The healthcare industry is dependent upon the provision of well designed medical devices. To achieve this it is recommended that user-centred design should begin early, and continue throughout device development. This is a challenge, particularly for smaller companies who may lack the necessary expertise and knowledge. The aim of this study was to conduct a rigorous yet focused investigation into the user requirements for a new medical imaging device. Open-ended semi-structured interviews were conducted with potential clinical users of the device to investigate the clinical need for the device and the potential benefits for patients and clinical users. The study identified a number of new and significant clinical needs that suggested that the concept of the device should be fundamentally changed. The clinical and organisational priorities of the clinical users were identified, as well as a number of factors that would act as barriers to the safe and effective adoption of the device. The developers reported that this focused approach to early requirements elicitation would result in an improved product, reduce the time to market, and save the time and cost of producing and evaluating an inappropriate prototype.

These findings suggest that HSI may predict healing in routine practice. The fact that the correlation between HSI and healing was negative could be explained by HSI being a measure of oxygenation of haemoglobin and there may be an inverse relationship between this and the oxygenation of extravascular tissue in people with neuropathy and/or microvascular disease.

Diabetic foot ulcers are a major complication of diabetes and present a considerable burden for both patients and health care providers. As healing often takes many months, a method of determining which ulcers would be most likely to heal would be of great value in identifying patients who require further intervention at an early stage. Hyperspectral imaging (HSI) is a tool that has the potential to meet this clinical need. Due to the different absorption spectra of oxy- and deoxyhemoglobin, in biomedical HSI the majority of research has utilized reflectance spectra to estimate oxygen saturation (SpO2) values from peripheral tissue. In an earlier study, HSI of 43 patients with diabetic foot ulcers at the time of presentation revealed that ulcer healing by 12 weeks could be predicted by the assessment of SpO2 calculated from these images. Principal component analysis (PCA) is an alternative approach to analyzing HSI data. Although frequently applied in other fields, mapping of SpO2 is more common in biomedical HSI. It is therefore valuable to compare the performance of PCA with SpO2 measurement in the prediction of wound healing. Data from the same study group have now been used to examine the relationship between ulcer healing by 12 weeks when the results of the original HSI are analyzed using PCA. At the optimum thresholds, the sensitivity of prediction of healing by 12 weeks using PCA (87.5%) was greater than that of SpO2 (50.0%), with both approaches showing equal specificity (88.2%). The positive predictive value of PCA and oxygen saturation analysis was 0.91 and 0.86, respectively, and a comparison by receiver operating characteristic curve analysis revealed an area under the curve of 0.88 for PCA compared with 0.66 using SpO2 analysis. It is concluded that HSI may be a better predictor of healing when analyzed by PCA than by SpO2.

(2018) Health technology assessment methods guidelines for medical devices : how can we address the gaps? The

New technologies have the potential to revolutionize the way we manage health and wellbeing now and in the future. But often seen as expensive and difficult to implement, the challenge is to identify the best technology to deliver real patient benefit and support its rapid adoption to help address the funding difficulties faced by all modern healthcare systems. In this paper we consider the traditional linear model of the technology adoption pathway as it pertains to healthcare, look at common challenges faced traversing this path and suggest solutions. In so doing, we recognise the limitations of the linear model and describe our version of a more realistic, non-linear model. Throughout, we will be looking at the key role of the Clinical Engineer to successful healthcare technology adoption based on our experience of supporting medical device products through to adoption and present the key lessons we learnt along the way.