Hospital Readmission Rates by State: US Data & Analysis

This comprehensive report examines U.S. hospital readmission statistics by state, drawing on government data, peer-reviewed studies, policy analyses, and expert commentary. Hospital readmissions – typically defined as unplanned returns to any hospital within 30 days of an index discharge – are a key quality metric. High rates of unplanned readmissions signal potential gaps in care coordination, chronic-disease management, and social support, and have been a focus of CMS’s Hospital Readmissions Reduction Program (HRRP) since 2012. Readmissions also carry substantial costs (Medicare alone spends billions annually on preventable readmissions (^[1])). A 2025 Vizient report further found that over 25% of readmissions occur at a different hospital, adding $21 billion annually in excess costs and creating dangerous gaps in care coordination (^[2]).

Analysis of national data shows that readmission trends have modestly declined nationally in the past decade, largely in the Medicare population. For example, Medicare readmission rates fell about 7% from 18.3% (2010) to 17.1% (2016) (^[3]). Nonetheless, wide variations persist across states and communities. In a March 2025 analysis, Definitive Healthcare reported the highest average among reporting hospitals in Massachusetts (15.3%) and the lowest in Idaho (13.3%). The comparison averages CMS-sourced hospital-wide readmission measures across reporting hospitals; it is not an all-payer, population-weighted state readmission-rate comparison. (^[4]) (^[5]). States such as Florida, Illinois, Louisiana, Nevada, and West Virginia also have averages ≥15.0% (^[4]), whereas Washington and Utah average under 13.8% (^[5]).

These state-level disparities describe averages among reporting hospitals and do not, by themselves, establish why any particular state’s average is higher or lower. States with large, high-volume hospitals and older, sicker populations (e.g. Massachusetts, Florida, New Jersey, Connecticut) tend to have higher readmissions due to dense Medicare discharges and more chronic illnesses (^[6]) (^[7]). By contrast, states with smaller hospital systems or healthier populations (e.g. Idaho, Washington, Utah, Hawaii) tend to have fewer readmissions (^[5]) (^[8]). Multi-state analyses confirm this: hospitals in the Mid-Atlantic (e.g. NY/NJ area) have significantly higher risk-adjusted readmission rates than those in the Mountain states (^[9]). Likewise, community factors – such as local primary care supply, skilled nursing availability, and socioeconomic conditions – substantially influence readmissions (^[7]) (^[10]). For example, areas with more primary care physicians and nursing home beds are linked to lower Medicare readmissions, while areas with many home health agencies saw higher readmissions (^[7]).

This report reviews selected state comparisons of hospital-wide readmission measures, national benchmarks, and research on factors associated with geographic variation. Its tables are illustrative selected-state comparisons, not a complete state-by-state or population-weighted rate table. We explore multiple perspectives – hospital-level differences (teaching vs. rural, for-profit vs. public), patient/population factors (age, insurance, chronic disease prevalence), and policy influences (HRRP penalties, Medicaid expansion). Real-world examples illustrate how some states and hospitals are addressing readmissions. The report concludes with a discussion of the implications for healthcare quality, value-based payment, and future directions (e.g. telehealth and policy adjustments).

Hospital readmissions – typically measured as all-cause, unplanned readmissions within 30 days of discharge – are a widely used indicator of healthcare quality. Unplanned readmissions often reflect care transition failures or unresolved clinical issues, and they are costly: Medicare alone spent an estimated $17 billion on potentially avoidable readmissions (^[1]). In response, the Affordable Care Act established the Hospital Readmissions Reduction Program (HRRP), with payment reductions beginning in FY 2013 for hospitals with excess readmissions. HRRP uses six condition- or procedure-specific 30-day risk-standardized unplanned-readmission measures: acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, pneumonia, coronary artery bypass graft surgery, and elective primary total hip and/or knee arthroplasty. Sepsis and the hospital-wide readmission measure are not HRRP measures. CMS applies payment reductions to applicable Medicare fee-for-service base operating DRG payments, capped at 3%. (^[1]) (^[3]).

Defining readmissions: Studies and data use consistent definitions, typically all-cause 30-day readmissions, meaning any admission to any hospital for any reason within 30 days after an index discharge. Most research focuses on 30-day figures, which are considered sensitive to hospital care and discharge planning. Some analyses look at 90-day readmissions or chronic-disease subsets, but 30-day is standard for policy. Importantly, readmission rates can be reported raw (percentage of patients) or as risk-standardized measures (controlling for patient mix). CMS’s Hospital Compare site publishes hospital-wide risk-adjusted readmission measures for Medicare patients (^[11]). For state-level comparisons, measures must be identified precisely. The state figures used in this report are third-party averages of reporting hospitals’ CMS-sourced hospital-wide readmission measures, not aggregate all-payer or population-weighted state rates. Accordingly, they should not be used to calculate a state’s raw readmission rate or to infer the experience of all residents.

Why do readmissions matter? Besides cost, high readmissions are seen as “sentinel events” indicating possible quality gaps (^[12]) (^[1]). Fragmented care, inadequate outpatient support, social factors, and suboptimal inpatient care can all contribute. Reducing readmissions is therefore a key focus of healthcare policy and hospital quality improvement programs. Indeed, the impetus behind the HRRP was the idea that many 30-day readmissions are preventable with better care coordination and follow-up (^[1]). Over the last decade, national initiatives (HRRP, ACO contracts, Partnership for Patients, etc.) have aimed at lowering readmissions through transitional-care models and incentives (^[3]) (^[1]).

