What Should You Know About Risk Adjustment and Intelligent Systems?

Health plans should use preemptive telemedicine and remote care approaches to help counterbalance the detrimental impacts of the pandemic on Risk Adjustment (RA) and performance assessment initiatives.

As a result of COVID-19, regular health interventions are being canceled or postponed, and anticipated risk ratings used to pay Medicare Advantage (MA) health insurers are drifting lower than expected. According to health plan data and Risk Adjustment Solutions, risk scores for the Health and Human Services (HHS) model used by the Affordable Care Act (ACA) for private insurers are also going lower than in previous years.

Healthcare plans should experience an increase in demand for optional and basic care as the pandemic declines, but the restricted capacity and re-strategizing necessary to fulfill that need may persist to impair risk adjustment scores and revenue-capturing possibilities.

Considering these interruptions, insurance providers should implement intentional and meaningful telemedicine and remote care activities to help limit the pandemic's negative impact on Risk Adjustment Solutions and quality assessment operations.

Analyzing the Effect of Machine Learning

During the pandemic, the disease aspect of risk adjustment scores has dropped by double digits in healthcare plans throughout the nation. Although telemedicine interactions have reached new highs, the average healthcare practitioner's face-to-face interactions are dropping.

It is frequently referred to as machine learning, deep learning, or Natural Language Processing (NLP) since it employs computer algorithms to imitate humans' learning processes more effectively and hence generate predictions.

Applications of Natural Language Processing

The use of computer algorithms to recognize essential features in common language and extract meaning from unstructured spoken or written input is an area of automation. NLP technology may be assigned the following tasks:

1.Representing long blocks of narrative text, including a diagnostic report or an academic published paper, by finding significant themes or keywords in the references.

2.Transferring unstructured text data components to structured elements in an electronic health record to enhance medical data consistency.

3.Data conversion from machine-readable formats to plain language for reporting and education.

4.Responding to one-of-a-kind free-text questions that necessitate the integration of the different databases.

5.Using computer vision technology to transform visuals, such as PDF files or scanning of care reports and imaging data, into text documents that can be processed and analyzed.

Addressing HCC Coding

The most critical problems for health plans are to tackle HCC Coding and reporting, risk adjustment, participant involvement, and healthcare provider skills training. Failure to effectively document and capture precise HCC Coding for a patient's risk may result in an unjustly reduced level of associated risk and, consequently, lower compensation.