Correction to: A blockchain-based healthcare data marketplace: prototype and demonstration.
[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].
Author(s):
DOI: 10.1093/jamiaopen/ooae046
[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].
Author(s):
DOI: 10.1093/jamiaopen/ooae046
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
To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.
Author(s): Jamie, Gavin, Elson, William, Kar, Debasish, Wimalaratna, Rashmi, Hoang, Uy, Meza-Torres, Bernardo, Forbes, Anna, Hinton, William, Anand, Sneha, Ferreira, Filipa, Byford, Rachel, Ordonez-Mena, Jose, Agrawal, Utkarsh, de Lusignan, Simon
DOI: 10.1093/jamiaopen/ooae034
The generation of structured documents for clinical trials is a promising application of large language models (LLMs). We share opportunities, insights, and challenges from a competitive challenge that used LLMs for automating clinical trial documentation.
Author(s): Landman, Rogier, Healey, Sean P, Loprinzo, Vittorio, Kochendoerfer, Ulrike, Winnier, Angela Russell, Henstock, Peter V, Lin, Wenyi, Chen, Aqiu, Rajendran, Arthi, Penshanwar, Sushant, Khan, Sheraz, Madhavan, Subha
DOI: 10.1093/jamiaopen/ooae043
Numerous studies have identified information overload as a key issue for electronic health records (EHRs). This study describes the amount of text data across all notes available to emergency physicians in the EHR, trended over the time since EHR establishment.
Author(s): Patterson, Brian W, Hekman, Daniel J, Liao, Frank J, Hamedani, Azita G, Shah, Manish N, Afshar, Majid
DOI: 10.1093/jamiaopen/ooae039
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
Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts.
Author(s): Sikora, Andrea, Keats, Kelli, Murphy, David J, Devlin, John W, Smith, Susan E, Murray, Brian, Buckley, Mitchell S, Rowe, Sandra, Coppiano, Lindsey, Kamaleswaran, Rishikesan
DOI: 10.1093/jamiaopen/ooae033
[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