Prior Authorization Payback: Make.com + Agentic AI for Health Plans

1. Problem / Context

Prior authorization (PA) remains one of the most labor-intensive and costly processes inside health plans—especially for mid-market organizations with 5–20 reviewers. Intake arrives through multiple channels, criteria are complex and frequently updated, and downstream denials trigger rework that erodes margins and frustrates providers and members. Manual triage, data entry across portals, and case assembly slow approvals, resulting in delayed starts of care and cash flow drag.

In this environment, leaders need a pragmatic way to shorten approval cycle times, reduce manual touches, and prevent avoidable denials—without risking compliance missteps. Make.com (formerly Integromat) and governed agentic AI can orchestrate the end-to-end PA workflow, from intake and validation through clinical review support and outbound determinations, while maintaining HIPAA-safe controls and audit trails.

2. Key Definitions & Concepts

• Prior Authorization (PA): The payer process to determine medical necessity and coverage before services are rendered. Failures drive avoidable denials and appeals workload.
• Agentic AI: AI-driven automations that not only analyze but also take actions across systems (e.g., assembling a case, checking criteria, drafting communications), governed by policies, guardrails, and human-in-the-loop checkpoints.
• Make.com: A visual automation platform for building versioned, monitored workflows across APIs, files, and portals. In PA, it coordinates intake, validations, task routing, and notifications while integrating with UM and core admin systems.
• Human-in-the-loop (HITL): Required checkpoints where clinical staff review AI-suggested triage or summaries, ensuring safety and compliance.
• Core metrics: approval cycle time, manual touch rate, cost per auth, denial overturn rate, and nurse FTE utilization.

3. Why This Matters for Mid-Market Regulated Firms

Mid-market plans operate with lean IT and clinical operations teams but face the same regulatory and audit burden as national carriers. Every minute of reviewer time matters. Without orchestration, approvals average multiple days and generate high provider abrasion. By applying governed agentic automation, plans can realistically reduce average turnaround from 5 days to 1 day and cut manual review volume by roughly 40%. Faster decisions trigger earlier starts of care, reduce avoidable denials, and improve cash flow and member experience—key advantages for regional competitiveness.

Kriv AI, a governed AI and agentic automation partner for the mid-market, focuses on making these gains durable by emphasizing HIPAA-safe design, auditability, and measurability from day one.

4. Practical Implementation Steps / Roadmap

1) Discover and map flows

• Inventory intake sources (fax/OCR, provider portal submissions, EDI 278, email) and destinations (UM system, core admin, document repository).
• Catalog policy criteria and common clinical scenarios (e.g., MRI, MSK therapy, sleep studies).
• Identify high-volume, rules-friendly segments for early automation.

2) Data readiness and connections

• Set up secure connectors between Make.com and UM/core systems, document stores, and identity provider (for RBAC).
• Implement HIPAA-safe OCR for faxed submissions; standardize data fields.
• Establish a structured policy/criteria store referenceable by automations and AI.

3) Build orchestrations in Make.com

• Intake triage: Validate completeness; request missing elements from providers via templated, secure emails/portal updates.
• Policy pre-checks: Match request to plan policy and clinical criteria; flag obvious approvals/deferrals; route edge cases.
• Case assembly: Fetch prior history, deduplicate attachments, and create a complete case packet in the UM system.
• AI assistance: Generate clinical summaries from records, highlight criteria alignment, draft determination templates—always reviewed by a nurse.
• Communications: Send status updates and final determination letters; log all interactions.

4) Human-in-the-loop safety

• Thresholds define when AI drafts are advisory only; nurses must affirm or correct suggestions.
• Require reason codes for overrides to enhance learning and audit quality.

5) Production safeguards

• Versioned Make.com flows with environment separation (dev/test/prod), change control approvals, and rollbacks.
• Monitoring on SLAs (cycle time, error rates) and health checks; alerting on integration failures.

[IMAGE SLOT: agentic prior authorization workflow diagram integrating fax OCR, EDI 278, UM system, and Make.com orchestration with human-in-the-loop checkpoints]

5. Governance, Compliance & Risk Controls Needed

• HIPAA and PHI protection: Enforce encryption at rest/in transit, signed BAAs, RBAC, and least-privilege access. Mask PHI in logs and redline prompts to prevent leakage.
• Auditability: Maintain end-to-end audit trails—who touched what, when, and why. Store versioned policies and criteria snapshots used in each decision.
• Model risk management: Document models, training data sources, and intended use; set guardrails so AI cannot render final clinical decisions without review.
• Policy change management: Version criteria and workflows; require approvals and automated regression checks before promotion.
• Vendor lock-in mitigation: Keep decision logic externalized (e.g., rules/criteria repositories) and maintain exportable data structures to ensure portability.

Kriv AI helps mid-market plans harden these controls, combining MLOps, policy governance, and Make.com flow design so compliance never becomes an afterthought.

