Implementation of a cloud-based electronic medical record exchange system in compliance with the integrating healthcare enterprise's cross-enterprise document sharing integration profile.

In 2011, the Ministry of Health and Welfare of Taiwan established the National Electronic Medical Record Exchange Center (EEC) to permit the sharing of medical resources among hospitals. This system can presently exchange electronic medical records (EMRs) among hospitals, in the form of medical imaging reports, laboratory test reports, discharge summaries, outpatient records, and outpatient medication records. Hospitals can send or retrieve EMRs over the virtual private network by connecting to the EEC through a gateway. International standards should be adopted in the EEC to allow users with those standards to take advantage of this exchange service. In this study, a cloud-based EMR-exchange prototyping system was implemented on the basis of the Integrating the Healthcare Enterprise's Cross-Enterprise Document Sharing integration profile and the existing EMR exchange system. RESTful services were used to implement the proposed prototyping system on the Microsoft Azure cloud-computing platform. Four scenarios were created in Microsoft Azure to determine the feasibility and effectiveness of the proposed system. The experimental results demonstrated that the proposed system successfully completed EMR exchange under the four scenarios created in Microsoft Azure. Additional experiments were conducted to compare the efficiency of the EMR-exchanging mechanisms of the proposed system with those of the existing EEC system. The experimental results suggest that the proposed RESTful service approach is superior to the Simple Object Access Protocol method currently implemented in the EEC system, according to the irrespective response times under the four experimental scenarios.

A decision support model of demand forecast for national immunisation vaccines.

This paper presents a demand forecast and optimal allocation model to support the decision making of national vaccine purchase. This research attempts to use ARIMA and Neural Network models respectively to forecast two individual values of 'The yearly total number of inoculations' and 'The annual birthrate' for the next year, and then choose the reasonable and better results generated from one of these two models to conduct a further forecast of 'The yearly total demand volume in doses of vaccines'. According to the experimental results, this research indeed may provide a feasible decision model for yearly vaccine procurement for Taiwan's government immunisation authority and establishes an optimal demand forecast model for a specific vaccine like DTP vaccine.