What you'll learn

Use Gen AI to support prior authorization, claims, denials and appeals workflows.
Identify documentation gaps that may cause PA failures, claim denials or appeal delays.
Analyse denial reasons using classification, CARC codes and root cause thinking.
Draft stronger appeal letters using SBAR, evidence checks and human review.
Apply safe AI practices by avoiding PHI, patient identifiers and unsupported outputs.
Build trackers, checklists and evidence libraries to improve claims workflow control.

Course content

1 section • 6 lectures • 6h 7m total length

Foundations of Prior Authorization: Concepts, Failures, and Public Resource Work57:04
What prior authorization means in real healthcare practice.
The difference between clinical need and payer approval.
Why a doctor’s order alone may not be enough for approval.
The key people involved in the PA process, including providers, PA coordinators, payers, pharmacies, PBMs and patients.
How handoff failures create delays, denials and rework.
Common reasons PA requests fail, including missing documentation, incorrect codes, timing issues and coverage problems.
How to separate administrative failures from clinical documentation failures.
Why step therapy, medical necessity and supporting records matter.
How AMA and CMS public resources can help learners understand PA burden, process timing, reform language and documentation expectations.
Why public resources are useful for training but cannot replace payer portals, provider records or patient specific workflows.
This module also includes practical case studies, role plays, workflow examples and knowledge checks to help learners apply concepts confidently. By the end of the module, learners will know how to think like a PA professional, identify workflow risks early, ask for the right documentation and reduce preventable prior authorization failures.
AI-Powered Evidence Tools for Effective Clinical PA Documentation57:25
Learners will explore:
What medical necessity really means from a payer’s point of view.
The five key questions payers check before approving a request.
How to prove diagnosis, guideline support, level of care, prior treatment history and clinical urgency.
The five common documentation mistakes that lead to denials.
How to turn vague notes into patient specific, evidence based documentation.
How to use the SBAR format to write clear prior authorization letters.
How to structure a PA letter using Situation, Background, Assessment and Recommendation.
Where to place diagnosis, treatment history, clinical evidence and approval request in the letter.
How free healthcare evidence tools can support stronger PA writing.
How PubMed.ai helps learners find relevant biomedical research.
How Elicit Basic helps learners extract trial outcomes and study evidence.
How EvidenceHunt Basic helps learners ask focused clinical evidence questions with citations.
How to use AI evidence tools safely without letting them make medical decisions.
Why every AI generated fact, statistic, guideline and citation must be verified by a clinician.
How to build an evidence package for appeals using the three tool evidence stack.
How to improve appeal reasoning when a payer says treatment is not medically necessary.
How specialty documentation changes across oncology, cardiology, neurology and behavioral health.
What oncology PA requests must include, such as cancer stage, histology, biomarkers and ECOG status.
What cardiology PA requests must include, such as LVEF, NYHA class, GDMT history and guideline support.
What neurology PA requests must include, such as MRI findings, relapse history, disability scores and safety labs.
What behavioral health PA requests must include, such as DSM 5 diagnosis, ASAM dimensions, safety risk and functional impairment.
Why missing small details like biomarkers, safety labs or treatment dates can trigger preventable denials.
How to apply learning through realistic cases, including rheumatoid arthritis, heart failure, asthma, multiple sclerosis, oncology and behavioral health.
How to identify whether a weak PA letter needs better chart facts, better evidence, stronger attachments or clearer clinical reasoning.
How to create PA documentation that is clear, complete, evidence based and payer friendly.
By the end of this module, learners will be able to write stronger PA letters, correct weak documentation, use free evidence tools responsibly and build more complete approval focused clinical documentation packages.
Decoding and Preventing Healthcare Claim Denials: Practical Tools and Tactics51:32
Learners will explore:
What an appeal really means in prior authorization, denials and revenue cycle work.
Why appeals fail when teams treat them like routine paperwork.
How to choose the right appeal route based on denial type, urgency and claim value.
The difference between Level 1 internal appeal, peer review, external independent review and regulatory escalation.
When a written Level 1 appeal is enough.
When peer review should be prepared early for urgent or high value denials.
When patient appeal rights and external review may apply.
Why regulatory escalation should only be used when there is a documented payer pattern.
How to use Zotero to build an appeal evidence library.
How Zotero helps organize guidelines, journal articles, drug labels, payer policies and clinical evidence.
Why evidence must be stored separately from patient information.
How to use LibreOffice Writer to create appeal letters and peer review battle cards.
How to build a reusable Level 1 appeal letter template.
How to structure an appeal using header, formal request, situation, background, assessment, rebuttal, requested action and supporting documents.
Why the rebuttal section is the most important part of an appeal letter.
How to copy the payer denial reason exactly and respond to it directly.
How to avoid the common mistake of repeating the original request without answering the denial reason.
How to prepare a peer review battle card for the treating physician.
What every peer review battle card must include, such as clinical snapshot, denial reason, evidence, payer objections, prepared counters and closing request.
How to help physicians stay focused during short payer review calls.
What language strengthens a peer review conversation.
What language weakens a peer review conversation.