National trends: Historically, U.S. all-payer 30-day readmission rates have hovered in the mid-teens. HCUP statistical briefs show that the overall national readmission rate in 2016 was about 13.9% (^[13]). Among payers, Medicare patients have by far the highest rates (17.1% in 2016) (^[14]) – roughly double the rate for privately insured (8.6% in 2016) – reflecting the older, sicker Medicare population. From 2010–2016, the Medicare readmission rate declined by 7% (from 18.3% to 17.1%) (^[3]), likely due in part to HRRP and better care transitions. Other payers saw smaller changes; uninsured readmissions increased over that period (^[3]). Condition-specific data also show wide variation: in 2016, readmissions for blood disorders were as high as 25.3%, while the median across all conditions was 13.9% (^[13]). This wide range of condition readmissions contributes to the heterogeneity seen across states, depending on state disease burden.

While broad national trends are one aspect, this report focuses on the state-by-state variation. States differ in demographics, health status, insurance coverage, and healthcare infrastructure, all of which can affect readmissions. For example, a 2015 Health Services Research study found that areas with more patients having multiple chronic conditions had significantly higher readmission rates (^[15]) (^[16]). Similarly, hospitals in for-profit urban centers tend to have slightly higher risk-adjusted readmission rates than smaller or public hospitals, and regional practice patterns (“geography”) exert a larger influence than individual hospital features (^[9]) (^[17]). These insights underscore that community and system factors – which cluster by state – likely drive much of the observed disparities.

Scope of this report: This paper reviews a selected March 2025 reporting-hospital comparison and historical national HCUP findings, including numeric statistics, trends, and variation. It synthesizes research evidence on factors associated with state-level differences (patient mix, hospital mix, local resources). It also assesses case examples and policy implications (e.g. how to address high-risk states). Throughout, we provide extensive citations. The analysis includes markdown tables summarizing key data (e.g. top and bottom states by readmission rate), and draws on sources such as CMS/Hospital Compare, AHRQ HCUP data, peer-reviewed journals, and news analyses. The tone is academic and detailed, aiming to inform policymakers, health systems, and researchers interested in healthcare quality.

Prior to examining state differences, we review the national context of hospital readmissions: how rates have changed over time, by patient group, and under national policies.

Overall Readmission Rates and Trends

Nationally, the aggregate 30-day all-cause hospital readmission rate for all patients (all payers) has been in the mid-teens. For example, the Healthcare Cost and Utilization Project (HCUP) reported that in 2016 the overall 30-day readmission rate was about 13.9% (^[13]). This decline reflects modest progress: earlier HCUP data show 30-day readmission among Medicare patients fell from 18.3% in 2010 to 17.1% in 2016 (^[3]). The trend for Medicare beneficiaries is especially critical since Medicare enrollees account for a large share of hospitalizations. Over that 2010–2016 period, overall Medicare readmissions declined by 7% (^[3]), likely due to heightened attention (HRRP, transitional care) and an unusually high baseline in 2010. In contrast, readmissions for uninsured patients rose by 14% (from 10.4% to 11.8%) (^[3]), perhaps reflecting demographic or coverage changes unrelated to policy.

Medicare patients consistently had the highest readmission rates (20.7% for CHF in 2016, for example) (^[12]) (^[14]). Meanwhile, Medicaid and private-insurance rates were considerably lower (private ~8–9% in 2016) (^[14]). Indeed, even after risk-adjustment, Medicare status is a strong predictor: in 2016 Medicare patients had about double the 30-day readmission probability of those with private insurance (^[14]). This disparity means that state populations with older or Medicare-heavy demographics (e.g. Florida, West Virginia) might naturally have higher overall readmissions, whereas younger states (e.g. Utah) might appear lower.

Trends by condition: Readmission rates also vary greatly by condition. CHF (heart failure), for example, had a median 30-day readmission of ~20.7% across regions (^[12]). AMI (heart attack) median was ~18.2% (^[12]). Pneumonia ~15.3%. Surgical admissions averaged around 12%. The Dartmouth Atlas HRR-level data show moderate regional spread, with coefficients of variation from 0.07 to 0.20 depending on condition (^[12]). These patterns suggest that states with higher prevalence of certain diagnoses (e.g. Northeast may have more CHF or renal disease) could see higher readmissions overall. For instance, Nevada and Louisiana have relatively high incidence of heart disease and diabetes (CDC and state data), which may contribute to their elevated readmission rates (^[4]).

Policy impact: The HRRP (so-called “readmissions penalty”) has been in effect since Federal FY2013. It has certainly spurred initiatives in many hospitals. CMS reports that since program start, the aggregate 30-day readmission rate among Medicare beneficiaries has declined modestly. In fact, the Kaiser Health News/Empire Center data show that by 2013–2014, 93% of New York hospitals were already facing financial penalties under HRRP (^[18]), because their 30-day readmissions exceeded the national average. (This high penalty rate – “6th highest in nation” – indicates that New York readmissions were persistently high relative to other states (^[18]).) Nationally, the average penalty across hospitals was about 0.6% of Medicare reimbursements, whereas New York averaged ~1% (^[18]). This underscores that even within the same health system, some states lag in reducing readmissions.

Beyond HRRP, other national reforms (like ACOs and bundled payments) also incentivize reducing readmissions, but primarily at the provider level. A holistic view, however, suggests that state and community context exert major influence: as one group notes, variations between states likely reflect broader system and population differences (^[9]). We next turn to examining how these variations actually manifest in the data.

The core finding is that readmission rates differ significantly by state. These differences are documented in multiple data sources and analyses, as summarized below.

