Welcome

The dataset on potentially preventable readmission (PPR) rates summarizes hospital-level readmission patterns across New York State from 2009 through the most recent release. This page presents an accessible, data-driven description of risk-adjusted PPR rates, highlighting statewide trends, the distribution of hospital rates, and variation across counties and hospital sizes. The narrative and figures below are intended to help policymakers, hospital quality teams, and community partners identify patterns that may warrant further investigation or quality improvement efforts.

Data Overview

This dataset includes hospital-level observed, expected, and risk-adjusted PPR rates together with counts of at-risk admissions for each discharge year. The records cover general acute care hospitals across New York State and include auxiliary metadata such as software version used to compute rates. The data are structured as a time series by discharge year and allow comparison of hospital performance relative to expected readmission patterns after accounting for measured patient- and hospital-level factors. Where present, statewide aggregate rows are retained in the raw import but excluded from most hospital-level analyses to avoid double counting.

How to Use These Data

Researchers and analysts can use these data to summarize temporal trends in readmission-related outcomes, to compare hospital performance within and across counties, and to prioritize hospitals or regions for further inquiry. The data are suitable for descriptive analyses, benchmarking, and exploratory modeling; however, investigators should pay attention to small-sample instability for hospitals with very low numbers of at-risk admissions. Because the risk-adjusted rates are already adjusted using the provider-supplied methodology, comparisons should be contextualized with information about case mix, hospital service lines, and changes in software versions that may affect calculations across years.

Why Are These Data Important?

Potentially preventable readmissions are a commonly used indicator of quality and coordination of care. Tracking risk-adjusted PPR rates over time provides insight into whether hospital-level readmission outcomes are improving, worsening, or stable after accounting for case mix. These data are useful to health system leaders, payers, and public health agencies seeking to target interventions that reduce unnecessary readmissions and improve care transitions. They are also valuable for community stakeholders interested in accountability and monitoring of hospital performance.

What These Data Show

The outputs below move from a statewide descriptive view to more focused hospital- and county-level summaries. They illustrate overarching time trends in mean risk-adjusted PPR rates, the hospital-level distribution in the most recent year, notable high- and low-rate hospitals, variation across counties with a minimum level of hospital coverage, and the relationship between hospital size and risk-adjusted rates.

What These Data Do Not Show

These data do not provide patient-level clinical detail, diagnoses, or the specific reasons for readmission, and therefore they cannot be used to attribute causality for elevated readmission rates. They also do not include comprehensive measures of outpatient services, social determinants, or community-level resources that influence readmissions. Where software versions differ across years, some changes in rates may be attributable to methodological updates rather than true changes in clinical performance.

Implications for Public Health Practice

For public health practice, the data offer a basis for monitoring hospital performance and identifying regions where readmissions may be concentrated. The findings can inform the targeting of transitional care programs, partnerships with community-based organizations, and investments in post-discharge support. Because the data are observational and aggregated, follow-up investigations and local clinical reviews are necessary before implementing system-level changes.

Analyses