An electronic healthcare record server implemented in PostgreSQL

This paper describes the implementation of an Electronic Healthcare Record server inside a PostgreSQL relational database without dependency on any further middleware infrastructure. The five-part international standard for communicating healthcare records (ISO EN 13606) is used as the information basis for the design of the server. We describe some of the features that this standard demands that are provided by the server, and other areas where assumptions about the durability of communications or the presence of middleware lead to a poor fit. Finally, we discuss the use of the server in two real-world scenarios including a commercial application

Open Science: Tools, approaches, and implications

The Pacific Symposium on Biocomputing is an annual meeting whose topics are determined by proposals submitted by members of the community. This document is the proposal for a session on Open Science, submitted for consideration for the PSB meeting in 2009

Developing digital interventions: a methodological guide.

Digital interventions are becoming an increasingly popular method of delivering healthcare as they enable and promote patient self-management. This paper provides a methodological guide to the processes involved in developing effective digital interventions, detailing how to plan and develop such interventions to avoid common pitfalls. It demonstrates the need for mixed qualitative and quantitative methods in order to develop digital interventions which are effective, feasible, and acceptable to users and stakeholders

Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities