# Reverse Engineering Value Based Care: Payers Adopting Clinical Variation Management Software

When we think about clinical variation management, we invariably think about Providers. In the journey to value based care, Providers are the ones most tasked with understanding evidence based medicine, and studying that data to determine how to deliver better care at lower costs.

Indeed, most of our clients in the space are Providers and they use our Clinical Variation Management application to develop the optimal way to deliver care – from pneumonia to knee replacement to coronary artery bypass graft (CABG) surgery.  The results are profound and the application has won numerous awards for its ability to see the subtle connections that doctors simply cannot. This is not about “eureka” moments (although they occur), this is about incremental, but continuous improvement and, just as importantly adherence. The success of Clinical Variation Management is also a function of the fact that it can deliver care paths against fine grained patient groups. This matters because there is rarely one thing contributing to a health event and the ability to find and study similar patients results in better care paths.

While these attributes contribute to Provider adoption, they also serve as the foundation for Payer adoption. This is where the similarities end, however, for the Payer has different data, different motivations and different incentives. Let’s explore them quickly.

Providers, even highly integrated ones, are focused on the patient first and foremost. This is reflected in the different operating models – non-profit, teaching, faith-based and even for profit hospitals. Payers are ultimately responsible for their members, but they are, in healthcare, profit making enterprises. They seek to balance the well-being of their membership with the cost of that care. Delivering against that balance leads Payers to view an individual members health more holistically, whereas a Provider views it it in the moment. While there is plenty of ambition in this area, it is how the U.S. healthcare system works.

Payers have access to longitudinal data. Providers have access to temporal slices of that data. Providers have a deeper picture but Payers have a wider one. As Physician from UCLA told us “everything that happens in my hospital started somewhere else.” The Payer has a better picture of where and when.

Finally, the Payer’s incentive to deliver an optimal outcome for its members is generally not unaligned with the Provider – although it can be unaligned with the individual Physician. This is one reason why Payers are adopting Clinical Variation Management.

Let me explain.

One of the top payers in the United States used our Clinical Variation Management application  to determine which doctors were the best at performing certain procedures. Then they directed their members to those doctors. In effect, they “engineered” value based care – but from the other direction.

They started with the procedures, medical administration (all pharmacy), diagnostics and authorization data for close to 8,000 patients who underwent coronary artery bypass graft surgery (CABG). The patients contained slightly more commercial members than medicare members.

Not included in this work but available for subsequent study was detailed EMR data, detailed inpatient/outpatient data, socioeconomic data, patient experience, referrals, benchmarks and business segment/geographic data.

Using this data in conjunction with Ayasdi’s Clinical Variation Management application, the Payer discerned the evidenced-based, optimal care process model for CABG based on several key outcome variables. Those included overall cost, length of stay, readmissions, complications and any ER post procedure.

With that optimal care process model identified, the Payer then identified providers with strong compliance to the care process model and validated the association between compliance and good outcomes (LOS, cost, adverse events). Those become “preferred” Providers for CABG. Indeed, most Payers already have this designation. This technology makes it far more efficient and effective.

This naturally leads to “learning opportunities” for those Providers with poor outcomes vs. the standard. It becomes a data-driven conversation at that point to drive changes and to identify specific opportunities for moving underperforming providers toward these superior outcomes.

In this particular case, those learning opportunities came in the form of specific medications  including beta blockers, calcium channel blockers, ACE inhibitor and ARBs. Groups with higher post-operative prescription fill rates for these medications were associated with lower cost and lower LOS. Additionally, cardiac  rehabilitation utilization post CABG was associated with lower costs and lower LOS.

Finally, within Providers, certain physicians will perform better against the standard (indeed, their performance is what creates the standard) and the Payer can guide their members toward these doctors – knowing that in doing so they are creating the best potential outcome for their members, their shareholders and the healthcare system as a whole.

While some pioneering payers are already moving in this direction, the fact of the matter is that this has a far larger impact on profitability than optimizing marketing. When coupled with fraud detection, there isn’t a larger opportunity for the bottom line. More importantly, Payers have the data, talent and clinical knowledge to operationalize this at scale.

Further, Payers can also use this same technology to assess the readiness or suitability for value-based care contracts by evaluating the performance of the Provider as well as their variance to the standard of care. As more and more contracts head in this direction, this becomes a highly efficient way to assess Providers.

Finally, Payers can, and are, leveraging CVM in their population health work. As noted elsewhere, the ability to get at fine grained patient groups with targeted care plans is immensely difficult. With Ayasdi’s Population Risk Stratification application working in conjunction with CVM, this becomes a far more manageable task.

The financial impact can be profound. Consider the following scenario. If a CABG procedure runs $60k on average and you take$7.5K out of that across the 400K procedures annually, it produces savings of $3B for the healthcare industry. One would not expect to be able to move the needle with all Providers, but a third is reasonable. This results in$1B in savings. Again, on a single procedure. This cannot be expected to work for all procedures, however, we have yet to encounter a procedure where we don’t see double digit opportunities for improvement.