EBMonFHIR-based tools and initiatives to support clinical research.
Author(s): Alper, Brian S
DOI: 10.1093/jamia/ocac193
Author(s): Alper, Brian S
DOI: 10.1093/jamia/ocac193
Privacy is a concern whenever individual patient health data is exchanged for scientific research. We propose using mixed sum-product networks (MSPNs) as private representations of data and take samples from the network to generate synthetic data that can be shared for subsequent statistical analysis. This anonymization method was evaluated with respect to privacy and information loss.
Author(s): Kroes, Shannon K S, van Leeuwen, Matthijs, Groenwold, Rolf H H, Janssen, Mart P
DOI: 10.1093/jamia/ocac184
Author(s): Petersen, Carolyn, Berner, Eta S, Cardillo, Anthony, Fultz Hollis, Kate, Goodman, Kenneth W, Koppel, Ross, Korngiebel, Diane M, Lehmann, Christoph U, Solomonides, Anthony E, Subbian, Vignesh
DOI: 10.1093/jamia/ocac192
Thoughtful integration of interruptive clinical decision support (CDS) alerts within the electronic health record is essential to guide clinicians on the application of pharmacogenomic results at point of care. St. Jude Children's Research Hospital implemented a preemptive pharmacogenomic testing program in 2011 in a multidisciplinary effort involving extensive education to clinicians about pharmacogenomic implications. We conducted a retrospective analysis of clinicians' adherence to 4783 pharmacogenomically guided CDS alerts that triggered [...]
Author(s): Nguyen, Jenny Q, Crews, Kristine R, Moore, Ben T, Kornegay, Nancy M, Baker, Donald K, Hasan, Murad, Campbell, Patrick K, Dean, Shannon M, Relling, Mary V, Hoffman, James M, Haidar, Cyrine E
DOI: 10.1093/jamia/ocac187
The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDMs) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500 000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM.
Author(s): Papez, Vaclav, Moinat, Maxim, Voss, Erica A, Bazakou, Sofia, Van Winzum, Anne, Peviani, Alessia, Payralbe, Stefan, Kallfelz, Michael, Asselbergs, Folkert W, Prieto-Alhambra, Daniel, Dobson, Richard J B, Denaxas, Spiros
DOI: 10.1093/jamia/ocac203
Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic [...]
Author(s): Peng, Le, Luo, Gaoxiang, Walker, Andrew, Zaiman, Zachary, Jones, Emma K, Gupta, Hemant, Kersten, Kristopher, Burns, John L, Harle, Christopher A, Magoc, Tanja, Shickel, Benjamin, Steenburg, Scott D, Loftus, Tyler, Melton, Genevieve B, Gichoya, Judy Wawira, Sun, Ju, Tignanelli, Christopher J
DOI: 10.1093/jamia/ocac188
Author(s):
DOI: 10.1093/jamia/ocac183
Lung transplant (LTx) saves lives in cystic fibrosis (CF). However, many potential candidates express uncertainty about LTx and die before receiving this treatment. CF guidelines recommend LTx education and clinical discussions well before the need for LTx arises, but limited patient resources exist.
Author(s): Hartzler, Andrea L, Bartlett, Lauren E, Hobler, Mara R, Reid, Nick, Pryor, Joseph B, Kapnadak, Siddhartha G, Berry, Donna L, Lober, William B, Goss, Christopher H, Ramos, Kathleen J, ,
DOI: 10.1093/jamia/ocac176
The aim of this article is to compare the aims, measures, methods, limitations, and scope of studies that employ vendor-derived and investigator-derived measures of electronic health record (EHR) use, and to assess measure consistency across studies.
Author(s): Rule, Adam, Melnick, Edward R, Apathy, Nate C
DOI: 10.1093/jamia/ocac177
Electronic health record audit logs capture a time-sequenced record of clinician activities while using the system. Audit log data therefore facilitate unobtrusive measurement at scale of clinical work activities and workflow as well as derivative, behavioral proxies (eg, teamwork). Given its considerable research potential, studies leveraging these data have burgeoned. As the field has matured, the challenges of using the data to answer significant research questions have come into focus [...]
Author(s): Kannampallil, Thomas, Adler-Milstein, Julia
DOI: 10.1093/jamia/ocac173