Management and Countermeasure Evaluation of Smart Community Health Information Sharing Platform Based on Medical Big Data

With the accelerating pace of human work and life, as well as the trend of aging population, the construction of smart communities has received more and more attention. Internet technology has promoted the health care service model. Community health information sharing platform has become an indispensable part of smart communities. At present, the health management industry has developed rapidly, but there are many problems. Therefore, it is necessary to establish a smart community health information sharing platform with creativity, strong cooperation ability, solid theoretical construction and multi-level in-depth decision-making analysis. This study first examined the conceptual significance and current status of intelligent community health information sharing platforms, explored the role of blockchain in the health and medical big data platform, and then proposed management strategies in the context of medical big data. The application of an advanced computing intelligence system based on secure medical images and the improvement of the open data supply of the community health information sharing platform were the two angles from which the management policy of a smart community health information sharing platform based on big data was proposed. Relevant algorithms for the management of smart community health information sharing platform under medical big data were mentioned. Finally, community A was selected to implement the methods provided in this paper and compared with community B that did not implement the management methods in this paper. It was concluded that the management of community A was 7.52% higher than that of community B. The easy, intelligent, and secure community health service system could be constructed to better protect the health services of community members through the management and countermeasure analysis of the smart community health information sharing platform based on medical big data.