Analytical Methods for a Learning Health System: 2. Design of Observational Studies. Issue 1 (7th December 2017)

• Record Type: Journal Article
• Authors: Stoto, Michael; Oakes, Michael; Stuart, Elizabeth; Priest, Elisa L.; Savitz, Lucy
• Is Part Of: EGEMS. Volume 5:Issue 1(2017)
• Publication Date: 2017-12-07
• Subjects: Medical records -- Data processing -- Periodicals; Medical care -- Data processing -- Periodicals; Medical Records; Automatic Data Processing; Medical care -- Data processing; Medical records -- Data processing; Periodicals; Periodicals; 651.504261
• DOI: 10.5334/egems.251
• Languages: English
• ISSNs: 2327-9214

Abstract

The second paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review summarizes study design approaches, including choosing appropriate data sources, and methods for design and analysis of natural and quasi-experiments. The primary strength of study design approaches described in this section is that they study the impact of a deliberate intervention in real-world settings, which is critical for external validity. These evaluation designs address estimating the counterfactual – what would have happened if the intervention had not been implemented. At the individual level, epidemiologic designs focus on identifying situations in which bias is minimized. Natural and quasi-experiments focus on situations where the change in assignment breaks the usual links that could lead to confounding, reverse causation, and so forth. And because these observational studies typically use data gathered for patient management or administrative purposes, the possibility of observation bias is minimized. The disadvantages are that one cannot necessarily attribute the effect to the intervention (as opposed to other things that might have changed), and the results do not indicate what about the intervention made a difference. Because they cannot rely on randomization to establish causality, program evaluation methods demand a more careful consideration of the "theory" of the intervention and how it is expected to play out. A logic model describing this theory can help to design appropriate comparisons, account for all influential variables in a model, and help to ensure that evaluation studies focus on the critical intermediate and long-term outcomes as well as possible confounders.