Philadelphia Woman Wins ADHD Medication Coverage with AI-But at What Risk?
After two denials for ADHD meds, an AI-structured appeal helped Joani Reisen win coverage. Expect more organized, evidence-backed appeals, and pressure for clearer, faster reviews.
AI-Tuned Appeals Are Hitting Insurance Desks. Here's What One Case Signals
After two failed appeals for ADHD medication coverage, Philadelphia resident Joani Reisen turned to artificial intelligence to structure her third attempt-and won. Her insurer had categorized methylphenidate (the active ingredient in Concerta) as "experimental," pushing her to a generic that left her groggy and unable to function at work.
The case is a microcosm of a larger shift: members are using AI to write sharper appeals, cite evidence, and map benefit language to clinical need. For insurance professionals, this means clearer arguments landing in your queue-and higher expectations for transparent, defensible decisions.
The case at a glance
- Diagnosis: ADHD for both Reisen and her son; Concerta helped her focus for nearly a decade.
- Coverage issue: Independence Blue Cross classified methylphenidate as "experimental" and required a switch to a generic.
- Clinical impact: The generic options made her fall asleep; two appeals failed despite physician support.
- Outcome: An AI-assisted appeal helped reverse the denial and restore coverage.
Why this matters for insurers
Marketplace insurers denied 19% of in-network claims in 2023, and more than half of appealed denials were upheld. Yet fewer than 1% of denials were appealed at all-until now, as AI lowers the barrier to filing organized, evidence-backed appeals.
ADHD prevalence remains high, and medication costs can be substantial for members without coverage. For example, listed cash prices for Concerta can exceed four figures for a 100-tablet supply depending on dose.
How members are actually using AI
- Drafting appeals that mirror policy language (medically necessary, step therapy outcomes, contraindications).
- Summarizing clinician notes and aligning them to plan criteria.
- Finding peer-reviewed references or guideline excerpts to support exceptions.
- Structuring timelines and prior auth histories to make review faster.
Risks you should anticipate
- Privacy exposure: Members may paste PHI into public tools that do not meet privacy or security standards.
- Hallucinated citations: AI can fabricate studies or misquote guidelines, creating review noise and risk.
- Outdated guidance: Models may pull from old clinical standards unless constrained by current sources.
- One-size-fits-all templates: Appeals may look polished but miss key plan-specific criteria, leading to churn.
Operational implications for plans
Better-structured appeals mean faster review is possible-but volume may rise. Your teams need clear criteria, efficient triage, and tooling that spots incomplete submissions early.
Expect more exception requests where generics are clinically ineffective for an individual. Extended-release formulations and brand-vs-generic differences, while bioequivalent on paper, can produce variable outcomes for some patients. That nuance will show up in AI-written appeals.
Practical steps insurers can take now
- Publish member-friendly criteria: Make coverage policies and exception pathways easy to find and reference. Plain language reduces back-and-forth.
- Tighten policy-to-evidence mapping: Link each criterion to current guidelines and acceptable documentation. Reduce room for misinterpretation.
- Offer secure intake: Provide a protected portal for appeals with prompts that collect the exact elements you need (diagnosis, failed therapies, adverse effects, dosing, duration).
- Add verification checks: Auto-flag missing data, questionable citations, or non-applicable guidelines before a case hits clinical review.
- Standardize clinician attestations: Provide downloadable templates for prescribers to document trial-and-failure, side effects, and contraindications.
- Clarify substitution policies: Be explicit about brand vs. generic exceptions, authorized generics, and what evidence supports a switch-back.
- Instrument the workflow: Track overturn reasons and cycle time to spot policy friction or education gaps.
- Train your teams on AI literacy: Teach reviewers to spot fabricated sources and request corrections efficiently. Consider controlled internal tools to summarize records, not to replace judgment.
What this signals for member experience
Members who feel unheard will look for leverage. AI gives them structure and confidence, which can turn a denial into a well-supported case. Meeting that with clear criteria, stable alternatives, and quick escalations will lower abrasion for both sides.
If you're upskilling teams on practical AI for insurance workflows
Focused training shortens the learning curve and reduces risk. See role-specific options here: AI courses by job.
Bottom line
AI isn't just a member tool-it's now part of the appeals ecosystem. Plans that combine transparent criteria, secure submission, and AI-aware review will cut cycle times and make fair decisions stick.
The Joani Reisen case is a signal: well-structured evidence wins. Make it easy to submit the right evidence, and you'll protect members, clinicians, and your own teams from unnecessary churn.