How to document the peer review call with reviewer name, credentials, reference number, authorization number, date and time.
How to support patient appeals in plain language.
How to help patients explain care impact, safety concerns and functional limitations.
Why external review may be useful when internal appeal options fail but the case remains clinically strong.
How to keep appeal communication compliant, documented and evidence based.
How to practise appeal decision making through a gamified appeal strategy challenge.
How to decide whether a case needs Level 1 appeal, peer review, patient support letter, external review or compliance escalation.
Why every appeal must be based on facts, evidence and the payer’s specific denial reason.
By the end of this module, learners will know how to select the correct appeal route, prepare evidence, support physicians during peer review and draft appeal letters that directly address payer denial reasons.
Module 4 Summary
Module 4 teaches learners how to turn appeals into a structured and evidence based process. Learners move beyond emotional or generic appeal writing and learn how to build focused responses that answer the denial reason clearly.
The key message is simple: a strong appeal is not a complaint. It is a clinical argument supported by patient facts, evidence and a clear request.
Learners will finish this module knowing how to use Zotero for evidence organization, LibreOffice Writer for appeal drafting, peer review battle cards for physician preparation and appeal strategy logic to choose the right next step.
Advanced Healthcare Appeals: Strategies for Evidence, Documentation, & Rebuttal1:08:42
What denial management really means in prior authorization and revenue cycle work.
The difference between hard denials and soft denials.
Why hard denials usually need a formal appeal or may lead to write off.
Why soft denials can often be corrected and resubmitted without a formal appeal.
How to avoid wasting time by treating every denial the same way.
How CARC codes help explain the reason behind a denial.
The most common CARC codes, including CO 4, CO 16, CO 29, CO 50, CO 97, CO 197, CO 204, PR 1, PR 2 and CO 109.
What action to take for each denial code.
Why CO 50 medical necessity denials need strong clinical appeals.
Why CO 197 prior authorization not obtained needs urgent action.
Why CO 29 timely filing denials are difficult to recover without proof.
How to classify denials within 24 hours so appeal deadlines are not missed.
How to use the 48 hour denial response protocol.
What to do in the first 4 hours after receiving a denial.
How to perform root cause analysis between hours 4 and 8.
How to collect missing documentation between hours 8 and 24.
How to draft the appeal or corrected claim between hours 24 and 36.
How to complete final review and submission between hours 36 and 48.
The five major root cause categories behind denials.
How front end errors happen when eligibility, demographics or PA checks are missed.
How clinical documentation errors happen when notes are vague or incomplete.
How coding errors happen when CPT, ICD 10, modifiers or revenue codes do not match.
How process errors happen when PA numbers, claim links or clearinghouse records fail.
How payer behaviour can create patterns such as auto denials, outdated policies or delayed responses.
How Notion free tier can be used to build a denial tracker.
How AI supported tracking can reveal patterns by payer, provider, procedure, code and dollar value.
Why denial patterns are more valuable than individual denials.
How to identify sudden denial spikes and investigate payer policy changes.
How to spot physician specific documentation patterns without blaming individuals.
How to use denial data for payer escalation, leadership reporting and workflow improvement.
How to build three AI checkpoints into the workflow.
How a scheduling checkpoint prevents PA not obtained denials.
How a submission checkpoint prevents wrong codes, missing information and weak documentation.
How an AR review checkpoint prevents timely filing misses and lost appeal deadlines.
How payer specific checklists can improve first submission approval rates.
How a 90 day prevention plan can improve approval rates using free tools.
Why every denial should become a learning signal, not just a billing problem.
How prevention saves time, protects revenue and reduces stress for patients and teams.
By the end of this module, learners will know how to classify denials correctly, choose the right response pathway, perform root cause analysis, use AI supported denial tracking and build prevention checkpoints that reduce repeated denials.
Module 3 Summary
Module 3 teaches learners how to turn denial management into a structured and proactive process. Instead of seeing denials as isolated problems, learners begin to see them as signals of deeper workflow issues.
The key message is simple: fixing one denial is useful, but fixing the pattern prevents many future denials.
Learners will finish this module with a practical method to classify denials, respond within 48 hours, track payer and provider patterns and create checkpoints that stop preventable denials before they happen.
Scaling Prior Authorization and Denial Workflows with LibreOffice Calc and Zoter1:10:53
Learners will explore:
What an AI powered PA and denial workflow looks like in real practice.
Why every workflow needs two layers: prevention and recovery.
How prevention stops avoidable denials before they happen.
How recovery helps teams respond when a denial has already occurred.
Why many practices spend too much time fixing denials instead of preventing them.
How AI tools can support PA requirement checks at scheduling.
How documentation checklists reduce missing information and weak submissions.
How quarterly payer policy reviews keep workflows current.
How denial receipt, classification, RCA, evidence building and appeals fit into the recovery layer.
How trend analysis helps teams update the prevention workflow.
How small practices can build a simple workflow with limited staff and zero budget.
How larger multispecialty groups can assign AI responsibilities by specialty.
Why each coordinator should become the expert for their specialty’s payer and procedure combinations.
What a team AI Prompt Library is and why it matters.
