Assessment of Manual Dexterity in VR: Towards a Fully Automated Version of the Box and Blocks Test

Proceeding of The 27th Australian National Health Informatics Conference (HIC 2019), 12-14 August 2019, Melbourne, AustraliaIn recent years, the possibility of using serious gaming technology for the automation of clinical procedures for assessment of motor function have captured the interest of the research community. In this paper, a virtual version of the Box and Blocks Test (BBT) for manual dexterity assessment is presented. This game-like system combines the classical BBT mechanics with a play-centric approach to accomplish a fully automated test for assessing hand motor function, making it more accessible and easier to administer. Additionally, some variants of the traditional mechanics are proposed in order to fully exploit the advantages of the chosen technology. This ongoing research aims to provide the clinical practitioners with a customisable, intuitive, and reliable tool for the assessment and rehabilitation of hand motor function.Work funded by the Spanish Ministry of Economy and Competitiveness (ROBOESPAS project DPI2017-87562-C2-1-R and mobility grant EST2019-013090), and by the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (S2018/NMT-4331).Publicad

The hare and the hortoise [sic]: The potential versus the reality of eTP implementation

In a health system increasingly driven by cost constraints, there is a focus on improved electronic transfer of information to support healthcare delivery. One area of healthcare that has moved more quickly than others to achieve this is prescribing in the primary care environment. Whilst the move to electronic transfer of prescriptions has reduced transcription errors, the regulatory environment persists with handwritten signatures. This constraint, whilst addressed slowly with technology solutions, needs support from legislative change. The ultimate step is to have a secure mobile model, which would support the move to a fully-electronic, paperless transaction model

Development of an at-risk assessment approach to dietary data quality in a food-based clinical trial

Accurate and valid dietary data is the basis to investigate diet-disease relationships. Potential data discrepancies may be introduced when collecting and analysing data, despite rigorous quality assurance protocols. The aim of this study was to identify at-risk areas of dietary data in a food-based clinical trial. Source data verification was performed on a 10% random sample (n=38) of paper-based baseline diet history interview records in a registered clinical trial. All items listed in the source data underwent 100% manual verification based on the food input data from FoodWorks nutrient analysis software. Food item discrepancies were explored using food categories and summarised based on meals. The differences in identified discrepancies for energy and macronutrient output generated from FoodWorks software between previously entered data and re-entered data were compared. An overall discrepancy rate of 4.88% was identified. It was found that dinner intake data were more prone to discrepancy incidences than breakfast, lunch and snacks. Furthermore, assessing intake based on reported quantity and frequency may be more effective to correct discrepancies for quality improvement. Therefore, the dinner meal appeared to be an at risk area of dietary data. The method implemented in this study offers a systematic approach to evaluating dietary data in a research setting

Future of Australia’s ETP: Script exchange, script vault or secure mobile alternative

Electronic transfer of prescriptions is an essential element of electronic medications management. Unfortunately, current manual and preliminary electronic transfer of prescription methods are not patient focussed, leading to a suboptimal solution for the patient. This is increasingly relevant in the push for more patient engagement in their own healthcare. The area is highly controlled by legislation and regulation. Through research and an analysis of the possible methods to improve and personalise electronic transfer of prescriptions, this paper provides an overview of these conclusions, and presents an alternative technical solution. The solution has been derived from a number of experiments in data transfer techniques using a mobile phone. The paper explains how this meets the current regulations and legislation, as well as providing a patient centred approach to the problem. Ultimately, healthcare outcomes will improve where patients are given the opportunity and the tools to better engage in their own healthcare management, and secure electronic transfer of prescriptions with patient access to their own medication lists may improve compliance and reduce healthcare costs

Content Analysis of Tweets by People with Traumatic Brain Injury (TBI): Implications for Rehabilitation and Social Media Goals

In this Twitter research, 6874 tweets of six adults with traumatic brain injury (TBI) were analyzed qualitatively and quantitatively using content classification [1], inductive coding of content themes, socio-linguistic analysis, and computational analysis in KH Coder. The results reflected that participants used Twitter for: (i) supporting others, including people with TBI; (ii) discussing society and culture, popular issues, news, and personal interests; (iii) connecting with others; (iv) sharing their experiences of life after TBI; (v) knowledge via exchanging information; and (vii) advocacy. ‘Emotional expression’, and ‘connection’ were common threads running across themes. Attending to the expressions of people with TBI on Twitter provides important insights into their lived experiences and could inform the development of user-centered cognitive-communication and social participation goals for people with TBI

Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter

The manner in which people preferentially interact with others like themselves suggests that information about social connections may be useful in the surveillance of opinions for public health purposes. We examined if social connection information from tweets about human papillomavirus (HPV) vaccines could be used to train classifiers that identify antivaccine opinions. From 42,533 tweets posted between October 2013 and March 2014, 2,098 were sampled at random and two investigators independently identified anti-vaccine opinions. Machine learning methods were used to train classifiers using the first three months of data, including content (8,261 text fragments) and social connections (10,758 relationships). Connection-based classifiers performed similarly to content-based classifiers on the first three months of training data, and performed more consistently than content-based classifiers on test data from the subsequent three months. The most accurate classifier achieved an accuracy of 88.6% on the test data set, and used only social connection features. Information about how people are connected, rather than what they write, may be useful for improving public health surveillance methods on Twitter

The Acceptance of National Electronic Health Records in Saudi Arabia: Healthcare Consumers’ Perspectives

This study aims to investigate factors impacting healthcare consumers’ acceptance of National Electronic Health Records (NEHRs) in Saudi Arabia. The study incorporated perceived security concerns and trust factors into the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A questionnaire survey was distributed among Saudi citizens to gain their perceptions, and 794 valid responses were collected. Structural Equation Modeling (SEM) was used to analyse the collected data. Both the measurement model and structural model proved a good fit to the research data. All research hypotheses were supported at the significance level of p \u3c 0.001 except the impact of social influence, which was significant at the level of p \u3c 0.005. The proposed model explained 56% of the variance in behavioural intention, implying the presence of additional factors that are not yet identified. A better understanding of these influential factors could prompt policymakers to effectively plan for and enhance the acceptance and use of NEHRs