Reducing Readmissions in the Healthcare System


Hospital readmission is the situation where a patient is readmitted to a hospital after he/she has been discharged for the same reason or for a related medical issue. Readmission rates are a common benchmarked used to measure healthcare systems and used in healthcare research. Reducing readmissions is a top priority for healthcare organizations because Centers for Medicare and Medicaid Services (CMS) as part of the Patient Protection and Affordable Care Act (ACA) of 2010, penalized hospitals and healthcare organizations that have high readmission rates. So how does the Center for Medicare and Medicaid Services calculate the rate of hospital readmission? They developed a formula which shows transparency and fairness for both small and larger organizations.


Formulas to Calculate the Readmission Adjustment Factor

Excess readmission ratio = risk-adjusted predicted readmissions/risk-adjusted expected readmissions

Aggregate payments for excess readmissions = [sum of base operating DRG payments for AMI x (excess readmission ratio for AMI-1)] + [sum of base operating DRG payments for HF x (excess readmission ratio for HF-1)] + [sum of base operating DRG payments for PN x (excess readmission ratio for PN-1)] + [sum of base operating DRG payments for COPD x (excess readmission ratio for COPD-1)] + [sum of base operating payments for THA/TKA x (excess readmission ratio for THA/TKA -1)]

*Note, if a hospital’s excess readmission ratio for a condition is less than/equal to 1, then there are no aggregate payments for excess readmissions for that condition included in this calculation.

Aggregate payments for all discharges = sum of base operating DRG payments for all discharges

Ratio = 1 - (Aggregate payments for excess readmissions/ Aggregate payments for all discharges)

Readmissions Adjustment Factor = the higher of the Ratio or 0.97 (3% reduction).

(For FY 2013, the higher of the Ratio or 0.99% (1% reduction), and for FY 2014, the higher of the Ratio or 0.98% (2% reduction).)

Health Catalyst is a leading authority in providing through viable research, proven ways that hospitals and health organizations can reduce readmission rates. They’ve identified that heart failure as one of the most common sources of readmissions. Heart failure, including what was formerly referred to as congestive heart failure is an extremely serious problem in the United States. There has been a high number of patients who are suffering and dying from this disease. Also, the financial burden to treat heart disease patients is becoming an alarming public health issue. Hence they have come up with ways that healthcare analytics can be leveraged to reduce heart disease readmission rates.


Understand your current readmission rates for your heart patients

It’s a general saying that you can’t improve what you can’t measure. Same goes for readmission rates. The first step toward quality improvement is to establish readmission baselines, track performance metrics and distribute information to every unit and individual involved in the processes of reducing readmissions. There is a healthcare analytics adoption model that acts as a scale to measure this. Any score above 5 shows that the healthcare organization will be able to achieve this goal.


Establish a 30 and 90-day readmission baseline measure

This is important so that data being used is always current and fresh. Old data dwindles in relevance and makes it difficult to engage clinicians in clinical improvement initiatives.


Keeping Balance Measures in mind

Health organizations need to be aware of balance measures. Balance measures are changes designed to improve one part of the system without causing new problems in another part of the system. Some important balance measures are; patient satisfaction rates, emergency room or emergency department visits and observation stay. These measures should also be tracked while trying to reduce the rate of hospital readmissions.


Using Enterprise Data Warehouse to integrate clinical, financial and patient satisfaction.

An enterprise healthcare data warehouse identifies all patients with a primary diagnosis of heart failure and then stratifies the populations as either high or low risk for readmission rates. With this data, multidisciplinary teams examine the root cause of readmission in order to implement evidence-based prevention plans.