Assessing Hospital Readmission: Calculating Repeat Admissions

Hospital readmission rates are a key indicator of the quality of care provided by a hospital. A readmission refers to a patient who has been admitted to a hospital within 30 days of being discharged, usually following heart attacks, heart failure, or pneumonia. There are various methods to calculate readmission rates, and these rates are used to compare hospital performance and incentivize hospitals to improve patient care and communication. Notably, the Hospital Readmissions Reduction Program (HRRP) is a Medicare value-based program that encourages hospitals to enhance care coordination and patient engagement to reduce avoidable readmissions. The calculation of readmission rates involves factors such as patient demographics, hospital characteristics, and the specific condition for which patients were initially treated. These calculations help identify areas for improvement and ensure that patients receive continuous and effective care.

Calculating the 30-day readmission rate

To calculate the 30-day readmission rate, also known as the 30-Day All-Cause Rehospitalization Rate, several steps must be taken. This rate is used to compute each hospital's 30-day risk-standardized readmission rate (RSRR).

First, the predicted 30-day readmission for a hospital is obtained from a hierarchical regression model. This predicted readmission is an estimate of the number of readmissions that would occur within 30 days of discharge for a particular hospital, given factors such as patient case mix and the hospital's quality of care.

Next, this predicted readmission value is divided by the expected readmission for that hospital, which is also derived from the regression model. The expected readmission represents the anticipated number of readmissions if the same patients were treated at an "average" hospital with average care.

The result of this division is then multiplied by the national unadjusted readmission rate for the specific condition across all hospitals. This step helps to adjust for the hospital's unique case mix and standardize the rates for comparison.

For example, consider a scenario where the model predicts that out of 10 heart attack patients admitted to Hospital A, 3 are expected to be readmitted within 30 days due to factors like age, gender, and pre-existing conditions. If these same patients were treated at an average hospital, the expected number of readmissions might be higher or lower, depending on the quality of care. By adjusting for the hospital's case mix and comparing it to the national average, we can calculate an adjusted readmission rate that allows for a more standardized comparison between hospitals.

Additionally, adjustments are made for small hospitals or those with a small number of cases. In these situations, the predicted readmission rate is influenced by the overall national readmission rate and data from all hospitals to ensure more reliable results and prevent extreme fluctuations in performance measurements.

Adjusting for small hospitals

When calculating hospital readmission rates, it is important to consider the specific context and characteristics of the hospital in question, especially for small hospitals. Small hospitals may have unique challenges and considerations that larger hospitals may not face.

One key adjustment for small hospitals is accounting for low patient volume. Small hospitals typically serve smaller populations, which can result in fewer readmissions overall. This low volume can make it difficult to detect meaningful patterns and draw statistically significant conclusions. To address this, consider using risk-adjustment models specifically designed for low-volume hospitals. These models account for the unique characteristics of small hospitals and can provide more accurate predictions and comparisons.

Additionally, small hospitals may have a higher proportion of rural or elderly patients, which can impact readmission rates. Rural patients may have distinct healthcare-seeking behaviours and face distinct challenges in accessing healthcare services, potentially influencing their readmission patterns. Likewise, elderly patients often present complex health needs and higher readmission risks. Adjusting for these demographic factors is crucial when calculating and interpreting readmission rates specific to small hospitals.

Another important consideration is the range of services offered by small hospitals. They may differ from larger hospitals in terms of specialties or service lines, which can impact the types and volumes of readmissions. For meaningful comparisons, grouping or stratifying hospitals based on their service offerings is essential. This allows for comparisons between hospitals with similar profiles and helps identify readmission patterns associated with specific service lines.

Small hospitals may also experience fluctuations in readmission rates due to seasonal changes or temporary shifts in patient demographics. For instance, a small hospital in a tourist destination might witness an increase in patients during peak travel seasons, potentially affecting the annual readmission rate. It is important to be mindful of these temporal variations and make adjustments as necessary when interpreting data.

In summary, when calculating and interpreting hospital readmission rates, particularly for small hospitals, it is imperative to consider adjustments for low patient volume, unique demographic factors, variations in service lines, and temporal fluctuations. By implementing these adjustments, healthcare administrators, policymakers, and clinicians can gain a more accurate understanding of readmission patterns specific to small hospitals and, consequently, develop effective strategies to enhance patient care and outcomes within these unique settings.

Using the Hospital Readmissions Reduction Program (HRRP)

The Hospital Readmissions Reduction Program (HRRP) is a Medicare value-based purchasing program that encourages hospitals to improve patient care and post-discharge planning, thereby reducing avoidable readmissions. The HRRP was established by the Affordable Care Act (ACA) in 2012 and has been subject to ongoing policy discussions and criticism.

