Lessons from the Electronic Data Methods Forum
Collaboration at the Frontier of Comparative Effectiveness Research, Patient-Centered Outcomes Research, and Quality Improvement
Bibliographic Data
| ID | 9102119 |
|---|---|
| Authors | Erin Holve (0000-0002-6024-0415, AcademyHealth, corresponding author), Ned Calonge (0000-0002-2653-6001, Colorado Trust) |
| Year | 2013 |
| Volume | 51 |
| Issue | Supplement 8Suppl 3 |
| Pages | S1-S3 |
| Publication date | 2013-08-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0b013e31829c518f |
| PMID | 23793048 |
| OpenAlex | W2000629254 |
| Language | EN |
BACKGROUND: The Electronic Data Methods (EDM) Forum, with support from the Agency for Healthcare Research and Quality, exists to advance knowledge and practice on the use of electronic clinical data (ECD) for comparative effectiveness research, patient-centered outcomes research, and quality improvement (QI). The EDM Forum facilitates collaboration between the Prospective Outcome Systems using Patient-specific Electronic data to Compare Tests and therapies, Scalable Distributed Research Network, and Enhanced registry projects funded by Agency for Healthcare Research and Quality. OBJECTIVES: This overview describes a second set of papers commissioned by the EDM Forum, published in this supplement to Medical Care. The papers that are included discuss challenges and innovations from the research and QI community using ECD. CONCLUSIONS: The papers in this supplement provide lessons learned based on experiences building transparent, scalable, reusable networks for research and QI. Through these papers, and a new open access e-journal, eGEMs, the EDM Forum is working to advance the science of health research and QI using ECD to improve patient outcomes
Data science · Computer Science · Electronic Health Records Systems · Health Policy Implementation Science · Machine Learning in Healthcare
| Highly cited | No |
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