Shared goals demand shared data: bringing together payer and provider information can help both sides reach cost and quality goals

Health Management Technology, Dec, 2004 by Rose Cintron-Allen

Discussions about the U.S. healthcare system tend to be complex and polarized. One side says providers are consumed with providing quality care, but have no regard for costs. Another side says insurers and HMOs care only about wringing costs from the system, with no regard for quality outcomes.

The truth is that there is relentless pressure on both payers and providers to achieve cost and quality improvements. The pressure has created healthcare trends that rely on a level of cooperation between payers and providers that has been largely absent since the advent of managed care. Two of the trends--next-generation disease management (DM) programs and pay-for-performance provider contracts--are especially demanding of a cooperative approach.

To achieve shared cost and quality goals, payers and providers need rapid access to a broad range of reliable data, some of which traditionally reside with payers, others with providers. Advances in data technology and data management make possible an enterprisewide view that links data warehousing, tailored logical data models, business intelligence applications and data mining tools.

Enhancing Disease Management Programs

DM, which has gained momentum recently with the announcement of CMS' Chronic Care Improvement Program, comes in many forms. Though it's been around for nearly a decade, many health plans, vendors and providers have struggled to document results, be they clinical, financial or utilization-related.

One factor is clear: DM programs function best with a complete view of the targeted patient population and the providers with whom they work. This view demands that all the data relating to the patient be gathered in one place.

Typically, a provider has medical records, services delivered by that organization, costs, clinical data and medical protocols. A payer has claims data that include cost and services incurred from all providers, prescriptions filled and practices of all providers in a region. Both have DM contact information and morbidity ratings from their respective programs. Separately, each has limited capability. Together, accuracy and opportunity increase exponentially.

Once a health plan, provider or DM vendor has access to all this information in one central location, it can make better use of predictive modeling and data mining to more accurately identify patients, understand their needs, understand likely patient cooperation levels and predict likely results. The DM program user then can make better decisions about which programs to pursue (diabetes, asthma or congestive heart failure) and how to work with individual providers and patients in the targeted population.

Evaluating Pay-for-Performance Contracts

Typically, pay-for-performance gives providers incentives to follow agreed-upon evidence-based protocols to achieve clinical and patient satisfaction outcomes. The hope is that lower costs will accompany better treatment.

Quality measures, which require constant, up-to-date and complex data from payers and providers, are at the heart of these arrangements. Ideally, payers and providers will be able to carefully track medical history, patient demographics, risk factors of the patient population (to unlace patient severity), up-to-date external guidelines for clinical best practices by disease state, diagnostics and therapies used, patient compliance with various prescribed therapies such as pharmacy use, costs over time for payers and providers, and medical outcomes.

Access to these kinds of data enables what-if analysis of alternative fee schedules and reimbursement arrangements using existing severity-adjusted claims experience during the negotiation process. The process works best if all of the data is consistent for everyone who is looking at it.

Data Mining for Operational Improvements

Many hospitals struggle to identify cost reduction opportunities. Yet if they could gather a full array of data in one place, their task would be made considerably easier. First, they would gather claims, accounts payable (A/P), accounts receivable (A/R), provider, supply chain and labor cost data. Then they would use data mining techniques, such as uncovering patterns that reveal certain payments are constantly delayed, to pinpoint cost reduction opportunities.

From there, they could drill deeper to determine what the time and effort required to track down the late payments was costing them. They also might be able to demonstrate to the payer that the late payments cost the health plan more than it's worth to delay the payments. The two then could create an A/R information dashboard that both parties can access and which generates automatic reminders or communication to appropriate people to resolve delayed payments.

Cooperative Data Management

From a data management perspective, these examples reflect a process that begins with a solid data foundation and leads upward to a pinnacle where analysis yields information on which the payer, provider or both can act. The approach includes five broad steps: collect, organize, analyze, uncover and act.