Clinical application tool to support dengue management in low and middle income countries.
Role: Postdoctoral researcher bridging the gap between clinicians and software developers
Dengue epidemics can rapidly increase demand in healthcare services across many endemic settings. However, there remains a lack of tools which can rapidly inform patient management and can be used at the point of care. Digital clinical decision-support systems (CDSS) allow for efficient organisation of care as well as improve the quality of patient management. It is important that these tools are designed for the end-user and with the healthcare setting in mind to increase adoption and usability.
We adopted a ground-up human-centred design approach to design a digital CDSS system for dengue management in Vietnam (D-CAT). A multidisciplinary team of data scientists, clinicians and social scientists were involved in a series of activities designed to map clinical processes, essential tasks and decision-making priorities which were crucial in the management of dengue at our hospital setting [1]. The desired features for the CDSS identified were: i) patient organisation, ii) availability of guidelines and calculators with easy access, iii) display of results and iv) inference models for dengue diagnosis on admission [2] and further risk-stratification for hospitalised patients based on possible complications [3]. A web-based reactive framework suitable for display on computers and tablets was produced. Priority was placed on usability and modularity so that the system can be re-purposed.
Dengue Clinical Application Tool (D-CAT) is a bespoke and rapidly scalable CDSS produced following clinical pathways, clinician’s needs, and usability in mind. Further work will focus on prospective evaluation and iterative improvement of the CDSS including (i) end-user testing and (ii) prospective model performance. If successful, the CDSS will be implemented and deployed to evaluate its clinical utility.
@article{nguyen2023mapping,title={Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools},author={Nguyen, Quang Huy and Ming, Damien K and Luu, An Phuoc and Chanh, Ho Quang and Tam, Dong Thi Hoai and Truong, Nguyen Thanh and Huy, Vo Xuan and Hernandez, Bernard and Van Nuil, Jennifer Ilo and Paton, Chris and others},journal={BMC Medical Informatics and Decision Making},volume={23},number={1},pages={24},month=feb,year={2023},publisher={Springer},doi={10.1186/s12911-023-02116-4},url={https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02116-4},}
@article{ming2022diagnosis,title={The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality},author={Ming, Damien K and Tuan, Nguyen M and Hernandez, Bernard and Sangkaew, Sorawat and Vuong, Nguyen L and Chanh, Ho Q and Chau, Nguyen VV and Simmons, Cameron P and Wills, Bridget and Georgiou, Pantelis and others},journal={Frontiers in Digital Health},volume={4},date={2022-03-14},year={2022},month=mar,publisher={Frontiers Media SA},doi={10.3389/fdgth.2022.849641},url={},}
@article{ming2022applied,author={Ming, Damien K and Hernandez, Bernard and Sangkaew, Sorawat and Vuong, Nguyen Lam and Lam, Phung Khanh and Nguyet, Nguyen Minh and Tam, Dong Thi Hoai and Trung, Dinh The and Tien, Nguyen Thi Hanh and Tuan, Nguyen Minh and others},journal={PLOS Digital Health},volume={1},number={1},pages={e0000005},date={2022-01-18},year={2022},month=jan,publisher={Public Library of Science San Francisco, CA USA},doi={10.1371/journal.pdig.0000005},url={},dimensions={true}}
