Publications
540 results found
Bene B, Ibeneme S, Fadahunsi KP, et al., 2024, Regulatory Standards and Guidance for the Use of Health Apps for Self-Management in Africa: Scoping Review (Preprint), Journal of Medical Internet Research
Sheng B, Guan Z, Lim L-L, et al., 2024, Large language models for diabetes care: Potentials and prospects., Sci Bull (Beijing), Vol: 69, Pages: 583-588
Xu Y, Jiang Z, Ting DSW, et al., 2024, Medical education and physician training in the era of artificial intelligence., Singapore Med J, Vol: 65, Pages: 159-166
With the rise of generative artificial intelligence (AI) and AI-powered chatbots, the landscape of medicine and healthcare is on the brink of significant transformation. This perspective delves into the prospective influence of AI on medical education, residency training and the continuing education of attending physicians or consultants. We begin by highlighting the constraints of the current education model, challenges in limited faculty, uniformity amidst burgeoning medical knowledge and the limitations in 'traditional' linear knowledge acquisition. We introduce 'AI-assisted' and 'AI-integrated' paradigms for medical education and physician training, targeting a more universal, accessible, high-quality and interconnected educational journey. We differentiate between essential knowledge for all physicians, specialised insights for clinician-scientists and mastery-level proficiency for clinician-computer scientists. With the transformative potential of AI in healthcare and service delivery, it is poised to reshape the pedagogy of medical education and residency training.
Yeo PME, Qin VM, Ang C-S, et al., 2024, Prevalence and correlates of depressive symptoms among matriculated university students in Singapore during Covid-19 pandemic: findings from a repeated cross-sectional analysis., BMC Public Health, Vol: 24
BACKGROUND: Depression is a common issue among university students and has been particularly exacerbated during the COVID-19 pandemic. However, limited research has specifically focused on depression among university entrants. OBJECTIVES: This study aimed to determine the prevalence of depression severity and identify associated factors during different phases of the COVID-19 pandemic using health screening questionnaires completed by matriculated university students in Singapore. METHODS: A repeated cross-sectional study was conducted at a public university in Singapore. Data from health screening questionnaires administered in 2020 and 2021, involving 15,630 newly enrolled university students, were analyzed. The questionnaires covered students' sociodemographic information, physical health status, own and family medical history, lifestyle behaviours, and the Patient Health Questionnaire (PHQ-9). The PHQ-9 was used to measure the severity of depressive symptoms, categorizing into moderate to severe depressive symptoms (MSDS), mild depressive symptoms (MDS), or no depressive symptom (NDS). Multinomial logistic regression was used to assess the sociodemographic, physical and behavioural correlates of depression. RESULTS: The prevalence of MSDS was 1% in both 2020 and 2021, while the rates for MDS were 1.93% in 2020 and 1.64% in 2021. In the 2020 cohort, male freshmen who reported better health had a lower likelihood of experiencing depression. Conversely, students of Malay ethnicity, those majoring in Engineering, those with multiple chronic diseases, monthly alcohol consumers, current smokers, and those with a family history of mental disorder had a higher likelihood of experiencing depression. Moreover, students who lived on-campus in the 2021 cohort were less likely to experience depression than those living off-campus. However, the associations between academic majors, alcohol consumption, and smoking with depression were not significant in the 2021 cohort. CONCL
Wang X, Sanders HM, Liu Y, et al., 2023, ChatGPT: promise and challenges for deployment in low- and middle-income countries., Lancet Reg Health West Pac, Vol: 41
In low- and middle-income countries (LMICs), the fields of medicine and public health grapple with numerous challenges that continue to hinder patients' access to healthcare services. ChatGPT, a publicly accessible chatbot, has emerged as a potential tool in aiding public health efforts in LMICs. This viewpoint details the potential benefits of employing ChatGPT in LMICs to improve medicine and public health encompassing a broad spectrum of domains ranging from health literacy, screening, triaging, remote healthcare support, mental health support, multilingual capabilities, healthcare communication and documentation, medical training and education, and support for healthcare professionals. Additionally, we also share potential concerns and limitations associated with the use of ChatGPT and provide a balanced discussion on the opportunities and challenges of using ChatGPT in LMICs.
