Data Analytics, Quality Measurement, and Population Health
Core concepts
Understand the measurement lifecycle: define the measure, specify the denominator and numerator, validate data, compute, stratify, and act. Learn the difference between process, outcome, balancing, and structural measures. Know HEDIS, eCQM, and CMS quality programs at a conceptual level. Understand risk adjustment, attribution, and panel management. Distinguish descriptive, predictive, and prescriptive analytics. Know basic statistics: sensitivity, specificity, PPV, NPV, incidence, prevalence, and how prevalence affects PPV. Understand data quality dimensions: completeness, accuracy, timeliness, consistency, and validity.
Traps
Questions often test whether a low-prevalence condition makes a screening tool's PPV low even with high sensitivity and specificity. Another trap: confusing incidence (new cases) with prevalence (all cases). Also watch for denominator exclusions that shrink the measured population and change rates.
Rule of thumb
Always ask: who is in the denominator, who is excluded, and what is the data source? A measure without an explicit denominator is not a measure.
Worked example
A diabetes A1c measure reports 70% compliance. Check exclusions (hospice, gestational diabetes), data source (claims vs. EHR), and whether the numerator requires the most recent value. Recompute after exclusions; the rate often changes materially.