When you make an important business decision, you are most likely basing that decision on data: sales, ROI, depreciation, value — these are all data-driven aspects of a sound decision-making process. Decision-making in health care is no different, and the power of data is transforming the way we look at care delivery and network performance.
In recent years, there have been unprecedented advancements in capturing and using data, and today, data is the foundation for fueling higher-performing health care. Robust data and analytics unlock collaborative opportunities between payers and providers and drive more informed consumer decisions, optimizing value-based health care. They also guide the design and use of supporting networks within benefit programs, which are increasingly tailored to best balance objectives around health care quality, cost and access.
Data is foundational to the improvement of care delivery performance and utilization. Blue Cross and Blue Shield (BCBS) companies, for instance, share data with providers to support more objective member referrals, improve care coordination across settings, understand gaps in care, identify at-risk patients, focus care management and target health interventions.
Predictive modeling further enhances effective collaboration between providers and payers. For example, data-driven models can help identify risk factors and predict hospital readmissions for patients with chronic diseases like diabetes to support preventive care and improve case management processes. The richer the data, the more powerful the model can be at uncovering accurate predictors that have the potential to keep your employees healthier and out of the hospital.
Ample underlying data is the basis of decision-making that furthers both health care performance and provider network performance. It includes historical data based on years of experience and multitudes of use cases in the geographic areas where employees may access the same care. This is essential to truly understand and leverage historical cost and outcomes within and across communities, providers and health cases to build credible solutions.
The reality is that there isn’t a one-size-fits-all solution when it comes to balancing quality, cost and access. Data can be analyzed to understand and target employee care needs across geographies. Sophisticated analytics can be used to model network and benefit designs and to project utilization scenarios, predicting total cost savings that can be achieved by various design levers. But achieving an accurate prediction requires validity and reliability that stem from an appropriate depth and breadth of data from case examples.
Modeling of this nature can support advanced and tailored solutions, such as tiered networks and benefits. These solutions are designed to shift utilization to higher-performing providers and require a rigorous application of deep claims data and national industry benchmarks to sufficiently evaluate provider performance. For example, for BCBS’s national tiered network, we apply data on 200 treatment types and over 1,600 condition episodes to establish provider cost and quality performance measures.
Data-driven network designs can enable you to guide your employees to higher-performing providers while providing optimal accessibility to care that meets their needs. These networks can also reduce your employees’ health care expenses and positively impact your organization’s bottom line. As such, ensuring that comprehensive data is put to work when designing or determining the right solution for your business is essential.