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## Hospital Readmissions Calculation and Reduction

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- Written by Living Wisely
- Category: Readmissions
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**Hospital readmissions** 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. Hospitals constantly seek to reduce their rate of readmissions for more reason than one. For one, Medicare penalizes hospitals when they don’t meet readmission benchmarks. Although hospitals reduced avoidable readmissions for Medicare patients by about 100,000 in 2015, and by a total of 565,000 since 2010, there’s still much room for improvement. The federal government has estimated the annual cost of Medicare readmissions to be $26 billion per year with $17 billion of that considered avoidable.

Lots of hospitals faced financial problems and they were first addressed this financial problem back in 2009 by publicly reporting hospital readmission rates on the Hospital Compare website. The goal of the public reporting of readmission metrics was to increase the transparency of hospital care, help consumers choose a care venue, and provide a benchmark for hospitals in their quality improvement efforts and overall improve patient satisfaction. In 2012, CMS launched the Hospital Readmissions Reduction Program (HRRP), which began to penalize hospitals with high rates of readmissions for acute myocardial infarction, heart failure, and pneumonia.

In 2013, the Medicare payment reduction (penalty) was one percent of the base rate. This increased to two percent in 2014 and was capped at three percent going forward from 2015. Also in 2015, chronic obstructive pulmonary disease (COPD) and total hip and knee arthroscopy were added to the program. As hospitals seek to reduce their readmission rates, some hospitals have hired specialists to focus in calculating their rate of readmission so they can track it appropriately and seek to reduce it. These are the standard formulas for this:

**Formulas to Calculate the Readmission Rate**

*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).)*

**Reducing Readmission Using predictive Analysis**

Predictive analysis is an advanced branch of analytics that uses data to make predictions about future events. Predictive analysis analyses data using techniques such as data mining, statistics, modeling, machine learning and artificial intelligence. Predictive analytics also automates complex decisions and trade-off to make recommendations based on predictions and then proactively make changes. Hence, predictive analysis can be applied to reduce the rate of readmission.

However, care must be taken because technology driven and more generalized prediction model that inputs big data and global features is that the target use or utility is often lost in translation. Also hospitals practitioners should not confuse data with insight. The prediction that is focused on a specific clinical setting or patient need will always trump a generic predictor in terms of accuracy and utility. This principle is particularly important because of how highly humans regard new technology.