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Importance of Performance Measurement and MCH Epidemiology Leadership to Quality Improvement Initiatives at the
Kristin M Rankin1, Loretta Gavin2, John W Moran3
1Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 Taylor St (m/c 923), Chicago, IL, 60612, USA. krankin@uic.edu.
Abstract:
Purpose In recognition of the importance of performance measurement and MCH epidemiology leadership to quality improvement (QI) efforts, a plenary session dedicated to this topic was presented at the 2014 CityMatCH Leadership and MCH Epidemiology Conference. This paper summarizes the session and provides two applications of performance measurement to QI in MCH. Description Performance measures addressing processes of care are ubiquitous in the current health system landscape and the MCH community is increasingly applying QI processes, such as Plan-Do-Study-Act (PDSA) cycles, to improve the effectiveness and efficiency of systems impacting MCH populations. QI is maximally effective when well-defined performance measures are used to monitor change. Assessment MCH epidemiologists provide leadership to QI initiatives by identifying population-based outcomes that would benefit from QI, defining and implementing performance measures, assessing and improving data quality and timeliness, reporting variability in measures throughout PDSA cycles, evaluating QI initiative impact, and translating findings to stakeholders. MCH epidemiologists can also ensure that QI initiatives are aligned with MCH priorities at the local, state and federal levels. Two examples of this work, one highlighting use of a contraceptive service performance measure and another describing QI for peripartum hemorrhage prevention, demonstrate MCH epidemiologists' contributions throughout. Challenges remain in applying QI to complex community and systems-level interventions, including those aimed at improving access to quality care. Conclusion MCH epidemiologists provide leadership to QI initiatives by ensuring they are data-informed and supportive of a common MCH agenda, thereby optimizing the potential to improve MCH outcomes.
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