Put Data to Use


With data, we can address the underlying factors that are growing rural health disparities. We can examine factors impacting the leading causes of avoidable death in rural area. We can identify underrepresented and historically marginalized areas of a state, county, or rural hospital.

This tool allows users to extract data and interact with a dashboard that uses data from multiple federal, publicly available datasets for population health planning. The toolkit also provides users educational resources about working with the datasets in Microsoft Excel and Tableau Public.

This tool aims to provide a web-based dashboard to educate state Medicare Rural Hospital Flexibility (Flex) Program Coordinators, a state office of rural health staff, critical access hospitals, rural health networks, and other rural health stakeholders on population health data analytics.

The data is organized into scenarios that explore health conditions and the leading causes of death in rural America. The scenarios also explore quality and access to care, using claims data, and understanding how social determinants of health and inequities that may impact health and well-being.

The tool consists of educational modules offering step-by-step instructions of common population health analytical procedures.


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

Note: Hospital 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 officials from the State Offices of Rural Health may access detailed financial data through a password-protected site. State Flex Coordinators and CAHs already have access to their own Medicare Beneficiary Quality Improvement Project (MBQIP) data from quarterly reports created by Flex Monitoring in support of Federal Office of Rural Health Policy.


Benchmarks provide a standard or point of reference for preparing data. View this brief video to learn how national and state-level benchmarks for appropriate variables can be utilized to compare scenario data available at the county or hospital levels.

Population Health Portal - Use of Benchmarks


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

The purpose of this analysis is to compare the incidence of cancer-related to risk factors and geographic characteristics such as access and use of preventative care, uninsured rates, smoking rates, access to broadband internet, race, and rurality for each county and state.
This analysis aims to compare the incidence of Chronic obstructive pulmonary disease (COPD) related to risk factors and geographic characteristics such as obesity, smoking, uninsured, race, and rurality for each county and state.
The purpose of this analysis is to compare diabetes rates and population age for each county and state.
The purpose of this analysis is to compare the incidence of strokes related to risk factors and geographic characteristics such as incidence of high blood pressure, high cholesterol, rate of the population that performs cholesterol screening, use of high cholesterol medicine, uninsured rates, race, and rurality for each county and state.
This analysis aims to identify a potential association between homicides, motor vehicle accidents, and injuries based on poverty rates for each county and state.

Scenarios Related to Quality, Access to Care, and Claims

The purpose of this analysis is to compare the rates of patients who reported that they were given information about what to do during their recovery at home, given the rates of readmissions and mortality for acute myocardial infarction, coronary artery bypass grafting (CABG), chronic obstructive pulmonary disease, stroke, heart failure, pneumonia, and hip/knee replacement at multiple levels, including state, county, and hospital type.
This analysis aims to identify wait times and leave percentages across different types of hospitals.
This analysis aims to compare patient-physician communication rates with heart failure readmission and mortality rates among different types of hospitals.
This assessment aims to determine if there is a difference in the relationship between the percentage of the population that reported they strongly understood discharge instructions and the percentage for the race categories at the county level.
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

This analysis first aims to determine whether an area's poverty rate is related to preventable hospitalizations and readmission rates.
This analysis aims to compare health status with social determinants of health by examining the rates of self-reported mental distress given population estimates, poverty rates, population size, and reported excessive drinking at the county level.
This analysis aims to explore the socioeconomic status and health-related outcomes (including diabetes rates, preventable hospital stays, household income, and access to healthy food) in relation to premature mortality.
This analysis aims to determine if there is a relationship between access to transportation and health status.
This analysis aims to compare the rates of poor mental health days by rates of drug overdose, excessive drinking, employment status, and health insurance coverage at the county level.