Many health plans today take a largely transactional approach to risk adjustment. When a submission deadline approaches, they may hastily compile a list of members to perform a “sweep” for their risk adjustment coding operations. There’s rarely enough time to take a thoughtful approach that streamlines the efforts.
The process entails gathering member charts and sending the charts to coders for review. Manual data acquisition is difficult because it usually requires onsite abstractors to copy, fax, or scan documents. In addition, the retrieved charts can be disorganized and muddled with documents that are not relevant to risk adjustment. Once clinical data are acquired, coders typically review all documents with no particular strategy or prioritization – or as many as they can before the submission deadline. Measuring coder productivity is difficult, which potentially reduces effectiveness.
With such a haphazard process, health plans may not fully understand their risk adjustment programs’ return on investment. With risk adjustment becoming increasingly important to the bottom line of health plans and the wellness of patients, organizations need to improve their programs to gain a competitive advantage.
Overcoming Time-to-Value Barriers
Prioritizing the efforts that go into risk adjustment can greatly reduce the waste inherent in the process. Rather than review charts unsystematically, coders can leverage technology that flags specific charts with the highest likelihood of yield.
Natural language processing (NLP) – which can be used to analyze free text in clinical documents – is proven to effectively prioritize risk adjustment efforts to maximize coder productivity and accuracy. Once evidence of risk conditions are identified via NLP, those documents can be presented to coders for review – effectively reducing the number of charts that coders are required to manually review. Organizations have been able to see up to 4x increase in coder productivity as a result.
Reducing Technology Adoption Barriers
For coders to adopt new technology, they often need to overcome a mindset that new technology is designed to automate and replace their roles. Coders are accustomed to doing manual chart reviews and may be wary when new technology is introduced that changes the established process – especially if that technology is able to identify additional risk factors that were overlooked during manual chart reviews.
Organizations that are looking to introduce NLP – or other technology – into their risk adjustment processes must emphasize and communicate that NLP is not intended to replace coders. Instead, NLP is designed to augment, supplement, and enhance the important work that coders perform. Rather than have coders waste their time reviewing charts without any risk factors, they can use their substantial experience to focus on the charts that will impact risk adjustment. The end result is that coders are more efficient and make a bigger impact on the organization, which can also improve job satisfaction.
Beyond prioritizing charts for coder review, technology can also be used to improve workflow. For example, workflow solutions can be used to provide coders with a consolidated view of all available records for a member so that the coder can have the most complete context of the patient’s health status when coding. Additionally, workflow solutions can reduce bias across coders and help standardize coding practices across the department.
Other Ways Technology Can Help
A key benefit of introducing technology into risk adjustment processes is that the quality of prospective analytics can vastly improve. In a typical retrospective review process, when a coder spots documentation that is insufficient to validate a diagnosis code – but is indicative of the patient likely having that condition – there is not much that the coder can do. However, this is extremely valuable information that can be used to target educational opportunities for providers and properly document the condition the next time around. Coding platforms can allow coders to flag instances of inadequately documented conditions; analytics can then be run upon this set of data to identify specific providers that most frequently under-document or specific conditions that are most frequently under-documented.
There are many other ways in which technology can help to streamline adjustment coding operations to increase productivity, accuracy, and compliance:
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Data acquisition – With a high penetration of EHRs, there’s potential to acquire charts via technology solutions rather than manual methods. This would help to receive the medical charts more quickly and accurately while decreasing administrative costs and provider abrasion.
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Activity monitoring – Technology can easily track who reviewed charts and what action was taken, allowing for better quality control and internal auditing of coding results. This can be especially helpful during CMS audits – the documentation used to substantiate codes can be easily retrieved for audit submission.
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Performance management – Technology can enable better visibility into coder performance, including productivity and error metrics. Coding managers can use data-driven approaches to prioritize quality assurance audits and ensure standardization across the coders.
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Compliance risk management – NLP can be used to flag conditions submitted for risk adjustment that do not have substantiating clinical documentation. This allows organizations to mitigate their compliance risk by proactively reviewing flagged issues.
The Culture Shift
There are technologies that can help organizations improve their risk adjustment programs, but only if they are adopted by end-users and used effectively. Organizations looking to thrive in the new healthcare environment must not only seek technologies to achieve their goals, but also create cultures that view technology not as a threat, but as a way to help them become more effective in the work they perform.
To discuss the latest risk adjustment strategies and best practices to improve your HCC adjustment coding operations, contact us.