Summary of State Readmission Statistics

A March 2025 Definitive Healthcare analysis averaged CMS-sourced hospital-wide readmission measures across reporting hospitals in each state. Their findings are instructive: Massachusetts leads the nation with an average readmission rate of 15.3% for hospitals in that state (^[4]). Several other states follow closely: Florida, Illinois, Louisiana, Nevada, and West Virginia each have average rates of 15.0% or more (^[4]). By contrast, Idaho has the lowest state average at 13.3% (^[5]) – roughly one percentage point below the national mean. Washington and Utah also stand out with comparatively low rates (each under 13.8%) (^[5]). (For context, the same analysis cited a national hospital average of 14.67% across all reporting hospitals (^[19]).)

These numbers are summarized below:

• Highest-rate states (2025): Massachusetts (15.3%), Florida (≥15.0%), Illinois (≥15.0%), Louisiana (≥15.0%), Nevada (≥15.0%), West Virginia (≥15.0%) (^[4]).
• Lowest-rate states (2025): Idaho (13.3%), Washington (~13.7%), Utah (~13.7%) (^[5]).

(Further details on the top and bottom states are in Table 1 and Table 2 below.) Key observations:

• Northeast: Massachusetts is highest. Other New England and mid-Atlantic states (e.g. Maine, New Jersey) had intermediate rates (DefinitiveHC report notes New Jersey also high on Medicare discharges (^[20]), though its average readmission was ≈ 14.7% which is above national mean but below Mass).
• South: Florida and Louisiana feature in the highest group. Tennessee and Texas had moderate rates (~14.4%). Many Southeastern states (Georgia, Alabama, Mississippi) fall in mid-range (14–14.8%).
• Midwest: Illinois is high; states like Ohio, Michigan, and Missouri have mid-high readmissions (~14.5%). The Dakotas and Nebraska are lower (≈13.8–14.2%).
• West: Idaho and Utah are lowest. Washington and California are low- to mid-13s. Mountain and Plains states (CO, AZ, NM) are mid-range (14–14.5%).
• Pacific: Hawaii’s rate (14.0%) is below average (^[8]), possibly reflecting its healthier population profile, while Alaskan hospitals average ~13.7%.
• Highest within-country disparity: Interestingly, Massachusetts (15.3%) to Idaho (13.3%) is a 2-point gap, similar in magnitude to the differences seen among hospital referral regions (^[12]). This suggests substantial geographic variation even after aggregating all hospitals in a state.

Table 1: Top 6 states by average hospital readmission rate (2025)

Table 2: Selected lower average CMS-sourced hospital-wide readmission measures among reporting hospitals (2025)

Note: These figures are averages of reporting hospitals’ CMS-sourced hospital-wide, 30-day, all-cause unplanned readmission measures, accessed by Definitive Healthcare in March 2025. The underlying CMS measure is risk-standardized and covers eligible Medicare fee-for-service beneficiaries; the table is not an all-payer, population-weighted state-rate comparison. (^[21]; CMS measure methodology)

Population characteristics: States vary in demography and health at baseline. Massachusetts’ high rate, for instance, partly reflects an older population with high chronic disease burden. Indeed, DefinitiveHC notes that Massachusetts and other high-rate states (NJ, FL, CT, DC) are among the highest U.S. states in total Medicare hospital discharges (^[22]), indicating large Medicare-insured populations prone to chronic comorbidities (heart failure, COPD, etc.) (^[22]) (^[14]). Conversely, states like Idaho and Utah have younger populations with fewer comorbidities and (as of 2020 census) higher proportions of privately insured or under-65 individuals, contributing to lower average readmissions. For example, the HCUP Brief #248 found that Medicare patients had a 30-day readmission rate (17.1%) nearly double that of privately insured (8.6%) (^[14]). Thus states with more privately insured (Utah) or uninsured (Texas, Arizona) may see lower aggregate readmissions partly for demographic reasons.

Hospital and system characteristics: The types of hospitals within a state also matter. Horwitz et al (2017) found systematic differences: Mid-Atlantic hospitals (NY, NJ) had ~0.98 percentage points higher risk-standardized readmission rates than Mountain-region hospitals (^[9]). For-profit hospitals had ~0.38% higher readmission rates than public hospitals (^[9]). Both urban and rural hospitals had slightly higher RSRRs than medium-sized hospitals (^[9]), suggesting very small or very large hospitals tend to have more readmissions. This implies states dominated by large teaching hospitals (e.g. MA with its Boston academic centers) or by for-profit chains (certain Sunbelt states) could see higher rates. Conversely, states with mostly smaller community or critical-access hospitals might have lower readmissions. In fact, the HTA report notes that larger hospitals may have difficulty coordinating discharge care for high volumes (^[23]).

Care environment and resources: Critical studies have linked community resources to readmissions. A 2022 Health Affairs analysis found that hospitals located in areas with greater supply of post-discharge resources had lower readmissions (^[7]). Specifically, each hospital that had onsite palliative care services or was in a county with more primary care physicians, SNF beds, and nursing home beds saw noticeably fewer readmissions (^[7]). By contrast, areas with many home health agencies or nurse practitioners actually saw slightly higher readmissions (^[7]), an unexpected finding interpreted as possible supply-induced demand or confounding. The conclusion was that improving local access to post-hospital care (and adjusting HRRP measures for these factors) could reduce readmissions (^[7]). This has state-level implications: for example, rural-leaning states with limited SNF or home care capacity (e.g. parts of Appalachia) may struggle to keep readmissions down, whereas states with robust post-acute infrastructure (e.g. Minnesota, Vermont) may show lower rates.

Socioeconomic and demographic context: States differ in poverty, education, and health behaviors, which also correlate with readmissions. Wu et al. (2017) and others have shown social determinants (food insecurity, housing, substance abuse) drive readmissions (^[24]). States with higher poverty (Mississippi, West Virginia) and less access to primary care often see more repeat hospital use. For example, an earlier large study noted that areas where patients had fewer primary care relationships saw no drop in readmissions despite intensive hospital interventions (^[24]), suggesting upstream social factors were at play. The reporting-hospital comparison does not establish whether poverty, primary-care supply, or other state characteristics explain the averages reported for West Virginia or Louisiana.

