Background: Studies have demonstrated variable adherence to published melanoma treatment guidelines. Payers have used algorithms to preapprove certain tests and treatments. Our objective was to develop a preoperative treatment assessment algorithm to ensure patients with melanoma receive recommended care. Methods: A treatment algorithm was developed using existing guidelines. Records of patients presenting with melanoma between September 2010 and May 2012 were reviewed. Surgical care was classified as having been adherent or nonadherent with the guideline-based algorithm. The algorithm was incorporated into a Web site for preoperative treatment verification. Results: A treatment algorithm was developed on the basis of three critical pieces of preoperative data: Breslow thickness, presence of adverse primary tumor prognostic factors, and regional lymph node metastases. Treatment options included wide local excision (WLE), sentinel lymph node biopsy (SLNB), and completion/formal lymph node dissection. Of 328 patients evaluated, 316 (96 %) were treated according to the guideline-based algorithm. Causes of variation from predicted treatment included patient preference, severe comorbidities, difficult tumor location, and uncertain depth. All departures (n = 12) were in clinically node-negative patients: six patients did not undergo SLNB as indicated, and six underwent SLNB that was not concordant with the algorithm. An algorithm-embedded Web site was designed for preoperative verification of planned procedures. Conclusions: As a proof of principle, an algorithm was developed to preoperatively verify that proposed melanoma treatments are concordant with established guidelines. These three pieces of information could be required during precertification, and nonconcordant treatment plans could prompt a peer-to-peer discussion. After additional pilot testing, the algorithm could offer a novel approach for quality improvement and provide a preoperative, prospective safeguard to ensure high-quality care for melanoma patients.
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