Quality improvement (QI) methods have been used for almost a decade in public health departments to increase effectiveness and efficiency. Although results are rapidly accumulating, the evidence for the science of improvement is shallow and limited. To advance the use and effectiveness of QI in public health, it is important to develop a science of improvement using practice-based research to build an evidence base for QI projects.
This purpose of this study is to advance the science of improvement in public health departments with 3 objectives: (1) establish a taxonomy of QI projects in public health, (2) categorize QI projects undertaken in health departments using the taxonomy, and (3) create an opportunity modes and effects analysis.
This study is a qualitative analysis of archival data from 2 separate large databases consisting of 51 QI projects undertaken in public health departments over the last 5 years.
The study involves 2 separate QI collaboratives. One includes Minnesota health departments; the other is a national collaborative.
We propose a standardized case definition, common metrics, and a taxonomy of QI projects to begin building the evidence base for QI in public health and to advance the science of continuous quality improvement.
All projects created an aim statement and used metrics while 53% used a specific QI model with an average of 3.25 QI techniques per project. Approximately 40% of the projects incorporated a process control methodology, and 60% of the projects identified the process from beginning to end, while 11 of 12 PHAB (Public Health Accreditation Board) domains were included.
The findings provide a baseline for QI taxonomy to operationalize a science of improvement for public health departments.
The purpose of this study was to advance the science of improvement in public health departments with the objectives to establish a taxonomy of QI projects in public health, to categorize QI projects undertaken in health departments using the taxonomy, and to create an opportunity modes and effects analysis.
School of Public Health (Dr Riley and Mr Parrotta and Mr Godsall), School of Medicine (Ms Lownik), University of Minnesota, Minneapolis; Arkansas Department of Health, Little Rock, Arkansas (Dr Halverson); Office of Performance Improvement, Minnesota Department of Health, Saint Paul (Drs Gyllstrom and Gearin); and Health Services and Systems Research, College of Public Health, University of Kentucky (Dr Mays).
Correspondence: William Riley, PhD, School of Public Health, University of Minnesota, 420 Delaware St S, Minneapolis, MN 55455. E-mail: firstname.lastname@example.org
G.M. is the 8th author in this manuscript. He contributed significantly to the data analysis and preparation of both the official draft and final draft of the manuscript.
This study was supported by the National Public Health Practice–Based Research Network Program of the Robert Wood Johnson Foundation, the Practice Based Research Network (PBRN) Grant from the University of Kentucky, and the Multi-State-2 Learning Collaborative Program supported by the Robert Wood Johnson Foundation.
The authors express appreciation to the local health departments that participated in the 2 collaboratives that conducted quality improvement projects, which provided data for this study. The Multi-State-2 Learning Collaborative was facilitated by the Minnesota Department of Health in conjunction with the National Network of Public Health Institutes, and the Establishing the Evidence-Based Collaborative was facilitated by the School of Public Health, University of Minnesota. Health departments in both collaboratives actively designed and implemented their projects and submitted routine reports and storyboards.
The authors declare no conflicts of interest.