Alert Fatigue and MedAware
MedAware uses a machine learning approach to identify prescriptions that are outliers, outside the ‘normal’ treatment spectrum.
Bell Eapen MD, PhD.
Bringing Digital health & Gen AI research to life!
Installing OSCAR EMR and OpenMRS EHR in your Laptop
Though there are several popular open-source products such as OSCAR EMR, installing them on your laptop can be cumbersome. Install in 5 easy steps using my puppet script!
EHR and Genomic Data
Pulling genomic data into EHR is a complex task because of the volume of genomic data and the difficulty in interpretation.
Googling Electronic Health Records with EMERSE
EMERSE designed to facilitate information retrieval (IR) from narrative documents stored in electronic health records (EHRs).
NoSQL in eHealth
In Part I of interoperability, I touched upon federated and centralized models and RDF and NoSQL paradigms for data persistence in centralized models. So what exactly is NoSQL in eHealth? NoSQL or ‘Not Only SQL’, unlike the structured tabular data storage method of relational database, use data storage methods such as key-value, graph, or document.
If you, like me, were first introduced to the world of EHRs through open source products such as McMaster’s OSCAR or WHO’s OpenMRS, it would be difficult to think beyond a relational database system with the household names of Oracle, SQL Server and MySQL. As we discussed before, structured relational database systems are not suitable for clinical workflows. This has led to the rejection of most EHR systems by the clinicians.
The fact of the matter is, a large number of legacy EHR systems, including the trusted VistaA EMR by The US Dept of Veterans’ Affairs’ (VA) use a database system unfamiliar to the new generation but nevertheless amenable to clinical workflows called Mumps (often abbreviated as just M). Mumps historically had all the versatilities of modern NoSQL databases that are gradually coming to the forefront such as mongoDB. Rob Tweed, the Director and co-founder of M/Gateway Developments Ltd attempts a reincarnation of M by introducing a javascript/Nodejs based connector called EWD.js
Whether EWD.js succeeds in reigniting interest in M, or the new generation NoSQL databases make their inevitable foray into eHealth, it is high time for a major overhaul in the existing EHR data models.
Related articles
- MongoDB Update Addresses User Criticism (datacenterknowledge.com)
- Modern SQL in PostgreSQL [and other databases] (Use-The-Index-Luke.com)
- NoSQL – Creating Waves In The Database Community (spec-india.com)
LesionMapper: Pictographic lesion encoder for Dermatology
An electronic medical record example (Photo credit: Wikipedia)
Grading systems and novel methods of symptom coding is becoming more and more important with the growth of telehealth and electronic health records. It is probable that in dermatology too, a significant number of consultations will move online soon.
Visual Analogue Scale (VAS) is a commonly used tool for measuring subjective sensations such as itching. There is evidence showing that visual analogue scales have superior metrical characteristics than discrete scales, thus a wider range of statistical methods can be applied to the measurements.
Couple of months back, I attended a thesis defense in McMaster in which an innovative web based tool called Pain-QuILTTM for visual self-report of pain was presented. The technique of iconography – pictorial material relating to or illustrating a subject – was employed to represent pain using a flash based web-interface. Pain-QuILTTM tracks quality, intensity, location and temporal characteristics of the pain. Quality is represented by different icons, intensity is represented by a visual analogue scale of 1 -10, location by the position of icon on the body image and temporal characteristics by the time stamp. The clinical feasibility of Pain-QuILTTM has been successfully validated and published (1).
Pain-QuILTTM is a property of McMaster University and is subject to McMaster University’s terms of use. It can be accessed here.
The iconographic symptom encoding could be applied easily to dermatology as well. Dermatology lesions are primarily visual and dermatological diagnosis to a great extend is based on the type, distribution, intensity and temporal characteristics of the skin lesions. However the representation may be challenging because of the diverse nature of lesions.
Recently I came across fabric.js a javascript library for image manipulation based on HTML5 canvas. Fabric.js was much more versatile and powerful than I expected. I could prototype LesionMapperTM (that is what I want to call it), in less than 24 hours. The type of lesions are symbolized by representative clinical pictures instead of icons, intensity is represented by the opacity/translucency of the image and the location and distribution by the position and size of the lesion respectively, on the body image. The images can be dragged, enlarged or rotated. The icing on the cake is the ability of fabric.js to rasterize the image into a JSON that can be stored easily in a database.
Update: 14- June – 2016
LesionMapper is available as an OpenMRS module and OSCAR eForm. OpenMRS module is opensource and can be downloaded here. The github repository is available here. If you need the OSCAR eForm version, please contact me.
Ref:
1. Lalloo, Chitra et al. “Pain-QuILT: Clinical Feasibility of a Web-Based Visual Pain Assessment Tool in Adults With Chronic Pain.” Journal of medical Internet research 16.5 (2014). [JMIR]
