Deliverable 3 - Locating Data Sources and Sets for Population Health ManagemenT

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Executive Summary: Assessing Data Sets for Population Health Management

Sonia Martinez

Rasmussen University

HSA 5300 Population Health

Dr. Point-Johnson

2/25/26

Executive Summary: Assessing Data Sets for Population Health Management in Miami-Dade County

This executive summary is a response to the request of the Board of Directors of our health system serving Miami-Dade County, Florida, to outline key data sets needed to support our new Population Health Management (PHM) program. The prior community needs assessment highlights challenges like a 10% diabetes prevalence, immigration-related mental health issues, elevated infant mortality rates, a 14.8% uninsured rate under 65, and environmental factors such as urban pollution and housing instability. The program is aimed at the prevention of the non-communicable diseases, cultural competency, and partnerships with such resources as Florida Department of Health clinics and federally qualified health centers. Our use of recent, pertinent, and reliable data helps to prioritize vulnerable populations, such as 70.3% Hispanic/Latino and 54.3% foreign-born residents, in the face of socioeconomic stressors such as a 14.1% poverty rate, to achieve better results, equity, and resource use.

Required Data Sets for PHM Program

In order to start and maintain our PHM program successfully in Miami-Dade County, we need to combine various data sets: federal, state, private, and academic. Such will be in line with our community health needs assessment to make informed decisions. The U.S. Census Bureau provides demographic and socioeconomic data, including population statistics of 2.8 million and 5.1% growth since 2020, age distributions of 19.7% under 18 and 17.2% over 65 years old, ethnic composition, 14.1% poverty, 83.1% high school, and income inequalities with a median household income of 68,694 (U.S. Census Bureau, 2024). This helps in dealing with the obstacles such as non-English speakers (75.2) and high living costs (18.9) that are above average.

The Florida Department of Health and CDC health indicators include chronic diseases such as diabetes and obesity, mental health indicators, maternal health including low birth weights, and behaviors including smoking, with monitoring of A1c levels below 8% (Florida Department of Health, 2022). AHRQ-collected access information and local measurements provide the uninsured rates, ratios of providers 10 per 10,000, and utilization in facilities such as Jackson Memorial (Chambers et al., 2025). The foundations, such as Robert Wood Johnson, and scholars, such as the University of Miami, provide environmental data on pollution and housing (Heenan et al., 2022). Florida CHIP performance data monitor such KPIs as 80% mammography rates and infant mortality less than 5 per 1,000 (Roorda et al., 2024). Collectively, these will allow customized interventions, including telehealth due to mental health through collaboration with NAMI, so that the program is tailored to the urban diversity and needs of immigrants in the county.

Role of Data in Empowering PHM

Data sets transform raw inputs into empowering insights for our PHM program, addressing Miami-Dade's needs systematically. As an example, demographic analysis can show the trends of hypertension related to diet and stress in ethnic groups, which can be used to direct specific programs. This is based on the Deming model in which data is converted into information, including 60% uncontrolled hypertension and knowledge, including adherence issues in seniors (Florida Department of Health, 2022).

They enable prioritization by focusing resources on high-risk immigrants through zip code pilots. Evidence-based actions use SMART objectives, like enrolling 250 in monitoring programs (Heenan et al., 2022). Assessment using KPIs, including ED visits, facilitates changes and ROI presentation. The cooperation is improved by the exchange of information, which decreases the differences with non-governmental organizations and employer associations (Roorda et al., 2024). This feedback mechanism enhances effectiveness, reduces hospitalization, and improves the quality of life, and incorporates cultural aspects to serve better foreign-born populations with language and access barriers (U.S. Census Bureau, 2024).

Importance of Quality Data Sets

In dynamic environments such as Miami-Dade, PHM success depends on the relevant, up-to-date, and accurate information. CHNAs data is relevant and focused on chronic matters, which eliminates wastage of resources (Florida Department of Health, 2022). Existing statistics follow trends such as population increase and enable maternal health to be adjusted in good time.

Accuracy eliminates KPI errors, which ensures credibility and sound assessment, including pregnancy reductions (Chambers et al., 2025). It demonstrates equity gains to guarantee ACA compliance and CDC funding. The stakeholders are engaged in the process of sustainability through the relevant data demonstrating the progress, such as reduced smoking (Heenan et al., 2022). In their absence, programs will be inefficient and goal-oriented when chronic management is concerned. Also, predictive analytics is backed by quality data in anticipating needs, including increased mental health demands due to socioeconomic pressures to guarantee proactive instead of reactive strategies (Roorda et al., 2024).

Conclusion

Integrating these data sets will drive our PHM program toward equitable, data-informed strategies for better health in Miami-Dade. Some of the recommendations involve developing an operational dashboard in real-time, collaboration with academia to conduct analytics, and the annual update of CHNAs. This is in line with the requirements of a resilient community, which focuses on continuous assessment to fine-tune interventions and create the greatest 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