Cognitive modelling and control of human error processes in human-computer interaction with safety critical IT systems in telehealth.
Professor Eyad Elyan email@example.com
The field of telehealth has developed rapidly in recent years. It provides medical support particularly to those who are living in remote areas and in emergency cases. Although developments in both technology and practice have been rapid, there are still many gaps in our knowledge with regard to the effective application of telehealth. This study investigated human colour perception in telehealth, specifically the colour red as one of the key symptoms when diagnosing different pathologies. The quality of medical images is safety critical when transmitting the symptoms of pathologies in telehealth, as distorted or degraded colours may result in errors. The study focused on the use of digital images in teleconsultation, particularly on images showing cellulitis (bacterial skin infection) and conjunctivitis (red eye) as case studies, as both of these pathologies involve the colour red in their diagnosis. The study proposed and tested the use of an image quality scale, which represented the level of image resolution; a red colour scale, which represented the intensity of redness in an image; and a confidence scale, which represented the levels of confidence that telehealth users had when judging the colour red. The research involved a series of experiments using hypothetico-deductive and formal hypothesis testing with two groups of participants, medical doctors and non-medical participants. The experiments were conducted in collaboration with the local National Health Service (NHS) Accident and Emergency (A&E) department at Aberdeen Royal Infirmary (ARI). Medical experts in ophthalmology and dermatology were also involved in selecting and verifying the relevant images. The study found that doctors and non-doctors were consistent in the majority of the experiments. The accuracy of the participants was demonstrably higher when using a colour scale with pictures, more so for the non-doctor group than the doctor group. It also found that the level of accuracy for both doctors and nondoctors was higher when using red colour scale of three divisions than when using a scale of five divisions. This result was supported by previous studies, which used telehealth for diagnosing extreme cases. The study also found that when the image quality was poor the participants had higher error rates and less consistency in their answers. The study found poor correlation between accuracy, confidence and time for both participant groups. The study found that most participants in both doctor and non-doctor groups had high confidence most of the time, whether the accuracy was high or low. It was also found that medical background or clinical experience had no effect on the accuracy level across the experiment sets. In some cases, doctors with no or little experience had higher accuracy than those with greater experience. This result may have significant implications for the feasibility of involving non-doctors in the management of telehealth systems, especially in tasks not requiring medical skills, such as colour classification. This has the potential to provide a considerable saving in resources and costs for healthcare providers. An auto-evaluation system was introduced, and proposed for further study, in order to improve the current telehealth diagnostic protocol and to avoid or prevent errors by making red colour classification more objective and accurate.
ALWAWI, I. 2017. Cognitive modelling and control of human error processes in human-computer interaction with safety critical IT systems in telehealth. Robert Gordon University, PhD thesis.
|Dec 1, 2017
|Jan 23, 2018
|Publicly Available Date
|Jan 23, 2018
|Telehealth; Medical support; Remote areas; Emergency; Human colour perception; Digital images; Tele consultation
ALWAWI 2017 Cognitive modelling and control
Publisher Licence URL
Copyright: the author and Robert Gordon University
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