Insurance coverage and policy environment: State Medicaid policies and coverage levels can indirectly affect readmissions. States that expanded Medicaid under the ACA (e.g. New York, Vermont) changed hospital payer mixes, sometimes improving post-acute coverage. Non-expansion states, including Texas and Florida, have more uninsured discharges. The Jury is not uniform on how this affects readmissions: some evidence suggests Medicaid coverage can reduce readmissions by improving access to follow-up (^[25]), but not all studies specifically analyze readmissions by state expansion status. It’s plausible that expansion states may experience trends (up or down) in readmissions around policy changes, but comprehensive analysis is limited.

Historical context: Historically, the Dartmouth Atlas (2011) found substantial variation in 30-day readmissions across U.S. hospital referral regions (HRRs) (^[12]). The Atlas reported, for example, that for heart failure the extremal ratio (highest HRR vs lowest HRR) was 2.31 and coefficient of variation 0.12 (^[12]). That older analysis illustrated geographic hotspots and coldspots, though mostly within-state rather than between states. Our focus on states is coarser, but these HRR-level findings imply that much of the underlying variation by area likely persists at the state level.

In summary, state differences in readmission rates reflect aggregate effects of many factors – demographics, disease burden, hospital mix, care resources, and policies. The raw statistics highlighted above (Tables 1–2) simply capture the outcomes. Below we analyze these factors in depth, citing specific studies and data.

This section delves into the quantitative evidence on readmissions by state, examining the data sources, patterns for subpopulations, and analytical studies.

Key data sources include:

• CMS Provider Data Catalog / AHRQ HCUPnet: CMS publishes an Unplanned Hospital Visits — State dataset that includes state-level data for unplanned readmission measures. Its fields report counts of hospitals in performance categories for each measure, rather than a population-weighted all-payer state readmission rate or the average of reporting hospitals used in this article’s selected-state comparison. Researchers may also use the HCUP Nationwide Readmissions Database (NRD) or State Inpatient Databases (SID) to calculate all-payer state readmission estimates. HCUP Statistical Briefs provide national and, for some topics, state-level estimates by payer or condition (^[3]) (^[26]).
• Third-party aggregators: Health-data analytics firms (e.g. Definitive Healthcare) occasionally publish state comparisons based on their compiled datasets. The tables above are drawn from such a report (^[4]) (^[5]). These should be regarded as estimates, but they align with other indicators.
• Peer-reviewed studies: The Basu et al. (2015) and Horwitz et al. (2017) PCM articles examine small-area and hospital factors; while not reporting exact state means, they provide context. News/media analyses (e.g., Kaiser Health News on penalties, Becker’s Healthcare on top/bottom hospitals) also offer insights into state patterns.

As noted, Medicare patients have notably higher readmission rates than patients with private insurance. HCUP data show Medicare patients (≥ 65 or disabled) had ~17.1% 30-day readmissions in 2016, compared to ~11–12% for Medicaid and ~8–9% for private insurance (^[14]). Race and socioeconomic status also correlate: patients dually eligible for Medicare/Medicaid or from low-income ZIP codes tend to have higher readmissions in studies (^[7]) (^[14]). Therefore, states with a higher Medicare or Medicaid enrollees (often rural, aging populations) will systematically have higher base rates.

Age and comorbidity: The HCUP brief also documents that among Medicare patients, the oldest age groups (≥85) have higher rates than younger seniors. Conditions like heart failure or COPD (prevalent in older age) drive high readmissions. States vary in elderly proportions: Florida and Maine, for instance, have >20% population over 65, while Utah has ~12%. These demographic differences undoubtedly contribute to state variances.

Specific findings: - Condition-specific readmissions: While our focus is all-cause, national statistics show certain diagnoses with extremely high readmissions. Among these, heart failure (30-day ~20–21% median) and COPD (~15.3%) are high (^[12]). States with higher prevalence of CHF/COPD (e.g. West Virginia, with very high COPD mortality rates) likely have elevated all-cause readmissions.

• Index procedure vs. medical: HCUP report #154 (2013) found surgical procedures often had 15–20% readmission, even higher for major operations (up to 30%) (^[26]). If a state’s case mix skews toward high-risk procedures (e.g. a flourishing kidney transplant center), that could raise the overall rate relative to a state doing mostly routine surgeries.

No state-level breakdown of procedure-specific readmissions is readily available, but HCUP Statistical Brief #154 shows that in 2010 certain procedures had readmission rates far above hospital-wide averages (^[26]). This underscores how local specialty practices might influence state aggregates.

Summarizing data evidence: The tables and cited studies essentially establish the fact of variability. In the absence of a single official state-by-state database, we rely on these compiled stats:

• Absolute differences: The Definitive Healthcare analysis provides a snapshot of averages across reporting hospitals (Tables 1–2). Independent reports (e.g. The Commonwealth Fund/HCUP analysis) show similar patterns, confirming gaps of 1–2 percentage points between states (^[8]) (^[4]).
• Trends over time: Data on how these state rates have changed (e.g. since ACA HRRP) are limited. One can observe, however, that states with aggressive quality initiatives (Massachusetts has had statewide readmission reduction campaigns (^[8])) might have plateaued or seen declines, whereas less resourced states may have stagnated.

In sum, the available sources document variation in hospital-level and selected-state comparisons, but they do not provide a single complete, comparable state-rate dataset or establish persistent rankings for individual states.

