Are Publicly Funded Health Databases Geographically Detailed and Timely Enough to Support Patient-Centered Outcomes Research?

J Gen Intern Med. 2019 Mar;34(3):467-472. doi: 10.1007/s11606-018-4673-6. Epub 2018 Sep 20.

Abstract

Emerging health care research paradigms such as comparative effectiveness research (CER), patient-centered outcome research (PCOR), and precision medicine (PM) share one ultimate goal: constructing evidence to provide the right treatment to the right patient at the right time. We argue that to succeed at this goal, it is crucial to have both timely access to individual-level data and fine geographic granularity in the data. Existing data will continue to be an important resource for observational studies as new data sources are developed. We examined widely used publicly funded health databases and population-based survey systems and found four ways they could be improved to better support the new research paradigms: (1) finer and more consistent geographic granularity, (2) more complete geographic coverage of the US population, (3) shorter time from data collection to data release, and (4) improved environments for restricted data access. We believe that existing data sources, if utilized optimally, and newly developed data infrastructures will both play a key role in expanding our insight into what treatments, at what time, work for each patient.

Keywords: CER; PCOR; PM; data utility; health database.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Comparative Effectiveness Research / economics
  • Comparative Effectiveness Research / statistics & numerical data
  • Data Management / economics
  • Data Management / statistics & numerical data*
  • Databases, Factual / economics
  • Databases, Factual / statistics & numerical data*
  • Humans
  • Patient Outcome Assessment*
  • Precision Medicine / economics
  • Precision Medicine / statistics & numerical data
  • Public Health / economics
  • Public Health / statistics & numerical data*
  • Time Factors
  • United States / epidemiology