The concept of value-based care where data is dynamic, patients are encouraged to proactively manage chronic conditions, and physicians are reimbursed on outcomes rather than total tests and procedures performed is seeing renewed interest during COVID’s remaking of healthcare.
New evidence of the trend was the announcement on Monday (Dec. 20) that health information technology solutions company Edifecs is acquiring Health Fidelity, whose Natural Language Processing (NLP) technology and inference platform analyzes unstructured patient data “to supply clinical and financial insights.”
The Health Fidelity deal builds on Edifecs’ September acquisition of Talix, whose software also uses natural language processing (NLP) and machine learning to analyze unstructured patient data, extracting billing codes and analyzing health trends in specific populations like the elderly.
According to a statement, “Edifecs will integrate Health Fidelity and Talix technology into its signature Encounter Management solution to provide customers the opportunity to operate risk adjustment processes more efficiently for Medicare Advantage, Managed Medicaid, and Affordable Care Act (ACA) products. The addition of Health Fidelity and Talix Natural Language Processing (NLP) and Artificial Intelligence (AI) technologies to Edifecs’ workflow solutions will create one of the most comprehensive Risk Adjustment offerings in the market.”
By integrating data technologies of Health Fidelity and Talix, Edifecs is building out its platform’s predictive risk adjustment capabilities. Per Healthcare.gov, risk adjustment is “a statistical process that takes into account the underlying health status and health spending of the enrollees in an insurance plan when looking at their health care outcomes or health care costs.”
Edifecs CEO Venkat Kavarthapu said, “Traditional risk adjustment solutions have rapidly outgrown their retrospective-only payer use cases. By combining Health Fidelity and Talix solutions with our own workflow, Edifecs can now offer payers and providers clinical and coding solutions that deliver risk adjustment insights across many operational areas all while enabling the shift to value-based care.”
According to an article from the National Center for Biotechnology Information (NCIB), “The big problem of healthcare fields is that about 80% of medical data remains unstructured and untapped after it is created (e.g., text, image, signal, etc.). Since it is hard to handle this type of data for Electronic Medical Record or most hospital information system, it tends to be ignored, unsaved, or abandoned in most medical centers for a long time.”
NLP is proving effective at taking masses of unstructured patient data — voice recordings, text, scans, images, test results and more — and extracting valuable information that can not only bring about better patient outcomes, but also help remove cost from healthcare.
Until recently, however, NLP has not typically been designed for healthcare use cases.
Healthcare IT News recently reported that “According to one recent survey, 36% of healthcare organizations plan to implement NLP this year.”
See also: Ex-Microsoft Exec Forms Truveta To Share Anonymized Patient Data
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