Reflections on the history of interoperability in hospitals.
To discuss the origins of HL7 and its subsequent impact on interoperability in hospitals.
Author(s): Simborg, Donald W
DOI: 10.1093/jamia/ocad185
To discuss the origins of HL7 and its subsequent impact on interoperability in hospitals.
Author(s): Simborg, Donald W
DOI: 10.1093/jamia/ocad185
Fully automated digital interventions show promise for disseminating evidence-based strategies to manage insomnia complaints. However, an important concept often overlooked concerns the extent to which users adopt the recommendations provided in these programs into their daily lives. Our objectives were evaluating users' adherence to the behavioral recommendations provided by an app, and exploring whether users' perceptions of the app had an impact on their adherence behavior.
Author(s): Sanchez-Ortuno, Maria Montserrat, Pecune, Florian, Coelho, Julien, Micoulaud-Franchi, Jean Arthur, Salles, Nathalie, Auriacombe, Marc, Serre, Fuschia, Levavasseur, Yannick, de Sevin, Etienne, Sagaspe, Patricia, Philip, Pierre
DOI: 10.1093/jamia/ocad163
This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings.
Author(s): Susanto, Anindya Pradipta, Lyell, David, Widyantoro, Bambang, Berkovsky, Shlomo, Magrabi, Farah
DOI: 10.1093/jamia/ocad180
Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations, and discusses potential innovations.
Author(s): Li, Siqi, Liu, Pinyan, Nascimento, Gustavo G, Wang, Xinru, Leite, Fabio Renato Manzolli, Chakraborty, Bibhas, Hong, Chuan, Ning, Yilin, Xie, Feng, Teo, Zhen Ling, Ting, Daniel Shu Wei, Haddadi, Hamed, Ong, Marcus Eng Hock, Peres, Marco Aurélio, Liu, Nan
DOI: 10.1093/jamia/ocad170
Patient portals are increasingly used to recruit patients in research studies, but communication response rates remain low without tactics such as financial incentives or manual outreach. We evaluated a new method of study enrollment by embedding a study information sheet and HIPAA authorization form (HAF) into the patient portal preCheck-in (where patients report basic information like allergies).
Author(s): Leuchter, Richard K, Ma, Suzette, Bell, Douglas S, Hays, Ron D, Vidorreta, Fernando Javier Sanz, Binder, Sandra L, Sarkisian, Catherine A
DOI: 10.1093/jamia/ocad164
The COVID-19 pandemic has significantly impacted daily activity rhythms and life routines with people adjusting to new work schedules, exercise routines, and other everyday life activities. This study examines temporal changes in daily activity rhythms and routines during the COVID-19 pandemic, emphasizing disproportionate changes among working adult subgroups.
Author(s): Luong, Nguyen, Barnett, Ian, Aledavood, Talayeh
DOI: 10.1093/jamia/ocad140
To determine whether data-driven family histories (DDFH) derived from linked EHRs of patients and their parents can improve prediction of patients' 10-year risk of diabetes and atherosclerotic cardiovascular disease (ASCVD).
Author(s): Barak-Corren, Yuval, Tsurel, David, Keidar, Daphna, Gofer, Ilan, Shahaf, Dafna, Leventer-Roberts, Maya, Barda, Noam, Reis, Ben Y
DOI: 10.1093/jamia/ocad154
Author(s): Bapna, Monika, Miller, Kristen, Ratwani, Raj M
DOI: 10.1093/jamia/ocad184
This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.
Author(s): Souza, Raissa, Wilms, Matthias, Camacho, Milton, Pike, G Bruce, Camicioli, Richard, Monchi, Oury, Forkert, Nils D
DOI: 10.1093/jamia/ocad171
To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes.
Author(s): Carrasco-Ribelles, Lucía A, Llanes-Jurado, José, Gallego-Moll, Carlos, Cabrera-Bean, Margarita, Monteagudo-Zaragoza, Mònica, Violán, Concepción, Zabaleta-Del-Olmo, Edurne
DOI: 10.1093/jamia/ocad168