Mastering Risk Adjustment: Technology Best Practices for Health Plan Executives

Author:
Dean Stephens
Date:
December 13, 2016

Last week, I discussed some of the key challenges payer organizations face with risk-based contracting. Now I’ll share some steps health plans can take to master risk adjustment through improved coding efficiency, productivity and accuracy.

Effectively addressing the challenges of risk adjustment starts with improving coder productivity and accuracy. Payers should leverage the new breed of sophisticated data analytics available and ensure their professional coding staff – whether made up of in-house, remote contractors, or outsourced to a third-party vendor – has the tools and capabilities that support best practices for high accuracy coding:

Automate the chart review process – Utilizing automated technology will help streamline the risk adjustment process by reducing the need for the “chart chase” and eliminating inefficient, time-consuming, manual chart reviews. These technology-driven data analytics tools give coders the ability to process a higher volume of patient charts with more accuracy for increased efficiency. Tools that are integrated into the coder’s workflow and feature an intuitive, easy-to-use interface allow coders to review more charts per hour, often dramatically increasing productivity. Health plans that leverage these tools can more easily respond to growing enrollment and expand their risk adjustment programs without the high costs associated with increasing their coder workforce.

Comprehensively review patient data – Finding and identifying chronic conditions is essential to improving patient care, controlling costs, and obtaining accurate risk scores and reimbursements – three critical components of successful risk adjustment programs. New technology, such as natural language processing (NLP), advanced semantic ontologies and machine-learning clinical rules engines, can collect and analyze large amounts of disparate patient data, including the valuable 80 percent of unstructured data hidden in consult notes, radiology reports, specialist charts, etc. Finding and documenting these missed conditions can have a significant effect on risk scores and reimbursement rates and can make the difference between receiving or paying out transfer payments. Implementing these powerful, automated analytics tools gives coders a more complete look at all patient data, allowing them to efficiently and accurately uncover missed HCC conditions and close documentation gaps without having to manually comb through reams of clinical data.

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Want to learn more? Download our latest white paper, “Winning in Risk Adjustment: A Guide for Health Plan Executives,” to find out what other important steps health plans can take to improve coding processes and win in risk adjustment. Plus, read how automated coding technology and data analytics helped one large payer coding organization increase productivity by 135 percent.

Dean Stephens is the CEO of Talix.
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