Introduction:
I feel that in order to build a mental framework as a physician-engineer, it helps to stop thinking of AI as a single "brain" and start thinking of it as a team of specialists with different cognitive styles.
In the Availity ecosystem, the distinction is between making a decision (the judge) and summarizing information (the scribe).
1. The Cognitive Map: Four Types of AI
| AI Type | The "Mental" Action | Real-World Analog | Role in Prior Auth |
|---|---|---|---|
| Deterministic AI | Following Rules | A strict protocol or "If/Then" flowchart. | The Foundation: Executes the "Codified Medical Policy" exactly as written. |
| Analytical AI | Calculating Logic | A specialist reviewing a chart for specific evidence. | The Evaluator: Compares patient data against rules to see if criteria are met. |
| Predictive AI | Forecasting | An actuary or weather reporter. | The Risk Scout: Flags cases likely to be denied or identifies high-risk patients. |
| Generative AI | Synthesizing | A medical scribe or resident writing a summary. | The Assistant: Drafts clinical summaries or identifies "gaps" in the documentation. |
2. Where Generative AI (GenAI) Fits In
Availity uses Generative AI (specifically through partnerships like Abridge, announced in early 2026) not to make the decision, but to bridge the gap between the conversation and the data.
Documentation Gap Visibility: During a patient encounter, GenAI "listens" and identifies if you’ve missed a clinical detail required by the insurance policy (e.g., "Doctor, you haven't mentioned the 3-month trial of physical therapy required for this MRI").
Clinical Summarization: Instead of a human reviewer reading 50 pages of records, GenAI creates a concise clinical abstract that highlights only the evidence relevant to the specific medical policy.
The "Drafting" Phase: GenAI can pre-fill the "Statement of Medical Necessity" based on your notes, but the Analytical AI is what actually verifies if that statement is true according to the policy.
3. Why Availity Avoids GenAI for the "Final Answer"
In your role building decision trees, you must maintain a "Firewall" between Generative and Analytical logic:
- The "Hallucination" Risk: Generative AI is probabilistic (it predicts the next most likely word). In medicine, you cannot have an AI "guess" if a BMI is 30.1 or 29.9.
- The "Black Box" Problem: If GenAI denies a claim, it can’t always explain why in a way that is legally defensible.
- The Solution (Deterministic AI): This is why Availity’s AuthAI™ is Deterministic. It uses the JSON decision trees we discussed earlier. If the data enters the tree, the result is mathematically certain and 100% traceable to the policy.
4. A Physician-Engineer’s Framework
When you sit down with engineers to build a new module (e.g., for a new cardiovascular drug), use this workflow:
- Step 1 (Generative): Use GenAI to scan the 40-page PDF policy and summarize the key requirements into a draft list for you to review.
- Step 2 (Human): You, the physician, verify that the draft requirements match clinical reality.
- Step 3 (Deterministic): The engineer translates those requirements into the JSON logic gates.
- Step 4 (Analytical): The system runs the live patient data through those gates.
- Step 5 (Predictive): The system looks at historical data to tell you, "Based on this payer's history, even though we meet the criteria, there is a 20% chance they will still flag this for manual review."
Mental Shortcut: * Generative AI is for reading and writing.
- Analytical/Deterministic AI is for deciding and auditing.
Summary statement, this essay is meant to help clear up the "semantic noise," of all the AI Agent types, but to be honest it does add to the pool of names, even though it attempts to shine a light on the problem. Next I will look at a specific clinical scenario for AI Deterministic Modelling.
5. Four Stage Functional Workflow with two agents
To clarify: I am describing a four-stage functional workflow, but these four "roles" are technically handled by two primary product engines (Fusion and AuthAI) and a strategic partnership (Abridge).
Availity "stacks" these as a vertical stack of products, but a horizontal chain of AI functions.
Here is the definitive breakdown of how these agents interact in the 2026 Availity ecosystem.
A. The Two-Product Vertical Stack (The "Hardware")
At the foundational level, Availity sells and operates two primary "engines." Everything else happens inside them:
- Availity Fusion™: The Data Engine. This is where the Deterministic and Analytical cleaning happens.
- Availity AuthAI™: The Decision Engine. This is where the Deterministic policy matching and Generative exception handling occur.
B. The Four-Agent Functional Chain (The "Software")
While there are two main products, they utilize four distinct "AI behaviors" (or agents) to get the job done. This is the "chain" I described:
Stage 1: Ambient/Generative Agent (The Partner: Abridge)
- Where it lives: At the point of care (during your patient exam).
- Role: This is a Generative AI agent. It "listens" to the conversation and creates the structured clinical note.
