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Colloquium Paper

Characterizing treatment pathways at scale using the OHDSI network

George Hripcsak, Patrick B. Ryan, Jon D. Duke, Nigam H. Shah, Rae Woong Park, Vojtech Huser, Marc A. Suchard, Martijn J. Schuemie, Frank J. DeFalco, Adler Perotte, Juan M. Banda, Christian G. Reich, Lisa M. Schilling, Michael E. Matheny, Daniella Meeker, Nicole Pratt, and David Madigan
  1. aDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032;
  2. bMedical Informatics Services, NewYork-Presbyterian Hospital, New York, NY 10032;
  3. cObservational Health Data Sciences and Informatics, New York, NY 10032;
  4. dEpidemiology Analytics, Janssen Research and Development, Titusville, NJ 08560;
  5. eCenter for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46205;
  6. fCenter for Biomedical Informatics Research, Stanford University, CA 94305;
  7. gDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea, 443-380;
  8. hLister Hill National Center for Biomedical Communications (National Library of Medicine), National Institutes of Health, Bethesda, MD 20894;
  9. iDepartment of Biomathematics, University of California, Los Angeles, CA 90095;
  10. jDepartment of Biostatistics, University of California, Los Angeles, CA 90095;
  11. kDepartment of Human Genetics, University of California, Los Angeles, CA 90095;
  12. lReal World Evidence Solutions, IMS Health, Burlington, MA 01809;
  13. mDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO 80045;
  14. nDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37212;
  15. oGeriatric Research, Education and Clinical Center, VA Tennessee Valley Healthcare System, Nashville, TN 37212;
  16. pDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA 90089;
  17. qDepartment of Pediatrics, University of Southern California, Los Angeles, CA 90089;
  18. rDivision of Health Sciences, University of South Australia, Adelaide, SA, Australia 5001;
  19. sDepartment of Statistics, Columbia University, New York, NY 10027

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PNAS July 5, 2016 113 (27) 7329-7336; first published June 6, 2016; https://doi.org/10.1073/pnas.1510502113
George Hripcsak
aDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032;
bMedical Informatics Services, NewYork-Presbyterian Hospital, New York, NY 10032;
cObservational Health Data Sciences and Informatics, New York, NY 10032;
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  • For correspondence: hripcsak@columbia.edu
Patrick B. Ryan
cObservational Health Data Sciences and Informatics, New York, NY 10032;
dEpidemiology Analytics, Janssen Research and Development, Titusville, NJ 08560;
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Jon D. Duke
cObservational Health Data Sciences and Informatics, New York, NY 10032;
eCenter for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46205;
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Nigam H. Shah
cObservational Health Data Sciences and Informatics, New York, NY 10032;
fCenter for Biomedical Informatics Research, Stanford University, CA 94305;
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Rae Woong Park
cObservational Health Data Sciences and Informatics, New York, NY 10032;
gDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea, 443-380;
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Vojtech Huser
cObservational Health Data Sciences and Informatics, New York, NY 10032;
hLister Hill National Center for Biomedical Communications (National Library of Medicine), National Institutes of Health, Bethesda, MD 20894;
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Marc A. Suchard
cObservational Health Data Sciences and Informatics, New York, NY 10032;
iDepartment of Biomathematics, University of California, Los Angeles, CA 90095;
jDepartment of Biostatistics, University of California, Los Angeles, CA 90095;
kDepartment of Human Genetics, University of California, Los Angeles, CA 90095;
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Martijn J. Schuemie
cObservational Health Data Sciences and Informatics, New York, NY 10032;
dEpidemiology Analytics, Janssen Research and Development, Titusville, NJ 08560;
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Frank J. DeFalco
cObservational Health Data Sciences and Informatics, New York, NY 10032;
dEpidemiology Analytics, Janssen Research and Development, Titusville, NJ 08560;
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Adler Perotte
aDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032;
cObservational Health Data Sciences and Informatics, New York, NY 10032;
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Juan M. Banda
cObservational Health Data Sciences and Informatics, New York, NY 10032;
fCenter for Biomedical Informatics Research, Stanford University, CA 94305;
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Christian G. Reich
cObservational Health Data Sciences and Informatics, New York, NY 10032;
lReal World Evidence Solutions, IMS Health, Burlington, MA 01809;
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Lisa M. Schilling
cObservational Health Data Sciences and Informatics, New York, NY 10032;
mDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO 80045;
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Michael E. Matheny
cObservational Health Data Sciences and Informatics, New York, NY 10032;
nDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37212;
oGeriatric Research, Education and Clinical Center, VA Tennessee Valley Healthcare System, Nashville, TN 37212;
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Daniella Meeker
cObservational Health Data Sciences and Informatics, New York, NY 10032;
pDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA 90089;
qDepartment of Pediatrics, University of Southern California, Los Angeles, CA 90089;
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Nicole Pratt
cObservational Health Data Sciences and Informatics, New York, NY 10032;
rDivision of Health Sciences, University of South Australia, Adelaide, SA, Australia 5001;
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David Madigan
cObservational Health Data Sciences and Informatics, New York, NY 10032;
sDepartment of Statistics, Columbia University, New York, NY 10027
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  1. Edited by Richard M. Shiffrin, Indiana University, Bloomington, IN, and approved April 5, 2016 (received for review June 14, 2015)