How a Prompt Library becomes institutional memory for the PA team.
Why Prompt Libraries reduce staff dependency and protect knowledge when people leave.
What to include in a Prompt Library, such as payer policy queries, evidence queries, appeal templates, peer review battle cards and denial code action guides.
How to organize Prompt Library entries by payer, specialty, procedure, drug and denial type.
Why Prompt Library entries must never contain real patient PHI.
How quarterly reviews keep payer policy queries up to date.
How AI supported onboarding can reduce PA coordinator training from months to weeks.
What a 4 week onboarding plan looks like for a new PA coordinator.
How Week 1 focuses on payer policy research.
How Week 2 focuses on documentation checklists and SBAR templates.
How Week 3 focuses on denial classification and the 48 hour protocol.
How Week 4 focuses on evidence based appeals.
How to measure whether a trainee is ready for independent work.
How to investigate coordinator performance gaps without blame.
How blind documentation audits reveal what is missing from denied submissions.
How templates and training can close performance gaps quickly.
Why quality improvement should focus on system improvement, not punishment.
The five key PA performance KPIs every team should track.
How to measure first submission PA approval rate.
How to measure denial recovery rate.
How to measure time per PA submission.
How to measure appeal turnaround time.
How to measure clean claim rate.
What benchmarks teams can use to compare performance.
How to calculate ROI from fewer denials, saved admin time and reduced rework.
How to present AI workflow improvement to leadership using real numbers.
Why the business case should focus on empowering the existing team, not replacing them.
How a zero tool cost implementation can still create measurable financial value.
Why institutional systems are more powerful than individual effort alone.
How a 90 day workflow implementation can improve approval rates, reduce time per PA and increase revenue recovery.
Why the strongest PA teams combine people, process, evidence, templates and measurement.
By the end of this module, learners will know how to design, scale and measure an AI powered prior authorization and denial workflow for different practice sizes, from small clinics to large health systems.
Module 5 Summary
Module 5 teaches learners how to turn PA and denial management into a scalable system. Instead of depending only on individual experience, learners build workflows, prompt libraries, onboarding plans, quality checks and KPI dashboards that the whole team can use.
The key message is simple: AI value comes from the system, not just the tool.
Learners will finish this module knowing how to prevent avoidable denials, organize team knowledge, train coordinators faster, measure performance and present a strong ROI case to leadership.
Advancing Your GenAI Proficiency: A Taxonomy-Based Guide for Researchers1:01:52
Learners will explore:
Why GenAI proficiency is more than knowing how to use a tool.
Why many users either overestimate or underestimate their GenAI skills.
How weak GenAI understanding can lead to poor outputs, overreliance or missed opportunities.
Why users need a clear pathway to measure and improve their GenAI capability.
The meaning of GenAI proficiency in academic and professional work.
How GenAI use changes depending on the task, context and level of thinking required.
The five progressive levels of GenAI proficiency.
What AI Assist means and how users rely on GenAI for simple support.
What AI Refine means and how users improve, rephrase, summarize or organize existing content.
What AI Interpret means and how users evaluate, compare, review and make sense of GenAI outputs.
What AI Solve means and how users use GenAI for structured problem solving and complex tasks.
What AI Innovate means and how users co create, design and build original solutions with GenAI.
How task complexity increases from simple requests to advanced real world applications.
How cognitive engagement increases as users move from basic prompting to critical thinking and innovation.
How the taxonomy connects with learning models such as the Dreyfus Model, SOLO Taxonomy and Visible Learning phases.
Why GenAI learning moves from surface level understanding to deep use and then transfer level application.
How the taxonomy was built from real user experiences and research literature.
How participants shared real examples of how they use GenAI in writing, coding, research, brainstorming and learning.
How the model helps classify different types of GenAI tasks.
How programming users can move from asking for a simple function to building and deploying a complete application.
How design users can move from listing basic space requirements to creating full design documentation.
How teachers can move from asking for definitions to co creating a complete course plan with assessments.
How researchers can move from finding papers to developing a full literature review or conceptual framework.
Why advanced GenAI use requires judgement, verification, ethics and human responsibility.
Why GenAI should support human thinking rather than replace it.
How learners can identify their current GenAI level for different tasks.
Why a person may be advanced in one GenAI task but basic in another.
How this taxonomy can support training, curriculum design, workplace upskilling and professional development.
Why organizations can use this model to design better AI learning pathways.
How learners can use the model as a roadmap for moving from AI assisted work to AI enabled innovation.
By the end of this module, learners will understand the five levels of GenAI proficiency and how to grow from simple tool use to confident, critical and creative AI collaboration.
Module Summary
This module teaches learners that GenAI proficiency develops step by step. It starts with simple support, then moves into refinement, interpretation, problem solving and finally innovation.
The key message is simple: strong GenAI use is not about asking more prompts. It is about thinking better with AI.
Learners will finish this module with a clear understanding of where they stand, how GenAI tasks become more complex and how to progress toward responsible, creative and high value AI use.

