Article Text

Download PDFPDF
Novel tools for a learning health system: a combined difference-in-difference/regression discontinuity approach to evaluate effectiveness of a readmission reduction initiative
  1. Allan J Walkey1,
  2. Jacob Bor2,
  3. Nicholas J Cordella3
  1. 1 Medicine, Evans Center of Implementation and Improvement Sciences, Boston University School of Medicine, Boston, Massachusetts, USA
  2. 2 Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA
  3. 3 Patient Quality and Safety, Boston Medical Center, Boston, Massachusetts, USA
  1. Correspondence to Dr Allan J Walkey, Medicine, Boston University School of Medicine, Boston, MA 02118, USA; alwalkey{at}bu.edu

Abstract

Current methods used to evaluate the effects of healthcare improvement efforts have limitations. Designs with strong causal inference—such as individual patient or cluster randomisation—can be inappropriate and infeasible to use in single-centre settings. Simpler designs—such as prepost studies—are unable to infer causal relationships between improvement interventions and outcomes of interest, often leading to spurious conclusions regarding programme success. Other designs, such as regression discontinuity or difference-in-difference (DD) approaches alone, require multiple assumptions that are often unable to be met in real world improvement settings. We present a case study of a novel design in improvement and implementation research—a hybrid regression discontinuity/DD design—that leverages risk-targeted improvement interventions within a hospital readmission reduction programme. We demonstrate how the hybrid regression discontinuity-DD approach addresses many of the limitations of either method alone, and represents a useful method to evaluate the effects of multiple, simultaneous heath system improvement activities—a necessary capacity of a learning health system. Finally, we discuss some of the limitations of the hybrid regression discontinuity-DD approach, including the need to assign patients to interventions based upon a continuous measure, the need for large sample sizes, and potential susceptibility of risk-based intervention assignment to gaming.

  • quality improvement methodologies
  • comparative effectiveness research
  • continuous quality improvement
  • health services research
  • implementation science
View Full Text

Statistics from Altmetric.com

Footnotes

  • Twitter @walkeyallan

  • Contributors NC participated in the acquisition, analysis and interpretation of data for the work; drafted the manuscript; provided final approval for its content; and agrees to be accountable for the work he has done. JB participated in the analysis and interpretation of data for the work; edited the manuscript for important intellectual content; provided final approval for its content; and agrees to be accountable for the work he has done. AW participated in the acquisition, analysis, and interpretation of data for the work; drafted the manuscript and revised it for important intellectual content; provided final approval for its content; and agrees to be accountable for the work he has done.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement No data are available.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Linked Articles