Spending on skin substitutes has surged in recent years, becoming one of the fastest-growing areas of wound care reimbursement. Yet, many applications lack consistent clinical justification, leaving payers struggling to separate appropriate use from costly overuse. Traditional review processes are manual, time-consuming, and variable, and often fail to keep pace with the complexity of evolving clinical and billing data.
This new case study explores how one national payer leveraged Machinify’s advanced AI technology to bring accuracy, consistency, and speed to the skin substitutes review process. By applying machine learning and clinical context at scale, the payer was able to streamline decision-making, improve provider communication, and reduce unnecessary manual reviews without compromising compliance or care quality. The results point to a scalable model for balancing cost control with clinical integrity.
Read the full case study to discover how a modern, AI-driven approach to skin substitutes sets a new standard for payment integrity.
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