[IMAGE SLOT: governance and compliance control map showing HIPAA safeguards, audit trails, model risk management, and change control across Make.com flows]

6. ROI & Metrics

Focus on a small number of measurable outcomes:

• Approval cycle time: Target reduction from 5 days to 1 day for common, rules-heavy requests (e.g., routine imaging). Faster starts of care improve member satisfaction and reduce network leakage.
• Manual touch rate: Aim for a 40% reduction in nurse/manual review volume by automatically triaging complete, criteria-aligned cases.
• Cost per authorization: Track fully loaded cost—including intake, review, and rework—to capture end-to-end savings.
• Denial overturn rate: Monitor pre-service vs. post-service appeal outcomes; better front-end decisions reduce costly downstream overturns.
• Nurse FTE utilization: Reallocate time from clerical assembly to true clinical judgment, increasing throughput per reviewer.

Illustrative health plan example

• Scale: 8,000 prior auths/year, mixed ambulatory imaging and MSK therapy; 12 reviewers.
• Baseline: 5-day average turnaround; cost per auth $18 fully loaded; 22% downstream avoidable denials.
• After 90 days of governed automation: 1-day average turnaround; 40% fewer manual reviews; cost per auth down to $11–$12; avoidable denials reduced by 6–9 points. Payback typically falls in the 4–8 month window, driven by labor savings, reduced rework, and earlier, appropriate starts of care that stabilize cash flow.

[IMAGE SLOT: ROI dashboard with cycle-time reduction, manual touch rate, cost per auth, and nurse utilization visualized for prior authorization]

7. Common Pitfalls & How to Avoid Them

• Over-automation without guardrails: Avoid letting AI finalize determinations. Require HITL and reason codes for exceptions.
• Brittle integrations: Portal layouts and document formats change. Use versioned connectors, smoke tests, and quick rollback paths.
• Unclear policy sources: If clinical criteria aren’t versioned and referenceable, automation quality suffers. Centralize criteria and link every decision to a version ID.
• Ignoring provider communication: Automations that don’t manage missing info loops cause delays. Use templated outreach and track response SLAs.
• Skipping data hygiene: Poor OCR or unstructured attachments increase rework. Standardize intake and reject unusable submissions with clear guidance.
• No measurement plan: Define baselines and dashboards before launch; otherwise ROI will be anecdotal.

30/60/90-Day Start Plan

First 30 Days

• Discovery: Map PA subtypes, intake channels, and clinical criteria sources; select one high-volume, rules-friendly cohort.
• Inventory workflows: Document every system touch (UM, core admin, document store, provider portal) and handoff.
• Data checks: Validate OCR quality, field completeness, and identity mappings; establish the policy/criteria repository.
• Governance boundaries: Define PHI handling, audit requirements, HITL thresholds, and change control gates.

Days 31–60

• Pilot workflows: Build Make.com orchestrations for intake validation, policy pre-checks, case assembly, and templated communications.
• Agentic orchestration: Add AI summarization and criteria alignment hints—always with nurse review.
• Security controls: Enforce RBAC, encryption, and PHI-masked logging; confirm BAA coverage.
• Evaluation: Compare pilot metrics vs. baseline—cycle time, manual touch rate, cost per auth, denial trends.

Days 61–90

• Scaling: Expand to additional PA cohorts; tune thresholds to preserve safety while maximizing automation.
• Monitoring: Operational dashboards, alerts, and weekly governance reviews; stress-test rollbacks and regression suites.
• Metrics: Lock in KPI reporting for executives and clinical leads, including payback tracking and FTE utilization.
• Stakeholder alignment: Formalize provider communications, feedback loops, and a quarterly criteria update calendar.

9. (Optional) Industry-Specific Considerations

• Medicaid vs. Commercial: Medicaid PAs may require additional documentation and state-specific criteria tracking; ensure criteria repositories capture jurisdictional differences.
• Medicare Advantage: Heightened audit readiness—retain source documents and decision rationales for each auth.
• Specialty UM (e.g., radiology): Integrate appropriateness criteria sources; automate pre-checks for standard studies with well-established guidelines.
• Appeals/Grievances: Feed determinations into appeals queues with full provenance to reduce overturns and speed resolution.

10. Conclusion / Next Steps

Mid-market health plans can capture fast, durable ROI by orchestrating prior authorization with Make.com and governed agentic AI—reducing average turnaround from days to a single day and cutting manual review volume significantly. The key is not just automation, but safe automation: HIPAA-safe design, audit trails, versioned flows, and disciplined change control that protect both members and margin.

If you’re exploring governed Agentic AI for your mid-market organization, Kriv AI can serve as your operational and governance backbone. As a mid-market-focused partner, Kriv AI helps with data readiness, MLOps, and workflow orchestration so lean teams can move from pilots to production with confidence—and measurable payback in months, not years.