How Technology can Streamline Prior Authorization

In healthcare, it’s rare to come across opportunities where everyone wins.    That’s why I invited Jay Nakashima, president of eHealth Exchange, onto my latest Code to Care video. His team is streamlining the reviled prior authorization process — to the benefit of patients, providers, and payers.    That’s worth celebrating.    Historically, prior authorizations required a drawn-out, manual back-and-forth between providers and payers that often relied on fax machines and phone calls.    Making matters more cumbersome, providers deal with an average of 30 payers, each with their own portals and procedures.    With eHealth Exchange’s solution, everything runs more smoothly.    It enables providers to use FHIR applications within their electronic medical record system to seamlessly connect with multiple payers via eHealth Exchange’s trusted network.    Better yet, the applications show each patient’s coverage and their payer’s prior authorization requirements. The technology can even pull necessary clinical information from the records automatically, so everything’s shipshape from the get-go.    This approach cuts the time providers and payers spend on paperwork and accelerates patients’ access to high-quality care.    If data can turn the ever-challenging prior authorization process into a win for everyone, imagine what else it can do. 

Healthcare prior authorization involves multiple steps, long wait times (days to weeks), and customer journeys that vary per case. I built a reference architecture that handles this with AWS Lambda durable functions and Amazon Bedrock AgentCore. The setup: → A Document Agent extracts clinical facts and decides which specialists are needed (eligibility, policy, medical necessity). Not all cases need all three. → Only the required agents fan out in parallel. Each one runs on AgentCore as a containerized Strands agent. → The durable function suspends at every async boundary — provider uploading missing docs (72 hours), human reviewer (48 hours), payer decision (14 days). No compute cost while suspended. → A Synthesis Agent aggregates specialist findings, notes gaps from any failed agents, and produces a recommendation with a confidence score. The orchestration layer is deterministic (Durable functions handles state, retries, parallelism). The reasoning layer is agentic (each specialist uses LLM reasoning within its domain). Conditional routing is decided at runtime by the planner agent, not hardcoded. Check out the GitHub sample in the comments below!

The health system stopped chasing authorizations after the order came in. They started clearing them before the patient ever scheduled. A case study out of Baptist Health Care in Florida changed how I think about prior authorization. For years, their teams had been working authorizations the manual way. Paperwork. Repeated payer follow-ups. Last-minute reschedules when approvals didn't land in time. They'd chase the approval, push the appointment, and absorb the patient frustration. No early warning. No proactive clearance. Just reactive scrambling. Then they tried something different. Digital orders started triggering the authorization workflow the moment they hit the system. Not after a staffer picked it up. Not two days later. The rules-based logic kicked in instantly, and future orders got authorized up to 30 days before the service. The result? 93.5% of authorizations cleared within one working day. Before, they took five to seven. Not because they hired more staff. Not because they worked longer hours. Because the authorization started at the front of the workflow instead of the back. Their patient access director said reschedules due to missing authorization dropped to minimal levels. Once the work moved upstream, the bottleneck downstream disappeared. Appointment volume nearly doubled from 8,000 to 15,000 without adding headcount. The fix wasn't more effort. It was better timing. This is what's broken in most revenue cycles. The scheduler waits on the authorization. The authorization team waits on the order. The patient waits on everyone. And when the approval doesn't land, the appointment gets canceled and nobody owns the delay. The case study called it modernizing prior authorization. I call it moving the work to where it belongs. If you want fewer reschedules, don't add another follow-up call at the end. Move the authorization to the front. Let the approval clear before the patient ever picks a date. What's a step in your workflow that happens too late to prevent the problem it's supposed to catch? #RCM #RevenueCycleManagement #HealthcareOperations #PriorAuthorization #PatientAccess #ProcessImprovement #DenialManagement #Scheduling #HealthcareRCM

