Construction of medical equipment-based doctor health monitoring system

The Performance Evaluation Mechanism Based on Information Construction for Large Stand-Alone Medical Equipment and Its Support for Decision-Making of Purchasing

BACKGROUND

Continuously deeper reform of public hospitals has put forward the need to innovate the philosophy and system of large medical equipment operation and management, and the phenomenon featuring "more attention to purchasing and less attention to management" need to be turned around.

METHODS

This research took use of information management to set up ID fields (unique number) for target stand-alone equipment; integrated statistics functions of HIS, PACS, LIS, RIS and equipment management system to get the basic operation data; informationized the work flow and reform technology to set up a post evaluation indictor system for the performance of stand-alone equipment; compared the service condition of newly purchased equipment with the feasibility application of relative department from various dimensions; designed objective post-evaluation indicators from various angles to scientifically manage existing medical equipment and support decision-making of new application for purchasing medical equipment.

RESULTS

Application performance of stand-alone equipment and clinical departments were ranked in a standardized manner. Decision-making mode based on data case of stand-alone equipment was set up. Net present value was evaluated. However, re-purchasing the instrument did not continuously increase the contribution of each instrument. The laboratory can purchase new instruments again, while the imaging department is not recommended to purchase.

CONCLUSION

The performance evaluation mechanism based on information construction for large stand-alone medical equipment and its support for decision-making of purchasing is of great significance to improve the service life of equipment, exert the maximum effect and reduce economic waste.

Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.