ABSTRACT
To achieve continuously physiological monitoring on hospital inpatients, a ubiquitous and wearable physiological monitoring system SensEcho was developed. The whole system consists of three parts: a wearable physiological monitoring unit, a wireless network and communication unit and a central monitoring system. The wearable physiological monitoring unit is an elastic shirt with respiratory inductive plethysmography sensor and textile electrocardiogram (ECG) electrodes embedded in, to collect physiological signals of ECG, respiration and posture/activity continuously and ubiquitously. The wireless network and communication unit is based on WiFi networking technology to transmit data from each physiological monitoring unit to the central monitoring system. A protocol of multiple data re-transmission and data integrity verification was implemented to reduce packet dropouts during the wireless communication. The central monitoring system displays data collected by the wearable system from each inpatient and monitors the status of each patient. An architecture of data server and algorithm server was established, supporting further data mining and analysis for big medical data. The performance of the whole system was validated. Three kinds of tests were conducted: validation of physiological monitoring algorithms, reliability of the monitoring system on volunteers, and reliability of data transmission. The results show that the whole system can achieve good performance in both physiological monitoring and wireless data transmission. The application of this system in clinical settings has the potential to establish a new model for individualized hospital inpatients monitoring, and provide more precision medicine to the patients with information derived from the continuously collected physiological parameters.
ABSTRACT
<b>Background:</b> When analyzing regional disparities in healthcare resources, hospital accessibility is given little consideration. We surveyed accessibility from residential districts to medical institutions using GIS (Geographic Information System) and estimated Gini coefficient for each hospital distribution.<br><b>Methods:</b> The subjects were 2,688 census mesh blocks ( “<i>Cho</i>-<i>cho</i>-<i>aza</i>” ) and 109 hospitals in Tochigi prefecture. The number of hospitals located within the road distances of 5 km, 10 km and 15 km from the geometrical center of each block was calculated using GIS. The Gini coefficient of each hospital per 100 residents was calculated among the regions located within 5 km, 10 km and 15 km from the geometrical center of the census mesh block.<br><b>Results:</b> The population of each block was 748±1,067 (mean±SD), and the road distance to the nearest hospital from the center of each block was 4.3±4.5 km. The number of census mesh blocks with distances from the center of each block to the nearest hospital within 5 km, 5-10 km, 10-15 km and more than 15 km were 1909 (71.0%), 561 (20.9%), 139 (5.2%) and 79 (2.9%) respectively. The number of hospitals located within 5 km, 10 km and 15 km were 3.3±4.7, 8.3±8.6 and 14.4±11.4. Gini coefficients were 0.65, 0.52 and 0.43.<br><b>Conclusion:</b> When analyzing regional disparities in healthcare resources, it is necessary to take into account not only the number of physicians and beds, but also accessibility. Gini coefficient is useful to estimate geographical distributions, and can be used as an indicator for improvement projects for hospitals.