X-PAT: a multiplatform patient referral data management system for small healthcare institution requirements

ISMAC: An Intelligent System for Customized Clinical Case Management and Analysis

Clinical cases are primary and vital evidence for Traditional Chinese Medicine (TCM) clinical research. A great deal of medical knowledge is hidden in the clinical cases of the highly experienced TCM practitioner. With a deep Chinese culture background and years of clinical experience, an experienced TCM specialist usually has his or her unique clinical pattern and diagnosis idea. Preserving huge clinical cases of experienced TCM practitioners as well as exploring the inherent knowledge is then an important but arduous task. The novel system ISMAC (Intelligent System for Management and Analysis of Clinical Cases in TCM) is designed and implemented for customized management and intelligent analysis of TCM clinical data. Customized templates with standard and expert-standard symptoms, diseases, syndromes, and Chinese Medince Formula (CMF) are constructed in ISMAC, according to the clinical diagnosis and treatment characteristic of each TCM specialist. With these templates, clinical cases are archived in order to maintain their original characteristics. Varying data analysis and mining methods, grouped as Basic Analysis, Association Rule, Feature Reduction, Cluster, Pattern Classification, and Pattern Prediction, are implemented in the system. With a flexible dataset retrieval mechanism, ISMAC is a powerful and convenient system for clinical case analysis and clinical knowledge discovery.

A non-repudiated and traceable authorization system based on electronic health insurance cards

Since anamnesis management in health care is directly relative to patients' privacy protection, how to resist malicious behaviors is an important issue in information security. In recent years, the developed electronic health insurance cards (eHIC) has been widely adopted as an identification certificate, which involves lots applications and provides convenience to both the patients and relative medical workers as well. There always existed some disputes and moral standards for these medical doctors who are to be confronted with these challenges. For example: The doctor discloses patient's anamnesis without patient's consent and anamnesis by the illegal access…etc. As required in E-Health, the current systems are almost offline system, which are not suitable to support online E-anamnesis sharing access to reduce the consumption of the medical treatment and fulfill a secure audit channel. In this paper, to solve these problems, an eHIC-based online authorization system with non-repudiated and traceable properties is proposed. According to our simulation results, not only the patient's privacy could be fully protected, but also the medical revenue could be raised extensively.