Seeing the Larger Picture on Patient Risk

In the era of value-based care, life science companies will be called upon to take on more risk for patient care – but how much risk are they willing to accept?

There may be doubt around many aspects of healthcare at present, but one aspect that isn’t in question is the transition to value-based care.

In the future, pharma companies will take on more risk for certain patients, a trend that has many concerned as they quickly discover that they don’t know what they don’t know about patients. This makes it difficult to make good decisions on the level of risk they are willing to accept.

Consider an example of a patient prescribed a medication for diabetes. Under value-based care principles, the life sciences company will be asked to assume a level of financial risk related to the medication or device. Yet, it is difficult to accept risk without knowing more about that patient’s overall condition. What if the patient has lower back pain and part of the success of treatment depends on regular exercise? The company may end up accepting more risk than it should. Even though factors may have been outside of its control, it may also gain an undeserved reputation for its products not working, hurting future sales.

To avoid these types of situations – and ensure the best health outcomes for patients – it is important for life sciences organizations and health plans/payers to break down their usual siloes and start sharing their data for the common good.


Connecting the dots

Payers are the connective tissue for the care a patient receives; as they receive claims from all the providers and healthcare organizations involved, they can provide a more complete view of a patient’s care. If he or she is being treated for back pain or another co-morbid condition that could affect the outcome of care, it will be visible in the payer’s data. Using this data, life sciences organizations can adjust their risk score appropriately.

Payers also have an incentive to share data as they can receive a de-identified view of the outcomes for all patients who received a treatment or device, not just those they cover. This larger dataset, when combined with predictive analytics, can help them make more accurate projections of the treatments or devices that should be applied to patients who fit certain profiles or personas. This can enable them to provide guidance that will improve the quality of care and patient outcomes while reducing costs. It can also help them adjust their risk projections.


Hitting the target

With this data from payers, life sciences organizations can use predictive analytics to inform the commercial teams, helping them target resources more effectively. For instance, payer data run through predictive analytics may show a high concentration of patients with a specific profile in a geographic area, allowing commercial teams to target providers with a specific product.

Armed with this data, companies can focus their efforts on the largest opportunities, and/or the lowest-hanging fruit, yielding the best ROI. They can also avoid going after areas where there is no competitive advantage, or where the financial risk outweighs the potential benefits.

Life sciences organizations do not typically have the data within their internal systems to make such sophisticated evaluations. Partnering with payers for a holistic view of patient care plus next-generation predictive analytics can reveal the bigger picture, allowing them to make more-informed decisions that reduce risk exposure, improve the bottom line, and enhance patient outcomes.

John Pagliuca is Vice President, Life Sciences at SCIO Health Analytics



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