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The Linear Health Journal

Healthcare AI insights for operational leaders

Practical guides to referral automation, prior authorization AI, care gap closure, AI voice agents, and patient scheduling. Written for the teams who run specialty practices, FQHCs, and PE-backed medical groups.

Key Takeaways

The Linear Health Journal publishes 162 practical guides on AI automation for healthcare operations: referral management, prior authorization workflows and CMS-0057-F compliance, care gap closure and HEDIS quality measures, AI voice agents for patient calls, patient scheduling and no-show reduction, and medical fax automation. Built for operational leaders at specialty practices, FQHCs and community health centers, and PE-backed multi-site medical groups.

Latest articles

Referral Intake Automation: One Accountable Record Across Every Channel

How to bring every intake channel into one accountable workflow without counting every message as a new referral.

How to Get Consult Notes Back: A Documentation Return Workflow

An administrative workflow for requesting, receiving, matching, and routing specialist documentation without confusing receipt with clinical review.

All articles

162 entries

Referral Intake Automation: One Accountable Record Across Every Channel

How to bring every intake channel into one accountable workflow without counting every message as a new referral.

How to Get Consult Notes Back: A Documentation Return Workflow

An administrative workflow for requesting, receiving, matching, and routing specialist documentation without confusing receipt with clinical review.

Automated Patient Outreach for Specialty Referrals: Design the Stop Rules First

A practical outreach design that coordinates automated attempts and human follow-up around the current referral state.

Inbound vs. Outbound Referrals: Who Owns Each Handoff?

One referral has a sending side and a receiving side. Map their responsibilities so a completed task at one office does not become missing work at the other.

How to Evaluate Healthcare AI Vendors: Ask for Evidence at Every Step

Evaluate healthcare AI vendors with a claim-to-evidence worksheet, failure demonstrations, reference questions, and a clear procurement decision record.

Referral Automation Implementation: A Phased Plan With Release Gates

A post-selection implementation plan that connects each launch decision to evidence, ownership, and a workable recovery path.

Referral Management Software Pricing: Compare Quotes on the Same Work

A practical pricing worksheet for comparing referral software on the same scope, volume and first-year cost assumptions.

Referral Bottleneck Analysis: Separate Staff Work From Waiting Time

Reconstruct the referral timeline before deciding whether the next improvement needs more staff, better information, or a different handoff.

Voice AI and EHR Scheduling: Ten Tests Before You Trust a Booking

Test voice AI scheduling integration with ten booking scenarios covering write-back, duplicate prevention, cancellations, outages, and recovery.

Patient Self-Scheduling: Design a Booking Flow Patients Can Finish

Build a patient self-scheduling flow with clear slot eligibility, confirmed bookings, accessible recovery paths, and a measurable booking funnel.

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Explore our complete guides: What is Referral Management? · Referral Software Buyer's Guide · Operational AI in Healthcare

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