- The Bridge: It uses Availity’s APIs to flag "documentation gaps" in real-time (e.g., "Doctor, you didn't mention the failed physical therapy trial required for this MRI").
Stage 2: Normalization Agent (The Product: Fusion™)
- Where it lives: The data intake layer.
- Role: This is an Analytical/Deterministic agent. It takes the notes from Stage 1 and "upcycles" them. It deduplicates records and translates "Myocardial Infarction" into a code the system can calculate.
Stage 3: Determination Agent (The Product: AuthAI™)
- Where it lives: The decision layer.
- Role: This is a Deterministic agent. It is the "Judge." It takes the cleaned data from Stage 2 and runs it through the JSON Decision Tree you helped build. It provides a "Yes/No" recommendation in under 90 seconds.
Stage 4: Exception/Summarization Agent (The Product: AuthAI™)
- Where it lives: The review layer (only if Stage 3 fails).
- Role: This is a Generative AI agent. If the case is too complex for the "Decision Tree," this agent summarizes the 40-page record into a 1-page brief for you (the human physician) to review.
3. Reconciling the "Two vs. Four" Framework
It is a vertical stack, but the "four agents" are the functional tasks that the stack performs. Think of it like a hospital department:
- The Department is the "Product" (AuthAI).
- The Triage Nurse, Resident, Attending, and Scribe are the "Agents."
| Task | AI Type | Handled By |
|---|---|---|
| Listen & Transcribe | Generative | Abridge (Partner) |
| Clean & Code Data | Analytical | Fusion™ |
| Apply Policy Logic | Deterministic | AuthAI™ |
| Summarize Complex Cases | Generative | AuthAI™ |
Summary for Your Mental Framework
Availity is "Vertical" because they own the pipeline (Fusion to AuthAI). They are "Chain-based" because they use different AI methodologies at each step of that pipeline.
As a physician-engineer, you aren't building four separate robots. You are building one Decision Tree (AuthAI) that relies on Clean Data (Fusion) and is fed by Ambient Notes (Abridge).
This is the alignment between the "Product Stack" and the "AI Roles" help stabilize the framework for
Human in the Loop HITL
In your dual role as a physician and a technical advisor to engineers, your job isn't to "check the AI's homework" on every case—it’s to design the answer key.
1. The "Human-in-the-Loop" Shift
Availity’s 2026 model follows a "Management by Exception" philosophy:
For 75–80% of cases: The Deterministic AI handles the "routine" (e.g., a standard MRI for a specific diagnosis). You don't see these; they are auto-approved in under 90 seconds.
For the "Exceptions": If a case is complex and doesn't fit the Decision Tree, the AI doesn't just guess. It uses Generative AI to "abstract" the 40-page record into a 1-page Clinical Summary.
Your Role: You only read that 1-page summary to make the final clinical "call." You are checking the summary, not the raw 40-page PDF.
2. Your Real Job: The "Policy Architect"
Instead of reading PDFs for individual patients, you spend your time Codifying the Policy. This is a one-time setup (per policy update) that allows the AI to run autonomously.
| Old Way (Manual) | New Way (Physician-Engineer) |
|---|---|
| Physician reads 40-page PDF for every single patient. | Physician reads 40-page PDF once to extract the logic. |
| Physician manually cross-references EHR notes. | Physician tells engineers: "If Lab X is > Y, it's a pass." |
| High burnout; 20-minute review per case. | Low touch; you build the JSON logic gate once. |
3. How Availity Protects Your License (The "Traceability" Feature)
You might worry: "What if the AI is wrong and I'm the doctor of record?" Availity’s AI is not a "Black Box." Every recommendation it makes comes with a "Evidence Citation":
- If the AI recommends "Approve," it provides a digital "breadcrumb" trail: "Approved because it found ICD-10 code M54.5 and 6 weeks of Physical Therapy in the Fusion™ normalized data stream."
- You (or the payer's medical director) can click a link and see the exact sentence in the medical record that triggered that logic gate.
4. Summary Framework for Your Engineers
When you are "helping engineers build the decision trees," you should tell them:
- "Use GenAI to Extract": Have the AI scan the 40-page PDF and propose a draft of the Decision Tree (e.g., "It looks like this policy requires three criteria...").
- "Physician Validates": You spend 5 minutes looking at that draft tree to ensure it’s clinically sound.
- "Deterministic Execution": The engineers lock that logic into the JSON/CQL format.
- "Human-in-the-Loop": Set a "Confidence Threshold." If the AI is only 70% sure a patient meets a criterion, it must kick it to you for a 60-second review of the summary.
The Bottom Line: You are the Architect of the system, not the Editor of its output. You read the policy once to build the "Machine," so the "Machine" can read the records for you from then on.