- January 30, 2019
- Posted in Identify Risk
This post was last updated on November 27, 2019 at 2:10 pm
By Frank Jackson, EVP, Payer Markets
When payers are identifying risk in Medicare, they start with the most easily accessible data: claims. It’s a reasonable place to start — but claims don’t begin to tell the whole story. Claims have just one field — the primary diagnosis code. They don’t record any secondary diagnoses, which may reveal information that could be crucial in determining a patient’s risk. If a Medicare Advantage payer wants to pull a patient’s chart for a clinical review, it can cost as much as $40 per chart, which is more money than most payers are willing to spend.
Lab data, on the other hand, is easy to obtain and it can provide telling insights on risk. As health technology has advanced and lab testing has become more accurate, lab tests have identified more and more conditions — most of which are covered by Medicare. Predictive models can use combinations of lab tests to predict which conditions a patient might have, which fully informs the payer to do a more comprehensive chart review.
All this, of course, is part of the complex process of risk-based contracting between providers and payers. Lab data collected by the payer can be used as a risk stratification tool for providers. So, when a provider group doesn’t have all the data it needs, payers can identify those who need the most attention, and providers can call them in to help better manage their disease or condition. This is a win-win for providers and patients, as it improves patients’ health while also inflating quality metrics.
The challenge of using lab data to identify risk is that the purpose of the claim is usually different than the content of the lab result. Results often have abbreviated fields that may be difficult to read, so within the medical records, there is a high degree of sharing. The problem is, there is no standard for sharing data outside the provider context. This is where Prognos can help payers — by doing the heavy lifting to standardize for sharing in other cases.
In other words, Prognos dives deep into the system to find fields of data that providers may have overlooked when determining a patient’s risk for disease. It uses artificial intelligence (AI) to aggregate that data, coming to conclusions based on billions of other data points from millions of other patients.
Not only can AI extract information from lab results that helps providers and payers negotiate risk, but it can also identify risk factors for disease to potentially save a Medicare Advantage patient’s life. Machine-learning models can use lab tests to predict the severity of a patient’s condition, which a payer can then relay to the provider. They can also identify clinical care gaps and be used to actually close those gaps, thereby eliminating the need for a labor-intensive chart review.
Identifying risk is not a simple task — but when you have the right partner that can sift through all the data for you, it becomes much easier to manage.