Anand Shroff

The U.S. healthcare system is currently shifting to an incentive structure where health plans and providers are rewarded for delivering higher quality care with better efficiency. This transition from fee-for-service to value-based care is not only changing how patients are cared for, but also how health plans and providers are measured and compensated for performance.

With an ever-growing number of patients covered under value-based care programs, it’s crucial for payers and providers to collaborate on risk-adjustment performance so that they can position themselves for financial success in risk-sharing arrangements and deliver value without disrupting existing work flow.

Risk adjustment in payment models refers to the practice of accounting for the differences in the underlying risk (i.e., expected costs) of patient populations. It would be unfair to compare the costs incurred of a healthy member to that of a sick member without properly adjusting for the expected cost of each person based on her/his health status. However, risk adjustment is not just a payment model mechanism. Successful capture of risk enables obtaining a complete and accurate picture of your members’ health status, which is critical to ensuring proper reimbursements, effectively managing costs of your high-risk members, and delivering high-quality care.

Managed care organizations, through participation in programs such as Medicare Advantage, Managed Medicaid, and ACA, are well-aware of the importance of accurate risk adjustment. However, currently risk adjustment is a very manual and inefficient process at most health plans-medical record acquisition and record review for coding are very expensive processes and leverage very little technology. Additionally, most of the analytics used to stratify members based on risk only utilize claims data leading to ineffective gap recognition and physician abrasion during the risk capture process.

New technologies that integrate with EHRs to acquire data and provide natural language processing (NLP) and machine learning (ML) tools dramatically change the risk adjustment expense and return. By automating medical record acquisition, an expensive part of the risk adjustment process can be eliminated. Additionally, using NLP and ML, the risk adjustment team can focus on members that have outstanding risk factors and save precious capacity from being spent on reviewing members with accurate health statuses. Finally, using NLP and ML based analytics that rely on clinical evidence to surface suspected conditions to fuel prospective risk adjustment campaigns have a much higher likelihood of success with low physician abrasion.

Even though health plans have been dealing with risk adjustment for years, the technology is now available to lower costs and improve financial returns on investment by a factor of five to 10.

Providers have recently begun entering risk-sharing arrangements through contractual vehicles such as Medicare ACOs as well as Medicare Advantage partnerships with health plans. As a result, effective risk capture is now a high priority for health systems and physician groups. Some of the key hurdles providers cite that prevent them from entering risk-sharing arrangements is the lack of access to administrative and claims data, and the required risk infrastructure to allow effective management of care and costs for patients. As organizations take on more risk burden across multiple patient populations, it is important to set up a comprehensive risk adjustment program that can cover all relevant populations.

For providers new to risk adjustment, a comprehensive technology platform that utilizes NLP and ML while integrating into their established EHR workflow is an essential step toward accurate risk capture. Such a platform would support starting with a post-encounter coding review often provides the highest immediate ROI because it enables risk capture from diagnoses that have already been documented, but not coded, without requiring physician work flow modification. By using technology and bringing the retrospective review more upstream for a concurrent work flow, providers can ensure that claims accurately reflect the care provided and diagnoses are adequately substantiated before they go out the door. Prior to putting any information in front of physicians, there is an opportunity to leverage NLP and ML analytics on historical patient data to identify prospective risk capture gaps pre-encounter. Providers can identify gaps such as patients with suspected conditions who lack the proper documentation to be submitted for reimbursement. Advanced analytics technology can also ensure that only the most accurate intelligence is being put in front of a physician at the point-of-care to increase trust of the providers and reduce any potential for physician abrasion.

During encounter with a patient, a point-of-care solution can deliver the gaps to the physician and allow the closure of gaps without departure from the physician’s EHR-centered work flow. A successful point-of-care solution must also facilitate the documentation of outstanding risk conditions through accurate and complete encounter documentation in the EHR. This allows providers to efficiently capture the risk of their populations, decrease the need for follow-up visits, and streamline coding review processes.

This encounter-centric framework and phased implementation approach allows organizations to develop a plan for continuous improvement and transformation. Once an organization has developed a more mature risk adjustment infrastructure, it can then layer in additional technology and analytics solutions to identify gaps prospectively and optimize their risk capture at the point-of-care.

The best start to improvement requires that organizations understand where they currently stand and, accordingly, set a baseline from which progress can be measured. Technology platforms that utilize NLP and ML are now available to help payers and providers achieve accurate risk capture at a fraction of the cost of legacy solutions. Organizations will need to adopt a comprehensive but customized solution that is fit for their readiness, infrastructure, process, and people that will serve as the blueprint for achieving success.

, a provider of technology solutions for healthcare organizations.

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Measuring success in the value-based care era requires establishing a baseline and implementing new technology tools.

How Technology Helps Healthcare Organizations be Successful in Value-Based Care

	The first-year results of the Affordable Care Act's (ACA) Risk Adjustment Program are in, and while the experience may not have been a home run, it might be characterized as an RBI double.

