Looking for clinician involvement under the wrong lamp post: The need for collaboration measures.
Author(s): Sendak, Mark P, Gao, Michael, Ratliff, William, Nichols, Marshall, Bedoya, Armando, O'Brien, Cara, Balu, Suresh
DOI: 10.1093/jamia/ocab129
Author(s): Sendak, Mark P, Gao, Michael, Ratliff, William, Nichols, Marshall, Bedoya, Armando, O'Brien, Cara, Balu, Suresh
DOI: 10.1093/jamia/ocab129
This study: 1) characterized the app market by EHR app gallery and type of app; 2) tracked changes in the EHR app galleries from the end of 2019 through 2020; and 3) examined how apps connect to EHR data systems, and if the apps support the HL7 FHIR standard.
Author(s): Barker, Wesley, Johnson, Christian
DOI: 10.1093/jamia/ocab171
Equitable distribution of vaccines is necessary to ensure those at highest risk of illness are protected from COVID-19 (coronavirus disease 2019). Unfortunately, there is significant evidence that vaccines have not been reaching the most vulnerable. At our large hospital system, we created interactive online tools to measure and visualize equitability of vaccine administrations and to help stakeholders identify populations at highest risk within state-designated eligible vaccine groups. Using race, ethnicity [...]
Author(s): Shaheen, Amy W, Ciesco, Eileen, Johnson, Kevin, Kuhnen, Greg, Paolini, Christopher, Gartner, Gary
DOI: 10.1093/jamia/ocab180
The study sought to evaluate the expected clinical utility of automatable prediction models for increasing goals-of-care discussions (GOCDs) among hospitalized patients at the end of life (EOL).
Author(s): Taseen, Ryeyan, Ethier, Jean-François
DOI: 10.1093/jamia/ocab140
To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.
Author(s): Yang, Jiannan, Xu, Zhongzhi, Wu, William Ka Kei, Chu, Qian, Zhang, Qingpeng
DOI: 10.1093/jamia/ocab162
Mobile-based interventions have the potential to promote healthy aging among older adults. However, the adoption and use of mobile health applications are often low due to inappropriate designs. The aim of this systematic review is to identify, synthesize, and report interface and persuasive feature design recommendations of mobile health applications for elderly users to facilitate adoption and improve health-related outcomes.
Author(s): Liu, Na, Yin, Jiamin, Tan, Sharon Swee-Lin, Ngiam, Kee Yuan, Teo, Hock Hai
DOI: 10.1093/jamia/ocab151
This study investigated how well-suited the International Classification of Diseases, 11th Revision, for Mortality and Morbidity Statistics, (ICD-11 MMS) is for 2 morbidity use cases, patient safety and quality, examining the level of detail captured, and evaluating the necessity for the development of a US clinical modification (CM).
Author(s): Fenton, Susan H, Giannangelo, Kathy L, Stanfill, Mary H
DOI: 10.1093/jamia/ocab163
Mobile health (mHealth) applications have the potential to improve health awareness. This study reports a quasi-controlled intervention to augment maternal health awareness among tribal pregnant mothers through the mHealth application. Households from 2 independent villages with similar socio-demographics in tribal regions of India were selected as intervention (Village A) and control group (Village B). The control group received government mandated programs through traditional means (orally), whereas the intervention group received [...]
Author(s): Choudhury, Avishek, Asan, Onur, Choudhury, Murari M
DOI: 10.1093/jamia/ocab172
At the onset of the COVID-19 (coronavirus disease 2019) pandemic, telemedicine was rapidly implemented to protect patients and healthcare providers from infection. It is unlikely that care delivery will fully return to the pre-COVID form. Telemedicine offers many opportunities to improve care efficiency, accessibility, and patient outcomes, but many challenges exist related to technology interoperability, the digital divide, and usability. We propose that telemedicine evolve to support continuity of care [...]
Author(s): Sun, Ran, Blayney, Douglas W, Hernandez-Boussard, Tina
DOI: 10.1093/jamia/ocab145
Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our objective was to identify the different computational and organizational setups that early-adopter health systems have utilized to integrate AI/ML clinical decision support (AI-CDS) and scrutinize their trade-offs.
Author(s): Kashyap, Sehj, Morse, Keith E, Patel, Birju, Shah, Nigam H
DOI: 10.1093/jamia/ocab154