Characterizing treatment pathways at scale using the OHDSI network.
Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.
Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032; Medical Informatics Services, NewYork-Presbyterian Hospital, New York, NY 10032; Observational Health Data Sciences and Informatics, New York, NY 10032; hripcsak@columbia.edu.
George
G
Hripcsak
Observational Health Data Sciences and Informatics, New York, NY 10032; Epidemiology Analytics, Janssen Research and Development, Titusville, NJ 08560;
Patrick B
PB
Ryan
Observational Health Data Sciences and Informatics, New York, NY 10032; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46205;
Jon D
JD
Duke
Observational Health Data Sciences and Informatics, New York, NY 10032; Center for Biomedical Informatics Research, Stanford University, CA 94305;
Nigam H
NH
Shah
Observational Health Data Sciences and Informatics, New York, NY 10032; Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea, 443-380;
Rae Woong
RW
Park
Observational Health Data Sciences and Informatics, New York, NY 10032; Lister Hill National Center for Biomedical Communications (National Library of Medicine), National Institutes of Health, Bethesda, MD 20894;
Vojtech
V
Huser
Observational Health Data Sciences and Informatics, New York, NY 10032; Department of Biomathematics, University of California, Los Angeles, CA 90095; Department of Biostatistics, University of California, Los Angeles, CA 90095; Department of Human Genetics, University of California, Los Angeles, CA 90095;
Marc A
MA
Suchard
Observational Health Data Sciences and Informatics, New York, NY 10032; Epidemiology Analytics, Janssen Research and Development, Titusville, NJ 08560;
Martijn J
MJ
Schuemie
Observational Health Data Sciences and Informatics, New York, NY 10032; Epidemiology Analytics, Janssen Research and Development, Titusville, NJ 08560;
Frank J
FJ
DeFalco
Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032; Observational Health Data Sciences and Informatics, New York, NY 10032;
Adler
A
Perotte
Observational Health Data Sciences and Informatics, New York, NY 10032; Center for Biomedical Informatics Research, Stanford University, CA 94305;
Juan M
JM
Banda
Observational Health Data Sciences and Informatics, New York, NY 10032; Real World Evidence Solutions, IMS Health, Burlington, MA 01809;
Christian G
CG
Reich
Observational Health Data Sciences and Informatics, New York, NY 10032; Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045;
Lisa M
LM
Schilling
Observational Health Data Sciences and Informatics, New York, NY 10032; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37212; Geriatric Research, Education and Clinical Center, VA Tennessee Valley Healthcare System, Nashville, TN 37212;
Michael E
ME
Matheny
Observational Health Data Sciences and Informatics, New York, NY 10032; Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089; Department of Pediatrics, University of Southern California, Los Angeles, CA 90089;
Daniella
D
Meeker
Observational Health Data Sciences and Informatics, New York, NY 10032; Division of Health Sciences, University of South Australia, Adelaide, SA, Australia 5001;
Nicole
N
Pratt
Observational Health Data Sciences and Informatics, New York, NY 10032; Department of Statistics, Columbia University, New York, NY 10027.
David
D
Madigan
D000928:Antidepressive Agents / Q000627:therapeutic use; D000959:Antihypertensive Agents / Q000627:therapeutic use; D016208:Databases, Factual; D003863:Depression / Q000188:drug therapy; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy; D057286:Electronic Health Records; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D038622:Internationality; D008490:Medical Informatics; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*
D000928:Antidepressive Agents; D000959:Antihypertensive Agents; D007004:Hypoglycemic Agents
data network; observational research; treatment pathways