Healthcare Data and Preventive Care: A Pathway to Success in Value-Based Healthcare
An area where healthcare data is beginning to make inroads and has tremendous potential for significant impact is in preventive healthcare.
Preventive care plays in important role in value-based healthcare – the direction healthcare models continue to evolve into as they move away from fee-for-service. A couple notable factors act as incentives for placing prevention at the forefront of care:
Changing Hospital Revenue Mix
Hospitals face changing payment mixes due to:
- High volume of Medicare usage by an aging population, notably the Baby Boomers.
- Increased numbers of Medicaid patients, in part, a result of the Affordable Care Act (ACA) Medicaid expansion.
- Decreasing reimbursements from commercial payers.
With payments for quality on the line, hospitals have a strong incentive to keep these populations on the preventive continuum of care, especially those most at risk for costly chronic health conditions like diabetes, seniors that need special home care (for more info check out homecaring.com.au).
There’s no doubt that hospitals with successful readmissions rates have leveraged big data to do so. This requires using data for proactive patient care, including using data platforms that can detect harmful drug side effects and interactions so pharmacists can assist doctors in tailoring a patient’s drug regimen to optimize its therapeutic effect while mitigating risk.
Feeling the Squeeze
Given changing revenue mixes, managing hospital readmissions and much more, it’s a rare hospital that’s not feeling the squeeze these days. Let’s look closer at how data can support preventive care, thus helping fuel success in a value-based model.
Before we jump in, we want to acknowledge what we all know and are watching unfold – it’s hard to say what will happen with ACA in the current political climate. If ACA is repealed, it’s also unclear what will emerge in its place. It does remain reasonable, though, to assume that a payment for outcomes versus payment for service model will likely continue to shape the healthcare industry.
For years, providers have submitted quality measures for programs such as Hospital Inpatient Quality Reporting (IQR), Hospital Outpatient Quality Reporting (OQR) and Physician Quality Reporting System (PQRS). Now these measures are tied to penalties and incentives. There is an opportunity here to leverage these quality and performance measures at a population health level – recognizing trends in vulnerable populations – for prevention. This will, however, require data tools in the form of sophisticated data analytics and data warehousing.
At a more granular level beyond broader population health, integrating healthcare data across data silos can allow hospitals to be more predictive and capture similarities across defined patient populations and at the individual level for more personalized risk profiling. The idea of precision health is the ability to narrow down to very specific segments of a population. Being able to predict risk at this more granular level will allow for proactive preventive treatment and improved cost management.
Claims Data and Preventive Healthcare
While we are in the business of providing healthcare claims data analytics, we recognize that claims data is a piece of the larger data puzzle for preventive healthcare. Integration of various forms of data for business intelligence and improved care may be the only way hospitals can weather shifting payment mixes, care models, penalties and more.
With Medicare setting clear goals for 90 percent of all payments to be tied to quality or value by 2018, healthcare data integration is a must to achieve this goal and bend the cost curve. Cloud technology and warehouse-based big data and analytics platforms are an investment for a hospital system. But without them, there will simply be no way to gain the insights hospitals need for success in preventive care or more broadly across their care outcome and business goals.
Get In Touch
(888) 214-1415 I email@example.com