Data Unlocks Decreased Costs and Better Health Outcomes

The healthcare industry has faced challenges brought on by the shift from volume-based care to value-based care for many years now. While this change has brought about obstacles and opportunities across the board such as new infrastructure and technology requirements, administrative changes, regulatory changes, and other areas, the impact on member engagement and population health management has been especially important. Managing the total cost of care for members, improving overall member health across all populations, and ensuring regulatory compliance can be difficult without the right tools, resources, and strategies. The right approach towards population health management and member engagement can help plans and providers successfully transition from volume-based to value-based care.

Developing a population health management strategy that takes an all-encompassing approach to member health and costs requires an in-depth understanding of the members who make up the populations being served. A deep data and artificial intelligence driven comprehensive view – one going far beyond the traditional views of members to include behaviors, engagement strategies, and opportunities for the greatest impact – can enable health plans to hurdle challenges that historically stood in their way.

When Income Depends on Outcomes, Health Care Organizations Must Understand Members

The traditional care management strategies that health plans employed before the passage of the Affordable Care Act are currently outdated and untenable. It’s a no outcome, no income world for health care organizations – if they don’t provide patients care that meets quality standards, their revenue is at risk. Additionally, the industry must now go beyond focusing on providing care whenever a member reactively presents themselves for care. To make the shift to comprehensively managing the health of members, care providers must strive toward a highly proactive approach to better health outcomes, mitigate risk and manage the total cost of care.

Extremely limited analytics and data integration often prevent a health plan from gaining a deep understanding of its members. The resulting fragmented view of a plan’s membership makes designing a population health strategy that addresses all of these different aspects nearly impossible. Health care organizations are challenged to identify which members require targeted interventions, which are the most likely to benefit from outreach, what care should be delivered, and how to best deliver that care. To elevate this struggle, these organizations have not historically had the analytics or resources to appropriately ensure the resources they are investing are delivering a return by accurately tracking and measuring the performance of their implemented programs – a key to achieving return on investment and improvements across cost, quality and utilization.

Additionally, all too often, data resides in many different systems. This makes it difficult to consolidate and analyze the information needed to gain an in-depth understanding of different populations and their specific needs.

Heightened Insights through Next Generation Data and Analytics: The Key to Population Health Success

Data and analytics are not new tools to population health management strategies, but advanced machine learning analytics, as well as predictive and prescriptive analytics, designed to deeply understand members and mitigate risk are. Such analytics deliver an understanding of current and rising risk, health conditions, barriers to care, and behaviors, and help care teams better map a member’s journeys and the best path forward to improve member outcomes. The insights gained from this analysis are the link to connecting traditional ways of population health management to a truly data-driven, proactive, and comprehensive member-centric approach. With an analytics maturity model that looks to descriptive, predictive and prescriptive analytics, health plans are sure to understand what is happening today, where intervention is needed, and what to prioritize in order to impact outcomes in the future, as well as how and who to best intervene with for effective engagement. This insight can help health plans take the guesswork out of where to allocate their extremely limited resources and put those resources to use in the most effective ways possible.

In order for the analytics to deliver the insights needed, health care organizations today are looking to also augment their own internal data with data from outside sources. Those that are able to build a robust data strategy with third-party data in addition to their own data can gain a wealth of insights. Social determinants of health (SDoH) data in particular is proving to be highly valuable as care teams look to more holistically understand members and patients. By combining such data as SDoH, medical claims, pharmacy claims, lab biometric, and other data, care teams are able to glean a better understanding of real health status and opportunities to engage and intervene. Fortunately, a large amount of SDoH data is available from public sources such as census and county health, EASI, Medical Expenditure Survey (MEPS), and other sources, and many plans are able to look to their analytics partner for additional data that is not publically available, such as outside consumer data and benchmarking. Layering predictive and prescriptive analytics on top of meaningful data layers, and incorporating these insights into different and current workflows, can enable a health plan to make data-driven decisions, design better programs and improve member engagement rates.

Best Practices for the Best Population Health Outcomes

For a health care organization with integrated, accessible data and strong analytics capabilities – whether built in-house or through strategic partnerships – it’s possible to achieve a comprehensive understanding that leads to better health outcomes, quality compliance, and patient satisfaction.

Go Beyond Measuring Clinical Risks

Data and analytics limitations often force organizations to focus only on metrics related to clinical performance. This, however, only presents one aspect of a patient’s journey, and cannot be used to accurately gauge the risk of a patient, both today or in the future. At one large regional Blue plan, they were able to pair clinical history, social impacts and behaviors with predictive and prescriptive analytics for a real understanding of the risk facing a member. Visibility into future risk and rising risk was ahead of the curve for this health plan, and allowed them to leap beyond traditional care management into a strategy that was far more proactive at managing member health and mitigating rising risk. With SDoH information at hand, care teams are able to look at the whole member, understand their barriers to care and design and implement programs that will be much more effective than traditional care program approaches.

Next Generation Strategies for Member Engagement

There are many approaches to engaging with members but two primary paths include 1) when a member is already ill or has been diagnosed with a condition that requires a care team and a prescribed plan of care, and 2) proactive engagement to mitigate negative future health outcomes. Both approaches are needed and both when combined can lead to optimal outcomes if the right information is presented in a way that empowers clinical teams to effectively intervene and engage. Effectively engaging with members requires understanding their current and future risk, impactability and their intervenability.

