Population Health Management
Population health management is the systematic use of data to identify, stratify, and proactively manage the health of defined patient panels — reducing care variation, closing care gaps, and improving quality measure performance across entire patient populations.
What is Population Health?
Population health management (PHM) applies a proactive, data-driven approach to patient care — moving beyond the traditional reactive model where clinicians only address problems when patients present with symptoms. The PHM model uses aggregated patient data to identify who in a defined population has a given condition, who is overdue for preventive services, who is at risk for deterioration or hospitalisation, and what resources each patient segment requires — before problems escalate.
Disease Registry Management
A disease registry is a structured list of all patients with a defined condition within a practice or health system. Effective registries are continuously updated from clinical data (not manually maintained), allow filtering by clinical parameters (e.g., all diabetic patients with A1C > 9%), and generate care gap lists for outreach. The diabetes, hypertension, COPD, and depression registries are the most commonly maintained in primary care for quality reporting and chronic disease management.
Attribution Models
Attribution determines which patients "belong" to a given provider or practice for quality reporting and cost accountability. Two primary models exist:
- Prospective attribution: Patients are assigned to a provider at the start of a measurement period based on their declared or historical utilisation patterns. Used in some ACO and capitation models.
- Retrospective attribution: Patients are assigned after the performance period ends based on the provider who delivered the plurality of primary care services during the year. Used in traditional MSSP ACO measurement. Creates uncertainty during the performance year about who is actually in your attributed panel.
Health Equity in Population Health
Effective PHM requires stratifying quality measure performance by race, ethnicity, language, and socioeconomic status to identify disparities and target improvement. CMS now requires reporting of stratified quality data for several MIPS and MSSP ACO measures. Practices that identify racial disparities in diabetes control or preventive screening rates can target language-concordant outreach and community health worker engagement to specific subpopulations.