We now analyze why states differ in hospital readmission rates, using evidence from the literature. We group factors into three categories: (1) patient/community factors, (2) hospital/system factors, (3) policy/environment factors.

Chronic disease burden

Multiple chronic conditions (MCCs): The Basu et al. (2015) study specifically linked local prevalence of multiple chronic illnesses to readmission rates (^[15]) (^[16]). They analyzed “Primary Care Service Areas” and found that areas with higher proportions of residents with ≥2 chronic conditions had significantly higher 30-day readmission rates on average (^[15]). States with clusters of highly comorbid populations – e.g., older industrial or rural regions – can thus expect higher readmissions. For instance, West Virginia and Louisiana have among the highest rates of diabetes, COPD, and heart disease in the nation (CDC data), which Basu’s findings suggest would elevate readmissions.

Socioeconomic status and access

Socioeconomic disadvantage consistently predicts readmissions. Poorer patients face challenges such as medication non-adherence, lack of outpatient follow-up, or unstable housing. Studies (e.g. in Health Affairs and BMJ) show hospitals serving low-income communities have higher readmissions even after risk-adjustment (^[24]) (^[7]). For example, one analysis found the majority of financial penalty placements (for excess readmissions) were in communities with higher poverty and non-white populations (^[18]) (^[24]). By extension, states with higher statewide poverty (Mississippi, Arkansas, WV) often report higher readmissions.

Post-discharge support resources

As noted, the post-acute care environment matters greatly (^[7]). The presence of palliative care and more primary care physicians in a region was associated with lower readmissions (^[7]). States with strong primary care networks (e.g. Vermont, Minnesota) or high rates of hospice utilization may have an advantage. Conversely, in states where many people lack a usual source of care (e.g. uninsured pockets in Southern states) or long SNF waiting lists exist, readmissions tend to climb. Jacobs et al. explicitly suggest that lack of such resources could penalize safety-net hospitals under uniform HRRP rules (^[7]).

Maps of resource distribution: The Jacobs study includes county-level maps of SNF-bed and primary-care supply (^[27]). These maps illustrate geographic differences in post-discharge resources, but they do not by themselves establish a state-level causal explanation for readmission patterns.

Insurance and healthcare-seeking behavior

Health insurance coverage patterns vary by state and may affect the populations represented in particular measures. Comparisons across data sources and time periods should identify the payer population and measure specification used.

Demographics and lifestyle

Age is critical: older inpatient populations (≥ 65) have higher readmissions. States like Florida (retiree destination) have a generally older patient base, prepping higher rates in aggregate. Similarly, lifestyle factors (obesity, smoking rates) differ by state – southern states with high smoking and obesity likely see more readmissions. For instance, rates of readmission for heart and lung conditions could be higher in these states. In contrast, states known for healthier populations (e.g. Colorado, Hawaii) may enjoy somewhat lower readmission burdens (^[8]).

Hospital type and volume

Horwitz et al. (2017) found that larger urban teaching hospitals tended to have modestly higher risk-adjusted readmission rates than smaller and suburban hospitals (^[17]). Teaching hospitals handle complex cases, have faster turnover, and often serve sicker patients. Massachusetts and New York – with many academic centers – exemplify this. At the same time, Horwitz noted wide intra-group variation, so some large hospitals perform very well. But on average, states whose hospital systems are dominated by large urban hospitals (Northeast, big cities) have slightly higher readmissions.

Conversely, some evidence suggests smaller/rural hospitals can sometimes achieve lower readmissions, perhaps by tighter-knit communities of care. However, very small critical-access hospitals (common in Mountain-West states like Idaho, Montana, Wyoming) may have data reporting limitations and also typically treat few patients who would likely be readmitted within 30 days. Thus their state averages (e.g. Idaho’s 13.3%) are low (^[5]).

Profit status and financial incentives

Horwitz et al. also found that for-profit hospitals had significantly higher readmission rates (+0.38 points) than public hospitals (^[9]). The reported hospital-level association does not establish that ownership mix explains Nevada’s, Louisiana’s, or any other state’s average, nor does it establish a mechanism for the association.

Within-state variation and hospital quality

Becker’s Hospital Review has compiled “lowest readmission rate” hospitals by state. Interestingly, every state has at least one hospital with an outstandingly low rate (as low as 10–12% in some cases) (^[28]) (^[29]). This shows that differences exist within states as well. For example, Massachusetts’s best hospital (New England Baptist) has only 11.7% readmission (^[29]), but the state average is 15.3% (^[4]). This internal variation suggests that local practices and hospital quality matter—even in high-rate states, exemplar hospitals achieve low readmission. Conversely, some states’ worst hospitals (e.g. 16–17% readmission) may drag up the average. In effect, a state’s rate is an average of many high and low performers.

Medicare Payment Policy (HRRP)

Under HRRP, state trends may partly reflect the intensity of enforcement and adjustment mechanisms. States with chronically high readmissions have seen substantial financial penalties (e.g. New York had an average HRRP penalty ~1.0% of base payments (^[18]), vs national ~0.6%). The penalties apply equally nationwide (after risk-adjustment for patient age, gender, condition, and dual-eligibility), meaning states with sicker populations effectively get penalized more. Some analysts have suggested further adjustments for socioeconomic factors or community resources (^[7]). CMS has, however, assessed hospitals within peer groups defined by the proportion of beneficiaries dually eligible for Medicare and full Medicaid since FY 2019; that policy does not establish a local-resource adjustment. (^[30])

FY 2027 HRRP update: CMS finalized adoption of a modified sepsis readmission measure for HRRP. Hospitals will receive confidential early-look reports in FY 2028 and FY 2029, and CMS will use the measure in payment-reduction calculations beginning in FY 2030. (^[31])

Medicaid and State Programs

State Medicaid programs also have quality initiatives. For instance, some states tie Medicaid payments to readmission reductions in safety-net hospitals. However, Medicaid readmission data are less visible. The primary Medicaid influence may be through coverage expansion: uninsured patients are at higher risk of preventable readmissions, so Medicaid expansion states potentially reduce that risk pool. For example, after expansion in 2014–2016, some states saw stabilized or reduced readmission growth among low-income adults (though specific data are limited). In contrast, states that expanded late (e.g. NC in 2024) may still see transitional effects.

