Embedded Health Systems Science as a driver of care improvement within an integrated delivery organization

Background: Embedded Health Systems Science (HSS) has the potential to reduce gaps between research and delivery of evidence-based medicine. Models are needed to guide the development of embedded HSS in health care delivery organizations particularly with the rise of value-based care. Methods: The development of HSS infrastructure at a large nonprofit health care delivery organization is described, along with an embedded HSS diabetes study to illustrate the integration of program specific data, electronic health records, and health care system data infrastructure. To compare diabetes outcomes across four evidenced-based programs, a control group was developed from EHR data using propensity score matching. Mixed effect adjusted models were used to estimate reductions in hemoglobin A1c (HbA1c) and body weight. Results: Adjusted analyses using an EHR derived comparison group demonstrated significantly different findings than unadjusted pre to post analyses. The embedded HSS study indicates that appropriate statistical methods, staff with required expertise, and integration with health system data infrastructure are needed to develop timely and rigorous HSS outcomes that effectively improve patient care. Conclusions: Embedded HSS has the potential to inform value-based care models and contribute to evidence-based medicine approaches that improve patient care. Although developing system wide integrated data structures and staff with the appropriate skills requires substantial effort, the outcome is more reliable evaluations that lead to higher quality and higher value care. Implications: Health care delivery organizations can improve patient care by dedicating resources to embed HSS into its routine operations.