Liew H, Pienkowska A, Ang C-S, et al., 2023, Empowering Foot Care Literacy Among People Living With Diabetes and Their Carers With an mHealth App: Protocol for a Feasibility Study., JMIR Res Protoc, Vol: 12, ISSN: 1929-0748
BACKGROUND: Diabetic foot ulcers (DFUs) cause significant morbidity affecting 19% to 34% of people living with diabetes mellitus. DFUs not only impair quality of life but may also result in limb loss and mortality. Patient education has been advocated to raise awareness of proper foot self-care and the necessity of seeking assistance when a foot wound occurs. Modern technologies, including mobile health (mHealth) interventions such as health apps, bring the potential for more cost-effective and scalable interventions. OBJECTIVE: This study aims to examine the feasibility and usability of a newly developed mHealth app called Well Feet, which is a diabetes and foot care education app for individuals at risk of developing DFU. METHODS: Well Feet was developed using an evidence-based and expert panel cocreation approach to deliver educational content available in 3 languages (ie, English, Chinese, and Malay) via animation videos and a range of additional features, including adaptive learning. A nonrandomized, single-arm feasibility study using a mixed methods approach with a series of validated questionnaires and focus group discussions will be conducted. In total, 40 patients and carers will be recruited from a tertiary hospital diabetes clinic to receive a 1-month mHealth intervention. The primary outcomes are the usability of the app and a qualitative perspective on user experience. Secondary outcomes include changes in foot care knowledge, self-management behaviors, and quality of life. RESULTS: Patient recruitment began in July 2023, and the intervention and data collection will be completed by the end of September 2023. This study has been approved by National Healthcare Group Domain Specific Review Board (2022/00614) on February 10, 2023. The expected results will be published in spring 2024. CONCLUSIONS: Through this feasibility study, the Well Feet DFU education app will undergo a comprehensive quantitative and qualitative evaluation of its usability and accept
Martinengo L, Lin X, Jabir AI, et al., 2023, Conversational Agents in Health Care: Expert Interviews to Inform the Definition, Classification, and Conceptual Framework., J Med Internet Res, Vol: 25
BACKGROUND: Conversational agents (CAs), or chatbots, are computer programs that simulate conversations with humans. The use of CAs in health care settings is recent and rapidly increasing, which often translates to poor reporting of the CA development and evaluation processes and unreliable research findings. We developed and published a conceptual framework, designing, developing, evaluating, and implementing a smartphone-delivered, rule-based conversational agent (DISCOVER), consisting of 3 iterative stages of CA design, development, and evaluation and implementation, complemented by 2 cross-cutting themes (user-centered design and data privacy and security). OBJECTIVE: This study aims to perform in-depth, semistructured interviews with multidisciplinary experts in health care CAs to share their views on the definition and classification of health care CAs and evaluate and validate the DISCOVER conceptual framework. METHODS: We conducted one-on-one semistructured interviews via Zoom (Zoom Video Communications) with 12 multidisciplinary CA experts using an interview guide based on our framework. The interviews were audio recorded, transcribed by the research team, and analyzed using thematic analysis. RESULTS: Following participants' input, we defined CAs as digital interfaces that use natural language to engage in a synchronous dialogue using ≥1 communication modality, such as text, voice, images, or video. CAs were classified by 13 categories: response generation method, input and output modalities, CA purpose, deployment platform, CA development modality, appearance, length of interaction, type of CA-user interaction, dialogue initiation, communication style, CA personality, human support, and type of health care intervention. Experts considered that the conceptual framework could be adapted for artificial intelligence-based CAs. However, despite recent advances in artificial intelligence, including large language models, the technology is not able to ensure