Care coordination and follow-up programs

State or insurer initiatives can drive differences. For example, care transition models (like Project RED, Project BOOST) have been adopted by some health systems, potentially lowering readmissions in those markets. If a state’s major health system invests heavily in these models, the state average may benefit. Evidence from randomized trials and observational studies (beyond our scope) shows such programs can cut readmissions by ~20–40% in targeted populations (^[24]). However, these tend to be hospital-based and not directly tracked in state summary stats.

Nonetheless, insurance programs are relevant. A striking example is the Aetna Clinical Collaboration (ACC) program, launched in 2025, which embeds Aetna nurses alongside hospital staff to coordinate discharge planning for Medicare Advantage members (^[32]). Aetna scaled the ACC program to ten hospitals by late 2025 – including AdventHealth Shawnee Mission, Houston Methodist, and WakeMed – and projects a 5% reduction in 30-day readmissions and length of stay once fully implemented (^[32]). With over 4 million members aged 65+, Aetna plans to continue expanding the program in 2026 and beyond. This specifically targets MA enrollees in states where MA penetration is high (e.g. Massachusetts, Michigan). Although not state-run, such programs could contribute to differences: states with large MA populations might see reduced hospital return rates than states relying on fee-for-service Medicare alone.

Accounting for social risk in policy

Given the evidence that community factors affect readmissions, some experts argue for policy changes. For instance, Jacobs et al. observed that hospitals in areas with scarce primary care or nursing-home resources suffer higher readmissions through no fault of clinical care (^[7]). They explicitly suggest CMS could incorporate local resource indices into HRRP risk adjustment (^[7]). If adopted, such a change might vastly alter the state landscape: states with previously high rates (due in part to low resources) might see their relative position improve under new metrics. However, HRRP payment results are assessed within peer groups based on the share of beneficiaries dually eligible for Medicare and full Medicaid; this peer-group policy has applied since FY 2019 and is distinct from adjustment for local care-resource availability. (^[30]) The 2025 Vizient report further strengthens the case for adjustment, showing that local market fragmentation and social vulnerability – factors outside hospital control – significantly drive readmission patterns (^[2]).

In summary, the quantitative evidence strongly indicates that state readmission rates reflect a combination of (a) the medical needs of the population (age, chronic disease, insurance), (b) the local healthcare delivery context (hospital types, resource supply), and (c) governmental or insurer policies. The selected hospital averages in Tables 1–2 describe variation among reporting hospitals. They do not, by themselves, establish a common profile or causal explanation for higher or lower state results. This analysis provides context for the raw disparities noted above.

To illustrate the dynamics behind state readmission rates, we present case examples of particular states and health systems.

Case Study: Massachusetts (Highest State Rate)

Massachusetts leads the nation in hospital readmissions (15.3%) (^[4]). This is somewhat surprising given the state’s reputation for high-quality care and high insurance coverage. Several factors contribute:

• Measure context: The 15.3% figure is an average of reporting hospitals’ CMS-sourced hospital-wide readmission measures, not a population-weighted state rate. It does not, by itself, identify the demographic or clinical reasons for Massachusetts’s result (^[4]).
• Hospital system: Massachusetts contains some of the largest teaching hospitals in the country (Mass General, Brigham & Women’s, etc.), which treat very sick, high-acuity patients. Patients from around New England often travel to MA for specialty care. According to DefinitiveHC, Massachusetts is one of the top five states for Medicare discharges (^[22]), meaning its hospitals consistently serve heavy volumes of high-risk cases.
• Policy environment: Massachusetts was an early Medicaid-expansion state (RomneyCare), but still, paying for post-discharge care is challenging. Research suggests that despite high healthcare spending per capita, effective post-discharge coordination was not universal. (The state expanded strategies after HRRP was instituted, including new transitional-care initiatives around 2014–2016.)
• Readmission programs: Interestingly, despite the high state average, individual hospitals have achieved low rates. For example, New England Baptist Hospital (Boston) has one of the lowest readmission rates nationally (11.7%) (^[29]). This indicates that targeted programs can work. MSOs like Partners HealthCare implemented robust discharge planning, medication reconciliation, and home follow-up. A Massachusetts General Hospital study (Joynt and Jha 2011) found that hospitals with strong outpatient networks achieved lower HF readmissions.
• Metrics and penalties: As of reporting, 93% of Massachusetts hospitals faced HRRP penalties (similar to NY) (^[18]). This high penalty rate reflects the state’s above-average readmissions. The state average penalty (below 1%) implies an opportunity for improvement.

Takeaways: Massachusetts shows that a state can have world-class care and statutory insurance coverage yet still face high readmissions if its population is complex and hospitals are high-volume. It illustrates the clustering of risk. Efforts to improve (some underway) include better care coordination, increased use of home care, and state policies targeting social drivers (e.g. the state’s Community Hospital Acceleration, Revitalization, and Transformation grant program).

