By Caroline Stampfel
Senior Epidemiologist, AMCHP
In MCH programs, we rely on epidemiology and evaluation data to tell us important information about our populations and how effectively we are serving them. Epidemiology data can signal problems to us using incidence or prevalence. Inferential studies can help us understand which groups and behaviors are good candidates for intervention, and what health outcomes are important for the populations we serve. Specific evaluative data from the programs and services we implement are critical: Process measures can tell us what we have accomplished, outcome measures point to what we have changed with our program or practice, and impact measures assess the ultimate difference for those we serve.
As MCH programs and more generally, public health agencies, focus on implementing practices designated as "evidence-based," there is a burgeoning need to examine and reinforce relevant data systems that will guide the implementation, monitoring and evaluation of evidence-based practices (EBP). Public health data systems need to support quality improvement (QI) techniques to determine if implementation is going as planned, where course-corrections are necessary, and whether the evidence-based practice needs to be adapted to meet the needs of a new population. QI, which, up until a few years ago, was more common in clinical settings, involves implementing rapid cycles of improvement. The Centers for Disease Control and Prevention (CDC) gathered some key information about quality improvement and has adopted a public health definition for QI proposed by William Riley et al as, "the use of a deliberate and defined process, such as Plan-Do-Check-Act, which is focused on activities that are responsive to community needs and improving population health. It refers to a continuous and ongoing effort to achieve measurable improvements in the efficiency, effectiveness, performance, accountability, outcomes, and other indicators of quality services or processes which achieve equity and improve the health of the community."
In order to achieve the key characteristics of QI, namely being continuous and responsive, we need to look beyond traditional public health data. For example, epi data, and vital records data in particular, are often not available in the time frame needed for meaningful QI. The process of reviewing vital data for quality and editing can take a year or more from the closing of a vital records year to the release of final data. While this delay has been acceptable for the purposes of final reports and trend analysis, and has even improved with advances in state and national data systems, these data are not timely enough to achieve QI goals. One example of growing recognition of the need for more timely data comes from the National Association for Public Health Statistics and Information Systems (NAPHSIS), which in November 2012 convened a symposium to examine the timeliness issue. The report resulting from that symposium and released in April 2013, More, Better, Faster: Strategies for Improving the Timeliness of Vital Statistics, outlines short- and long-term strategies for the state and national vital event systems to improve timeliness, centered on capital – financial, political and human. Short-term strategies for achieving success include professional development for data providers and the next generation of vital records leaders, evaluation of system performance and return on investment, releasing data to the National Center for Health Statistics more quickly, and sharing best practices through learning networks.
AMCHP sponsored training on QI in San Antonio in 2012, with resources available here. At the AMCHP annual conference this past February, QI was the subject of a mini-plenary where a distinguished panel discussed implementation of QI in public health from different vantage points. To access a recording of this discussion, click here. Panel member Kaye Bender provided this advice to attendees: do something, start small and remember that you can’t really break anything by doing an improvement initiative. Since implementation of QI is relatively new to public health, getting our minds around what kind of data are needed and how they can and should be used might take a little time, a lot of practice, and maybe even making a few mistakes until we learn how to do it right.