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BPaaS + Analytics = Cost savings and better patient outcomes for health insurers (Part 2)

October 23, 2020 - 234 views

BPaaS + Analytics = Cost savings and better patient outcomes for health insurers (Part 2)

By adding predictive insights to BPaaS solutions, health insurers can reduce costs, improve member experiences and boost health outcomes.

Reducing total cost of ownership (TCO) is a key objective for healthcare organizations. That doesn’t just mean driving down operational costs – it also means changing the economics of care by moving away from volume-based fee-for-service models to outcome-driven value-based care. As discussed in part one of this blog series, health insurers can use business process as a service (BPaaS) to significantly reduce costs while improving efficiencies.

Additionally, payers can supercharge BPaaS with business intelligence. Using predictive analytics, AI and machine learning (ML) tools, health insurers can drill deeply into their own data stores to anticipate macro market shifts. They can also glean granular insights to ensure claims are paid properly, identify at-risk populations and best interventions, and improve member experiences and outcomes.

Payers’ data-driven insights can accelerate the compression of front-, middle- and back-office operations to support a revamped value chain based on interactions with their customers. Here are a few key areas in which health insurers can achieve substantive results from investing in analytics tools to generate business intelligence: 

  • Payment integrity. Based on our experience, payers can save millions of dollars annually by using predictive analytics to uncover whether they are paying claims properly. In fact, analytics really are the only way for health insurers to glean insights from their vast quantities of data, which are too great for humans to process. The processing of a single claim may generate over 1,000 data points; multiply that by millions of claims, and the result is an enormous data set to be capitalized on. Analytics, ML and AI, plus robotic process automation, can work together to eliminate inefficiencies, such as duplicated claims, and identify fraud, waste and abuse patterns. These tools can also analyze whether claims slated for rejection will ultimately be approved after providers resubmit them. As bots learn from transactions, they can make autonomous decisions, greatly streamlining processing while improving accuracy. Payers also may use analytics to improve payment integrity from the outset. One of our clients says it has automated 82% of its preauthorization requests using a touchless, rules-based process, shaving its cycle time from days to minutes. The federal interoperability rules taking effect next January should enhance these capabilities by permitting access to more member data for automating utilization decisions.
  • Provider performance. Knowing whether providers are achieving quality outcomes can have a big impact on reimbursement rates. Predictive insights can help health insurers forecast which provider is the best match for a specific member. A member predicted to be at high risk for complications from hip replacement surgery, for example, could be directed to a facility with a great track record for managing complex cases. That decision could lead to a more satisfactory outcome for the member and avoid costly readmissions. For their part, providers need integrated data, analytics and technology to identify their highest risk, highest need patients and populations for targeted interventions. They also need simpler health plan structures so they can more effectively support benefits coverage. Payers can provide the analytics-based insights and collaborate with providers to make fact-driven decisions to ultimately reduce total costs.
  • Better outcomes and quality ratings. The Affordable Care Act and the Centers for Medicare and Medicaid Services (CMS) are driving regulations and reimbursement practices that emphasize the attraction and retention of desired members, “right-time, right-place” health and care management, and cost containment and reduction. The analytical insights needed to achieve these goals must come from multiple data sets – financial, administrative, clinical, structured and unstructured – that create a full view of individuals and populations. Members themselves would benefit from decision support tools to inform their care choices, such as information on their accumulated deductible, price and quality. Further, with a “whole-person” view, payers could personalize member care. Custom-care management, outreach programs and alternative care providers could encourage member engagement, improve outcomes and reduce cost of care.
  • IT operations. Analytics embedded in core administrative systems monitor system health and performance. Systems with closed feedback loops continually learn and improve to increase speed and accuracy. These analytics help systems deliver reliable, repeatable performance through increased stability and resilience to operational disturbances, such as volume increases, new diagnostic codes and regulatory changes.
Prerequisites for Optimizing Analytics

Health insurers have rich troves of utilization data within their core administrative systems. Now they need to unlock these storehouses to make data accessible to analytics tools and other systems so it becomes actionable. This will require a comprehensive data management strategy encompassing data maintenance, storage, accessibility and security. Adopting the Fast Healthcare Interoperability Resources (FHIR) standard will accelerate compliance with the federal interoperability rules and enable smoother data flows. Payers also need to invest in data orchestration, enabling data to flow among core administrative systems and other business and clinical systems in real time to support event-based automated actions. 

Becoming a data-driven payer organization will take time. Yet payers can still get short-term wins with intelligent solutions. One of our clients told us it saw ROI within six weeks of deploying a bot-based solution to clear its claims backlog. It eventually grew into a broader intelligent solution that the client said improved its payer’s net promoter score by 10 basis points and netted $10 million in savings.

Adding predictive insights to BPaaS solutions like this one will be key to unlocking more cost savings, improving member experiences – and generating still more data that payers can use to improve health outcomes.

Digital Business & Technology AI , bpaas , business intelligence , health insurers , predictive analytics

John Reynolds

John Reynolds, Ph.D., has over 30 years of healthcare services, benefit consulting and financial technology experience. He is...

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