Toward a Harmonized WHO Family of International Classifications Content Model.

An overarching WHO-FIC Content Model will allow uniform modeling of classifications in the WHO Family of International Classifications (WHO-FIC) and promote their joint use. We provide an initial conceptualization of such a model.

Fully-Online, Interoperable Clinical Trial Management System for Multi-Interventional RCT: Maintain Your Brain Digital Platform.

Maintain Your Brain (MYB)i is a randomised controlled trial (RCT) of multiple online interventions designed to target modifiable risk factors for Alzheimer's disease and dementia. Traditional clinical trial management systems (CTMS) requirements consist of features such as management of the study, site, subject (participant), clinical outcomes, external and internal requests, education, data extraction and reporting, security, and privacy. In addition to fulfilling these traditional requirements, MYB has a specific set of features that needs to be fulfilled. These specific requirements include: (i) support for multiple interventions within a study, (ii) flexible interoperability options with third-party software providers, (iii) study participants being able to engage in online activities via web-based interfaces throughout the trial (from screening to follow-up), (iv) ability to algorithmically personalize trial activities based on the needs of the participant, and (v) the ability to handle large volumes of data over a long period. This paper outlines how the existing CTMSs fall short in meeting these specific requirements. The presented system architecture, development approach and lessons learned in the implementation of the MYB digital platform will inform researchers attempting to implement CTMSs for trials comparable to MYB in the future.

Design and Development of the Brain Training System for the Digital "Maintain Your Brain" Dementia Prevention Trial.

BACKGROUND: Dementia is the leading cause of disability worldwide, and interventions aimed at reducing the prevalence and burden of the disease are urgently needed. Maintain Your Brain (MYB) is a randomized controlled trial of a multimodal digital health intervention targeting modifiable dementia risk factors to combat cognitive decline and potentially prevent dementia. In addition to behavioral modules targeting mood, nutrition, and physical exercise, a new Brain Training System (BTS) will deliver computerized cognitive training (CCT) throughout the trial to provide systematic, challenging, and personally adaptive cognitive activity. OBJECTIVE: This paper aimed to describe the design and development of BTS. METHODS: BTS has been designed with a central focus on the end user. Raw training content is provided by our partner NeuroNation and delivered in several innovative ways. A baseline cognitive profile directs selection and sequencing of exercises within and between sessions and is updated during the 10-week 30-session module. Online trainers are available to provide supervision at different levels of engagement, including face-to-face share-screen coaching, a key implementation resource that is triaged by a "red flag" system for automatic tracking of user adherence and engagement, or through user-initiated help requests. Individualized and comparative feedback is provided to aid motivation and, for the first time, establish a social support network for the user based on their real-world circle of friends and family. RESULTS: The MYB pilot was performed from November 2017 to March 2018. We are currently analyzing data from this pilot trial (n=100), which will make up a separate research paper. The main trial was launched in June 2018. Process and implementation data from the first training module (September to November 2018) are expected to be reported in 2019 and final trial outcomes are anticipated in 2022. CONCLUSIONS: The BTS implemented in MYB is focused on maximizing adherence and engagement with CCT over the short and long term in the setting of a fully digital trial, which, if successful, could be delivered economically at scale. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618000851268; https://www.anzctr.org.au /Trial/Registration/TrialReview.aspx?id=370631&isReview=true.

Comparative Study of Medical Reference and Information Mobile Apps for Healthcare Professionals and Students.

This paper presents a framework upon which medical applications can be evaluated both in terms of basic functionality, and their target area of users. The study was conducted on 40 mobile applications that were published within Google Play and Apple App store targeted for clinicians, medical practitioners and students. It was important to first classify the mobile apps selected because there are many generic applications, and some focus on special areas within the medical field. The classification process included determining the specialist areas of these applications such as educational and training, nursing, diagnosis and treatment, patient monitoring, testing and laboratories, and social networking. After the classification, a criterion to evaluate applications within individual categories, as well as in more general aspects such as their performance, security, user interface, and other software quality attributes, was developed. Test data was used to test the applications using the developed evaluation criteria, and the results were then used to determine the apps with surpassing features. As per the category-wise results, Medicine References and Education & Training categories had applications that had better scores than other categories. However, an equally important finding concluded that there are not enough applications to help with lab testing, and this gap needs to be filled. Most applications lacked the usability aspect and needed work in user interface (UI) and user experience (UX) areas.

Comparative Analysis of Algorithmic Approaches for Auto-Coding with ICD-10-AM and ACHI.

Clinical coding is done using ICD-10-AM (International Classification of Diseases, version 10, Australian Modification) and ACHI (Australian Classification of Health Interventions) in acute and sub-acute hospitals in Australia for funding, insurance claims processing and research. The task of assigning a code to an episode of care is a manual process. This has posed challenges due to increase set of codes, the complexity of care episodes, and large training and recruitment costs of clinical coders. Use of Natural Language Processing (NLP) and Machine Learning (ML) techniques is considered as a solution to this problem. This paper carries out a comparative analysis on a selected set of NLP and ML techniques to identify the most efficient algorithm for clinical coding based on a set of standard metrics: precision, recall, F-score, accuracy, Hamming loss and Jaccard similarity.

Representation of ICD-10-AM/ACHI Using Classification Markup Language (ClaML).

This paper outlines the Australian experience in adopting the Classification Markup Language (ClaML) to represent two clinical classification systems: ICD-10-AM (International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification) and ACHI (Australian Classification of Health Interventions). The primary goal of this process is to share classification data efficiently with relevant parties in a consistent format. This paper outlines extensions implemented in ClaML to facilitate the representation of the above classifications, with validation of the resulting ClaML files verified through XML stylesheet transformation (XSLT) to render the data on a browser, in the same format of printed ICD-10-AM and ACHI books.

Evaluation of Visualisation Techniques for Meaningful Representation of Clinical Classification Data Sets.

In the field of clinical classifications, such as ICD-10 (International Classification of Disease, version 10), there is a transition from paper-based books towards digital systems to have an open process, powered by collaboration. Most clinical classification systems contain massive volume of terms that are hierarchically organised. There are variety of approaches to visualise hierarchical data, in digital systems. In this paper, a selected set of technologies, such as tree views, tree view diagrams, force-directed graphs are investigated to evaluate the suitability of those to accommodate a large clinical classification data sets. Our findings suggest that tree view diagram is the most appropriate technique, due to its ability to accommodate the multi-parent structure of some clinical classifications, and represent large amounts of data in a visually appealing manner.

Health Behaviour Change Through Computer Games: Characterising Interventions.

Recently games in the form of video, computer, or mobile apps have been utilised as an effective component of interventions for health behaviour change. This paper provides an overview of related projects reported in peer-review literature in the period 2006 to 2016. Nine highly relevant references were considered for analysis. The findings are presented according to 3 dimensions of characterisation: health intention, behaviour change principle, and health purpose.