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More Risk, Better Data

Robust and timely information on the performance of targeted patient populations is critical to success in risk-based payment models. Healthcare providers depend on their data and analytics technologies to be able to coordinate and synchronize care across the continuum, identify what’s working well in care delivery and what needs improvement, and design and target interventions to improve outcomes and lower costs. Data and analytics are a foundational capability of effective population health management, and population health management is central to value-based payment.

These needs only intensify as organizations take on more risk, and there’s no question that the industry is headed this way.

The IT roadmap: Key priority areas

For providers that are engaged in any value-based payment agreement, especially a risk-bearing model, following a roadmap will make best use of limited IT resources for the biggest impact on performance.

It should include four key components:

  1. Claims analytics and benchmarking to prioritize high-level areas of opportunity
  2. Risk-stratify patients and target care to address high risk patients
  3. Determine interventions and build them into clinical workflow to close gaps in care
  4. Regular monitoring of progress and feedback to providers

Determine areas of opportunity

Organizations must clearly define their population health priorities and create a data analytics solution to support their strategy. The most effective way to identify the greatest areas of opportunity is through claims analytics and benchmarking.

Claims analytics are effective because the time to implement is quick and they can provide a full picture of a target patient population, including out-of-network utilization. Claims analytics also allow organizations to benchmark performance to prioritize the areas of care that will be most effective for reaching their goals.

There are three ways to benchmark performance to determine priorities:
  • Compare your current performance to historical performance over time
  • Benchmark against risk-adjusted peer groups
  • Risk-adjusted benchmarking customized for your population

Opportunities may be discovered in clinical areas of care such as avoidable inpatient admissions, overutilization of skilled nursing services, unnecessary emergency department visits, or underutilization of hospice services. Other areas to understand include the demographics of a patient population, changes to plan coverage or reimbursements over time, and the impact of high-cost beneficiaries on overall costs. And while cost and utilization remain important factors in value-based program success, benchmarking a population’s performance in quality measures will also help identify areas of clinical intervention.

By focusing on the top areas of opportunity, organizations can focus limited resources on the areas that make the biggest impact. With claims analytics and benchmarking in place, data can be used to identify patients for a more sophisticated care management program.

Build interventions

Another important strategy for improving the cost and quality of patient populations is using claims and EHR data to identify high-risk patients. The methods for doing this vary widely from home-grown algorithms to vendor-purchased solutions. It’s important to note that high cost and high risk are not necessarily interchangeable. When deciding which patients care management resources should add to their services, identifying patients with avoidable spend and services is an important factor. For example, a patient with cancer may be high cost, but an organization’s ability to affect their cost trajectory is smaller than someone with chronic heart failure who was recently identified as a high emergency room utilizer.

Once top opportunities and the patients affected by this strategy are identified, providers can take steps to build clinical interventions into EHR workflows. For instance, a benchmarking comparison may identify the need to reduce inpatient hospitalizations of chronic obstructive pulmonary disease and dehydration. One population health strategy to curb hospitalizations is to improve outpatient care of these chronic and acute conditions. With the right technology, providers can identify and prioritize these patients for care management services and build clinical pathways into the EHR to avoid hospitalizations and high cost.

Monitor progress

Regular monitoring of progress, using a combination of claims and clinical data, is an important step in building an effective value-based IT program. While clinical data can provide access to more timely process measures, claims data will continue to provide full coverage of the outcomes needed to measure success. It’s important to also remember the provider perspective and build reporting capabilities that include cost, utilization and quality for providers across the network as well.

Challenges with current state

While technology has evolved and improved at a rapid pace, there are still challenges to be aware of. For one, current technology isn’t yet optimized for population health management. A significant challenge faced by most organizations is the integration of data from multiple sources. In order to have a complete picture of patient health, providers ideally need information from adjudicated claims, clinical and social data, and health risk assessments from payers or other sources. And population health metrics rely on complex informatics processes that demand a lot of investment in time and personnel. Ultimately, organizations that can prioritize their strategy, then develop their limited IT needs around the strategy, will succeed in risk-based payment models.

For more building blocks for success in risk-based payment models, read Building Successful Two-Sided Risk Models. We’ve also underscored the importance of taking a methodical approach to care delivery optimization to ensure organizational readiness before assuming more risk.

For more information on building a successful risk-based payment strategy, visit www.premierinc.com/vbc.

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