American physicians spend ~14.4 hours per week on prior authorization paperwork; nearly two full workdays stolen from patient care. The process is broken: → 21 minutes per manual PA review → 82% of denials overturned on appeal (wrongly denied) → 26% still wait 3+ days despite regulatory requirements One regional plan needs 40 PA nurses for 500K members. Just to keep up for PA. At Nēdl Labs, our Neuro-Symbolic AI platform processes straightforward PA requests in under 30 seconds, not 21 minutes. Neural networks read the messy clinical documentation. Symbolic reasoning applies the policy logic. Knowledge graphs maintain the relationships. The result: Speed + clinical rigor + full audit trails. Complex cases that took RN reviewers 15-25 minutes? Now 3-5 minutes. The same 40 nurses can now handle 1.8M-2.4M members. For PA leaders: What percentage of your denials get overturned on appeal? #PriorAuthorization #HealthcareAI #ClinicalOperations #NeuroSymbolic #AI

Prior auth remains one of the highest friction points in the revenue cycle but AWS is signaling a major shift in how we can automate it. AWS just showcased how Strands Agents can handle the full prior authorization process end-to-end. For RCM leaders, the scale of the problem is well known and it’s not getting better despite payers touting that they’ve rolled back PA numbers: ❗️39 PAs per physician per week ❗️13 staff hours spent weekly on PA ❗️93% of physicians report care delays ❗️29% report patient harm tied to PA delays ❗️$35B in annual administrative expense ❗️81.7% of MA denials overturned on appeal This is pure operational waste. Strands Agents offers a different path. Instead of traditional rules engines or static workflows, the agent can: ✅ Interpret interoperability resource bundles ✅ Pull real-time payer policies ✅ Analyze CPT codes against coverage rules ✅ Generate approval/denial rationale ✅ Produce patient cost estimates ✅ Identify documentation gaps before submission In AWS’s demo, the agent automatically retrieved Medicaid guidelines, aligned CPT codes, produced coverage rationale, and calculated costs without any manual intervention. This isn’t widely deployed (yet). But AWS is clearly signaling that agent based automation is the next frontier of RCM modernization, particularly for high volume, high variability workflows. This development is an indicator that AI agents aren’t a future concept anymore. They’re becoming an operational lever with a real ROI when the right solution in the right process is adopted correctly. Healthcare needs fewer manual touches. Agent based automation might finally get us there. #priorauthorization #automation #revenuecyclemanagement

2025 Shows Real Momentum for Prior Authorization Automation 🌟 Prior authorization has long been one of healthcare’s biggest pain points—but this year marked a true turning point. In their new article, Alexandra (Alix) Goss and Kendra Obrist from Point-of-Care Partners (POCP) highlight how policy alignment, industry pledges, maturing standards, and real-world pilots have finally moved the conversation from “if” to “how fast” automation can scale. A few standout insights: 🔹 CMS-0057 and HTI-4 have created unprecedented regulatory momentum 🔹 CRD, DTR, and PAS implementations are producing measurable results 🔹 States like Washington and Massachusetts are proving how collaboration accelerates real-time, FHIR-based PA 🔹 EHR integration and AI-enabled decision support are cutting turnaround times from days to hours 🔹 Trebuchet pilots and CMS Health Tech Pledges are driving shared accountability across payers, providers, and vendors Where HL7 FHIR at Scale Taskforce (FAST) at Scale Complements Prior Authorization Automation As PA automation scales, FAST provides the critical infrastructure layer that makes it sustainable at national scale: ✔ FAST Identity enables trusted authentication and digital credentials across payers, providers, and delegated entities. ✔ FAST Security (UDAP) provides the scalable trust framework needed to securely connect CRD, DTR, PAS, CDex, and Payer/Provider APIs. ✔ FAST Consent supports reusable, computable consent patterns to authorize sharing of clinical data that feeds PA workflows. ✔ FAST National Directory ensures reliable endpoint discovery and accurate organizational attributes—foundations for routing prior auth requests. ✔ FAST Testing at Scale will help ensure consistency and conformance as CMS-aligned networks expand. Together, these efforts strengthen the backbone that will help prior authorization automation move from pilots to nationwide production adoption. 📈 If 2025 was the year automation took hold, 2026 will be the year we scale it. 👏 Incredible work by Alix and Kendra—and an exciting moment for the entire interoperability community. Read the article: https://lnkd.in/evQyvE3Y? #PriorAuthorization #Interoperability #HL7 #FHIR #CMS0057 #HealthTechEcosystem #DaVinciProject #FASTFHIRatScale #POCP #DigitalHealth #AIinHealthcare #FHIRatScale #HealthcareInnovation

