At one point in the not-too-distant past, hospitals analyzed medical data with one goal in mind: improving patient care. But that moment is already beginning to feel like part of a simpler time. Increasingly, it seems that for hospitals, analyzing data is not just an optional method for improving patient outcomes, but a necessity for ensuring their own survival.
In recent years, hospitals and other healthcare providers have made great leaps in using data to improve their quality of care. But like many powerful tools, data also presents risks. The sheer volume of information can overwhelm any provider, and even those with the resources to sift out important data points may fail to draw the right lessons from them.
Now, with the rise of value-based healthcare models, the stakes for providers in getting analytics right have soared even higher. The value-based approach is quickly becoming a reality for most providers, if it isn’t already. According to survey results released in February by HealthEdge Voice of the Market, nearly half of healthcare executives report that the majority of their contracts are value-based models. Insurance giant Humana last year launched a value-based Hospital Incentive Program and has attracted Cleveland Clinic Florida and Georgia’s WellStar Health System as participants. Some states, such as California, are moving to a value-based model.
With this shift, hospital administrators have begun to feel a financial squeeze. Porter Research just found that 62 percent identified reimbursement as one of their top challenges. On a bleaker note, last year Moody’s cited the shift to value-based care as one reason that the nonprofit and public hospital sector as a whole is on an “unsustainable” path.
With reimbursement tied to patient outcomes, the financial health of hospitals now depends at least in part on their ability to make the most of their data. Used correctly, analytics will not only help patients, but also make hospitals more efficient and assist them in striking more favorable deals with insurers. What specific steps can they take to turn the mounting wave of information to their advantage?
When Poor Analytics Become a Tripwire
In our work advising hospitals, we see wide variations in practices. Some, for example, carefully analyze data on MRI usage in their facilities. They track the number of tests that each doctor prescribes, compare that data to benchmarks, review patient outcomes, and, with all that information in hand, create best-practice guidelines. Others don’t analyze MRI data at all. That failure results in inconsistent practices across staff and a heightened possibility that patients will be exposed to unnecessary radiation or fail to receive needed treatment.
We’ve also seen that hospitals that don’t use analytics properly are disadvantaged in negotiations with insurance companies. A payer, for example, might withhold reimbursement if a hospital’s practices fall outside the norm, something it would not likely be aware of without analytics. Those using data intelligently can identify potential billing issues before an insurer flags them. If a dispute over coverage crops up, they’ll be armed with information that gives them bargaining leverage. A smart analytics strategy can act as a sword as well as a shield. Tracking reimbursement levels from individual insurers allows hospitals to identify those with poor reimbursement histories. That’s an insight that can drive decisions to negotiate more forcefully with those insurers or avoid them altogether.
A 3-Step Plan to Implement Analytics
To start addressing these issues, hospitals and other providers can follow a few best practices that we’ve helped clients implement.
- First, run regular reports on your payers, identifying the five best and worst. Examine the worst to find out why your reimbursement rate is poor. Identify the pressure points and start a dialogue about a solution, which may require you to change some of your practices and policies.
- Second, identify a few key areas where the data indicate over-use. Are your doctors prescribing too many MRIs, for example?
- Third, analyze whether you’re collecting the data you need to drive quality of care. Many emergency rooms study the “door-to-doctor” time to measure the waiting time before a patient sees a doctor. How much time elapses before patients identified as having life-threatening sepsis receive medication?
To follow these practices, your staff may need additional training or guidelines. You may also need to hire new professionals with the ability to identify, collect and analyze the right data points. But the exercise doesn’t stop there. You need people who can formulate and implement an action plan that leads to better outcomes for your patients and your institution.
Prioritizing the smart use of analytics may seem like a huge undertaking, but it will pay off. If you’re using your data thoughtfully and continually identifying areas where you’re seeing problems, you’ll create a better foundation for patient well-being and institutional stability. Just as important, as the health care system evolves, you’ll be ready for the coming changes that value-based models will drive.