperforms GPT-3.5, ClinicalBERT, and BlueBERT. However, the number of in-context examples needed to reach maximum performance was similar to GPT-3.5.</p><p><strong>Conclusion: </strong>LLMs show great potential to rapidly extract structured data from sigs in no-code fashion for clinical and research applications.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 2","pages":"ooae051"},"PeriodicalIF":2.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447237","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":"Transforming and facilitating health care delivery through social networking platforms: evidences and implications from WeChat.","authors":"Jiancheng Ye","doi":"10.1093/jamiaopen/ooae047","DOIUrl":"10.1093/jamiaopen/ooae047","url":null,"abstract":"<p><strong>Objectives: </strong>Telehealth or remote care has been widely leveraged to provide health care support and has achieved tremendous developments and positive results, including in low- and middle-income countries (LMICs). Social networking platform, as an easy-to-use tool, has provided users with simplified means to collect data outside of the traditional clinical environment. WeChat, one of the most popular social networking platforms in many countries, has been leveraged to conduct telehealth and hosted a vast amount of patient-generated health data (PGHD), including text, voices, images, and videos. Its characteristics of convenience, promptness, and cross-platform support enrich and simplify health care delivery and communication, addressing some weaknesses of traditional clinical care during the pandemic. This study aims to systematically summarize how WeChat platform has been leveraged to facilitate health care delivery and how it improves the access to health care.</p><p><strong>Materials and methods: </strong>Utilizing Levesque's health care accessibility model, the study explores WeChat's impact across 5 domains: Approachability, Acceptability, Availability and accommodation, Affordability, and Appropriateness.</p><p><strong>Results: </strong>The findings highlight WeChat's diverse functionalities, ranging from telehealth consultations and remote patient monitoring to seamless PGHD exchange. WeChat's integration with health tracking apps, support for telehealth consultations, and survey capabilities contribute significantly to disease management during the pandemic.</p><p><strong>Discussion and conclusion: </strong>The practices and implications from WeChat may provide experiences to utilize social networking platforms to facilitate health care delivery. The utilization of WeChat PGHD opens avenues for shared decision-making, prompting the need for further research to establish reporting guidelines and policies addressing privacy and ethical concerns associated with social networking platforms in health research.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 2","pages":"ooae047"},"PeriodicalIF":2.5,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11138362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181084","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":"An infrastructure for secure data sharing: a clinical data implementation.","authors":"Joanna F DeFranco, Joshua Roberts, David Ferraiolo, D Chris Compton","doi":"10.1093/jamiaopen/ooae040","DOIUrl":"10.1093/jamiaopen/ooae040","url":null,"abstract":"<p><strong>Objective: </strong>To address database interoperability challenges to improve collaboration among disparate organizations.</p><p><strong>Materials and methods: </strong>We developed a lightweight system to allow broad but well-controlled data sharing while preserving local data protection policies. We used 2 NIST-developed technologies-Next-generation Database Access Control (NDAC) and the Data Block Matrix (DBM)-to create a proof-of-concept system called the Secure Federated Data Sharing System (SFDS). NDAC controls access to database resources down to the field level based on attributes assigned to users. The DBM manages and shares authoritative user-attribute assignments across a federation of organizations, implemented using a modified open-source permissioned blockchain, to manage and share authoritative user-attribute assignments across a federation of organizations. We used synthetic data to demonstrate a clinical research data-sharing use case using the SFDS.</p><p><strong>Results: </strong>We demonstrated, through consent, the onboarding of previously unknown users into NDAC via assignments to their DBM-validated attributes, allowing those users policy-preserving access to local database resources. The SFDS main system components-NDAC and DBM-also showed excellent performance metrics.</p><p><strong>Discussion: </strong>The SFDS provides a generic data-sharing infrastructure that effectively and securely achieves data-sharing objectives. It is completely transparent to the otherwise normal business operations of participating organizations. It requires no changes to database management systems or existing methods of authenticating and authorizing local user access to local resources.</p><p><strong>Conclusion: </strong>This efficiency, flexibility of deployment, and granularity of control make this new infrastructure solution practical for meeting the data-sharing and protection objectives of the clinical research community.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 2","pages":"ooae040"},"PeriodicalIF":2.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11095973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946254","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":"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":"7 1","pages":"ooae022"},"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}