Effective population health management (PHM) requires strategies to reach the individual consumer or patient at all stages of life in the manner most appropriate for each individual. PHM must use a set of patterns of population health strategies that describe people and their preferences. These pattern classifications help healthcare organizations begin to understand how they should develop a robust PHM that serves the population needs.
Each market and population is unique. Market position, service offerings, health status, predominant diseases, and geographic and community features are all unique factors that need to be addressed. As your healthcare system gains a greater understanding of their local population needs, the PHM program you are implementing needs to develop criteria that will be assigned to specific population cohorts to define the various proactive health interventions and care delivery.
Locating Data Sources and Sets for Population Health Management
Sonia Martinez
Rasmussen University
HSA 5300 Population Health
Dr. Point-Johnson
3/4//26
Locating Data Sources and Sets for Population Health Management
As the Population Health Management (PHM) program leader for our Miami-Dade County health system, this assessment focuses on diabetes management as the key medical case, addressing the 10% prevalence rate highlighted in the community health needs assessment (CHNA). This aligns with priorities like non-communicable disease prevention, cultural competency for the 70.3% Hispanic/Latino and 54.3% foreign-born population, and partnerships with entities such as Florida Department of Health (DOH) clinics and federally qualified health centers (FQHCs) (U.S. Census Bureau, 2024). Big data aggregation from diverse sources is essential to build comprehensive patient profiles for risk scoring and interventions. The top three critical data sources driving decisions are: (1) U.S. Census Bureau for demographics and socioeconomic insights, (2) Florida DOH for health indicators like diabetes metrics, and (3) electronic health records (EHRs) from local hospitals for clinical and utilization data. This paper identifies big data sources and types, describes necessary data elements for immediate and future gains, and assesses readily available data as a foundational step.
Big Data Sources and Types for the PHM Program
Big data for diabetes management draws from federal, state, private, and academic sources to support informed, equitable strategies. Federal sources include the U.S. Census Bureau, providing quantitative demographic data (e.g., 2.8 million residents, 5.1% growth since 2020, 14.1% poverty rate) and socioeconomic indicators (e.g., 75.2% non-English speakers, median income $68,694), which highlight barriers in diverse urban areas (U.S. Census Bureau, 2024). The Centers for Disease Control and Prevention (CDC) supplies health surveillance data, such as national diabetes trends for benchmarking.
State sources like the Florida DOH offer quantitative health indicators, including diabetes prevalence, obesity rates, and behavioral data (e.g., smoking), alongside qualitative community reports on immigrant stressors (Florida Department of Health, 2022). Private sources encompass EHRs from facilities like Jackson Memorial and Baptist Health, yielding clinical (e.g., lab results) and utilization data (e.g., provider ratios of 10 per 10,000, 14.8% uninsured rate) (Chambers et al., 2025). Academic and foundation sources, such as the University of Miami and Robert Wood Johnson Foundation, provide environmental data on pollution and housing, often mixing quantitative metrics (e.g., air quality indices) with qualitative analyses (Heenan et al., 2022). These sources enable a multifaceted view, primarily quantitative for measurable outcomes, with qualitative elements for contextual depth, facilitating risk stratification and tailored interventions (Roorda et al., 2024).
Specific Data Elements for Immediate Gains and Future Best Practices
Specific data elements are crucial for immediate patient well-being improvements in diabetes management while establishing best practices. Demographic elements (e.g., age, ethnicity, foreign-born status) from Census data identify high-risk groups, enabling targeted outreach. Clinical elements, such as ICD-10 codes (e.g., E11.9 for type 2 diabetes) and hemoglobin A1c levels (target <8%), from EHRs support real-time monitoring to reduce complications (Florida Department of Health, 2022). Socioeconomic elements like poverty and insurance status address access barriers, while behavioral data (e.g., smoking rates) and environmental factors (e.g., pollution exposure) inform holistic interventions.
For immediate gains, these elements allow providers to aggregate data for risk scoring, such as using A1c and demographic data to enroll 250 high-risk Hispanic seniors in a culturally adapted telehealth program, reducing uncontrolled hypertension (60%) and ED visits through diet/stress management (Heenan et al., 2022). A clear example: Demographic and clinical data translate to a targeted diabetes outreach intervention, where zip code analysis identifies immigrant-heavy areas for mobile clinics offering free A1c testing and Spanish-language education, yielding quick adherence improvements. For future best practices, these elements create feedback loops via key performance indicators (KPIs) like A1c control rates (70% target), supporting predictive analytics for scalability, ROI evaluation, and integration with partners like NAMI for mental health comorbidities, ensuring long-term equity (Roorda et al., 2024).
Assessment of Readily Available Data as a First Step
Utilizing readily available data like demographics, ICD-10 codes, and admission-discharge-transfer (ADT) alerts is a vital initial step toward integrating complex big data into PHM. Demographics from Census sources enable rapid population segmentation, identifying vulnerabilities like language barriers for diabetes education pilots in high-poverty areas (U.S. Census Bureau, 2024). ICD-10 codes from EHRs provide diagnostic precision, allowing quick trend analysis (e.g., diabetes clusters) and basic risk scoring without advanced infrastructure (Chambers et al., 2025). ADT alerts facilitate timely post-discharge follow-ups, preventing readmissions and promoting continuity.
This foundation builds data literacy, supports immediate interventions (e.g., alerting providers to high-risk admissions for insulin education), and ensures Affordable Care Act compliance while fostering collaborations (Florida Department of Health, 2022). It transitions PHM from reactive to proactive by preparing for complex data like wearables or genomic profiles, enabling predictive modeling for socioeconomic pressures and reducing disparities in immigrant communities (Heenan et al., 2022).
Conclusion
Aggregating big data sources and elements for diabetes management empowers equitable PHM in Miami-Dade, with readily available data as the gateway to advanced integration. Recommendations include a real-time dashboard for monitoring and annual CHNA updates to sustain impact on vulnerable populations.
References
Chambers, D., Mawson, R., Mettle-Nunoo, J., Sutton, A., & Booth, A. (2025). A systematic review of international performance indicators and metrics relevant to UK general practice. BMJ Open Quality, 14(4). https://bmjopenquality.bmj.com/content/14/4/e003477
Florida Department of Health. (2022). Community Health Assessment: Miami-Dade County. https://www.floridahealth.gov/_media/miami-dade/community-reports/miamidade-cha.pdf
Heenan, M. A., Randall, G. E., & Evans, J. M. (2022). Selecting performance indicators and targets in health care: An international scoping review and standardized process framework. Risk Management and Healthcare Policy, 747-764. https://doi.org/10.2147/RMHP.S357561
Roorda, E., Bruijnzeels, M., Struijs, J., & Spruit, M. (2024). Business intelligence systems for population health management: A scoping review. JAMIA Open, 7(4), ooae122. https://doi.org/10.1093/jamiaopen/ooae122
U.S. Census Bureau. (2024). QuickFacts: Miami-Dade County, Florida. https://www.census.gov/quickfacts/fact/table/miamidadecountyflorida/POP060210
