Using Data Science to Tackle Hospital Readmissions Head On

By Wes Little

Across the United States, hospital readmissions are a serious problem. Readmission: When a patient is readmitted to a hospital within 30 days of being discharged.

In 2013, for example,

home health care patients were readmitted to the hospital at a cost of $8.3 billion. 

750,000

While the economic impact of these readmissions is

staggering, it’s not the only one.  

Events like these also takes a

huge toll on the millions of

Americans who either find

themselves back in the hospital or

caring for someone who is.

Our Industry

’s Challeng

e

of Medicare hospital readmissions are potentially preventable. *MedPAC

*Source: MedPAC http://www.medpac.gov/documents/reports/Jun07_Ch05.pdf

76%

That means that 570,000 home

health care patients went back into the hospital in 2013 when it could

have been avoided.

That’s $6.3 billion in wasted taxpayer

dollars.

So if most hospital readmissions can be prevented, how do we make sure that they are?

The key is to know the

warning signs so that you can

give extra attention and

care to those at highest risk.

And that’s where data science can play a role. But to get meaningful results,you need a lot of data to work with.

At Kinnser Software, we’ve got that in spades. In fact, we have a bigger dataset than the three largest US home health care agencies combined.

More than 3,000 home

health providers

Over 10 years of

data collection

Data on more than

3.5 million patient episodes

Using that data, we’ve identified specific characteristics that are common among the patients with the highest rates of hospital readmission.

And we’ve incorporated those insights into a

new predictive model that medical

professionals can use to identify at-risk patients.

While these characteristics aren’t necessarily the cause of readmission, there is a strong correlation that can be used to predict hospitalization based on what we’ve observed in our data.

Those patient characteristics include things like:

Shortness of breathIncontinenceOxygen usageHistory of congestive heart failureNeeding help with injected medicationDecreased appetiteForgetfulness

And while many of these characteristics are already widely known to be predictors of increased hospitalization risk...

… For clinical managers, staying on top of those variables across an average of 50 patients can be a major challenge.

In fact, staying up to date on that many patients, and knowing which ones are at the highest risk, can be overwhelming.

It means combing through all of the documentation that was entered into the EHR system the night before patient by patient.

In the process, clinical managers look for notes that will alert them to changes in the status of each patient.

It’s a manual and time-

consuming process. As a result, most

clinical managers rely on what their

nurses tell them to direct their

efforts.

But that’s not a reliable solution and it can lead to errors.

The good news is

that there’s a

better way.

RiskPointTM is a new tool that will make clinical managers’ lives a lot

easier.

It systemizes and profiles risk so that clinical managers don’t have to rely on their gut or anecdotal information to do their job.

Instead, RiskPoint pulls all of the relevant information from your

EHR system in one easy-to-read, prioritized page.

What that means is that if you’re a clinical manager, it will quickly direct your attention

to those patients who need it most.

And it works. Community Home Health in Oklahoma saw hospital readmissions reduced by 29 percent in the first two months of use compared to the same two-month period the year before.

Want to learn more?

Visit:www.kinnser.com/riskpoint