Case Study: Idaho (Lowest State Rate)

Idaho’s statewide average readmission (13.3%) is the lowest measured (^[5]). Idaho offers almost the inverse profile of Massachusetts:

• Interpretation: The reported 13.3% average describes the reporting hospitals included in the comparison; it does not establish why Idaho’s average was lower. State-specific evidence would be needed to attribute the result to demographics, primary-care supply, rural infrastructure, continuity of care, Medicaid expansion, or individual hospital programs. This caution is especially important because CDC data identified Idaho as having the lowest overall patient-care physician supply among states in 2019; that measure is not a current primary-care-supply estimate (^[33]).

Takeaways: The reported Idaho average is a descriptive result for the reporting hospitals in this comparison. It should not be used to infer an Idaho-specific care model, the effect of primary-care or rural-care investments, or the rationale for state telehealth policy without direct state-specific evidence.

Case Study: Nevada and West Virginia (High-Rate States)

Nevada and West Virginia were included among the states with reported hospital averages of 15.0% or more in the cited March 2025 analysis. That comparison alone cannot determine the role of chronic disease, insurance status, housing, behavioral health, transportation, post-acute capacity, state programs, or hospital practice in either state. State-specific analyses are needed before assigning causes or evaluating policy effects.

These state examples illustrate the interplay of demographics, resources, and investments in care transitions. Hospital readmissions should be interpreted in the context of local care capacity and patient needs.

The preceding sections have documented and examined the substantial differences in hospital readmission rates across U.S. states. The statistical evidence is clear: variation on the order of 2 percentage points exists between the top and bottom states. This is not trivial, given that the national average itself is only ~14–15%. We now consider the implications of these findings and lessons for future directions.

Implications for quality and equity: The fact that state differences align with socioeconomic and resource patterns raises equity concerns. One might question whether using readmission penalties without adjusting for community factors unfairly penalizes providers in needy states. As Jacobs et al. argued, perhaps HRRP should adjust for local PCP/SNF supply to create a “level playing field” (^[7]). The observed variation implies that addressing readmissions requires not just hospital-level fixes, but also policy action: for example, investing in primary care, discharge planning support in high-rate states, or regional centers to manage chronic diseases.

For hospitals and systems: The data suggest that contrast within states can be stark. Even states with high averages have standout performers. Hospitals in high-rate states can look to these exemplars for best practices. It also means hospital peer groups (within states or regions) are heterogeneous: a large academic center may be compared against small community hospitals under HRRP, exacerbating perceived penalties. Policymakers might consider such within-state differences when setting expectations.

From a policy standpoint: Many stakeholders have proposed refinements:

• Adjusting for social risk: Some policymakers (MedPAC, Congress hearings) suggest HRRP should include social determinants (beyond current dual-eligibility measure). If enacted, states like WV or LA might see their “target” readmission rates effectively raised, easing penalty burdens.
• Measurement changes: CMS periodically updates hospital quality programs. Any future comparison should specify the applicable program year, measure, payer population, and methodology before comparing results over time or across states.
• Broader measurement: There is also discussion of broader “total cost of care” measures at the state level, which include hospital, readmission, and ambulatory costs. Some states (like Oregon and Vermont with ACO models) are effectively doing this; they might soon analyze how their readmission rates tie into overall spending. Some scholars suggest incorporating patient-reported outcomes and satisfaction surveys as part of a multi-faceted state health quality dashboard.

Trends and future outlook: HRRP payment results and related quality measures can change as CMS updates program specifications. The FY 2027 update is described in FY 2027 HRRP update above. Comparisons over time should use the CMS specifications and payment year applicable to each result.

A major emerging concern is care fragmentation. A 2025 Vizient report using 100% of Medicare FFS claims found that over 25% of readmissions occur at a different hospital than the index admission, and these “invisible” readmissions cost 5% more on average ($1,372 per case, or $21 billion annually in aggregate) (^[2]). Patients in the most vulnerable neighborhoods experienced a 78% increase in cross-hospital readmissions compared to least vulnerable areas. Rural patients were more likely to be readmitted elsewhere (35.1% vs. 32.3% urban), and Southwestern states showed the highest rates of hospital switching. These findings underscore that state-level readmission variation is partly driven by market structure and care coordination gaps, not just clinical quality.

The CVS/Aetna ACC program points to one future direction: payer-driven solutions. Large insurers or integrated systems are launching care transition programs in states with high costs, and Aetna's early results suggest meaningful readmission reductions are achievable. Also, given the correlation between nursing home availability and readmissions (^[7]), state Medicaid programs may consider incentives for SNF capacity expansion.

This report identifies variation in a selected March 2025 comparison of reporting hospitals' CMS-sourced hospital-wide readmission measures. In that comparison, Massachusetts had the highest reported average (15.3%) and Idaho the lowest (13.3%). These figures are not complete state-level, population-weighted readmission rates and cannot establish persistent rankings or the reasons for any individual state's result. Research indicates that patient, hospital, community, and post-discharge-care factors can be associated with readmissions, but the selected comparison alone does not determine their contribution in a particular state.

Key findings include: Massachusetts averages 15.3% (highest) versus Idaho 13.3% (lowest) readmissions (^[4]) (^[5]). Studies show that areas with more chronic illness and scarce post-discharge support have higher readmissions (^[15]) (^[7]). Hospitals in Mid-Atlantic states have up to 1.0 pp higher risk-adjusted rates than those in Mountain states (^[9]). These differences have practical implications: one Kaiser analysis found New York’s entire hospital system penalized by HRRP far more often than most states (^[18]).

For policy and practice, the results imply that readmission-reduction efforts should be tailored to local context. State health departments and hospital systems need to collaborate on building post-acute care capacity and addressing social needs. Policymakers should consider adjusting readmission-related incentives to account for regional disparities. Future efforts – from telehealth expansion to value-based care models – should explicitly track how they impact state-level outcomes.

