The Multi-State Learning Collaborative: Lead States in Public Health Quality Improvement (MLC) brought state and local health departments in 16 states together with public health system and national partners to prepare for national voluntary accreditation and to implement quality improvement (QI) practices.
The MLC has collected the single largest repository of qualitative public health QI data to date. A preliminary study was conducted to explore the potential merits of further mining data sets of this size and scope and examining them quantitatively.
We addressed the following research question: What characteristics of QI projects/mini-collaboratives make them more or less likely to attain their stated objectives? Qualitative MLC data were modified and coded as quantifiable measures using categorical or Likert scale measures analyzable through quantitative methods. Descriptive and inferential statistics were calculated.
Of the 156 mini-collaboratives with complete data, chronic disease was the most commonly selected target area. Among the 4 dependent variables, results varied somewhat by outcome. There was support in 1 or more analytical models for a positive relationship between aim statements that included target objectives, time frames, measurable goals, and well-defined processes. The degree to which the intervention was logically aligned with the aim and the comprehensiveness of the QI project were also positively associated with 1 or more outcomes. The large number of statistical tests conducted may have led to type I errors for some comparisons.
Quantitative analysis and modeling of public health QI activities are feasible and desirable. It may provide critical information leading to incremental improvement in QI performance within public health practice. This work can inform the nascent national accreditation program and the developing QI in Public Health Practice Exchange.
This article describes a synthesis of quality improvement (QI) projects conducted by the Multi-State Learning Collaborative. The preliminary study was conducted to explore the potential merits of further mining data sets of this size and scope and examining them quantitatively to assess the full impact of public health QI.
Center for Medicine and Public Health, Florida State University College of Medicine, Tallahassee (Drs Beitsch and Carretta); and National Network of Public Health Institutes, New Orleans, Louisiana (Mss McKeever and Gillen and Mr Pattnaik).
Correspondence: Leslie M. Beitsch, MD, JD, Center for Medicine and Public Health, Florida State University College of Medicine, 1115 W Call St, Tallahassee, FL 32306 (firstname.lastname@example.org).
The authors thank the Robert Wood Johnson Foundation for its unflagging support through the Multi-State Learning Collaborative grants, and for financially underwriting the quality improvement work described herein.
The authors declare no conflict of interest.