In recent years, Indian economic growth and improvement in living standards have driven significant advancements in medical services and healthcare technologies. With the continuous promotion and deepening of the Internet initiative across various industries, the development of Internet+ Healthcare has experienced rapid growth. As data processing technologies such as machine learning and data mining continue to evolve, the risk of personal medical privacy data breaches during online medical services has attracted widespread attention from researchers. Integrating the current research status of privacy protection motivations in online healthcare, mechanism design theory is utilized to create incentive-compatible mechanisms that motivate both users and platforms to prioritize privacy protection. Given the strong willingness of users to continue using online medical platforms while selectively providing private information, a Markov Decision Process (MDP) is adopted to model the sequential decision-making process between users and online medical platforms. The study reveals the changing tendencies of both parties, analyzes the evolutionary game dynamics under different model parameters, and examines the evolving strategies of both sides over time. It concludes that when the parameter condition is satisfied, users begin to shift from agreeing to share privacy data to refusing to share it as the interaction progresses. Simulation experiments verify these conclusions. Based on these findings, practical policy recommendations are provided from both the online medical platform and user perspectives to achieve privacy protection through the privacy protection motivations of both parties in the online healthcare process.