Health Plans Are Using AI To Address The Issue Of Insufficient Data
Adequate, precise, and reliable member data is required for health plans to assess patients' medical conditions, anticipate potential risks, and identify treatment gaps that must be filled. However, patient records that meet these requirements are unusual, and real difficulty in leveraging patient data stems from its vast quantity. Managing confusing and inaccurate patient data adds to payers' problems. Employing automated technologies such as Electronic Health Record (EHR) interoperability and conventional paper-based patient records extraction, the Risk Adjustment Solution detects, tracks, and records accurate diagnoses that are not generally adequately or sufficiently recorded by a health plan's core claims mechanisms. Health insurance companies responsibly concentrated early compliance on giving access to structured data, but they should now incorporate unstructured data for improving the database's utility to patients. Unstructured data can help payers with a variety of reporting needs, including Risk Adjustment (RA). Health plans have used previously unexplored insights to generate concrete data that supports predictive analytics, thereby improving patient outcomes, lowering costs, and enhancing risk management.
Increasing the Effectiveness of Medicare Advantage Risk Adjustment Inconsistent and inaccurate HCC Coding can lead to huge spending for health insurance companies, forcing many health plans to recruit huge teams of chart reviewers to manually trawl through patient data in quest of additional insights into these datasets. To meet the challenges of this constraint, several health technologists are increasingly using Natural Language Processing (NLP) to supplement the chart review method. The healthcare plans have two distinct aims for their NLP initiative: one, to speed up the review procedure so physicians can assess more records per hour, and second, to collect diagnoses related with HCC codes that chart review teams may have neglected. Several new Risk Adjustment Solution models now include cutting-edge machine learning and AI to assist with previously ignored insights. Such algorithms recognized characteristics for HCC codes with greater than 90% accuracy, processing huge records. NLP speeds up chart evaluations and allows reviewers to be more efficient and productive by processing hundreds of thousands of complex and time - consuming health data. Identifying and Addressing Social Determinants of Health (SDoH) Issues That most of the data about social determinants of health is stuck in unstructured data including hospitalizations, discharge, and treatment plans. By applying Natural Language Processing (NLP) to search for effective social features, health plans can take initiatives aimed at improving communication with patients who are thought to be at risk of skipping visits and having severe uncontrolled illness progression.