Impactable members are those with gaps in care that can be prioritized and remedied. They have conditions that can be managed and improved through intervention. If a member has a serious condition, such as diabetes, and is non-compliant with their care regimen, they would be considered to be highly impactable. Patients with non-treatable conditions, such as terminal cancer, or healthy patients would have a low impactability score. Impactability analysis allows organizations take their focus to new heights. Not only can they look to high or rising risk members, but with the added layer of Impactability, they can only focus on those members or conditions where they will gain the greatest impact, defined as potential to avoid future ED or Inpatient visits.

As health plans know all too well, just because a member is identified as needing intervention, does not mean that member will be willing or able to comply with given interventions. This is where health plans can look to prescriptive analytics, such as intervenability, to measure how likely a member is to respond to attempts to change their behavior to close these gaps in care or change behavior. This can be measured by examining any psychosocial, lifestyle, and other factors that may affect their willingness and ability to engage in their care plan and improve health outcomes. A member with a high intervenability score likely has the right resources available to them to change their health outcomes, and looks to have the right behaviors that demonstrate their willingness to make improvements to their health.

In addition to getting to the right members for intervention, health plans can look to data on preferred communication channels and bring this information into current workflow in order to effectively engage and communicate with members. Preferred communication channels should be identified and used within care programs in order to drive greater engagement.

By looking to the right data and using advanced analytics, health plans are able to gain a more complete picture of their members to truly understand their needs. By closing care gaps and engaging with high-priority, high-cost patients, plans can improve patient satisfaction, health outcomes, and decrease medical spend.

A Proven System for Turning Insights into Results

Any insights gleaned – holistic member profiles, risk, conditions, gaps, Impactability, Intervenability, barriers to care, and others - may be useless unless they are turned into action. To reap the full benefit of the data and analytics, organizations must incorporate this information about their members into their care management workflow. By tying the identification of which members are the best candidates for intervention, what care should be prioritized and delivered, and how to best deliver that care into the care management workflow tool the plan uses is critical to achieving a closed loop and streamlined process. However, to fully close the loop on population health, organizations must be able to track, trend and measure the success and outcomes of their programs. Are they moving the dial? Were there improvements across health or financial savings? Are they seeing a return on our investment? Should they reallocate resources to other higher performing programs? To ensure the organization is on track with the population health goals identified, they must look to outcomes analysis to gain a better understanding and transparency.

Putting the Network to Work for Quality Care Delivery

Population health doesn’t just require insight into members; health plans and providers must understand if they are offering the right level of care at the right time. By knowing both the needs of their members in areas including utilization patterns, emerging risk, and medication requirements, they can determine if the member’s needs are being met by the prescribed care plan.

For instance, if a plan identifies that many of its members in rural areas live 30 or more miles away from a primary care physician, they can evaluate whether interventions such as telemedicine would be effective at enhancing member health, ensuring compliance and closing care gaps. Depending on how granular of an understanding a health plan or provider has of its members and patients, these strategies can be further individualized. Another application is around identifying risk across members, patients and providers for opioid abuse. Tracking, trending and monitoring utilization and prescribing patterns can help care teams understand if abuse is occurring or if utilization patterns indicate a need to adjust prescribed care plans.

Another key aspect of looking at care delivery is by the quality of care being delivered through the provider network. Tracking quality compliance, care delivery and overall cost and efficiency performance across network providers can deliver meaningful insights into high and low performing providers. This data driven understanding of performance can be used to steer care to become more efficient, quality driven and can serve as intelligence to design network structures.

Right Channel, Right Connection, Right Member Outcomes Apps, web portals, text messaging and other forms of digital engagement are all tools currently entrenched within a health care ecosystem As these platforms and channels offer a multitude of opportunities to not only track how members are engaging with them, but what information they seek from their health plan or providers. Analytics can be a major driver of revealing what channel will be most effective.

Best Practices for Population Health

For health plans to innovate with new population health strategies, they must look to new innovation and approaches that include next-generation data and analytics that will deliver deeper insights to proactively and comprehensively manage member health. Health plans that consider the following best practices as part of their own population health strategies will be well positioned to achieve their goals:

  • Develop a data strategy that looks beyond traditional data and includes medical, pharmacy, surveys, lab, biometric, clinical, benchmarking, social determinants of health, consumer and third-party data sources
  • Create a deep, meaningful longitudinal and 360 degree view of members
  • Leverage descriptive, predictive and prescriptive analytics that take the guesswork out of where and how to focus care resources
  • Embed analytics and insights into current medical management operational workflow
  • Track program performance and ROI
  • Ensure performance is optimal
  • Develop strong payer-provider value based partnerships
  • Examine and fine-tune digital channels
  • Interoperability and putting data in the hands of consumer

Population health today is not what it was yesterday. Looking to new strategies, new innovations and new technologies to support member centric care in managing the whole health of members is key. By building up the capabilities discussed within this white paper, organizations can break down barriers to holistically managing member and practice intervention strategies focused on identifying and mitigating rising risk across populations in order to improve health and financial outcomes.


Written by EXL Healthcare Team

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