The Future of Medicaid: Standardized Data, Equitable Care, and AI-Powered Access

Ryan Bullock, chief strategy officer at Aeroflow Health discusses how the Centers for Medicare & Medicaid Services (CMS)–driven push for interoperability standards, real-time patient data integration, expanded telehealth access, and the use of artificial intelligence (AI)–enabled tools is reshaping Medicaid’s approach to chronic condition management, maternal health equity, and accessible care for non-English speaking, low-literacy, and digitally underserved populations.

How do you see the Centers for Medicare & Medicaid Services’ (CMS’s) emphasis on patient data reshaping Medicaid's approach to chronic condition management?

CMS’s emphasis on patient data is driving Medicaid fee-for-service and managed care plans to adopt interoperability standards based on the Fast Healthcare Interoperability Resources (FHIR) framework. This development supports seamless data exchange among patients, providers, and payers.

This shift ensures that accurate and comprehensive health data truly follow the patient, enabling more informed, consistent, and equitable chronic condition management.

One immediate benefit is better prior authorization decisions. When all relevant clinical data is aggregated from multiple sources and linked to the patient, we find that approval or denial decisions become more reliable, reducing delays and errors.

Beyond prior authorizations, this interoperability creates new opportunities to reshape chronic care:

• Sleep Apnea Management: For patients using continuous positive airway pressure (CPAP) machines, daily data such as usage patterns, sleep quality, and frequency of apneic events can be monitored. This allows for timely interventions if adherence drops or symptoms worsen.

• Diabetes Management: Continuous glucose monitoring (CGM) devices can provide near real-time readings, including trends in glycated hemoglobin (A1C) and episodes of hypoglycemia or hyperglycemia. These data give care teams actionable insights to adjust treatment promptly.

The operational impact is just as significant. Today, customer service and billing staff spend valuable time “chasing” documentation from sleep labs, endocrinologists, and other providers.

Centralizing and sharing this data would eliminate redundancies, reduce administrative burden, and free up resources to focus on patient care.

In short, when high-quality data flow seamlessly across the care ecosystem, chronic condition management becomes more proactive, personalized, and efficient—benefiting both patients and the Medicaid system as a whole.

What challenges exist in integrating patient data from multiple sources (eg wearables, electronic health records [EHRs], home devices) across Medicaid programs, and how might CMS's modernization initiative help address them?

One of the biggest challenges in integrating patient data from multiple sources, like wearables, EHRs, and home devices, is the lack of data standardization and normalization. Different systems use different naming conventions, formats, and metadata structures. For example, Aeroflow Health may label a field “HIC Number” while another provider calls it “Member ID” or “Insurance ID.”

Without a standardized approach, it’s difficult to match records accurately and build a complete, reliable patient history.

When data are not normalized, care teams risk working with incomplete or outdated information. A patient may have started a new medication the previous day, but without real-time, standardized updates, that change could be missed, impacting both clinical decisions and billing accuracy.

CMS’s modernization initiative, including FHIR-based interoperability, directly addresses these challenges by creating a common data framework. This makes it easier to:

• Match and identify members across disparate systems.

• Ensure consistency in metadata so that intent and context of the data are clear.

• Deliver a real-time, unified view of patient information to improve clinical decision-making.

Standardization doesn’t just improve patient care. It also drives operational efficiency. By eliminating the manual work of “chasing” documentation and reconciling mismatched records, resources can be redirected to patient support, leading to both cost savings and better outcomes.

Given CMS's push for technology equity, how should telehealth be prioritized to close maternal health gaps in rural and underserved communities?

To close maternal health gaps in rural and underserved communities, International Board Certified Lactation Consultants (IBCLCs) and Certified Lactation Counselors (CLCs) should be included in the federal telehealth list of covered practitioners. This would allow these specialists to provide virtual lactation consulting and related services to areas with severe shortages of maternal health professionals.

In many states, lactation consulting via telehealth is not covered by Medicaid or commercial payers, limiting access for those who need it most. Including IBCLCs and CLCs as eligible telehealth providers would address:

• Access Gaps: Expanding reach to communities without in-person lactation support.

• Workforce Shortage: Meeting demand where there are too few qualified practitioners.

• Health Equity: Ensuring mothers in underserved areas can get the same quality of postpartum and breastfeeding care as those in urban centers.

By expanding telehealth coverage to include lactation professionals, CMS could leverage existing networks and technology to provide critical education, early intervention, and ongoing breastfeeding support, helping to improve maternal and infant health outcomes while reducing disparities.

How can artificial intelligence (AI) tools help Medicaid programs better serve non-English speaking, low-literacy, or digitally underserved populations—without sacrificing clarity or compassion?

AI can play a critical role in helping Medicaid programs serve non-English speaking, low-literacy, and digitally underserved populations, while maintaining both clarity and compassion.

First, AI-powered omnichannel communication helps ensure that messages reach patients in their preferred and most comprehensible format. These formats may include written materials, plain-language summaries, video content, audio messages, or formats compliant with the Americans with Disabilities Act (ADA). For example, study notes or health instructions could be converted into a podcast, while visual aids could be paired with captions and translations for accessibility.

Second, AI can assess literacy levels in real time. If a patient’s reading ability falls below a sixth-grade level, the system can automatically simplify the content, avoiding medical jargon while preserving accuracy and empathy. This capability extends to language translation, enabling clear communication in a patient’s preferred language without losing cultural context or tone.

Third, AI can be context-aware, pulling from the patient’s known health history and disease state to deliver information with compassion. This means recognizing sensitive topics, such as chronic illness, maternal health risks, or mental health concerns, and tailoring the tone accordingly.

By combining adaptive content delivery with patient-specific insights, AI can help Medicaid programs break down barriers to understanding, promote trust, and improve adherence to care plans—particularly for those who have historically been left behind in digital transformation efforts.