Achieving a more complete view of member health status is vital for risk adjustment. First, the industry relied on claims and pharmacy data to develop analytical models to calculate member risk and identify missing Hierarchical Condition Categories (HCCs).
More recently, innovative healthcare organizations have identified ways to incorporate encounter data into risk modeling. Now it’s time to add laboratory data into the mix to gain an even more complete, accurate view of the member.
Read on to learn more about:
- Early HCC identification with lab data
- How lab data can be harmonized from multiple lab companies
- Ways to make lab data actionable and add into existing workflows
- Sample impact of using lab data at a health plan
New answers for a maturing industry
Medicare Advantage has grown steadily since its inception in 2008, from 22 percent to 34 percent of Medicare beneficiaries, according to Kaiser Family Foundation analysis. Health plans offering Medicare Advantage, managed Medicaid, and ACA products have developed proven processes and analytics for demographic, claims, and encounter data to manage member risk. With precise analytics and the emerging use of artificial intelligence, vendors and health plans have been able to identify retrospective and prospective gaps with increasing accuracy. “Advances in data integration and analytics has enabled the intelligent use of a financial transaction – a medical claim – to identify risk gaps,” observes Frank Jackson, executive vice president of payer markets at Prognos. “However, claims lack clinical granularity, may be incomplete, and often there is limited claims history.”
To incorporate the clinical detail held in encounter data, innovators now use natural language processing and normalized analytics. Over the past few years, the practice has continued to evolve and has become a vital part of many risk adjustment methodologies. By extracting and leveraging the clinical data in EHRs, health plans gain a more comprehensive view of the member profile.
However, one piece of data still missing from many member profiles is lab data. Using historical, current, and cross-payer lab data to calculate risk scores ensures all clinical conditions and comorbidities are factored into risk adjustment calculations. This, in turn, leads to more complete and accurate reimbursement.
More clinical insights mean more precise risk adjustment.
Early HCC identification with lab data
Clinical specificity and disease burden extracted from laboratory data may be directly tied to new or previously undetected patient diagnoses. These HCCs can be used retrospectively to generate more accurate reporting of patient health and associated costs earlier than traditional claims-based programs where claims are often delayed.
For example, lab data serves as supplemental information for diabetes with vascular manifestation, vascular disease with complications, and chronic heart failure. Without lab data, the health plan would need to reach out to the provider, retrieve charts, or do an in-home member assessment. “Laboratory data is a clinically rich goldmine for assessing member health and identifying true health status,” says Denise Olivares, vice president of product development at Prognos. “Our data science team has leveraged the power of thousands of analytics and our registry of more than 25 billion clinical records for 200 million patients in over 50 disease areas to build analytical models that bring to life the insight available in lab data.”
“When using lab data, health plans are better able to accurately identify chronic disease and lower false positive, which leads to more targeted and effective member outreach and chart retrieval and review.”
Increase HCC identification accuracy and lower costs
The added insight provided by lab data enables health plans to identify health risk far better than using claim based methods. The lab data includes cross-payer lab data, 24-month historic member lab results from existing and prior health plans, and ongoing views into member health history. Seamless integration allows for more efficient risk adjustment workflows. Lab data maps directly to 79 CMS HCCs for Medicare Advantage, 129 HHS HCCs for ACA, and the various state-specific managed Medicaid risk adjustment models.
Research by Prognos Health on two health plans, one national and the other regional, found an incremental 15 percent more confirmed HCCs than traditional claim-based and chart retrieval methods. In addition to the additional revenue from HCCs, the plans lowered operational costs. This resulted in nearly $2-3 million in value – an ROI of more than 20 to 1. The results were achieved with virtually no additional IT resources from the health plans.
In addition to having a positive impact on health plan programs, accurate member profiles can lower operational costs associated with member outreach. Frank Jackson points out, “When using lab data, health plans are better able to accurately identify chronic disease in a more timely manner and lower false positives, which leads to more targeted and effective member outreach and chart retrieval and review.” Operations teams benefit by having access to more complete member profiles to inform their efforts.
Most health plans have used one or more national lab partners and may have relationships with regional and hospital labs as well. However, each lab presents the data differently, resulting in clinical data that is disorganized, incomplete, and difficult to interpret and use. As a result, health plans may only have lab data on 20 to 30 percent of their membership. And because this data is spare and not organized to be actionable, this data often is not integrated into their risk adjustment, quality, and care management programs.
Over the past nine years, Prognos has developed data harmonization processes to normalize disparate lab data and apply clinical guidelines to the interpretation of the lab data. These steps have made the data actionable for risk adjustment and quality initiatives at health plans.
Making lab data actionable
Drawing on decades of experience in data and analytics, Frank Jackson notes, “Health plans that invest time and resources into normalizing disparate lab data find it challenging as the processes are complex and difficult to scale.” Prognos has spent the last decade building data transformation processes that integrate lab data in five steps. Our HITRUST-certified cloud synthesizes the raw lab data and transforms it into standardized information and actionable insights.
Sample impact of lab data for a health plan ACA population
Another example is a health plan who gave a sample of its ACA member population to Prognos. The health plan was interested in finding how many HCCs that lab insights could identify that claims-based methods did not. After processing 50,000 members, Prognos’ analytics found a comprehensive list of HCCs for these members, but only counted the ones that were not detected by claims or chart retrieval methods.
The result is that Prognos identified 12% confirmed incremental HCCs in addition to those found by claims. At an average net revenue per HCC of $8,807, the health plan significantly improved its risk adjustment reimbursement. The findings indicated that lab insights not only find confirmed risks, but also high-value HCCs.
Improve your current workflow with lab data
Lab data drives approximately 70 percent of medical decisions and, unlike claims data, is available in near real-time. It also provides an unrivaled level of severity and specificity for clinical conditions. When lab data is integrated into a plan’s claims and chart-based programs, health plans achieve earlier, more comprehensive clinical insights that elevate the effectiveness of care management for both existing and new members.
Prognos works closely with health plans to deliver the most complete member information available when it’s needed most: early in the risk adjustment process. Our AI-based solutions leverage weekly member lab test results going back as far as two years, even before they became a member of the health plan. This allows health plans to recoup reimbursement losses for existing members.