A study on information induced medication errors

The electronic health record (eHR) system has recently been considered one of the biggest advancements in healthcare services. A personally controlled electronic health record (PCEHR) system is proposed by the Australian government to make the health system more agile, secure, and sustainable. Although the PCEHR system claims the electronic health records can be controlled by the patients, healthcare professionals and database/system operators may assist in disclosing the patients’ eHRs for retaliation or other ill purposes. As the conventional methods for preserving the privacy of eHRs solely trust the system operators, these data are vulnerable to be exploited by the authorised personnel in an immoral/unethical way. Furthermore, issues such as the sheer number of eHRs, their sensitive nature, flexible access, and efficient user revocation have remained the most important challenges towards fine-grained, cryptographically enforced data access control. In this paper we propose a patient centric cloud-based PCEHR framework, which employs a homomorphic encryption technique in storing the eHRs. The proposed system ensures the control of both access and privacy of eHRs stored in the cloud database

Lamina propria macrophage phenotypes in relation to Escherichia coli in Crohn's disease

Background: Abnormal handling of E. coli by lamina propria (LP) macrophages may contribute to Crohn’s disease (CD) pathogenesis. We aimed to determine LP macrophage phenotypes in CD, ulcerative colitis (UC) and healthy controls (HC), and in CD, to compare macrophage phenotypes according to E. coli carriage. Methods: Mucosal biopsies were taken from 35 patients with CD, 9 with UC and 18 HCs. Laser capture microdissection was used to isolate E. coli-laden and unladen LP macrophages from ileal or colonic biopsies. From these macrophages, mRNA was extracted and cytokine and activation marker expression measured using RT-qPCR. Results: E. coli-laden LP macrophages were identified commonly in mucosal biopsies from CD patients (25/35, 71 %), rarely in UC (1/9, 11 %) and not at all in healthy controls (0/18). LP macrophage cytokine mRNA expression was greater in CD and UC than healthy controls. In CD, E. coli-laden macrophages expressed high IL-10 & CD163 and lower TNFα, IL-23 & iNOS irrespective of macroscopic inflammation. In inflamed tissue, E. coli-unladen macrophages expressed high TNFα, IL-23 & iNOS and lower IL-10 & CD163. In uninflamed tissue, unladen macrophages had low cytokine mRNA expression, closer to that of healthy controls. Conclusion: In CD, intra-macrophage E. coli are commonly found and LP macrophages express characteristic cytokine mRNA profiles according to E. coli carriage. Persistence of E. coli within LP macrophages may provide a stimulus for chronic inflammation

Big data in healthcare: and what is it used for?

Big data analytics is a growth area with the potential to provide useful insight in healthcare. Whilst many dimensions of big data still present issues in its use and adoption, such as managing the volume, variety, velocity, veracity, and value, the accuracy, integrity, and semantic interpretation are of greater concern in clinical application. However, such challenges have not deterred the use and exploration of big data as an evidence source in healthcare. This drives the need to investigate healthcare information to control and reduce the burgeoning cost of healthcare, as well as to seek evidence to improve patient outcomes. Whilst there are a number of well-publicised examples of the use of big data in health, such as Google Flu and HealthMap, there is no general classification of its uses to date. This study used a systemic review methodology to create a categorisation of big data use in healthcare. The results indicate that the natural classification is not clinical application based, rather it falls into four broad categories: administration and delivery, clinical decision support (with a sub category of clinical information), consumer behaviour, and support services. Further, the results demonstrate that the use of big data in all examples in the literature is not singular in its approach and each study covers multiple use and application areas. This study provides a baseline to assess the proliferation of the use of big data in healthcare and can assist in the understanding the breadth of big data application