Yeung AWK, Torkamani A, Butte AJ, et al., 2023, The promise of digital healthcare technologies, FRONTIERS IN PUBLIC HEALTH, Vol: 11
Chan DYS, Surendra NK, Ng YZ, et al., 2023, Prospective study on the clinical and economic burden of venous leg ulcers in the tropics, JOURNAL OF VASCULAR SURGERY-VENOUS AND LYMPHATIC DISORDERS, Vol: 11, Pages: 954-963, ISSN: 2213-333X
Shin YH, Hwang J, Kwon R, et al., 2023, Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019, ALLERGY, Vol: 78, Pages: 2232-2254, ISSN: 0105-4538
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Lin X, Martinengo L, Jabir AI, et al., 2023, Scope, Characteristics, Behavior Change Techniques, and Quality of Conversational Agents for Mental Health and Well-Being: Systematic Assessment of Apps, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 25, ISSN: 1438-8871
GBD 2021 Diabetes Collaborators, 2023, Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021., The Lancet, Vol: 402, Pages: 203-234, ISSN: 0140-6736
BACKGROUND: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. METHODS: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the com
Lo ZJ, Chong B, Tan E, et al., 2023, Patients, carers and healthcare providers' perspectives on a patient-owned surveillance system for diabetic foot ulcer care: A qualitative study, DIGITAL HEALTH, Vol: 9, ISSN: 2055-2076
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Castro O, Mair JL, Salamanca-Sanabria A, et al., 2023, Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders, FRONTIERS IN DIGITAL HEALTH, Vol: 5
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Lo ZJ, Tan E, Chandrasekar S, et al., 2023, Diabetic foot in primary and tertiary (DEFINITE) Care: A health services innovation in coordination of diabetic foot ulcer (DFU) Care within a healthcare cluster-18-month results from an observational population health cohort study, INTERNATIONAL WOUND JOURNAL, Vol: 20, Pages: 1609-1621, ISSN: 1742-4801
Bojic I, Mammadova M, Ang C-S, et al., 2023, Empowering Health Care Education Through Learning Analytics: In-depth Scoping Review, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 25, ISSN: 1438-8871
Tan B, Pereira M, Yang S-Y, et al., 2023, COLLABORATIVE MODEL OF CARE BETWEEN ORTHOPAEDICS AND ALLIED HEALTHCARE PROFESSIONALS (CONNACT) FOR KNEE OSTEOARTHRITIS: 12-MONTHS FOLLOW-UP OF AN EFFECTIVENESS-IMPLEMENTATION HYBRID TRIAL, World Congress of Osteoarthritis Research Society International (OARSI), Publisher: ELSEVIER SCI LTD, Pages: S16-S17, ISSN: 1063-4584
Tan BY, Goh ZZS, Lim CJ, et al., 2023, Singapore KneE osTeoarthritis CoHort (SKETCH): protocol for a multi-centre prospective cohort study, BMC MUSCULOSKELETAL DISORDERS, Vol: 24
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- Citations: 1
Teixeira F, Li E, Laranjo L, et al., 2023, Digital maturity and its determinants in General Practice: a cross- sectional study in 20 countries, Frontiers in Public Health, Vol: 10, Pages: 1-10, ISSN: 2296-2565
Background: The extent to which digital technologies are employed to promote the delivery of high-quality healthcare is known as Digital Maturity. Individual and systemic digital maturity are both necessary to ensure a successful, scalable and sustainable digital transformation in healthcare. However, digital maturity in primary care has been scarcely evaluated.Objectives: This study assessed the digital maturity in General Practice (GP) globally and evaluated its association with participants' demographic characteristics, practice characteristics and features of Electronic Health Records (EHRs) use.Methods: GPs across 20 countries completed an online questionnaire