If you’ve been following along with this blog series, I hope you’ve enjoyed our deep dive into healthcare analytics and how it’s poised to change with the implementation and growth of predictive analytics and machine learning. If you haven’t, I’d really encourage you to take a minute to check out the previous posts linked here.
What I’d like to do today is take a step back from the trenches of implementation, operational vs. clinical analytics, and future trends to look at the bigger picture for a moment. I’d like for us to answer probably the most important question of them all—why does all of this matter? What are the tangible benefits of predictive analytics in the healthcare space? Why are we spending the time and resources we are to dive so deep?
Let’s answer these questions. But instead of just giving you over-arching thoughts, let’s look at three real-world ways that predictive analytics are changing the healthcare space.
Patient Outcomes with FOTO
The most exciting parts of predictive analytics in the healthcare space always revolve around clinical outcomes. It doesn’t mean operational analytics aren’t worth their weight in gold, but they aren’t always the head-turners like predictive analytics solutions.
One example of the head-turning clinical analytics solutions in the real world is FOTO, an analytics company that analyzes recent and large sample sizes of past cases of therapy patient outcomes and provides the data to providers in an easy-to-digest format. The benefits? It’s a long list. Here are a few:
- Higher patient buy-in as you can show a much more realistic look at how long treatment is likely to take and how much improvement to expect based on the patient’s individual traits
- The ability to benchmark facilities and clinicians against the rest of the country using predictive models to level out the differences between providers’ patient case mixes
- Quick identification of facilities that are outperforming to find processes to share to other facilities
By being able to better predict patient outcomes, FOTO empowers the relationship between provider and patient. Once patients have a realistic view of what their treatment path may look like and when they may see results, you can expect an easier “sell” to get them committed to the process.
Here’s a link to learn more about FOTO.
Prevent Hospital Readmissions
Hospital readmission is not only one of the worst things for a patient but also one of the costliest things in the healthcare space—especially a preventable one. Predictive analytics are now being used to help providers identify patients who may be at a higher risk of readmission.
A real-world example of this is in Skilled Nursing Facilities (SNFs). By using analytics solutions that look at post-acute data, SNFs can be alerted of a patient who may be at a higher risk for readmission. This allows the providers to intervene and provide the additional care needed to try and mitigate the identified risks.
Here’s a link to learn more about how Net Health is using post-acute analytics to prevent hospital readmissions and adverse events.
Predicting Risk in Wound Care Settings
The treatment of wounds can be a challenging task, especially with the risks that can arise when things don’t go according to plan. As a provider, having as much knowledge as possible about the likelihood of adverse events is huge.
This, again, is where predictive analytics shines. Net Health has approached this with a two-part clinical analytics model. It consists of a wound trajectory indicator that predicts the likelihood a wound will heal in a specific timeframe and a risk of amputation indicator that predicts the likelihood that a wound may lead to amputation. The benefits of this information are invaluable to providers and to patients. To learn more about these clinical predictive models, here’s a link to the breakdown.
The Bottom Line
As you can see, predictive analytics bring a lot to the table in terms of benefits for the healthcare space. What’s most exciting is that a lot of these solutions don’t require unrealistic investments, even for the smallest of providers. If you’re enjoying this discussion, I’d encourage you to follow this account as we’ll continue going back into the trenches of predictive analytics and how you might be able to use them to transform your organization. And if you haven’t read my previous articles and discussions, here they are.
