Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Inovalon Launches Race & Ethnicity Data Enrichment Offering as Part of its QSI-XL Software Solution

New Offering Helps Health Plans Dramatically Improve Data Completeness to Better Identify Care Disparities Across Member Populations and Supports New HEDIS Stratification Requirements

Inovalon, a leading provider of cloud-based software solutions empowering data-driven healthcare, announced a new data enrichment offering that allows health plans to improve the completeness and accuracy of race and ethnicity data for their members. By using algorithms uniquely available through the Inovalon ONE Platform, the offering fills in information gaps that can lead to care disparities across member populations and helps plans meet new stratification requirements for five Healthcare Effectiveness Data and Information Set (HEDIS) measures for measurement year 2022.

Latest Aithority Insights: Detecting, Addressing and Debunking the Hidden AI Biases

Health plans face inherent challenges in directly capturing race and ethnicity data from members such as member distrust, unwillingness to self-identify, or inadequate staff training. These challenges can affect the completeness of population data and hinder efforts to incorporate race and ethnicity data into holistic member-centric care and intervention planning, which can also significantly impact a health plan’s overall quality ratings.

The National Committee for Quality Assurance (NCQA) has approved the use of indirect methods by health plans to improve the collection and use of race and ethnicity data to advance health equity efforts. While promising, some indirect methods pose challenges as well. Many health plans do not have the expertise or resources to produce indirect race and ethnicity data enrichment files. Further, tight regulatory reporting timelines prevent health plans from re-surveying their member populations on a regular basis to obtain this data and fill their information gaps.

Related Posts
1 of 40,749

AI and ML News: AI: Continuing the Chase for Brain-Level Efficiency

To address these challenges, health plans can now leverage the Inovalon ONE Platform, available public data sources, and Inovalon’s proprietary algorithms to analyze membership data and produce indirect supplemental membership datasets to enrich their race and ethnicity data. In a recent study of a large not-for-profit health plan, Inovalon’s data enrichment offering helped reduce the percentage of members classified as unknown race from 41.2% to .8% and unknown ethnicity from 100% to 1.8%. This dramatically improved the health plan’s ability to identify care disparities within its member population and comply with new regulatory requirements.

“Lack of specialized expertise and resources make it difficult for health plans to collect member data with the completeness and confidence needed for both compliance and deriving insights,” said Courtney Breece, Associate Vice President of Product at Inovalon. “This new offering lets health plans easily integrate supplemental data in just days, easing compliance and improving analysis and decision-making.”

AI ML in Marketing: AI and Big Data Analysis Used to Find Brands’ Emotional Connection

[To share your insights with us, please write to sghosh@martechseries.com]

Comments are closed.