Cognitive walkthrough - an element in system development and evaluation:experiences from the eWALL telehealth system

AbstractEpidemiological changes in the population lead to an increasing number of elderly people with a chronic disease. Telehealth is proposed as one of the solutions for the growing challenges of the health care system caused by these changes. The telehealth system eWALL seeks to promote the independent living of people with chronic obstructive pulmonary disease, mild cognitive impairment or age related impairments. The eWALL system is in a developmental stage in which partners from 14 different European countries are included. A three phase cognitive walkthrough-approach was performed on the eWALL system in order to evaluate the usability of the system. First the cognitive walkthrough performed by experts, second, rating of the identified usability problems identified by other medical partners, and third, discussion on a plenary telecommunication call among medical partners and technical partners. (n=119) usability problems were identified distributed among the 14 functionalities of the telehealth system. The majority of the usability problems were discovered in the functionalities: ‘TV’ (n=21), ‘Calendar’ (n=20), and ‘Environmental box’ (n=18). The least usability problems were identified in the functionalities: ‘My sleep’ (n=1), ‘Photo frame’ (n=2), and ‘My Everyday Life’ (n=3).The results of the cognitive walkthrough served as a concrete, structured and constructive collaborative tool between the medical partners and the technical partners involved in the eWALL project

Development of a multivariable prediction model for early revision of total knee arthroplasty - The effect of including patient-reported outcome measures

BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As revision is becoming increasingly common in patients under 65 years, the need for improved preoperative patient selection is imminently needed. Therefore, this study aimed to identify the most important factors of early revision and to develop a prediction model of early revision including assessment of the effect of incorporating data on patient-reported outcome measures (PROMs). MATERIAL AND METHODS: A cohort of 538 patients undergoing primary TKA was included. Multiple logistic regression using forward selection of variables was applied to identify the best predictors of early revision and to develop a prediction model. The model was internally validated with stratified 5-fold cross-validation. This procedure was repeated without including data on PROMs to develop a model for comparison. The models were evaluated on their discriminative capacity using area under the receiver operating characteristic curve (AUC). RESULTS: The most important factors of early revision were age (OR 0.63 [0.42, 0.95]; P = 0.03), preoperative EQ-5D (OR 0.07 [0.01, 0.51]; P = 0.01), and number of comorbidities (OR 1.01 [0.97, 1.25]; P = 0.15). The AUCs of the models with and without PROMs were 0.65 and 0.61, respectively. The difference between the AUCs was not statistically significant (P = 0.32). CONCLUSIONS: Although more work is needed in order to reach a clinically meaningful quality of the predictions, our results show that the inclusion of PROMs seems to improve the quality of the prediction model