Author(s): Sittig, Dean F, Petersen, Carolyn, Downs, Stephen M, Lehmann, Jenna S, Lehmann, Christoph U
DOI: 10.1055/s-0042-1744385
Author(s): Turer, Robert W, Levy, Bruce P, Hron, Jonathan D, Pageler, Natalie M, Mize, Dara E, Kim, Ellen, Lehmann, Christoph U
DOI: 10.1055/s-0042-1744386
The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic threatened to oversaturate hospitals worldwide, necessitating rapid patient discharge to preserve capacity for the most severe cases. This need, as well as the high risk of SARS-CoV-2 transmission, led many hospitals to implement remote patient monitoring (RPM) programs for SARS-CoV-2 positive patients in an effort to provide care that was safe and preserve scarce resources.
Author(s): Lara, Brenda, Kottler, Janey, Olsen, Abigail, Best, Andrew, Conkright, Jessica, Larimer, Karen
DOI: 10.1055/s-0042-1742370
Predictive analytic models, including machine learning (ML) models, are increasingly integrated into electronic health record (EHR)-based decision support tools for clinicians. These models have the potential to improve care, but are challenging to internally validate, implement, and maintain over the long term. Principles of ML operations (MLOps) may inform development of infrastructure to support the entire ML lifecycle, from feature selection to long-term model deployment and retraining.
Author(s): Bai, Eric, Song, Sophia L, Fraser, Hamish S F, Ranney, Megan L
DOI: 10.1055/s-0041-1740923
Personal health records (PHRs) can facilitate patient-centered communication through the secure messaging feature. As health care organizations in the Kingdom of Saudi Arabia implement PHRs and begin to implement the secure messaging feature, studies are needed to evaluate health care providers' acceptance.
Author(s): Yousef, Consuela C, Salgado, Teresa M, Farooq, Ali, Burnett, Keisha, McClelland, Laura E, Abu Esba, Laila C, Alhamdan, Hani S, Khoshhal, Sahal, Aldossary, Ibrahim, Alyas, Omar A, DeShazo, Jonathan P
DOI: 10.1055/s-0041-1742217
Sepsis is associated with high mortality, especially during the novel coronavirus disease 2019 (COVID-19) pandemic. Along with high monetary health care costs for sepsis treatment, there is a lasting impact on lives of sepsis survivors and their caregivers. Early identification is necessary to reduce the negative impact of sepsis and to improve patient outcomes. Prehospital data are among the earliest information collected by health care systems. Using these untapped sources [...]
Author(s): Desai, Manushi D, Tootooni, Mohammad S, Bobay, Kathleen L
DOI: 10.1055/s-0042-1742369
Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team has documented the design, development, and use cases of a dashboard solution to visualize OCOI.
Author(s): Jonnalagadda, Pallavi, Swoboda, Christine, Singh, Priti, Gureddygari, Harish, Scarborough, Seth, Dunn, Ian, Doogan, Nathan J, Fareed, Naleef
DOI: 10.1055/s-0041-1741482
Automation of health care workflows has recently become a priority. This can be enabled and enhanced by a workflow monitoring tool (WMOT).
Author(s): Wu, Danny T Y, Barrick, Lindsey, Ozkaynak, Mustafa, Blondon, Katherine, Zheng, Kai
DOI: 10.1055/s-0041-1741480
Author(s): Wong, Lori, Liu, Daniel, Thompson, Cori, Margo, Todd, Yu, Feliciano
DOI: 10.1055/s-0041-1740922
This study aimed to develop a virtual electronic health record (EHR) training and optimization program and evaluate the impact of the virtual model on provider and staff burnout and electronic health record (EHR) experience.
Author(s): English, Eden F, Holmstrom, Heather, Kwan, Bethany W, Suresh, Krithika, Rotholz, Stephen, Lin, Chen-Tan, Sieja, Amber
DOI: 10.1055/s-0041-1740482