Take 100 patients who are candidates for colorectal screening. We know that a group of them will try to avoid the procedure or postpone it for as long as possible. Evidence of this, for example, would be rescheduling appointments multiple times to later dates.
We also know that among this 100 is another group of people who need a colonoscopy more than others. A family history of cancerous polyps is a good indicator, for example.
Find where these two groups overlap, focus more staff time on prodding those patients to at least do a Cologuard home screening as a first step – and you have a simplified example of how predictive analytics can improve population health and cut patient healthcare expenditures without adding to provider costs.
That, in essence, is what it means to deliver value for all concerned.
What are predictive analytics?
Basically, it’s the ability to look at data sets and learn where to expect problems within given patient populations you manage. Selected data can help guide where you can act and positively change outcomes. Done right, predictive analytics can keep patients healthier longer, help them avoid expensive medical care, and identify cost drivers – and ways you can better control costs.
Before predictive analytics, we tended to react after something happened. Here’s an example: a patient with hypertension and diabetes. We know that, over time, this patient will have heart disease. You could write prescriptions and watch for symptoms, but otherwise wait until the onset of heart disease before intervening.
Why do this within an information vacuum? Ask if this patient is refilling prescriptions regularly. Taking medicine at the right times, in the right dosages.
How much do you know about this patient’s lifestyle? Familial support? Financial situation? How might all this then impact the patient’s self-care to prevent heart disease?
Determining what’s appropriate
Predictive analytics can ascertain how often such circumstances can be expected and in what subpopulations within this group. They can point to other areas of concern. They can guide us to ask further, appropriate questions, reach appropriate conclusions, and provide appropriate care at the appropriate time at the appropriate facility.
The potential is great. We can make patient population management more effective with the least amount of adverse effects going forward.
Practices adopting this approach can lead the way in bending the cost of healthcare downward. This creates a marketplace and opportunity to separate from competitors.
Already, we have some evidence of success. Take the colonoscopy example. For our Medicare population, we have increased the percentage of those up to date on their colorectal screenings to 73 percent, up from 65 percent just a few years ago.
This was accomplished without having to add more staff to our outreach teams. We used improved technology to uncover which patients require extra attention to get them to act and integrated it with existing resources.
Meanwhile, data collection and analysis continue to be driving forces in the tech industry. Improvements in predictive analysis will be coming – and at a faster pace. Already, there are approaches available that utilize artificial intelligence and machine learning. In many ways, all this is mindboggling.
We are not there yet as far as selecting the latest high-tech tools, but the prospects are exciting to contemplate. New and improved predictive analytics are in our future.
Provider-patient Bond
Amidst all of this, we must remember predictive analytics are informed calculations to help us manage patient populations. Still more important – indeed, most important – is the one-on-one relationship providers have with each patient.
“Population heath” cannot occur without these relationships. Without the provider-patient bond, data that drive predictive analytics cannot be accumulated, and the benefits cannot be realized.
Jay R. Zdunek is a family medicine physician and chief medical officer, Austin Regional Clinic, Austin, TX.
Image credit: Shutterstock.com
