Healthcare Transformation in Singapore With Artificial Intelligence

Liu, Siqi; Ko, Qianwen Stephanie; Heng, Kun Qiang Amos; Ngiam, Kee Yuan; Feng, Mengling

doi:10.3389/fdgth.2020.592121

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Singapore is a technologically savvy country, and citizens avidly use messaging apps. In addition, Singapore’s ministry of health has proposed the increasing use of conversational agents in health care in tackling issues such as the rising chronic disease burden and the aging population [ 15 ] that can lead to more primary care appointments. The ministry envisions a near future where a conversational agent can collect a patient’s history from them before their consultation, streamlining the primary care visit and thus cutting down waiting times.…”

Section: Introductionmentioning

confidence: 99%

Conversational Agent for Healthy Lifestyle Behavior Change: Web-Based Feasibility Study

Dhinagaran¹,

Sathish²,

Soong³

et al. 2021

JMIR Form Res

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Background The rising incidence of chronic diseases is a growing concern, especially in Singapore, which is one of the high-income countries with the highest prevalence of diabetes. Interventions that promote healthy lifestyle behavior changes have been proven to be effective in reducing the progression of prediabetes to diabetes, but their in-person delivery may not be feasible on a large scale. Novel technologies such as conversational agents are a potential alternative for delivering behavioral interventions that promote healthy lifestyle behavior changes to the public. Objective The aim of this study is to assess the feasibility and acceptability of using a conversational agent promoting healthy lifestyle behavior changes in the general population in Singapore. Methods We performed a web-based, single-arm feasibility study. The participants were recruited through Facebook over 4 weeks. The Facebook Messenger conversational agent was used to deliver the intervention. The conversations focused on diet, exercise, sleep, and stress and aimed to promote healthy lifestyle behavior changes and improve the participants’ knowledge of diabetes. Messages were sent to the participants four times a week (once for each of the 4 topics of focus) for 4 weeks. We assessed the feasibility of recruitment, defined as at least 75% (150/200) of our target sample of 200 participants in 4 weeks, as well as retention, defined as 33% (66/200) of the recruited sample completing the study. We also assessed the participants’ satisfaction with, and usability of, the conversational agent. In addition, we performed baseline and follow-up assessments of quality of life, diabetes knowledge and risk perception, diet, exercise, sleep, and stress. Results We recruited 37.5% (75/200) of the target sample size in 1 month. Of the 75 eligible participants, 60 (80%) provided digital informed consent and completed baseline assessments. Of these 60 participants, 56 (93%) followed the study through till completion. Retention was high at 93% (56/60), along with engagement, denoted by 50% (30/60) of the participants communicating with the conversational agent at each interaction. Acceptability, usability, and satisfaction were generally high. Preliminary efficacy of the intervention showed no definitive improvements in health-related behavior. Conclusions The delivery of a conversational agent for healthy lifestyle behavior change through Facebook Messenger was feasible and acceptable. We were unable to recruit our planned sample solely using the free options in Facebook. However, participant retention and conversational agent engagement rates were high. Our findings provide important insights to inform the design of a future randomized controlled trial.

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Section: Introductionmentioning

confidence: 99%

Conversational Agent for Healthy Lifestyle Behavior Change: Web-Based Feasibility Study

Dhinagaran¹,

Sathish²,

Soong³

et al. 2021

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…In the realm of healthcare, Singapore has rolled out several initiatives that make use of technology to meet healthcare needs, such as introducing telehealth, as well as implementing robotics and assistive technology solutions to help older adults and those with mobility issues (Smart Nation and Digital Government O ce, 2023). AI has also been used in specialties such as radiology and ophthalmology, and is expected to be a transformational force in healthcare (Liu et al, 2020).…”

Section: State Of Digitalization and Digital Training Among Medical S...mentioning

confidence: 99%

Barriers to the Incorporation of Digital Health Competencies in the Medical School Curriculum: A Qualitative Study of Doctors in Organizational Leadership

Zainal

Xin

Thumboo

et al. 2023

Preprint

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Purpose Digital technologies have influenced healthcare delivery worldwide and are increasingly being used in clinical practice. However, studies have shown a lag in the implementation of guidelines for the training of digital health competencies (DHC) in medical schools. Furthermore, few existing frameworks on DHC have examined barriers commonly faced by developed countries to equip medical students with DHC. Using Singapore as a case study, and by exploring the perspectives of doctors in organizational leadership positions, this paper identifies reasons for this lag and a common framework that can help overcome these barriers. Methods Findings were drawn from individual semi-structured interviews with 33 doctors in executive and organizational leadership roles. Participants were recruited using purposive sampling. Data were interpreted using qualitative thematic analysis. Results Participants were currently (n = 26) or formerly (n = 7) in organizational leadership. They highlighted six reasons for the lag in DHC integration in the curricula: bureaucratic inertia, expectations to pursue traditional career pathways, lack of protective mechanisms for experiential learning and experimentation, lack of clear policy guidelines for clinical practice, need for more evidences on the effectiveness of digital technologies, and need for a strong and integrated IT infrastructure in healthcare institutions. Conclusions Applying Damschroder et al.’s (2009) Consolidated Framework for Implementation Research (CFIR) would illuminate the importance of considering both structural and institutional barriers to training students with relevant DHC. Showing how relevant CFIR constructs might be applied to integrating DHC in the medical school curriculum would help ensure consistency of implementation across time and contexts.

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“…Another limitation is the possible limited generalizability of the findings as interviewee responses are likely to be tied to the specifics of Singapore health care system, the exposure to innovation, and the embedded attitudes to technological innovation potentially shaped by Singapore's strategy for AI in health care [74].…”

Section: Limitationsmentioning

confidence: 99%

Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

Vijayakumar,

Lee,

Leong

et al. 2023

JMIR Hum Factors

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Background Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are being explored. Therefore, understanding physicians’ perceptions can be critical toward clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSSs), there remains a need to gain an in-depth understanding of physicians’ perceptions and expectations toward their downstream implementation. This paper explores physicians’ perceptions of integrating CURATE.AI, a novel artificial intelligence (AI)–based and clinical stage personalized dosing CDSSs, into clinical practice. Objective This study aims to understand physicians’ perspectives of integrating CURATE.AI for clinical work and to gather insights on considerations of the implementation of AI-based CDSS tools. Methods A total of 12 participants completed semistructured interviews examining their knowledge, experience, attitudes, risks, and future course of the personalized combination therapy dosing platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, and coded manually. The data were thematically analyzed. Results Overall, 3 broad themes and 9 subthemes were identified through thematic analysis. The themes covered considerations that physicians perceived as significant across various stages of new technology development, including trial, clinical implementation, and mass adoption. Conclusions The study laid out the various ways physicians interpreted an AI-based personalized dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that physicians’ expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation that are relevant for technology developers and researchers.

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Healthcare Transformation in Singapore With Artificial Intelligence

Cited by 8 publications

References 20 publications

Conversational Agent for Healthy Lifestyle Behavior Change: Web-Based Feasibility Study

Conversational Agent for Healthy Lifestyle Behavior Change: Web-Based Feasibility Study

Barriers to the Incorporation of Digital Health Competencies in the Medical School Curriculum: A Qualitative Study of Doctors in Organizational Leadership

Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

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