between June and September 2020. Demographic data, practice characteristics, and features of EHRs use were collected. Digital maturity was evaluated through a framework based on usage, resources and abilities (divided in this study in its collective and individual components), interoperability, general evaluation methods and impact of digital technologies. Each dimension was rated as 1 or 0. The digital maturity score was calculated as the sum of the six dimensions and ranged between 0 to 6 (maximum digital maturity). Multivariable linear regression was used to model the total score, while multivariable logistic regression was used to model the probability of meeting each dimension of the score.Results: One thousand six hundred GPs (61% female, 68% Europeans) participated. GPs had a median digital maturity of 4 (P25–P75: 3–5). Positive associations with digital maturity were found with: male gender [B = 0.18 (95% CI 0.01; 0.36)], use of EHRs for longer periods [B = 0.45 (95% CI 0.35; 0.54)] and higher frequencies of access to EHRs [B = 0.33 (95% CI 0.17; 0.48)]. Practicing in a rural setting was negatively associated with digital maturity [B = −0.25 (95%CI −0.43; −0.08)]. Usage (90%) was the most acknowledged dimension while interoperability (47%) and use of best practice gen
Bojic I, Halim J, Suharman V, et al., 2023, A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets, Pages: 19-32
Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets1. We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality.
Bojic I, Chen J, Chang SY, et al., 2023, Hierarchical Evaluation Framework: Best Practices for Human Evaluation, Pages: 11-22
Human evaluation plays a crucial role in Natural Language Processing (NLP) as it assesses the quality and relevance of developed systems, thereby facilitating their enhancement. However, the absence of widely accepted human evaluation metrics in NLP hampers fair comparisons among different systems and the establishment of universal assessment standards. Through an extensive analysis of existing literature on human evaluation metrics, we identified several gaps in NLP evaluation methodologies. These gaps served as motivation for developing our own hierarchical evaluation framework. The proposed framework offers notable advantages, particularly in providing a more comprehensive representation of the NLP system’s performance. We applied this framework to evaluate the developed Machine Reading Comprehension system, which was utilized within a human-AI symbiosis model. The results highlighted the associations between the quality of inputs and outputs, underscoring the necessity to evaluate both components rather than solely focusing on outputs. In future work, we will investigate the potential time-saving benefits of our proposed framework for evaluators assessing NLP systems.
Pienkowska A, Ang C-S, Mammadova M, et al., 2023, A Diabetes Education App for People Living With Type 2 Diabetes: Co-Design Study, JMIR FORMATIVE RESEARCH, Vol: 7
Bojic I, Liu J, Ong QC, et al., 2023, Identifying at-risk university students: A system for longitudinal monitoring of sleep health, IEEE International Conference on Digital Health (IEEE ICDH) at the IEEE World Congress on Services (SERVICES), Publisher: IEEE COMPUTER SOC, Pages: 143-149
Lo ZJ, Harish KB, Tan E, et al., 2023, A feasibility study on the efficacy of a patient-owned wound surveillance system for diabetic foot ulcer care (ePOWS study), DIGITAL HEALTH, Vol: 9, ISSN: 2055-2076
Martinengo L, Lum E, Car J, 2022, Evaluation of chatbot-delivered interventions for self-management of depression: Content analysis, JOURNAL OF AFFECTIVE DISORDERS, Vol: 319, Pages: 598-607, ISSN: 0165-0327
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Reiner RC, LBD Triple Burden Collaborators, Hay SI, 2022, The overlapping burden of the three leading causes of disability and death in sub-Saharan African children, Nature Communications, Vol: 13, ISSN: 2041-1723
Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival.