HRRP relies on patient-level administrative billing data to determine index admission and readmissions. CMS data captures all inpatient episodes of care for which Medicare received a bill from an acute care hospital. CMS calculates the payment reduction and component results for each hospital based on their performance across the readmission measures during the HRRP performance period. The payment adjustment factor corresponds to the percentage by which a hospital's payment is reduced and is a weighted average of the hospital's performance. Hospitals with a high number of readmissions will have a high adjusted readmission rate.

CMS sends confidential Hospital-Specific Reports (HSRs) to hospitals annually, giving them 30 days to review their HRRP data, ask questions, and request corrections. The 30-day Review and Correction period allows hospitals to review and correct their HRRP payment reduction and component result calculations, such as the Payment Adjustment Factor, Dual Proportion, and Peer Group Assignment, prior to them being used to adjust payments.

HRRP uses the excess readmission ratio (ERR) to assess hospital performance, which measures a hospital's relative performance and is the ratio of predicted-to-expected unplanned readmissions. The 30-day risk-standardized unplanned readmission measures capture unplanned readmissions that happen within 30 days of discharge from the initial admission, including readmissions to the same hospital or another applicable acute care hospital. Hospitals must have at least 25 initial hospitalizations for a diagnosis to be measured. The risk adjustment measures are based on hierarchical logistic regression models derived using Medicare claims data and are used to calculate hospital risk-standardized 30-day all-cause readmission rates.

CMS operates via two mechanisms: public reporting of hospital readmission rates and monetary penalties. Hospitals are financially penalized if they have higher than expected risk-standardized 30-day readmission rates for conditions such as heart attack, heart failure, and pneumonia. The penalty is a percentage of total Medicare payments to the hospital, and the savings are added to the Medicare Hospital Insurance Trust Fund.

Considering the number of avoidable readmissions

While calculating hospital readmission rates is important for benchmarking and quality assessment, it is also crucial to consider the number of avoidable readmissions. A systematic review of 34 studies revealed that the median proportion of preventable readmissions was 27%, ranging from 5% to 79%. An observational study of 1,000 patients readmitted within 30 days of discharge showed that around 27% of these readmissions were considered potentially avoidable. The most common preventable factors included emergency department decision-making, failure to relay crucial information to outpatient providers, premature discharge, and a lack of care discussions with patients suffering from serious illnesses.

A readmission is considered potentially preventable if there was a reasonable expectation that it could have been avoided through one or more of the following measures:

• Provision of quality care during the initial hospitalisation
• Adequate discharge planning
• Adequate post-discharge follow-up
• Improved coordination between inpatient and outpatient healthcare teams

For instance, a readmission for a urinary tract infection 10 days after a hernia repair with a perioperative Foley catheter is plausibly related to care during the initial admission. Similarly, a readmission for diabetes following an initial admission for the same condition or a closely related problem is also considered clinically related.

Healthcare institutions can significantly reduce readmissions and improve patient outcomes by addressing the root causes and implementing strategies centred on patient education, care coordination, and tackling social determinants of health. This holistic approach not only benefits individual patients but also enhances the cost-effectiveness and efficiency of the healthcare system as a whole.

Comparing hospital performance

The 30-day all-cause rehospitalization rate is a critical metric for assessing hospital performance. Each hospital's 30-day risk-standardized readmission rate (RSRR) is calculated using a hierarchical regression model. This model accounts for various factors, including patient characteristics and the hospital's quality of care. The predicted readmission rate for a hospital is compared to the expected readmission rate if the same patients were treated at an "average" hospital. Hospitals with lower-than-expected readmission rates relative to the national average are considered better performers.

For example, if Hospital A has a predicted 30-day readmission rate of 10 for heart attack patients, based on age, gender, and health conditions, and an expected rate of 15 at an average hospital, its RSRR would be 13.3%. In this case, Hospital A performed better than the national average with a lower readmission rate. However, if the expected rate was 9, Hospital A's RSRR would be higher at 13.3%, indicating worse performance.

Additionally, hospitals are compared using performance scores and readmission rates for specific conditions such as pneumonia, AMI (acute myocardial infarction), and orthopedic surgery. While the correlation between performance scores and readmission rates is generally weak, hospitals with higher adherence to recommended care processes may achieve slightly better readmission rates. The payment adjustment factor, corresponding to the percent reduction in a hospital's payment, is also used as a performance indicator, incentivizing hospitals to improve patient care and reduce readmissions.

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