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.

 

 

Data Governance: A New Tool for Population Health can Help Reduce Readmission Rates

We live at a time that is completely unprecedented in all of human history: data available at the touch of a button, communicative processes that happen in a matter of moments, medical procedures and remedies to handle many of life’s wicked twists and turns and a plethora of other things that make life easier or better. With various devices and data at our fingertips, the realization that this data can be applied to so many other things is still dawning on us. One of those is helping to reduce readmission rates. High readmission rates is a problem no healthcare organization wants to have to deal with. 

 Looking at the big picture a little more particularly, every day, it is estimated that we create 2.5 quintillion bytes of data. One sector that holds vast amounts of data which needs extensive governance is in the healthcare industry. I’m sure that we have all been through a similar sequence of appointments and referrals, such as the family doctor, x-ray technician, specialist to read an x-ray, specialist for specific care, and follow up with the family doctor. With each visit, there are new forms to fill out, but basically, the same questions asked and answered. All of our information is entered into different databases and only accessible at the individual locations. If medical data is forwarded to another doctor or specialist, it must be entered into the new recipient’s database.

Data governance is defined as the process of managing not only the security but also the quality, usability, consistency, and availability of information to doctors. There is no doubt that privacy and security of an individual’s records are of the utmost importance, but have you ever thought about the significance of how your data could be used to make your life easier and make you healthier? When an establishment is able to use your information to process payments more efficiently or let you know when it is time to make an appointment, everyone could be a beneficiary. And when they can also use the information to determine if you are at risk of readmission and then proceed to prevent it, it is a win-win for all.

 

 

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