Biomedical and health informatics continue to contribute to COVID-19 pandemic solutions and beyond.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocab130
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocab130
Develop and evaluate an interactive information visualization embedded within the electronic health record (EHR) by following human-centered design (HCD) processes and leveraging modern health information exchange standards.
Author(s): Thayer, Jeritt G, Ferro, Daria F, Miller, Jeffrey M, Karavite, Dean, Grundmeier, Robert W, Utidjian, Levon, Zorc, Joseph J
DOI: 10.1093/jamia/ocab016
To assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use.
Author(s): Apathy, Nate C, Vest, Joshua R, Adler-Milstein, Julia, Blackburn, Justin, Dixon, Brian E, Harle, Christopher A
DOI: 10.1093/jamia/ocab024
The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.
Author(s): Klann, Jeffrey G, Estiri, Hossein, Weber, Griffin M, Moal, Bertrand, Avillach, Paul, Hong, Chuan, Tan, Amelia L M, Beaulieu-Jones, Brett K, Castro, Victor, Maulhardt, Thomas, Geva, Alon, Malovini, Alberto, South, Andrew M, Visweswaran, Shyam, Morris, Michele, Samayamuthu, Malarkodi J, Omenn, Gilbert S, Ngiam, Kee Yuan, Mandl, Kenneth D, Boeker, Martin, Olson, Karen L, Mowery, Danielle L, Follett, Robert W, Hanauer, David A, Bellazzi, Riccardo, Moore, Jason H, Loh, Ne-Hooi Will, Bell, Douglas S, Wagholikar, Kavishwar B, Chiovato, Luca, Tibollo, Valentina, Rieg, Siegbert, Li, Anthony L L J, Jouhet, Vianney, Schriver, Emily, Xia, Zongqi, Hutch, Meghan, Luo, Yuan, Kohane, Isaac S, , , Brat, Gabriel A, Murphy, Shawn N
DOI: 10.1093/jamia/ocab018
Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier-the Research Master Identifier (RMID)-for tracking research studies across disparate systems and a data warehouse-inspired model-the Research Integrated Network of Systems (RINS)-for integrating data [...]
Author(s): He, Wenjun, Kirchoff, Katie G, Sampson, Royce R, McGhee, Kimberly K, Cates, Andrew M, Obeid, Jihad S, Lenert, Leslie A
DOI: 10.1093/jamia/ocab023
To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time.
Author(s): Melnick, Edward R, Ong, Shawn Y, Fong, Allan, Socrates, Vimig, Ratwani, Raj M, Nath, Bidisha, Simonov, Michael, Salgia, Anup, Williams, Brian, Marchalik, Daniel, Goldstein, Richard, Sinsky, Christine A
DOI: 10.1093/jamia/ocab011
The study sought to develop an in-depth understanding of how hospitals with a long history of health information technology (HIT) use have responded to the COVID-19 (coronavirus disease 2019) pandemic from an HIT perspective.
Author(s): Malden, Stephen, Heeney, Catherine, Bates, David W, Sheikh, Aziz
DOI: 10.1093/jamia/ocab057
Accurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement.
Author(s): D'Amore, John D, McCrary, Laura K, Denson, Jody, Li, Chun, Vitale, Christopher J, Tokachichu, Priyaranjan, Sittig, Dean F, McCoy, Allison B, Wright, Adam
DOI: 10.1093/jamia/ocab039
Patient-generated health data (PGHD) are important for tracking and monitoring out of clinic health events and supporting shared clinical decisions. Unstructured text as PGHD (eg, medical diary notes and transcriptions) may encapsulate rich information through narratives which can be critical to better understand a patient's condition. We propose a natural language processing (NLP) supported data synthesis pipeline for unstructured PGHD, focusing on children with special healthcare needs (CSHCN), and demonstrate [...]
Author(s): Hussain, Syed-Amad, Sezgin, Emre, Krivchenia, Katelyn, Luna, John, Rust, Steve, Huang, Yungui
DOI: 10.1093/jamiaopen/ooab084
[This corrects the article DOI: 10.1093/jamiaopen/ooab063.].
Author(s): Mistry, Sejal, Gouripeddi, Ramkiran, Facelli, Julio C
DOI: 10.1093/jamiaopen/ooab080