Lack of technical readiness—from legacy technologies to data silos—continues to hold health plans back from achieving the promise of interoperability. It also creates barriers to innovation.
These are challenges payers must overcome before the next phase of interoperability takes effect: widespread adoption of application programming interfaces (APIs) such as Fast Healthcare Interoperability Resources (FHIR), which will give consumers the power to access their healthcare data with the touch of a smartphone.
Breaking down barriers to technical readiness
More than six months after the new interoperability rules took effect, health plans still struggle to develop a cohesive enterprise data strategy and infrastructure. This makes it challenging to develop a single source of truth that can help identify member risk, close care gaps for those with chronic conditions, or even determine the quality of care that members receive, whether virtually or in person.
Further, many plans struggle to draw actionable insight from the data they collect. As a result, 62% of health plan leaders say improving AI and machine learning capabilities and driving their adoption are “extremely high priorities” for their organization.
These breakdowns in technical readiness will become even more apparent in January 2023, when the Centers for Medicare & Medicaid Services will begin to require some payers to implement FHIR-based APIs that:
- Give patients the ability to access claims and encounter data as well as pending and active prior authorization decisions
- Make provider directories publicly available
- Enable patients to request that certain clinical data be exchanged with other payers
Such changes could make a big impact on the member experience, given the hurdles members typically face in obtaining their health information. Today, some 40% of patients must travel to a healthcare provider’s office or hospital to obtain copies of medical records or imaging scans. While 66% of consumers have access to a patient portal, only 18% of those surveyed have ever been able to receive digital records via the portal.
Closing readiness gaps
How can health plans make the leap from emerging technical readiness to a state where they can not only comply with API standards, but also use APIs to power internal workflows? Here are three approaches to consider.
Break down internal and external data silos. Achieving the promise of healthcare interoperability requires that all key stakeholders have access to connected data. Yet even within health plans, data silos between departments and divisions are not uncommon. For example, data silos between pharmacy and medical benefits management limit health plans’ ability to gain a comprehensive view of the cost drivers for a specific population. When health plans integrate data from these divisions, the effects on health outcomes and costs are dramatic: One health plan achieved reduced emergency department visits, lower rates of hospital admissions, and higher member engagement with care management programs with reduced costs of $117 per member per month by integrating pharmacy and medical benefit plans.
Leaders at an Appalachian health plan found the organization could more effectively bolster Star rating performance under Medicare Advantage by ensuring that performance is tracked in one place. This meant eliminating information silos so that team members could more effectively leverage data analytics to pinpoint critical gaps in care and work together to identify strategies for improvement. Customized dashboards tracked the plan’s efforts. The impact? It became one of 110 Medicare Advantage plans to earn 4-Star status for 2021.
Developing secure and accurate master data sets for patients and providers is critical to breaking down data silos. With ongoing governance, master data management technology can enable a faster path to interoperability. In addition, leveraging standards to rationalize and govern patients’ clinical data allows for the semantic translation of clinical data between providers and drives value in AI use cases requiring rich data sets.
Harness the power of cloud-based analytics. More and more, health plans are leveraging cloud computing to make sense of disparate data types—from social determinants of health data to medical records, pharmacy and laboratory information—to gain a longitudinal view of members. Relying on cloud-based analytics enables health plans to overcome the analytics challenges associated with legacy technologies by accessing AI and machine learning software via cloud platforms. This eliminates the challenges associated with fragmented and siloed data. It also provides a more detailed understanding of the action steps needed to improve outcomes and reduce risk.
One industry survey shows 78% of healthcare organizations have incorporated cloud computing into their operations, and another 20% plan on investing in the cloud. Meanwhile, nearly half of health plan leaders surveyed say their organization has a dedicated innovation lab for AI and machine learning to support enterprise-wide adoption.
Smaller health plans can make the move to the cloud by leveraging the infrastructure and applications of cloud providers through a platform-as-a-service model.
Bolster capabilities around fraud, waste and abuse prevention. While there are concerns among some industry groups that new interoperability rules put patient privacy at risk, such concerns should not be a roadblock to innovation. Instead, as the health insurance industry faces growing risk from cybersecurity threats due to the pandemic, with Fitch Ratings reporting a significant rise in insurance claims related to ransomware attacks, health plans should double down on their cybersecurity defense. One recommendation is to implement a zero-trust security model that requires all users to be authenticated, authorized, and continuously validated before gaining access to the health plan’s applications and data.
Eighty-two percent of health plans also create rules and scoring that automatically detect potential fraud schemes, according to one survey. These include development of logic-based rules for specific types of claims as well as user-defined rules that proactively flag claims with specific procedure codes and modifiers for review. Some plans choose to apply rules and scoring to specific populations, such as Medicaid managed care.
Leading health plans are leaning on AI to spot suspicious activity, such as impossible-day billing and instances where simple encounters have been coded to appear more complex. While just 12% of surveyed health plans have made this move, 83% plan to invest in AI for fraud, waste and abuse prevention.
Investing in a future-forward approach
While interoperability is a driver for digital health adoption among health plans, organizations must strengthen their technical readiness for widespread data sharing and analytics to establish a solid foundation for transformation. Developing a cohesive strategy for data capture, analysis and protection, supported by strong internal collaboration and a commitment to innovation, is an excellent place to start.
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