Creating pragmatic, rapid-cycle, evidence-based innovation: The Kaiser Permanente Northern California Delivery Science and Applied Research (DARE) program

Introduction

Ongoing crises in the quality, affordability, sustainability, value, and equity of U.S. healthcare call for rapid, massive-scale innovations across multiple specialties. Physician groups and healthcare organizations commit significant monetary and personnel investments for innovation and improvement efforts, but most lack over-arching systems theory-supported conceptual frameworks for efficiently coordinating the timely, large-volume idea generation, refinement, prioritization, evidence development, implementation, and re-evaluation strategies needed for rapid-cycle improvements in health care delivery, outcomes, and value.

Methods

This article describes one large-scale, generalizable model: The Permanente Medical Group's (TPMG) Delivery Science and Applied Research (DARE) program within Kaiser Permanente Northern California. This organization-level initiative was designed to: (1) elicit clinical innovation and quality concerns across clinical specialties; (2) prioritize these questions for next-step evaluations; (3) create dedicated analytic, research, and clinical expertise to rapidly study the questions generated; (4) develop communities of clinician-researchers, embedded within their specialties' clinical operations, who gather ideas for evaluation, generate evidence, and facilitate implementation of research results (a.k.a. evidence-based innovators); and (5) broadly disseminate findings, to connect results with potential next-step implementation.

Results

The DARE program and its components rapidly led to more than 200 recently completed or ongoing projects, informed care changes, influenced national guidelines, developed communities of evidence-based clinician innovators in 15 specialties, and empowered new paths for career diversity and physician wellness. Key factors in the DARE program's success include explicitly defining high-impact ideas; engaging researchers with analysts experienced with large clinical data systems; developing several research funding mechanisms scaled to project size; prioritizing rapid and efficient project completion; supporting clinician-investigators embedded within their specialties; and structured organization-wide dissemination of findings for informing potential implementation.

Conclusions

High-volume, evidence-based innovation programs can be important, scale-able, reproducible models for different settings for increasing quality, affordability, sustainability, value, and equity in healthcare and furthering the difficult-to-achieve concept of developing sustainable learning healthcare systems.