A PSCI Customer Transformative Success Story
Co-Authored with Medical Clinic of North Texas

The Affordable Care Act of 2010 (ACA) opens the door to a wealth of opportunities for hospitals and physician groups. They are beginning to adapt to the new pay-for-performance and bundled payment systems and develop population-based care management programs.  While the goal of ACA is to hold hospitals and physicians jointly responsible for quality and cost of care, the new payment models span the entire care continuum, including primary care physicians (PCPs), specialists, hospitals, post-acute care, and re-admissions. The biggest winners will be those who can improve quality of care while driving down costs. Those that focus first on preventive care for top chronic illnesses will be the first to cross the finish line.

For years, insurance (actuarial) companies have been mining their claims data for chronic patients and running predictive models to identify the high risk patients.  Armed with these reports, case managers and care coordinators have been designing intervention programs with the objective of keeping the chronic patients from developing complications and thereby prevent ER/hospitalizations.  This approach has huge limitations applying to healthcare population management in Accountable Care Organizations (ACOs).

Why “What-if” Modeling is critical but difficult to implement in healthcare quality improvement project planning process.

To perform “What-if” analysis is to understand the impact of the linkages of quality to cost, one needs to link quality and costs measures at the patient level.

Unfortunately, a lot of quality data in the hospital systems is collected at facility level and on sample-basis. Hospitals need to develop unique methodologies to drive these quality score to patient level with a high degree of certainty.

Most current healthcare analytics applications are single dimensional in approach. They risk-adjust patient data and perform analysis at a physician-centric level. But healthcare is complex and multi-dimensional.

One cannot completely attribute a physician responsible for cost or quality issues. Providers need to understand the cost-quality issues with a multi-dimensional approach.

Why are the costs-quality variances of this specific group of physicians working on a particular group patients at the facility XYZ high? Solutions need to help understand the problems in context of each other  at a multi-dimensional level (cost, quality, patient, physician, facility, risk). A clinician could be doing a great job in some situations versus the others. We need to identify the best practices and share with the rest of the team.