Using an AI Chatbot to Generate Cochlear Implant Insurance Appeal Letters: An Assessment of Accuracy and Citation Reliability

The study systematically assessed ChatGPT's guideline concordance on the specific task of cochlear implant insurance appeal letters, finding that 79.3% of responses partially aligned with American Cochlear Implant Alliance guidelines and 96.6% listed multiple benefits of cochlear implants for asymmetric hearing loss or single-sided deafness. Prior work on large language models in clinical writing has largely focused on general medical question answering or note summarization; this study narrows the evaluation to insurance appeal letters, a task the authors describe as time-consuming and repetitive and one that has explicit guideline criteria. Three cochlear implant providers produced 87 scores across 29 unique responses, a small multi-rater scoring design reported as percentages.

The study quantified fabrication in the generated content: 51.7% of responses contained hallucinated cochlear implant benefits, and of 96 citations only six (6.3%) accurately referenced peer-reviewed sources, while 57.3% were hallucinated, 24% erroneous, and 10% irrelevant. Rather than judging only whether the text reads plausibly, the study checked each citation for authenticity and relevance, presenting citation reliability in professional appeal documents as a separate metric. Citation checking covered all 96 citations with explicit category counts; the sample is limited but the classification is clear.

The study concludes that ChatGPT produced cochlear implant insurance appeal letters inconsistently, often mixing correct guideline-based content with erroneous or misinterpreted information, so cochlear implant providers should exercise caution and verify AI-generated materials before clinical or advocacy use. This gives clinicians a concrete boundary for use, positioning AI output as a draft requiring human verification rather than a finished document ready for submission. The conclusion rests on the guideline-concordance scores and citation checks described above and is a descriptive summary of the same set of 29 responses.

The study addresses cochlear implant insurance denial appeal letters for asymmetric hearing loss or single-sided deafness, evaluates zero-shot ChatGPT output, scores against American Cochlear Implant Alliance guidelines, and uses three cochlear implant providers as raters. Its conclusions apply to settings where AI-generated appeal letters serve as drafts verified by clinicians with cochlear implant expertise; other conditions, insurance systems, or languages would require separate evaluation.

This reading is at the abstract level and does not include figures or full methodological detail, so the specific design of the prompt variations, inter-rater agreement, and the exact types of hallucinated benefits remain unclear. Open questions to watch include whether different prompting strategies reduce fabricated citations, whether citation checking can be automated, and how much time human verification adds in a real appeal workflow.