Fadahunsi KP, Wark PA, Mastellos N, et al., 2022, Assessment of clinical information quality in digital health technologies: an international eDelphi study, Journal of Medical Internet Research, Vol: 24, Pages: 1-10, ISSN: 1438-8871
Background:Digital health technologies (DHTs), such as electronic health records and prescribing systems, are transforming health care delivery around the world. The quality of information in DHTs is key to the quality and safety of care. We developed a novel clinical information quality (CLIQ) framework to assess the quality of clinical information in DHTs.Objective:This study explored clinicians’ perspectives on the relevance, definition, and assessment of information quality dimensions in the CLIQ framework.Methods:We used a systematic and iterative eDelphi approach to engage clinicians who had information governance roles or personal interest in information governance; the clinicians were recruited through purposive and snowball sampling techniques. Data were collected using semistructured online questionnaires until consensus was reached on the information quality dimensions in the CLIQ framework. Responses on the relevance of the dimensions were summarized to inform decisions on retention of the dimensions according to prespecified rules. Thematic analysis of the free-text responses was used to revise definitions and the assessment of dimensions.Results:Thirty-five clinicians from 10 countries participated in the study, which was concluded after the second round. Consensus was reached on all dimensions and categories in the CLIQ framework: informativeness (accuracy, completeness, interpretability, plausibility, provenance, and relevance), availability (accessibility, portability, security, and timeliness), and usability (conformance, consistency, and maintainability). A new dimension, searchability, was introduced in the availability category to account for the ease of finding needed information in the DHTs. Certain dimensions were renamed, and some definitions were rephrased to improve clarity.Conclusions:The CLIQ framework reached a high expert consensus and clarity of language relating to the information quality dimensions. The framework can be used b
Amornsriwatanakul A, Rahman HA, Wattanapisit A, et al., 2022, University students' overall and domain-specific physical activity during COVID-19: A cross-sectional study in seven ASEAN countries., Heliyon, Vol: 8, ISSN: 2405-8440
This research investigated the overall and domain-specific physical activity (PA) of university students in the coronavirus disease 2019 (COVID-19) pandemic. A cross-sectional study was applied to socioeconomic (SE) and PA online data collected from 15,366 students across 17 universities in seven Association of Southeast Asian Nations (ASEAN) countries. Statistical analyses using logistic regressions established SE-PA relationships. Over half (60.3%) of ASEAN university students met age-span specific PA guidelines. Students participated in recreational PA the most, followed by study-related activities and 44.1% of students engaged in >8 hrs/day of sedentary time (ST). Compared to students with a normal body mass index (BMI), students who were underweight (UW), overweight (OW), and obese (OB) respectively, had a 14% (UW odds ratio (OR) = 1.14, p = 0.005), 25% (OW OR = 1.25, p < 0.001), and 24% (OB OR = 1.24, p = 0.005) greater probability of meeting PA guidelines. Those who engaged in active transport and belonged to a sports club (SC) had 42% (SC OR = 0.58, p < 0.001, for both) less probability of meeting the PA guidelines, compared with those who travelled inactively and did not belong to a sports club, respectively. Students who participated in 4-6 sport or exercise activities had ten times more likelihood of meeting PA guidelines (OR = 10.15, p < 0.001), compared with those who did not play any sport or do any exercise. Students who spent >8 hrs/day of ST had 32% (ST OR = 0.68, p < 0.001) less probability of meeting PA guidelines, compared with those who spent <3 hrs of ST. These data showed that over half of ASEAN university students achieved PA guidelines and were highly sedentary during the COVID-19 pandemic. Recreational and study-related activities were important for students to maintain sufficient PA and should be actively promoted within the restrictions imposed during periods of the COVID-19 pandemic lockdowns.
Wattanapisit A, Abdul Rahman H, Car J, et al., 2022, The clusters of health-risk behaviours and mental wellbeing and their sociodemographic correlates: a study of 15,366 ASEAN university students, BMC PUBLIC HEALTH, Vol: 22
Lee KFA, Lee EH, Roberts AC, et al., 2022, Effects of fun-seeking and external locus of control on smoking behaviour: a cross-sectional analysis on a cohort of working men in Singapore, BMJ OPEN, Vol: 12, ISSN: 2044-6055
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