By Jon Warner, May 29, 2024
While the definition has shifted somewhat in the last 25 years, population health data refers to health information collected about a group of individuals. This could include data on demographics, medical diagnoses, lifestyle factors, social determinants of health, and more. With the growing digitization of health and other records, there is an abundance of population data available, but simply collecting it is not enough. New insights must be generated from analyzing these large datasets for policymakers and organizations across healthcare and beyond to truly improve population health outcomes. Here are some approaches that can be used to create new insights from population health data:
Use Predictive Analytics Models: One of the most powerful ways to gain new understanding from population health data is through predictive analytics. Machine learning algorithms can be applied to identify patterns and relationships within large datasets. These models can predict future health risks, disease progression, healthcare utilization, and costs. For example, a predictive model may find that individuals with a certain combination of medical conditions, lifestyle factors, and social determinants have a much higher five-year risk of developing diabetes. This insight allows for more targeted prevention and management programs. Predictive analytics also helps identify at-risk populations that may need previously unused innovation and technology or additional support services or technology products.
Compare Subgroups: Breaking the population down into meaningful subgroups is a simple but effective way to generate new insights. Data can be analyzed by factors like age, gender, ethnicity, geographic location, education level, income level, insurance type, and many more. Looking for differences in health outcomes, risk profiles, or care patterns between subgroups often surfaces important findings. For example, an analysis may show that a certain ethnic group has significantly higher rates of a preventable disease despite similar risk factors as other groups. This discovery could prompt culturally tailored interventions. Comparing subgroups within population health data also then highlights health disparities and reveals where more attention and resources are needed.
Longitudinal Analysis: Most population health data contain a time component, with repeated measurements collected over months or years. Conducting longitudinal analysis examines how individual and population-level health metrics change over extended periods. This type of analysis can provide insights into disease progression, the impact of interventions, changing risk profiles as populations age, and more. For example, a longitudinal study may find that obesity rates in a region have been steadily increasing each year despite particular wellness interventions or programs. This indicates the programs need modification to achieve better results. Longitudinal analysis is especially useful for evaluating long-term outcomes and return on investment (ROI) of programs targeting chronic conditions.
Geospatial Mapping: Mapping population health metrics and outcomes onto geographic information systems reveals important spatial patterns. This may show clusters of high disease burden, health resource utilization, or social risk factors concentrated in certain neighborhoods or communities. Geospatial analysis can identify "hot spots" where targeted public health interventions may have the greatest impact. It also helps determine if adequate healthcare access exists throughout the service area. With location data, population health insights can be generated by examining relationships between environmental factors and community characteristics for individual and group health outcomes.
Natural Language Processing: An increasing amount of unstructured clinical notes and narratives exist within population health datasets. Applying natural language processing (NLP) techniques allows these text-based data sources to be analyzed at scale. NLP can extract meaningful insights by identifying symptoms, diagnoses, social factors, behaviors, and more that were previously hidden in notes. It also facilitates longitudinal analysis by connecting details across multiple clinical encounters over time. NLP applied to population health data broadens understanding of disease progression, symptom prevalence, care quality, and service gaps mentioned within clinical documentation.
Data Integration and Linkage: Most valuable insights emerge from analyzing population health data in an integrated manner. Linking disparate data sources, such as medical claims with social services records, for example, expands the breadth of information available. It allows examination of how non-medical factors influence health outcomes. Integrating data from multiple providers and communities generates a more holistic view of population health trends, risks, and resource needs across a wider geographic area. Data integration is critical for understanding the full spectrum of determinants impacting population wellness.
In summary, simply collecting population health data is just the starting point. Advanced analytics, often powered by AI, are crucial for organizations working with individual and public health data. Well-designed dashboards presenting these insights visually and intuitively unlock strategic decision-making. Predictive modeling, subgroup comparisons, longitudinal analysis, geospatial mapping, natural language processing, and integrated data views all provide pathways to a deeper understanding of population needs.
The insights uncovered can then guide more effective programs, resource allocation decisions, and policies aimed at improving community health outcomes on a large scale, particularly in previously known challenge areas such as women’s health, older adult groups, rural locations, and other well-described “vulnerable populations.” The ongoing analysis of population health data ensures efforts remain focused on the areas presenting the greatest opportunities to enhance short and long-term well-being and the greatest possible health span for individuals and groups of people.
This article was written by Jon Warner, Executive Chair of Citizen Health Strategies (CHS). Citizen Health Strategies optimizes the end-to-end care experience with advisory, consulting, and product-building services to help deliver the Quintuple Aim – enabling better, faster, and more personalized well and sick care for all.
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