Web3-based storage solutions for biomedical research and clinical data exchange.
Author(s): Tugaoen, Julian, Becker, Alana, Guo, Chenmeinian, Parasidis, Efthimios, Venkatakrishnan, Shaileshh Bojja, Otero, José Javier
DOI: 10.1093/jamia/ocad227
Author(s): Tugaoen, Julian, Becker, Alana, Guo, Chenmeinian, Parasidis, Efthimios, Venkatakrishnan, Shaileshh Bojja, Otero, José Javier
DOI: 10.1093/jamia/ocad227
The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data harmonization.
Author(s): Reich, Christian, Ostropolets, Anna, Ryan, Patrick, Rijnbeek, Peter, Schuemie, Martijn, Davydov, Alexander, Dymshyts, Dmitry, Hripcsak, George
DOI: 10.1093/jamia/ocad247
The 2021 US Cures Act may engage patients to help reduce diagnostic errors/delays. We examined the relationship between patient portal registration with/without note reading and test/referral completion in primary care.
Author(s): Bell, Sigall K, Amat, Maelys J, Anderson, Timothy S, Aronson, Mark D, Benneyan, James C, Fernandez, Leonor, Ricci, Dru A, Salant, Talya, Schiff, Gordon D, Shafiq, Umber, Singer, Sara J, Sternberg, Scot B, Zhang, Cancan, Phillips, Russell S
DOI: 10.1093/jamia/ocad250
Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities.
Author(s): Mullie, Louis, Afilalo, Jonathan, Archambault, Patrick, Bouchakri, Rima, Brown, Kip, Buckeridge, David L, Cavayas, Yiorgos Alexandros, Turgeon, Alexis F, Martineau, Denis, Lamontagne, François, Lebrasseur, Martine, Lemieux, Renald, Li, Jeffrey, Sauthier, Michaël, St-Onge, Pascal, Tang, An, Witteman, William, Chassé, Michaël
DOI: 10.1093/jamia/ocad235
This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data.
Author(s): Davis, Heath A, Santillan, Donna A, Ortman, Chris E, Hoberg, Asher A, Hetrick, Joseph P, McBrearty, Charles W, Zeng, Erliang, Vaughan Sarrazin, Mary S, Dunn Lopez, Karen, Chapman, Cole G, Carnahan, Ryan M, Michaelson, Jacob J, Knosp, Boyd M
DOI: 10.1093/jamia/ocad236
Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters.
Author(s): Barwise, Amelia K, Curtis, Susan, Diedrich, Daniel A, Pickering, Brian W
DOI: 10.1093/jamia/ocad224
The HIV epidemic remains a significant public health issue in the United States. HIV risk prediction models could be beneficial for reducing HIV transmission by helping clinicians identify patients at high risk for infection and refer them for testing. This would facilitate initiation on treatment for those unaware of their status and pre-exposure prophylaxis for those uninfected but at high risk. Existing HIV risk prediction algorithms rely on manual construction [...]
Author(s): May, Sarah B, Giordano, Thomas P, Gottlieb, Assaf
DOI: 10.1093/jamia/ocad217
Enhanced recovery pathways (ERPs) are evidence-based approaches to improving perioperative surgical care. However, the role of electronic health records (EHRs) in their implementation is unclear. We examine how EHRs facilitate or hinder ERP implementation.
Author(s): Wu, JunBo, Yuan, Christina T, Moyal-Smith, Rachel, Wick, Elizabeth C, Rosen, Michael A
DOI: 10.1093/jamia/ocad237
To provide balanced consideration of the opportunities and challenges associated with integrating Large Language Models (LLMs) throughout the medical school continuum.
Author(s): Benítez, Trista M, Xu, Yueyuan, Boudreau, J Donald, Kow, Alfred Wei Chieh, Bello, Fernando, Van Phuoc, Le, Wang, Xiaofei, Sun, Xiaodong, Leung, Gilberto Ka-Kit, Lan, Yanyan, Wang, Yaxing, Cheng, Davy, Tham, Yih-Chung, Wong, Tien Yin, Chung, Kevin C
DOI: 10.1093/jamia/ocad252
National attention has focused on increasing clinicians' responsiveness to the social determinants of health, for example, food security. A key step toward designing responsive interventions includes ensuring that information about patients' social circumstances is captured in the electronic health record (EHR). While prior work has assessed levels of EHR "social risk" documentation, the extent to which documentation represents the true prevalence of social risk is unknown. While no gold standard [...]
Author(s): Iott, Bradley E, Rivas, Samantha, Gottlieb, Laura M, Adler-Milstein, Julia, Pantell, Matthew S
DOI: 10.1093/jamia/ocad261