Put Data to Use


This web-based tool uses data from multiple federal, publicly available datasets to educate rural health stakeholders on population health data analytics. The site allows users to extract data and interact with scenarios that explore health conditions, quality and access to care, and social determinants of health in rural America. The tool is organized by specific scenarios that cover important health topics for rural areas. Each scenario provides educational modules on how to work with the data on a dashboard published through Tableau Public. Additionally, the datasets from each scenario can be downloaded in a Microsoft Excel data file. 

The tool aims to educate state Medicare Rural Hospital Flexibility (Flex) Program Coordinators, state office of rural health (SORH) staff, employees of critical access hospitals (CAHs), rural health networks, and other rural health stakeholders on population health data analytics for rural areas. The tool can support efforts to uncover factors leading to health disparities in rural areas to address policy changes to impact the health of the rural population.


The data included in this web-based tool are publicly available and consist of, but are not limited to:

Note: Care Compare data in the Toolkit does not include data that has been suppressed due to small numbers. The Critical Access Hospital Measurement and Performance Assessment System (CAHMPAS), maintained by the Flex Monitoring Team, provides access to financial, quality, and community-benefit performance data of CAHs at the state and hospital level. Community and quality data in CAHMPAS are available to the public. Critical access hospitals (CAHs), state Flex Coordinators, and SORH staff may access detailed financial data through a password-protected site. State Flex Coordinators and CAHs already have access to Medicare Beneficiary Quality Improvement Project (MBQIP) data from quarterly reports created by Flex Monitoring Team in support of Federal Office of Rural Health Policy.

Population Type and Size

Each scenario can be summarized by the population type. The population type is determined based on the population size of a specific county. The population types include metro, nonmetro cities, and nonmetro towns. These types are adapted from the rural-urban commuting area codes (RUCA) and core-based statistical areas (CBSA) definitions of rural and urban. The population types for counties are defined as follows:

  • Metro - A population of 50,000 or more
  • Nonmetro cities - A population between 2,500 and less than 50,000
  • Nonmetro towns - A population of less than 2,500


Limitations apply to the population health planning tool. The data is limited to the data sets that are publicly available and permitted to be repurposed on this website. The data sets are also limited to the most recent data published by federal agencies. Finally, the developed scenarios and the educational materials produced are not all-encompassing. They are examples of the types of analysis that can be conducted using public data for population health planning.

Scenarios Related to Diagnosis

This analysis aims to identify the factors that influence the risk and survival of cancer and how where you live affects your access to and quality of cancer care.
This analysis shows that COPD is a major public health problem that affects different regions and populations differently.
The purpose of this analysis is to compare diabetes rates and population age for each county and state.
This analysis shows that strokes are a major public health problem that affects regions and populations differently.
The aim of this analysis is to explore if relationships between homicides, motor vehicle accidents, and injuries, based on poverty rates in different counties and states.

Scenarios Related to Quality, Access to Care, and Claims

The goal of this analysis is to figure out how long people have to wait to be seen and how many decide to leave before being seen at different hospitals.
In this assessment, we will examine how these factors influence the comprehension of discharge instructions among hospital patients.
The purpose of this resource is to provide examples of analyzing claims data. Specifically, the resource offers explanations and videos on using synthetic claims data developed by the Centers for Medicare & Medicaid Services (CMS) and instructions on acquiring and using the data.

Scenarios Related to Social Determinants of Health

The objective of this analysis is to establish how poverty rates in a region relate to the frequency of preventable hospitalizations and readmissions.
The goal of this analysis is to delve into how socioeconomic status ties into health-related outcomes, encompassing factors like diabetes prevalence, preventable hospital stays, household income, and access to healthy food, all in relation to premature mortality.
This analysis looks at how transportation options, being active, and staying healthy are related.
In this analysis, we will explore how different variables at the county level, such as drug overdoses, excessive drinking, employment status, and health insurance coverage, are related to the rates of poor mental health days among the population.