Performance measurement part I: Foundational knowledge for measure development.

As medicine is moving toward performance and outcome-based payment and is transitioning away from productivity-based systems, value is now being appraised in healthcare through "performance measures." Over the past few decades, assessment of clinical performance in health care has been essential in ensuring safe and cost-effective patient care. The Centers for Medicare & Medicaid Services is further driving this change with measurable, outcomes-based national payer incentive payment systems. With the continually evolving requirements in health care reform focused on value-based care, there is a growing concern that clinicians, particularly dermatologists, may not understand the scientific rationale of health care quality measurement. As such, in order to help dermatologists understand the health care measurement science landscape to empower them to engage in the performance measure development and implementation process, the first article in this 2-part continuing medical education series reviews the value equation, historic and evolving policy issues, and the American Academy of Dermatology's approach to performance measurement development to provide the required foundational knowledge for performance measure developers.

Performance measurement part II: A guide to developing dermatology performance measures.

Throughout the 21st century, national and local governments, private health sectors, health insurance companies, healthcare professionals, labor unions, and consumers have been striving to develop an effective approach to evaluate, report, and improve the quality of healthcare. As medicine improves and health systems grow to meet patient needs, the performance measurement system of care effectiveness must also evolve. Continual efforts should be undertaken to effectively measure quality of care to create a more informed public, improve health outcomes, and reduce healthcare costs. As such, recent policy reform has necessitated that performance systems be implemented in healthcare, with the "performance measure" being the foundation of the system in which all of healthcare must be actively engaged in to ensure optimal care for patients. The development of performance measures can be highly complex, particularly when creating specialty-specific performance measures. To help dermatologists understand the process of creating dermatology-specific performance measures to engage in creating or implementing performance measures at the local or national levels, this article in the two-part continuing medical education series reviews the types, components, and process of developing, reviewing, and implementing performance measures.

COMBINING INFORMATION FROM MULTIPLE DATA SOURCES TO ASSESS POPULATION HEALTH.

Information about an extensive set of health conditions on a well-defined sample of subjects is essential for assessing population health, gauging the impact of various policies, modeling costs, and studying health disparities. Unfortunately, there is no single data source that provides accurate information about health conditions. We combine information from several administrative and survey data sets to obtain model-based dummy variables for 107 health conditions (diseases, preventive measures, and screening for diseases) for elderly (age 65 and older) subjects in the Medicare Current Beneficiary Survey (MCBS) over the fourteen-year period, 1999-2012. The MCBS has prevalence of diseases assessed based on Medicare claims and provides detailed information on all health conditions but is prone to underestimation bias. The National Health and Nutrition Examination Survey (NHANES), on the other hand, collects self-reports and physical/laboratory measures only for a subset of the 107 health conditions. Neither source provides complete information, but we use them together to derive model-based corrected dummy variables in MCBS for the full range of existing health conditions using a missing data and measurement error model framework. We create multiply imputed dummy variables and use them to construct the prevalence rate and trend estimates. The broader goal, however, is to use these corrected or modeled dummy variables for a multitude of policy analysis, cost modeling, and analysis of other relationships either using them as predictors or as outcome variables.

ChIP-Enrich: gene set enrichment testing for ChIP-seq data.

Gene set enrichment testing can enhance the biological interpretation of ChIP-seq data. Here, we develop a method, ChIP-Enrich, for this analysis which empirically adjusts for gene locus length (the length of the gene body and its surrounding non-coding sequence). Adjustment for gene locus length is necessary because it is often positively associated with the presence of one or more peaks and because many biologically defined gene sets have an excess of genes with longer or shorter gene locus lengths. Unlike alternative methods, ChIP-Enrich can account for the wide range of gene locus length-to-peak presence relationships (observed in ENCODE ChIP-seq data sets). We show that ChIP-Enrich has a well-calibrated type I error rate using permuted ENCODE ChIP-seq data sets; in contrast, two commonly used gene set enrichment methods, Fisher's exact test and the binomial test implemented in Genomic Regions Enrichment of Annotations Tool (GREAT), can have highly inflated type I error rates and biases in ranking. We identify DNA-binding proteins, including CTCF, JunD and glucocorticoid receptor α (GRα), that show different enrichment patterns for peaks closer to versus further from transcription start sites. We also identify known and potential new biological functions of GRα. ChIP-Enrich is available as a web interface (http://chip-enrich.med.umich.edu) and Bioconductor package.