Bringing Artificial Intelligence to Healthcare: Enhancing Risk Models to Predict the Future Cost of Care

Abhinav Shashank
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Once while I was scrolling through the news feed on my phone, there was one specific line that really made me wonder: “There’s a 40% chance of gusty and blustery winds today.”Statements such as this one strongly influence people’s behavior, as they are based on evidence or data findings from years of surveying, studying, and analyzing past trends and occurrences.

However, my question is “Why are we not able to make such claims in healthcare- even today?”

Can we predict the vulnerabilities a patient might face in the future or the current health risks a population segment faces?

Is risk scoring the answer we have been looking for?

Almost all kinds of care organizations have some risk scoring methodology to target care interventions. With quality, costs, and patient experience taking the center stage in healthcare, care organizations need to stratify patients based on their need for immediate intervention.

The need of the hour is to address high-risk issues that impact large groups of patients and ensure that these needs are met in a timely fashion. Often, frequent fliers among high-risk patients come into the emergency department as if it’s their second home.

What if we take the method of risk scoring to a whole new level?

Traditionally, providers and health systems have relied on claims-based risk models, such as CMS-HCC, ACG, and DxCG, which were built to forecast the risk of populations/sub-populations but not for individual patients. Hence, these models give an accurate prediction of the average risk of the population but exhibit very poor accuracy if used to predict risk for individual patients.

Although risk scoring has turned out to be a key factor in addressing the needs of the patient population, this method cannot provide all the important insights that are needed to drive necessary interventions.

Since healthcare already has the right data from sources such as EHRs, claims, labs, pharmacy, SDOH, and others, can we predict the future cost of care instead of just stating the risk score of the patient?

The right machine learning-driven approach to predict the future cost of care for patients

It all starts with the right data. The first step is to integrate the data from multiple sources- whether it is clinical or non-clinical data such as Social Determinants of Health. The data from these sources can allow us to use the comprehensive patient’s data for multiple predictive models to predict future health cost with greater accuracy.

Time plays a very important role in this analysis. In order to predict the right outcomes in the upcoming year, we need to have a clear view of what happened with the patient in the past. The predictive model needs to take into account the past years' cost, utilization, and diagnosis data to help the model understand the patient’s condition in depth.

In order to predict the future cost of care without any discrepancy, the final key ingredient is the inflation rate. Not just the financial aspect but some demographic adjustment is also required to understand the cost of care that will be incurred for a patient in the upcoming year.

In what ways can a revolutionary approach to risk scoring open opportunities for healthcare?

Imagine what we can achieve if we have both: the right data and the dollar amount that the patient might spend on his/her illness. This approach will help organizations to stratify patients for immediate care interventions and make cost-cutting easier as the organization becomes aware of precisely what to improve on and how to improve it.

The analysis of the patient’s future cost of care will help in achieving multiple targets, such as:

  • Better stratification of patients for care management and outreach
  • Better identification of patients with future liabilities

The road ahead

Predicting the future has always intrigued mankind. What seemed like a dream a long time ago has finally been made possible by a combination of technology and evidence. From assisting providers to use data-driven insights to target patients, to projecting the cost of care- predictive analytics can change the dynamics of healthcare. We are stepping into an age of intelligence where patients will know what will happen to them in the care process even before they become sick. They will know when, how, and where to spend their healthcare dollars. The future is here, and we just need to brace ourselves.

To learn how predictive analytics can lead you to better and more affordable healthcare, get a demo.

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