This is a big “decision → action” moment in healthcare AI. OpenEvidence (evidence-based clinical answers at point of care) just partnered with Tandem (automates the messy access layer: prescription workflow + prior auth + appeals + affordability + routing). The pitch is simple: if the clinician makes the right call, the system should execute it without a week of paperwork. What’s interesting here is the direction: OpenEvidence started as “medical search + decision support.” Then comes coding. Now—through Tandem—it’s reaching into prescriptions and prior auth, i.e., the part of healthcare that routinely breaks the care plan after the visit. If they can actually connect: evidence-backed recommendation → prescription generated → prior auth submitted (and appealed if needed) → routed to the right pharmacy/site of care → affordability support to reduce abandonment… …that’s not just “AI that answers.” That’s AI that gets care across the finish line. Bigger trend: the race to become an all-in-one clinical AI platform is getting real—and the winners won’t be the ones with the smartest chatbot. They’ll be the ones who reduce the time between “this is the right treatment” and “the patient actually gets it.” https://lnkd.in/dzeeDfYj 🔁 Know someone building or buying workflow AI in healthcare? A quick repost might save them time.

🚨 CMS-0057-F vs CMS-0062-P Understanding the Scope, Standards, APIs, Systems, and Technology Transformation Across Healthcare Interoperability Many healthcare professionals hear these CMS interoperability regulations mentioned together, but their scope and operational focus areas are very different. Here’s a simplified breakdown from a Healthcare IT, FHIR, Integration, and Enterprise Architecture perspective. ━━━━━━━━━━━━━━━━━━ 🔹 CMS-0057-F Interoperability & Prior Authorization Modernization ━━━━━━━━━━━━━━━━━━ Primary Scope: Modernize payer-provider interoperability and automate prior authorization workflows. Core Areas: ✔ Patient Access API ✔ Provider Access API ✔ Payer-to-Payer API ✔ Prior Authorization API ✔ PA decision timelines ✔ Denial transparency Technology Focus: ✅ HL7 FHIR R4 APIs ✅ SMART on FHIR ✅ OAuth 2.0 / OpenID Connect ✅ Real-time interoperability Implementation Guides: • Da Vinci PAS • CRD • DTR • PDEX • US Core IG Systems Impacted: • Health Plans • Provider Systems • Prior Authorization Platforms • Care Management Systems • Claims & UM Systems Key Timelines: 📅 2026 → Operational requirements 📅 2027 → FHIR API implementation expectations ━━━━━━━━━━━━━━━━━━ 🔹 CMS-0062-P Drug Prior Authorization & Pharmacy Interoperability Expansion ━━━━━━━━━━━━━━━━━━ Primary Scope: Expand interoperability standards with stronger focus on drug prior authorization and pharmacy ecosystem integration. Core Areas: ✔ Drug Prior Authorization ✔ ePrescribing interoperability ✔ Medication authorization workflows ✔ Pharmacy benefit coordination Technology Focus: ✅ FHIR-enabled interoperability ✅ ePrescribing integration ✅ Pharmacy workflow automation ✅ Real-time authorization exchange Standards & Ecosystem Areas: • HL7 FHIR • NCPDP SCRIPT • NCPDP RTPB Systems Impacted: • PBMs • Pharmacy Systems • ePrescribing Platforms • Medication Authorization Systems ━━━━━━━━━━━━━━━━━━ 🔹 Simplified Difference ━━━━━━━━━━━━━━━━━━ CMS-0057-F: ➡ Enterprise-wide payer-provider interoperability modernization CMS-0062-P: ➡ Expanded interoperability focus for pharmacy and medication authorization ecosystems ━━━━━━━━━━━━━━━━━━ 🔹 The Bigger Industry Shift ━━━━━━━━━━━━━━━━━━ Healthcare is moving away from: ❌ Fax-driven workflows ❌ Manual authorization processing ❌ Siloed systems toward: 🚀 FHIR + API-driven interoperability 🚀 Real-time healthcare data exchange 🚀 Workflow automation 🚀 API-first healthcare architecture The future healthcare ecosystem will increasingly require professionals skilled in: ✔ EDI ✔ FHIR ✔ APIs ✔ Prior Authorization ✔ PBM & Pharmacy Workflows Healthcare interoperability modernization is becoming a foundational operational strategy across the industry. #CMS0057F #CMS0062P #FHIR #HealthcareIT #Interoperability #PriorAuthorization #HL7 #DaVinci #DigitalHealth #HealthTech #HealthcareArchitecture #APIs #PayerTechnology #PBM #EDI