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Abstract

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.

  • observational research
  • data network
  • treatment pathways

Footnotes

  • ↵1To whom correspondence should be addressed. Email: hripcsak{at}columbia.edu.
  • Author contributions: G.H., P.B.R., J.D.D., N.H.S., C.G.R., and D. Madigan designed research; G.H., P.B.R., J.D.D., N.H.S., R.W.P., V.H., M.A.S., A.P., J.M.B., and D. Madigan performed research; G.H., P.B.R., J.D.D., M.A.S., M.J.S., F.J.D., and D. Madigan contributed new reagents/analytic tools; G.H., P.B.R., J.D.D., N.H.S., R.W.P., V.H., M.A.S., F.J.D., A.P., J.M.B., and D. Madigan analyzed data; and G.H., P.B.R., J.D.D., N.H.S., R.W.P., V.H., M.A.S., M.J.S., F.J.D., A.P., J.M.B., C.G.R., L.M.S., M.E.M., D. Meeker, N.P., and D. Madigan wrote the paper.

  • The authors declare no conflict of interest.

  • This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, “Drawing Causal Inference from Big Data,” held March 26–27, 2015, at the National Academies of Sciences in Washington, DC. The complete program and video recordings of most presentations are available on the NAS website at www.nasonline.org/Big-data.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1510502113/-/DCSupplemental.

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Treatment pathways using the OHDSI network
George Hripcsak, Patrick B. Ryan, Jon D. Duke, Nigam H. Shah, Rae Woong Park, Vojtech Huser, Marc A. Suchard, Martijn J. Schuemie, Frank J. DeFalco, Adler Perotte, Juan M. Banda, Christian G. Reich, Lisa M. Schilling, Michael E. Matheny, Daniella Meeker, Nicole Pratt, David Madigan
Proceedings of the National Academy of Sciences Jul 2016, 113 (27) 7329-7336; DOI: 10.1073/pnas.1510502113

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Treatment pathways using the OHDSI network
George Hripcsak, Patrick B. Ryan, Jon D. Duke, Nigam H. Shah, Rae Woong Park, Vojtech Huser, Marc A. Suchard, Martijn J. Schuemie, Frank J. DeFalco, Adler Perotte, Juan M. Banda, Christian G. Reich, Lisa M. Schilling, Michael E. Matheny, Daniella Meeker, Nicole Pratt, David Madigan
Proceedings of the National Academy of Sciences Jul 2016, 113 (27) 7329-7336; DOI: 10.1073/pnas.1510502113
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