Home Healthcare + Data Science: A Prescription For Our Nation's Readmissions Challenge
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Transcript of Home Healthcare + Data Science: A Prescription For Our Nation's Readmissions Challenge
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Using Data Science to Tackle Hospital Readmissions Head On
By Wes Little
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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.
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In 2013, for example,
home health care patients were readmitted to the hospital at a cost of $8.3 billion.
750,000
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While the economic impact of these readmissions is
staggering, it’s not the only one.
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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.
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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%
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That means that 570,000 home
health care patients went back into the hospital in 2013 when it could
have been avoided.
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That’s $6.3 billion in wasted taxpayer
dollars.
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So if most hospital readmissions can be prevented, how do we make sure that they are?
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The key is to know the
warning signs so that you can
give extra attention and
care to those at highest risk.
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And that’s where data science can play a role. But to get meaningful results,you need a lot of data to work with.
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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
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Using that data, we’ve identified specific characteristics that are common among the patients with the highest rates of hospital readmission.
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And we’ve incorporated those insights into a
new predictive model that medical
professionals can use to identify at-risk patients.
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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.
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Those patient characteristics include things like:
Shortness of breathIncontinenceOxygen usageHistory of congestive heart failureNeeding help with injected medicationDecreased appetiteForgetfulness
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And while many of these characteristics are already widely known to be predictors of increased hospitalization risk...
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… For clinical managers, staying on top of those variables across an average of 50 patients can be a major challenge.
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In fact, staying up to date on that many patients, and knowing which ones are at the highest risk, can be overwhelming.
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It means combing through all of the documentation that was entered into the EHR system the night before patient by patient.
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In the process, clinical managers look for notes that will alert them to changes in the status of each patient.
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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.
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But that’s not a reliable solution and it can lead to errors.
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The good news is
that there’s a
better way.
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RiskPointTM is a new tool that will make clinical managers’ lives a lot
easier.
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It systemizes and profiles risk so that clinical managers don’t have to rely on their gut or anecdotal information to do their job.
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Instead, RiskPoint pulls all of the relevant information from your
EHR system in one easy-to-read, prioritized page.
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What that means is that if you’re a clinical manager, it will quickly direct your attention
to those patients who need it most.
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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.