Learning health systems use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning. Even without randomization, observational studies can play a central role as the nation’s health care system embraces comparative effectiveness research and patient-centered outcomes research. However, neither the breadth, timeliness, volume of the available information, nor sophisticated analytics, allow analysts to confidently infer causal relationships from observational data.
Four new papers in AcademyHealth’s peer-reviewed, open access journal eGEMs by Dr. Michael Stoto and colleagues explore how learning health systems can use routinely collected EHD to advance knowledge and support continuous learning:
- The introduction to the series, this review begins with a discussion of the kind of research questions that EHD can help address, noting how different evidence and assumptions are needed for each.
- The second paper summarizes study design approaches, including choosing appropriate data sources, and methods for design and analysis of natural and quasi-experiments.
- The third paper in the series describes how analytical methods for individual-level EHD, including regression approaches, interrupted time series (ITS) analyses, instrumental variables, and propensity score methods, can also be used to address the question of whether the intervention “works.”
- The series' fourth paper describes how delivery system science provides a systematic means to answer questions that arise in translating complex interventions to other practice settings.