A sharable cloud-based pancreaticoduodenectomy collaborative database for physicians: emphasis on security and clinical rule supporting.

BACKGROUND: Pancreaticoduodenectomy (PD) is a major operation with high complication rate. Thereafter, patients may develop morbidity because of the complex reconstruction and loss of pancreatic parenchyma. A well-designed database is very important to address both the short-term and long-term outcomes after PD. OBJECTIVE: The objective of this research was to build an international PD database implemented with security and clinical rule supporting functions, which made the data-sharing easier and improve the accuracy of data. METHODS: The proposed system is a cloud-based application. To fulfill its requirements, the system comprises four subsystems: a data management subsystem, a clinical rule supporting subsystem, a short message notification subsystem, and an information security subsystem. After completing the surgery, the physicians input the data retrospectively, which are analyzed to study factors associated with post-PD common complications (delayed gastric emptying and pancreatic fistula) to validate the clinical value of this system. RESULTS: Currently, this database contains data from nearly 500 subjects. Five medical centers in Taiwan and two cancer centers in Mongolia are participating in this study. A data mining model of the decision tree analysis showed that elderly patients (>76 years) with pylorus-preserving PD (PPPD) have higher proportion of delayed gastric emptying. About the pancreatic fistula, the data mining model of the decision tree analysis revealed that cases with non-pancreaticogastrostomy (PG) reconstruction - body mass index (BMI)>29.65 or PG reconstruction - BMI>23.7 - non-classic PD have higher proportion of pancreatic fistula after PD. CONCLUSIONS: The proposed system allows medical staff to collect and store clinical data in a cloud, sharing the data with other physicians in a secure manner to achieve collaboration in research.

A physician order category-based clinical guideline comparison system.

Today, in order to provide high-quality medical services and to extend resources and reduce costs, many large hospitals have adopted clinical guidelines as a structured way to manage medical activities. However, customization of clinical guidelines in order to treat a large number of patients is a major challenge. In this paper, we present a physician order category-based clinical guideline comparison system. The system uses a preprocessor software to convert the clinical guidelines from a Microsoft Word document into XML format, and it can also compare clinical guidelines over the conceptual view such as the physician order category. The system has already been used to compare the HCC surgical clinical guidelines of Taiwan and Mongolia-resulting in some differences being found, for which possible causes were discussed. Therefore, it can be seen that our research provides a practical and convenient way in which to compare clinical guidelines based on physician order category-thereby saving time and enabling physicians to quickly resolve discrepancies and make necessary adjustments to clinical guidelines.

A data-mining framework for transnational healthcare system.

Medical resources are important and necessary in health care. Recently, the development of methods for improving the efficiency of medical resource utilization is an emerging problem. Despite evidence supporting the use of order sets in hospitals, only a small number of health information systems have successfully equipped physicians with analysis of complex order sequences from clinical pathway and clinical guideline. This paper presents a data-mining framework for transnational healthcare system to find alternative practices, including transfusion, pre-admission tests, and evaluation of liver diseases. However, individual countries vary with respect to geographical location, living habits, and culture, so disease risks and treatment methods also vary across countries. To realize the difference, a service-oriented architecture and cloud-computing technology are applied to analyze these medical data. The validity of the proposed system is demonstrated in including Taiwan and Mongolia, to ensure the feasibility of our approach.