CFOs should use classic payer measures, such as PMPM, in addition to traditional revenue cycle measures, to determine the success of population health strategies.
As the industry transitions to value-based reimbursement models, shared accountability agreements are creating both financial incentives and challenges for providers. Population health management has proven successful in curbing costs and improving care quality under these new payment models (Bright, R., Sakurada, B., “ A Population Health Strategy for Diabetes: New Partners, New Opportunities,” National Academy of Medicine, February, 2016.) In addition, a Frost & Sullivan report shows most U.S.-based payers and providers are poised to progressively embrace health IT solutions and services facilitating population health management. Providers can develop a clear, measurable approach when adopting and leveraging these technologies through effective data gathering, measuring patient risk, and identifying gaps in care.
Start with Data Housekeeping
The first step to managing population health is the integration of traditionally siloed data across financial, operational, and clinical domains. This data can range from adjudicated claims to lab and immunization records.
For example, a provider cannot determine the Healthcare Effectiveness Data and Information Set (HEDIS) quality measure HbA1C control with just claims data; that will only show that the test was conducted. An accurate measurement must include lab data. And to coalesce disparate data sets into singular, holistic patient views, providers should establish enterprise master patient indexes (EMPIs)—tools that ensure accurate patient identity across disparate systems and points of care. This foundational element will ensure that all patient data is being evaluated for overall risk and compliance. Providers will benefit from more precise analyses when more data is organized.
For example, providers often encounter challenges creating consistent patient IDs across the many disparate systems they use. These challenges are further compounded with the hospital consolidation trend as these merged or acquired hospitals are taking on multiple additional technology systems with each acquisition. This makes it more challenging but also further emphasizes the importance of enabling true, holistic pictures of patients to create true population health management programs.
However, observing and reporting on aggregate level data is only the beginning. This data must also come with the ability to drill down into patient segments and then into individual patient data so providers can get accurate measures of which patients are influencing which scores.
Think (and Measure) More Like a Payer
Providers must also have measurement frameworks and the ability to identify factors that increase patients’ risks. Yet many do not have the data needed to create a comprehensive portrait of total risk. For example, most providers do not consider all of their patients’ claims data across all payers or third party administrators. However, by using well-known risk stratification groupers, such as the Centers for Medicare and Medicaid Services hierarchical condition categories or commercial software applications, they can lay out the risk factors and better prioritize patients and identify those that require interventions outside the traditional theaters of care.
CFOs also need full financial views of their hospitals’ fee-for-service and fee-for-payment lines of business. Most health systems will slowly migrate individual service lines to new payment models, so it is important to understand their entire financial portfolio during transitions. To do this, CFOs should look at the performance of classic revenue cycle measures such as admissions, discharged not final billed, cost per case, and readmissions, and simultaneously consider classic payer measures like per member per month (PMPM), network leakage, and generic utilization. The reporting systems must also allow leaders to interrogate data and understand what “levers” impact these measures.
For example, examining practice variation―the differences in care within or across a health system for the same service lines―can identify opportunities for maximizing care while curbing costs. A classic example is correlating the different stents that physicians use to both clinical outcomes, such as length of stay or readmissions, as well as direct costs.
Build on Accurate Measurements and Data
Once organizations can accurately identify risks, they should stratify their patients and utilize gaps-in-care alerts, which allow hospital leaders to view and refine populations by chronic conditions and unveil holes in process and care. For example, analyzing patient groups with depression and other chronic conditions―who are less likely to follow their discharge instructions and take their prescribed medications―may uncover patterns of patient compliance and care that can drive care management follow up.
Gaps-in-care models can also drive work lists with specific intervention instructions, such as identifying diabetic patients who have missed their regular foot exams; these levels of preventive care will have significant impacts on the overall health of the population.
Reap the Benefits of Data
As payment incentives shift to the overall results of care, providers must leverage their own data to reduce their per capita costs while still achieving the same level of care or better. Population health management, with a measurement framework that taps into payer processes, is a strategic approach in healthcare’s shifting environment as more emphasis is placed on predicting new opportunities, curbing costs, and improving quality.
Christian Wieland is vice president of product management, MedeAnalytics.
Forum members: What do you think? Please share your thoughts in the comments section below.
• What have been some of your most successful population health strategies?
• What analytics tools or strategies have you implemented to support population health programs?