How primary care physicians can navigate business intelligence challenges under risk models

Primary care physicians have been enlisted in the value-based healthcare effort, with revenues linked to meeting targeted cost measures to entice practice participation, but many of these physicians lack the business intelligence capabilities needed to be successful under these models.

The new Medicare value-based care primary care models implemented by the Centers for Medicare & Medicaid Services (CMS) require physicians not only to be accountable for their patients’ long-term health, but also to control the costs of services that are beyond their scope of services, such as hospital inpatient and outpatient costs, specialty physician services and rehabilitation. The type of accountable expenses vary by model. 

Physicians can find it difficult to get a handle on these costs because they tend to lack the business intelligence capabilities required for success under these models, including systems for tracking patients by condition and risk and for measuring cost performance against targets.

The primary care models described in the sidebar on page 63 require practices to actively manage their patients’ risks and care, whether or not they directly provide that care. Therefore, they must create a referral network that will help them control the costs. Doing so requires specialists and hospitals who will communicate with the practices and ensure that primary care physicians continue to be part of the medical decision- making process that drives costs. 

Practices will require knowledge and data — through business intelligence technology and services — that they don’t have now. Tools must enable them to achieve savings and incentive revenues through better choosing referral physicians and effectively managing resources. 

5 business intelligence strategies

The following five strategies and using the right business intelligence technology are the most critical to the success of primary care practices.

1. Collect risk data on patients, including social determinants of health and patient preferences. Using electronic health records (EHRs) that are interoperable and certified, primary care practices under financial risk are responsible for organizing patient information that can be used to develop care pathways. Data can then be used in coordination of care. 

2. Organize patients in risk registries that reflect barriers and clinical status, and use that information to implement interventions. There are affordable outsourced vendors and systems such as clinical data registries or population health vendors that will aggregate data and perform population health management if a practice’s EHR does not have this functionality. 

3. Measure cost performance and select referral networks based on performance on cost and outcomes. Total cost per beneficiary and by category-of-care utilization measures and episodic costs should be used where feasible. Although some costs will require claims data, CMS is likely to provide quarterly data reflecting performance. If CMS does not include that data, practices should request it during the initial five-year testing period. The direct contracting groups accepting global capitation will have their own internal claims data to develop analytics.

4. Track and test interventions implemented to improve cost performance trends and patient outcomes. Cost control is an iterative process, and clinical registry and other analytics partners can help physicians adopt interventions based on measured success.

5. Organize consumer-focused efforts to engage patients. Depending on the patient population, practices should implement convenience features and substantive patient medical decision- 
making initiatives to create trust and achieve better results. 

As primary care physicians enter the fray of provider risk, they require business intelligence to help them steer toward success. Coordination of care, by itself, cannot fuel the practice transitions necessary to achieve good care at lower cost. Value is attained by properly measuring and then navigating positive and negative effects of interventions on patients and costs.