[This retracts the article DOI: 10.1093/jamiaopen/ooad090.].
Author(s):
DOI: 10.1093/jamiaopen/ooae036
To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms.
Author(s): Thayer, Daniel S, Mumtaz, Shahzad, Elmessary, Muhammad A, Scanlon, Ieuan, Zinnurov, Artur, Coldea, Alex-Ioan, Scanlon, Jack, Chapman, Martin, Curcin, Vasa, John, Ann, DelPozo-Banos, Marcos, Davies, Hannah, Karwath, Andreas, Gkoutos, Georgios V, Fitzpatrick, Natalie K, Quint, Jennifer K, Varma, Susheel, Milner, Chris, Oliveira, Carla, Parkinson, Helen, Denaxas, Spiros, Hemingway, Harry, Jefferson, Emily
DOI: 10.1093/jamiaopen/ooae049
To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models.
Author(s): Scott, Ian A, De Guzman, Keshia R, Falconer, Nazanin, Canaris, Stephen, Bonilla, Oscar, McPhail, Steven M, Marxen, Sven, Van Garderen, Aaron, Abdel-Hafez, Ahmad, Barras, Michael
DOI: 10.1093/jamiaopen/ooae031
The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.
Author(s): Essaid, Shahim, Andre, Jeff, Brooks, Ian M, Hohman, Katherine H, Hull, Madelyne, Jackson, Sandra L, Kahn, Michael G, Kraus, Emily M, Mandadi, Neha, Martinez, Amanda K, Mui, Joyce Y, Zambarano, Bob, Soares, Andrey
DOI: 10.1093/jamiaopen/ooae045
[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].
Author(s):
DOI: 10.1093/jamiaopen/ooae046
To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data.
Author(s): Nottke, Amanda, Alan, Sophia, Brimble, Elise, Cardillo, Anthony B, Henderson, Lura, Littleford, Hana E, Rojahn, Susan, Sage, Heather, Taylor, Jessica, West-Odell, Lisandra, Berk, Alexandra
DOI: 10.1093/jamiaopen/ooae041
Integrating clinical research into routine clinical care workflows within electronic health record systems (EHRs) can be challenging, expensive, and labor-intensive. This case study presents a large-scale clinical research project conducted entirely within a commercial EHR during the COVID-19 pandemic.
Author(s): Goldhaber, Nicole H, Jacobs, Marni B, Laurent, Louise C, Knight, Rob, Zhu, Wenhong, Pham, Dean, Tran, Allen, Patel, Sandip P, Hogarth, Michael, Longhurst, Christopher A
DOI: 10.1093/jamiaopen/ooae023
This paper reports on a mixed methods formative evaluation to support the design and implementation of information technology (IT) tools for a primary care weight management intervention delivered through the patient portal using primary care staff as coaches.
Author(s): Kukhareva, Polina V, Weir, Charlene R, Cedillo, Maribel, Taft, Teresa, Butler, Jorie M, Rudd, Elizabeth A, Zepeda, Jesell, Zheutlin, Emily, Kiraly, Bernadette, Flynn, Michael, Conroy, Molly B, Kawamoto, Kensaku
DOI: 10.1093/jamiaopen/ooae038
[This corrects the article DOI: 10.1093/jamiaopen/ooae003.].
Author(s):
DOI: 10.1093/jamiaopen/ooae030
A data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons (PRC) is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic.
Author(s): Trunnell, Matthew, Frankenberger, Casey, Hota, Bala, Hughes, Troy, Martinov, Plamen, Ravichandran, Urmila, Shah, Nirav S, Grossman, Robert L, ,
DOI: 10.1093/jamiaopen/ooae025