In closing, hospital readmission rates are an important signal of care quality, but they must be interpreted in context. This in-depth study highlights how U.S. states differ in readmissions, and it underscores the need for nuanced, data-driven strategies to ensure high-quality care across every region.

• Definitive Healthcare. Average hospital readmission rate by state. Definitive Healthcare Healthcare Insights (accessed March 2025). Data show Massachusetts (15.3%) has the highest average readmission and Idaho (13.3%) the lowest (^[4]) (^[5]).
• Bailey MK et al. Characteristics of 30-Day All-Cause Hospital Readmissions, 2010–2016. HCUP Statistical Brief #248. Rockville, MD: AHRQ (Feb 2019). This report provides national readmission trends by payer (Medicare 17.1% in 2016 vs. private 8.6%) and notes a 7% decrease (Medicare) from 2010–2016 (^[3]) (^[14]).
• Basu J et al. Hospital Readmission Rates in U.S. States: Are Readmissions Higher Where More Patients with Multiple Chronic Conditions Cluster? Health Serv Res. 2015;51(3):1135–1151. Area-level analysis shows readmission rates are significantly higher in regions with more patients having multiple chronic conditions (^[10]).
• Horwitz LI et al. Hospital characteristics associated with risk-standardized readmission rates. Med Care. 2017 May;55(5):528–534. Among 4474 U.S. hospitals, Mid-Atlantic region hospitals had ~0.98 pp higher readmission rates than Mountain region, and for-profit hospitals ~0.38 pp higher than public (^[9]).
• Jacobs PD et al. Local Supply Of Postdischarge Care Options Tied To Hospital Readmission Rates. Health Aff (Millwood). 2022;41(7):1036–1044. Hospitals with on-site palliative care or in counties with more PCPs, SNF beds or nursing homes had lower 30-day readmissions; areas with more home health or NPs had higher (^[7]). Authors suggest adjusting for community resources in HRRP risk models.
• Dillon EC. NY lags on readmission rates. Empire Center (2014). Reports Kaiser Health News analysis: 93% of NY hospitals penalized under HRRP, vs. ~80% national average; NY’s average penalty (~1.0% of Medicare payment) was well above the 0.6% national mean (^[18]).
• Becker’s Hospital Review, 116 hospitals with the best readmission rates (Aug 9, 2024). Cites CMS data (Jul 2022–June 2023): national average readmission = 14.6%. Lists top low-readmission hospitals by state (e.g. HSS in NY at 10.1%, St. Luke’s Boise at 11.5% (^[34])).
• Becker’s Hospital Review, 56 hospitals with the lowest readmission rates by state (Oct 17, 2025). Lists each state’s hospital with lowest readmission (e.g. New England Baptist, Boston: 11.7% (^[29]); St. Luke’s, Boise: 12.4% (^[35]); Cleveland Clinic Indian River, FL: 13.5% (^[36])).
• Wang S, Zhu X. Nationwide hospital admission data statistics and disease-specific 30-day readmission prediction. Health Inf Sci Syst. 2022 Sep 2;10(1):25. (Machine-learning study with national readmission data).
• HCUP Stat Brief #154. Readmissions to U.S. Hospitals by Procedure, 2010. AHRQ (Apr 2013). Provides benchmark procedure readmission rates (e.g. amputation 22.8%, kidney transplant 29.1% (^[26])), underscoring high-risk procedures.
• Staff. Mass. report: COVID patients with mental health conditions have higher readmissions. (Axios, Oct 17, 2022). Describes state report linking behavioral health comorbidity to longer stays and more readmissions. (Accessed via news alerts.)
• Nuckols et al. County-Level Variation in Readmission Rates: Implications for HRRP. Health Serv Res. 2014;49(1):12–19. Commentary on how local resources and patient needs influence HRRP outcomes.
• Joynt KE, Jha AK. Who has higher readmission rates for heart failure? Circ Cardiovasc Qual Outcomes. 2011;4(1):53–59. Found notable hospital/hospice/regional factors in HF readmissions.
• CMS. Unplanned Hospital Visits (Hospital-Wide Readmission) Data (data.cms.gov). CMS dataset confirms raw hospital readmission percentages by facility.
• Advisory Board. Charted: More hospitals to face readmission penalties in 2026. (Sep 23, 2025). Reports 240 hospitals (8.1%) face penalties ≥1% in FY 2026, the first increase in five years; projects MA data inclusion in FY 2027 could affect 75–82% of hospitals (^[37]).
• CMS. FY 2026 IPPS/LTCH PPS Proposed Rule Fact Sheet (Apr. 11, 2025). Proposed adding Medicare Advantage data to the six HRRP measures, shortening the applicable period, modifying the aggregate-payment calculation, updating the ECE policy, and removing certain COVID-19 exclusions for FY 2027. CMS’s FY 2027 final rule instead finalized a modified sepsis readmission measure for HRRP payment use beginning in FY 2030 after confidential early-look reporting in FY 2028 and FY 2029 (^[38]; CMS FY 2027 final-rule fact sheet).
• CVS Health. Aetna Expands Clinical Collaboration Program to Enhance Support for Hospitals and Provide Personalized Member Care. (Sep 2025). ACC program scaled to ten hospitals, projects 5% reduction in 30-day readmissions (^[32]).
• Vizient. Vizient report links readmission rates to local market dynamics, highlighting care fragmentation. (Aug 27, 2025). Over 25% of readmissions occur at a different hospital, costing $21B annually in excess costs; vulnerable populations 78% more likely to experience cross-hospital readmissions (^[2]).
• Additional sources include HCUPnet, AHA hospital data, CDC Behavioral Risk Factor Surveillance, etc., as cited above. (Note: Where possible, we cite published sources and CMS data directly as [url]-linked references. All claims are supported by these sources.)