Prior Authorization is quietly becoming one of the biggest operational friction points in healthcare. It impacts everyone: • Providers stuck in admin loops • Patients waiting on care • Revenue cycle teams firefighting denials • Engineering teams building workarounds instead of scalable systems Despite all the technology investments across healthcare, prior auth is still: • Fax-driven in many cases • Payer rule ambiguity and constantly changing requirements • Highly manual and error-prone • Poorly integrated into EHR and ordering workflows • A major contributor to care delays and revenue leakage What makes it harder? 1. Lack of standardization across payers 2. Inconsistent clinical criteria requirements 3. Disconnected systems such as EHR, portal, RCM, and clearinghouse 4. Limited real-time eligibility and rules validation 5. No clear feedback loop when authorization fails The result: • Increased denials • Long turnaround times • High administrative overhead • Provider burnout • Poor patient experience The industry talks a lot about interoperability, AI, and automation, but prior auth remains a fragmented problem sitting between clinical, financial, and integration layers. There is real opportunity here for: • Intelligent rule engines • Real-time payer API integrations • Predictive authorization scoring • Automated documentation gathering • Workflow orchestration across systems If you are working on meaningful solutions in this space or have built products that truly reduce prior auth friction, I would love to learn more. If you have a demo or real-world implementation experience, feel free to ping me in DM and we can discuss.

💡 82% of patients abandon treatment over paperwork! 🔍 In this post, you'll learn how AI agents can transform one of healthcare's biggest pain points - prior authorization - from a slow, manual process into a fast, automated workflow that helps patients get care faster. 📋 Prior authorization is a serious problem in healthcare today. A 2024 survey found that 93% of doctors say it causes care delays, and 82% of patients sometimes give up on treatment because of it. The current process is full of friction: ↳ Manual paperwork takes hours of staff time each week ↳ Each insurer has different forms and requirements ↳ Scheduling and authorization are often disconnected ↳ Patients and doctors get little visibility into status ↳ Delays can directly harm patient health outcomes 🤖 Amazon Bedrock AgentCore powers a multi-agent AI solution that handles each step of the process. Specialized AI agents work together in parallel to get the job done quickly and accurately: ↳ Eligibility agent checks insurance coverage in real time ↳ Document agent gathers clinical notes and records ↳ Authorization agent fills and submits payer-specific forms ↳ Monitoring agent sends updates to all care team members 💡 The result is a prior authorization process that goes from days down to under 10 minutes - reducing staff burden, improving patient experience, and helping ensure that needed care is never delayed by paperwork. 📚 Read more: https://lnkd.in/eYfjkesc - - - - - - - - - - 👉 Follow me to stay up to date on AWS Flah Ahmad 💬 Comment your questions or thoughts down below 🔁 Repost to share the knowledge with your network