	A major component of the ACA, the risk adjustment program is intended to spread out the cost of covering patients with pre-existing conditions by distributing payments, in part, according to the risk factors of those insured patients. The goal was to encourage insurers to cover high-risk patients by transferring funds from insurers focused on relatively low-risk populations.

 by the American Academy of Actuariesâ Risk Sharing Subcommittee, the program is largely headed in the right direction. Among its primary findings, the committee noted that the program âcompressed the loss ratio differences among insurers with low loss ratios and reduced loss ratios for insurers with high loss ratios.â Simply put, the program did its job of shifting funds from plans with low-cost enrollees to others with high-cost enrollees.

	Not everyone, however, benefitted equally. The report says that âRisk adjustment experience can vary among insurers due to operational issues (e.g., technical issues with loading enrollment and claims data, timely processing of claims), which may have impacted some small or new insurers to a greater degree than large and more established insurers.â It also noted that the impact of premiums, upon which risk adjustment factors had a strong influence, also came into play for various payers.

	âRisk adjust transfers as a percent of premium were more variable and likely to be higher for insurers with a smaller market share,â the report states. âInsurers with a larger market share were by definition closer to the market average while small-market-share insurers were more likely to be skewed toward either low-risk or high-risk individuals.â

	Across the U.S., plans that are new have been understandably hindered the most in mastering the intricacies of the program. A survey by a coalition of 35 small insurers participating in ACA exchanges found that 27 were compelled to write checks to the program as a result of risk adjustments. Even some longtime payers were impacted; Earlier this year, 

 that a decades-old South Florida HMO, Preferred Medical Plan, received a risk adjustment bill of $97 million.

	Larger plans generally fare better because they have the resources to chase claims and can more quickly identify high-risk members. Nonetheless, several such plans have struggled with ACA exchange participation since 2014. Humana Inc., while reporting a small profit on individual plans in Q1 of 2016, says 

 that may include statewide market and product exits, both on- and off-exchange. UnitedHealth Group reported a $720 million loss for 2015 on its exchange business segment, of which $245 million is a set aside for an anticipated loss of $500 million for 2016. 

 from all but a small portion of the 34 states in which it offers exchange plans by 2017.

	Uncertainty and even turmoil is not uncommon when fundamental change hits an industryâand the growing pains involved with ACA implementation are no exception. As insurers struggle to find their footing in this new era, significant financial shifts will undoubtedly continue, although they should lessen in intensity over time.

	Premiums, in particular, will fluctuate. It should be noted, however, that the risk adjustment program is a zero-sum endeavor, meaning thatâat least as plannedâpayers who are penalized under the program will be offset by others who benefit through accurate measurement and reporting of their risk. The federal government is also offering premium subsidies for many lower-income consumers; flowing directly to the insurer, the subsidies reduce the impact of large premium increases.

	As the healthcare industry looks ahead, itâs clear that the balance of risk, cost and revenue will be tricky to master. Risk adjustment, for example, drives more than transfer payments; the projected risk factor for a plan has a direct role in premium prices. Health plans that underestimate their risk may underprice their premiums and jeopardize profitability, while also attracting a much higher number of new enrollees with unknown health risks. Underestimating risk may also prevent a health plan from being eligible for transfer payments because its risk score was too low. On the other hand, overestimating risk may result in health plans raising their premium rates and losing membership because they are not as competitive in state marketplaces.

	Preparing for the next round of risk payments, for nearly all insurers, will require a much tighter assessment of the risk level of their populations. For many payers, determining risk scores is a tedious process. Claims are often analyzed manually; moreover, any sources of data are typically structured, meaning that the information is already stored in digital formats.

	The trouble is, much of the most influential information isnât in digital form at all. Unstructured dataâphysician notes, lab reports, personal health records, forums, affinity groups and the likeâare either handwritten or a part of forms and files that cannot be converted. Unstructured data is typically left out of risk calculations, despite the fact that such information can provide better insight and understanding of a patientâs overall health status. It can also help uncover misdiagnosed or undiagnosed conditions, improve treatment recommendations, and ensure that chronic conditions are correctly managed for better long-term clinical and financial outcomes.

	As insurers take a hard look at their approach to risk assessment, it will be critical to incorporate more sophisticated forms of technology into the resource mix. Many insurers are only now grasping the full challenge presented by the shift to value-based careâan environment where complete and compelling data, submitted to the rigorous standards of the ACA and its federal administrators, is king.

	Undoubtedly the mixed results of the ACA Risk Adjustment Program will improve in the years ahead as state and federal agencies refine the standards and procedures involved. It will be the job of insurers and providers, however, to respond to those refinements with infrastructure that ensures fair and equitable payments for all concerned. It remains to be seen if the program can bat 1.000 in its stated goalsâbut with the right technologies in place, everyoneâs average can become a lot higher. 

Anand Shroff is co-founder, chief technology and product officer of San Mateo, California-based 

The first-year results of the ACA’s Risk Adjustment Program are in. Find out how it worked out and what insurers need to do next.

Author | Anand Shroff

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How Technology Helps Healthcare Organizations be Successful in Value-Based Care

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