The COVID-19 pandemic exposed many weaknesses in the US healthcare system. Among them is the Medicaid program’s antiquated IT infrastructure. As millions of people lost their jobs and insurance coverage, Medicaid/CHIP enrollment surged, growing nearly 20% between February 2020 and September 2021. The sudden demand for coverage put a strain on technology and resources nationwide, leaving many eligible Americans temporarily uninsured.

At the same time, Medicaid is also grappling with many of the same challenges facing traditional health plans, including an increased urgency to adopt the latest big data technologies (such as artificial intelligence (AI) and machine learning), mounting pressure to meet the interoperability requirements of the Cures Act, the urgent need to transition to whole patient care using social determinants of health (SDOH), and myriad other competing priorities. Adding to the complexity is the fact that Medicaid is administered at the state level, which means upgrading its IT infrastructure involves overhauling as many independent IT systems as there are states.

Meeting these and other imperatives requires CMS and state Medicaid programs to make significant investments in clinical data, including advanced data infrastructure and broader data sharing. CMS is currently pouring billions into helping states with digital transformation efforts. Some states, including North Carolina, Connecticut, Arizona, California, and Michigan, have developed substantial plans to transform their Medicaid infrastructure due to the COVID-19 pandemic.

North Carolina is investing $430 million in a Medicaid Transformation Fund to transition beneficiaries to managed care and expand pilots for providing social services in lieu of medical care. Connecticut’s lawmakers expanded Medicaid to undocumented children and passed far-reaching health equity reforms declaring racism a public health crisis and investing in better data collection. Meanwhile, Arizona’s Whole Person Care Initiative is providing social support and referrals to Medicaid enrollees in partnership with managed care plans. The California Advancing and Innovating Medi-Cal Program includes ambitious efforts to improve prevention, address social determinants of health, offer social services, integrate behavioral health, and manage population health for the state’s 12 million Medicaid enrollees.

In a recent Health Affairs blog post entitled, “A Strategic Vision for Medicaid and Children’s Health Insurance Program (CHIP)1,” CMS emphasized the urgent need for states to collect, understand, and use data related to coverage and access, equity, innovation, and whole-person care, while also making this information transparent to stakeholders.

While digital transformation over the past decade has concentrated primarily on the rapid adoption of electronic health records (EHR) and increasing medical record sharing among providers, expanding data infrastructure now includes ambitious initiatives focused on population health management for people with complex medical needs and improving health disparities.

One challenge that providers and health plans face is making sense of the reams of fragmented data they receive. As with Missouri’s Medicaid leaders calling themselves “data-rich and information-poor,” many of these organizations have high volumes of data but are still struggling with the tools and teams to clean the data and unify records to create a “whole-person” view for every patient. Since EHR systems are not designed for data curation, much of the data being compiled is not clean enough for analysis using the latest tools.

In response, many states are working with regional and state health information exchanges (HIEs) to create this essential data infrastructure. The HIEs are building interfaces to provider EHRs, as well as cleaning, managing, and storing the data so it can be used to improve care.

The need for clean, normalized data was strongly underscored in the recent Medicaid strategy outline:  

“First and foremost, we need accurate data. We can’t fix what we don’t know, and we can’t measure progress without a baseline. Reporting on race, ethnicity, language, disability status, and sexual orientation and gender identity are inconsistent at best—as are clear, consistent, and comparable stratification of critical quality and outcome metrics across the program. CMS will work with states to improve measurement of health disparities across a core set of stratified metrics.”

The adoption of data curation and normalization technologies offers a wealth of benefits to Medicaid programs and commercial plans alike. The benefits stem from the ability to analyze data at scale while combining previously unconnected sources. Some of the most valuable advantages include:

• Enhance value of care and resource optimization. Data analysis can help reduce costs for payers and providers while delivering a more comprehensive service to patients. Looking at data combined from different sources, providers can achieve a more holistic view of each patient’s health and optimize resources to avoid unnecessary expenses.
• Better patient outcomes. Data that has been properly curated and normalized helps clinicians diagnose diseases more accurately, predict patient outcomes, and devise more effective treatment plans. 
• Efficiency boost for clinical research. Sharing research data is much easier when the data has gone through standardization processes. In addition, cloud-based AI systems can dramatically reduce the time needed for analysis with properly structured datasets.
• New medical products. With quicker and more reliable testing based on more refined data, companies can develop new wearable devices and pharmaceutical products in less time. Similarly, healthcare providers and insurers can join efforts in devising new plans and packages.
• Improve financial performance. Clean, accurate data opens the door to AI, machine learning, and predictive analysis that can be applied to financial data as well. With the help of these tools, healthcare organizations can obtain a clearer view of their financial operations, remove inefficiencies, and streamline revenue cycle management.
• Prevent fraud, waste, and abuse. Data collection and aggregation can effectively reduce the number of fraudulent claims. By exchanging data and applying predictive analysis, insurance companies can detect potential “red flags” and pause payment for such claims.
• Encourage Value-based care. AI-powered analysis using high quality data also helps shift the insurance paradigm to a more value-based approach. Insights from aggregated data suggest that covering preventative care and wellness visits is the path to affordable and efficient insurance. 

As states continue rebuilding Medicaid’s health data infrastructure, there is a clear need to implement innovative technologies designed to cleanse and normalize the data collected. Doing so will make the data truly actionable, allowing providers and health plans to fully leverage the incredible computational power of data analytics to improve the quality of healthcare for all.

For more information about the benefits of data curation and enrichment, visit https://www.verinovum.com/dcaas/