Requirements

No prior Gen AI experience is required.
Basic awareness of healthcare claims or insurance is helpful but not mandatory.
Learners should be interested in claims, denials, appeals or healthcare operations.
Access to a computer and internet connection is recommended.
This course explains all tools step by step for beginners.

Description

This course contains the use of artificial intelligence.

Healthcare claims, prior authorization, denials, and appeals are no longer just billing tasks. They influence how quickly patients receive care, how effectively providers protect revenue, and how confidently teams respond when payers push back.

This course shows how Generative AI can be used safely and practically to understand claims, support prior authorization, reduce denial risks, strengthen documentation, build appeal packets, and create smarter revenue cycle workflows without replacing human judgment.

This course is designed for anyone who wants to understand how Generative AI can support healthcare claims processing, prior authorization, denials, and appeals in a practical and responsible way.

You will not learn vague AI theory. You will learn practical workflows, documentation logic, denial response methods, and appeal preparation strategies that can be applied in healthcare revenue cycle settings.

Across six focused modules, you will learn how to:

Understand how prior authorization works from request to payer decision.
Identify why prior authorization requests fail before the denial is received.
Separate clinical need from payer approval requirements.
Recognize weak documentation that may cause medical necessity denials.
Use the five-point payer test to strengthen prior authorization submissions.
Write clear prior authorization letters using the SBAR structure.
Use free healthcare evidence tools to support clinical documentation safely.
Classify denials as hard or soft before taking action.
Decode common CARC denial codes and select the correct response.
Apply a 48-hour denial response process to reduce missed deadlines.
Perform root cause analysis to identify the real reason behind denials.
Use denial data to spot patterns by payer, provider, procedure, and code.
Build stronger appeal letters that directly answer the payer’s denial reason.
Prepare physicians for peer review calls using concise battle cards.
Organize appeal evidence using Zotero.
Draft structured appeal documents using LibreOffice Writer.
Understand patient appeal rights, external review, and escalation pathways.
Build AI-supported workflows for prior authorization prevention and denial recovery.
Create a team prompt library that protects institutional knowledge.
Train new prior authorization coordinators using structured AI-supported onboarding.
Track key performance metrics such as approval rate, recovery rate, and turnaround time.
Calculate ROI from fewer denials, saved administrative time, and reduced rework.
Use Generative AI responsibly without entering real patient data into unsafe tools.
Verify every AI-supported output against charts, payer rules, official evidence, and clinician judgment.
Build confidence in claims, prior authorization, denials, and appeals using repeatable workflows.

Who this course is for:

Healthcare claims processors who want to use Gen AI in daily claims workflows.
Insurance and TPA professionals handling claims, denials, appeals or approvals.
Medical billers and coders who want to understand claims and denial workflows.
Medical insurance brokers who want practical knowledge of healthcare claims.
Beginners who want to enter healthcare claims, insurance or Gen AI healthcare roles.
Hospital and clinic staff involved in billing, prior authorization or RCM.
Reinsurance professionals working with health claims review or claims operations.

What you'll learn

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Course content

Introduction6 lectures • 6hr 7min

Requirements

Description

Who this course is for: