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The Industrial Internet of Things (IIoT) has become a transformative force in various healthcare applications, providing integrated services for daily life. The app healthcare based on the IIoT framework is broadly used to remotely monitor clients health using advanced biomedical sensors with wireless technologies, managing activities such as monitoring blood pressure, heart rate, and vital signs. Despite its widespread use, IIoT in healthcare faces challenges such as security concerns, inefficient work scheduling, and associated costs. To address these issues, this paper proposes and evaluates the Blockchain-Based Deep Reinforcement Learning System for Optimizing Healthcare (BDRL) framework. BDRL aims to enhance security protocols and maximize makespan efficiency in scheduling medical applications. It facilitates the sharing of legitimate and secure data among linked network nodes beyond the initial stages of data validation and assignment. This study presents the design, implementation, and statistical evaluation of BDRL using a new dataset and varying platform resources. The evaluation shows that BDRL is versatile and successfully addresses the security, privacy, and makespan needs of healthcare applications on distributed networks, while also delivering excellent performance. However, the framework utilizes high resources as the size of inserted data increases. [ABSTRACT FROM AUTHOR]