LesionMapper: Pictographic lesion encoder for Dermatology

An electronic medical record example

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:
1Lalloo, 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]

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Bell Eapen

Dermatologist, programmer geek,information systems Ph.D. student, OpenMRS supporter, armchair philosopher, loves Canadian wine and beer,believes in coding to save lives. [Resume]
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