Population Health Analytics — populationhealth • ClinicoPath

Population Health Analytics provides comprehensive cohort-level health status monitoring and epidemiological analysis with population health metrics, risk stratification, geographic health mapping, and predictive health outcome modeling for public health surveillance, population management, and healthcare system optimization through evidence-based population health insights.

Usage

populationhealth(
  data,
  patientID,
  healthOutcomes,
  demographics,
  geographic,
  timeVariable,
  riskFactors,
  healthcareUtilization,
  population_stratification = TRUE,
  risk_stratification = TRUE,
  geographic_analysis = TRUE,
  temporal_trends = TRUE,
  health_disparities = TRUE,
  predictive_modeling = TRUE,
  population_type = "general",
  analysis_scope = "comprehensive",
  geographic_level = "county",
  time_window = 12,
  risk_threshold = 0.7,
  disparity_analysis = TRUE,
  social_determinants = TRUE,
  intervention_analysis = FALSE,
  comparative_analysis = TRUE,
  quality_metrics = TRUE,
  surveillance_system = TRUE,
  outcome_prediction = TRUE,
  resource_allocation = FALSE,
  population_visualization = TRUE,
  geographic_mapping = TRUE,
  trend_visualization = TRUE,
  disparity_plots = TRUE,
  interactive_dashboard = TRUE
)

Arguments

data: .
patientID: Unique patient identifier for population tracking and analysis
healthOutcomes: Health outcome measures (BMI, blood pressure, lab values, disease indicators)
demographics: Demographic characteristics (age, gender, ethnicity, socioeconomic status)
geographic: Geographic identifiers (zip code, county, region, coordinates) (optional)
timeVariable: Time variables for temporal health trend analysis (optional)
riskFactors: Risk factors (smoking, lifestyle, comorbidities, environmental) (optional)
healthcareUtilization: Healthcare utilization metrics (visits, admissions, procedures) (optional)
population_stratification: Enable population stratification analysis by demographics and risk factors
risk_stratification: Perform population-level risk stratification and early identification
geographic_analysis: Enable geographic health mapping and spatial analysis
temporal_trends: Analyze temporal trends in population health outcomes
health_disparities: Assess health disparities across demographic and socioeconomic groups
predictive_modeling: Enable predictive modeling for population health outcomes
population_type: Type of population for specialized analysis approaches
analysis_scope: Scope of population health analysis
geographic_level: Geographic level for spatial health analysis
time_window: Time window for population health analysis and trending
risk_threshold: Threshold for high-risk population identification
disparity_analysis: Enable advanced health disparity analysis methods
social_determinants: Include social determinants of health in analysis
intervention_analysis: Analyze impact of population health interventions
comparative_analysis: Enable comparison across different population segments
quality_metrics: Calculate population health quality and performance indicators
surveillance_system: Enable public health surveillance and monitoring capabilities
outcome_prediction: Predict future population health outcomes and trends
resource_allocation: Analyze healthcare resource allocation and optimization
population_visualization: Create comprehensive population health visualizations
geographic_mapping: Generate geographic health maps and spatial visualizations
trend_visualization: Create temporal trend visualizations for population health
disparity_plots: Generate health disparity and equity visualizations
interactive_dashboard: Create interactive population health dashboards and monitoring tools

Value

A results object containing:

`results$todo`					a html
`results$populationSummary`					a html
`results$demographicsTable`					a table
`results$healthOutcomesTable`					a table
`results$riskStratificationTable`					a table
`results$geographicAnalysisTable`					a table
`results$temporalTrendsTable`					a table
`results$healthDisparitiesTable`					a table
`results$predictiveModelingTable`					a table
`results$surveillanceTable`					a table
`results$qualityMetricsTable`					a table
`results$interventionAnalysisTable`					a table
`results$resourceAllocationTable`					a table
`results$populationReport`					a html
`results$executiveSummary`					a html
`results$populationDashboard`					a html
`results$populationOverviewPlot`					an image
`results$geographicHealthMap`					an image
`results$temporalTrendsPlot`					an image
`results$riskStratificationPlot`					an image
`results$healthDisparityPlot`					an image
`results$predictiveModelingPlot`					an image
`results$interactivePopulationDashboard`					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$demographicsTable$asDF

as.data.frame(results$demographicsTable)

Examples

data('population_health', package='ClinicoPath')

# Basic population health analytics
populationhealth(population_health,
               patientID = 'PatientID',
               healthOutcomes = c('BMI', 'BloodPressure', 'Cholesterol'),
               demographics = c('Age', 'Gender', 'Ethnicity'),
               geographic = 'ZipCode',
               risk_stratification = TRUE,
               predictive_modeling = TRUE)