全球智库信息集成服务系统

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. However, depending on the research question, careful study design and appropriate analytical methods can improve the utility of EHD.

The introduction to a series of four papers, 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. We argue that when the question involves describing the current (and likely future) state of affairs, causal inference is not relevant, so randomized clinical trials (RCTs) are not necessary. When the question is whether an intervention improves outcomes of interest, causal inference is critical, but appropriately designed and analyzed observational studies can yield valid results that better balance internal and external validity than typical RCTs. When the question is one of translation and spread of innovations, a different set of questions comes into play: How and why does the intervention work? How can a model be amended or adapted to work in new settings? In these “delivery system science” settings, causal inference is not the main issue, so a range of quantitative, qualitative, and mixed research designs are needed.

We then describe why RCTs are regarded as the gold standard for assessing cause and effect, how alternative approaches relying on observational data can be used to the same end, and how observational studies of EHD can be effective complements to RCTs. We also describe how RCTs can be a model for designing rigorous observational studies, building an evidence base through iterative studies that build upon each other (i.e., confirmation across multiple investigations).