Health may be priceless, but healthcare is not. With medical costs on the rise, insurance companies and government payers are increasingly asking pharmaceutical companies and device manufacturers to prove—and quantify—the value of their therapies.

The Push for Value-Based Purchasing

Under the Affordable Care Act, The Centers for Medicare & Medicaid Services (CMS) instituted the Hospital Value-Based Purchasing (VPB) Program. This model provides cash incentives for hospitals that meet or improve certain Healthcare Quality Indicators (QIs). This is part of a larger plan to shift more Medicare and Medicaid payments to value-based models.

What does this mean for the industry? In short, CMS wants to pay for results rather than simply covering costs of treatments. Increasingly, reimbursement decisions for drugs, medical devices and therapies are based on their demonstrated impact on patient outcomes. Private insurers are starting to follow suit with their own value-based purchasing programs.

With money on the line, hospitals and healthcare providers are putting pressure on medical device manufacturers and pharmaceutical companies to prove the value of their devices and therapies. This means going beyond the efficacy data generated in clinical trials. Hospital purchasers and insurance companies are turning to data analytics to answer complex questions like:

• How much value does a medical device or therapy add for patients?
• How does it compare to other treatment options in terms of cost vs. value added?
• How do patient profiles, patient compliance and other variables interact to influence the efficacy of the intervention?

Quantifying the Value of Treatment

How is the added value of a therapy measured? In a value-based purchasing model, the value of a device or therapy is calculated based on the quantifiable impact it has on patient outcomes and QIs. For example, Quality Adjusted Life Years (QALY) gained is a measure of the additional years of life gained and the quality of life in those extra years.

A medical product’s value can also be measured by its impact on hospital QI statistics such as mortality rates and causes, readmission rates, rates of healthcare-associated infections, and surgical outcomes and complications. A device or therapy that produces a measurable reduction in readmission rates, for example, has a demonstrated value based on money saved by reducing the need for future care.

Finding these correlations requires mining and analyzing data from patient Electronic Health Records (EHRs) and hospital QIs using sophisticated data analytics. Technology exists today that can integrate and analyze data from EHRs, medical sensor data, mHealth apps and many other disparate sources in order to quantify the value added by a device or therapy. These technologies use advanced machine learning and pattern recognition, which enable them to be able to predict how a therapy will impact individual patient and hospital QI measures. It’s one more way that Big Data is helping stakeholders in the healthcare industry make better decisions.