AI Foundations Pt. 1: An Operating System for AI in Healthcare

It’s now beyond question that AI has a vital role to play in healthcare administration. What’s urgently needed, however, is a robust and transparent way to implement it—one that is both efficient and safe while keeping people firmly in control. In other words, we need a solid foundation for AI in healthcare, something akin to an operating system (OS). Let’s explore what this OS might look like, layer by layer.

What is an Operating System for AI in Healthcare?

Think of a standard computer OS: It’s the fundamental layer that lets all applications function— without each app needing to reinvent the basics. As TechTarget succinctly puts it, “The OS provides a consistent and repeatable way for applications to interact with the hardware and other system-level functions without the applications needing to know any details about them.”

Components of an AI Healthcare OS

A practical healthcare OS can be visualized as three layers built atop a data foundation. Picture a house:

1. Data is the land on which everything is built.
2. Foundational Models form the foundation of the house.
3. Autonomous Task Execution is the first floor.
4. Program Optimization is the top floor.

Within this structure, we identify six key components:

Data Layer

• Medical Data Store
  Claims data captures key insights about billing, reimbursements, and patient coverage, while medical records offer a clinical view of diagnoses, treatments, and outcomes. By unifying these two sources, the OS gains a dynamic “single source of truth”—one that updates in real time and continuously cross-references administrative and clinical information. This integrated approach enables applications to detect anomalies, identify emerging patterns, and refine decision-making at every level of healthcare operations.
• Policy Database
  Healthcare administration is governed by a maze of complex regulations. Take claims processing: not only are there multiple intricate steps, but each is subject to a host of regulatory rules. By housing these policies and linking them to patient-provider data, the OS gains a critical ingredient for informed decision-making.

Foundational Models Layer

• Foundational AI Models for Complex Questions
  Foundational models underpin the AI healthcare OS. A piecemeal approach—where you build separate AI for each use case—can quickly become untenable, a game of “whack-a-mole” where solving one problem spawns the need for another siloed solution. Foundational models provide an adaptable backbone that supports numerous use cases—current and future. These models run the gamut from policy expertise to clinical decision interpretation to care trajectory prediction.

Autonomous Task Execution Layer

• Complex Reasoning with Humans in the Loop
  Armed with accurate data, AI systems can automate decisions that were once purely manual. But especially in healthcare, there are moments where human oversight is essential. The OS must balance autonomy with opportunities for human intervention to maintain trust and accountability.

Program Optimization Layer

• Policy Enforcement
  Policy enforcement ensures that every automated decision aligns with regulatory requirements and organizational values. This includes:
  • Monitoring: Defining key performance indicators (KPIs) and setting real-time alerts for anomalies.
  • Governance: Creating oversight processes or committees to mitigate bias and ensure regulatory compliance.
• Feedback Loops for Continuous Improvement
  Effective AI systems are never “set and forget.” A robust OS continually learns and refines its foundational models by ingesting new data, evaluating performance, and iterating—all in real time.

Once this layered architecture is in place, the same principles and data can be leveraged to solve a wide range of problems, whether it’s detecting a miscoded claim or enhancing diagnostic accuracy.

Machinify as a Real-World AI OS

Machinify’s AI OS exemplifies a comprehensive platform that streamlines decision-making and enables automation in healthcare. It combines foundational and task-specific AI models to extract valuable insights from billions of claims, medical records, and policies—upholding transparency, compliance, and operational efficiency along the way.

Rather than simply automating individual tasks, Machinify’s OS serves as an end-to-end AI decisioning framework. Its specialized models are fine-tuned for various applications, such as:

• Predicting the outcome of coding audits to improve claim accuracy.
• Retrieving relevant answers from large datasets of clinical notes.
• Automating prior authorization and payment integrity checks to reduce administrative burden.

Core Components of Machinify’s OS

Machinify’s OS is, of course, built on foundational models. These proprietary models ensure accuracy when it comes to interpreting and organizing medical data. 

• Medical Coding Models
  These models accurately classify procedures and diagnoses.
• Policy and Contract Models
  These models interpret and enforce complex payer rules with precision on real-time streams of claims.
• Medical Record Models
  These extract structured insights from unstructured clinical notes and documents to enable rigorous evaluation of care patterns.

These models are continually refined through sophisticated feedback loops that incorporate real-world outcomes, ensuring ongoing improvements. In addition, Machinify’s crucial optimization layer—the top floor—allows each health plan to embed its unique objectives into the AI workflow, ensuring compliance, efficiency, and financial sustainability for every decision.

There’s More to Unpack

Think of a healthcare OS as a fully integrated ecosystem of data and decision-making tools. Each layer is interdependent, and when they work seamlessly together, the result is transformative. With the OS established, we can now focus on leveraging these foundational models to deliver remarkable results in identifying claim errors, optimizing workflows, and reducing administrative overhead.

In the next installment of this AI Foundations series, I’ll dive deeper into how these models work in practice. In the meantime, to learn about how Machinify can help transform your program with AI, contact us today.