Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment

The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review

INTRODUCTION

Stakeholder preference evaluations are increasingly emphasized in healthcare policy and health technology assessment. Discrete choice experiments (DCEs) are the most common method for quantifying preferences among patients, the public, and healthcare professionals. While prior reviews (1990-2017) have examined DCE trends, no comprehensive synthesis exists for studies published since 2018. This updated review (2018-2023) provides critical insights into evolving methodologies and global trends in health-related DCEs.

METHODS

A systematic search (2018-2023) of Medline, Embase, and Web of Science identified relevant studies. Studies were screened for inclusion and data were extracted, including details on DCE design and analysis. To enable trend comparisons, the search strategy and extraction items aligned with previous reviews.

RESULTS

Of 2663 identified papers, 1279 met the inclusion criteria, reflecting a significant rise in published DCEs over time. DCEs were conducted globally, with a remarkable increase in publications from Asia and Africa compared with previous reviews. Experimental designs and econometric models have advanced, continuing prior trends. Notably, most recent DCEs were administered online.

DISCUSSION

The rapid growth of DCE applications underscores their importance in health research. While the methodology is advancing rapidly, it is crucial that researchers provide full transparency in reporting their methods, particularly in detailing experimental designs and validity tests, which are too often overlooked. Key recommendations include improving reporting of experimental designs, applying validity tests, following good practices for presenting benefit-risk attributes, and adopting open science practices. Ensuring methodological rigor will maximize the impact and reproducibility of DCE research in health economics.

Anxiety about artificial intelligence from patient and doctor-physician

OBJECTIVE

This paper investigates the anxiety surrounding the integration of artificial intelligence (AI) in doctor-patient interactions, analyzing the perspectives of both patients and healthcare providers to identify key concerns and potential solutions.

METHODS

The study employs a comprehensive literature review, examining existing research on AI in healthcare, and synthesizes findings from various surveys and studies that explore the attitudes of patients and doctors towards AI applications in medical settings.

RESULTS

The analysis reveals that patient anxiety encompasses algorithm aversion, robophobia, lack of humanistic care, challenges in human-machine interaction, and concerns about AI's universal applicability. Doctors' anxieties stem from fears of replacement, legal liabilities, emotional impacts of work environment changes, and technological apprehension. The paper highlights the need for patient participation, humanistic care, improved interaction methods, educational training, and policy guidelines to foster public understanding and trust in AI.

CONCLUSION

The paper concludes that addressing AI anxiety in doctor-patient relationships is crucial for successfully integrating AI in healthcare. It emphasizes the importance of respecting patient autonomy, addressing the lack of humanistic care, and improving patient-AI interaction to enhance the patient experience and reduce medical errors.

PRACTICE IMPLICATIONS

The study suggests that future research should focus on understanding the needs and concerns of patients and doctors, strengthening medical humanities education, and establishing policies to guide the ethical use of AI in medicine. It also recommends public education to enhance understanding and trust in AI to improve medical services and ensure professional development and stable work environment for doctors.

Exploring parental preferences for childhood obesity prevention program in China: a discrete choice experiment

BACKGROUND

Childhood obesity has emerged as one of the most critical public health challenges in China. Despite its urgency, the existing research on parental preference for tackling childhood obesity remains insufficient. This study aimed to determine the factors that parents prioritise most when commissioning hypothetical programs that target childhood obesity prevention in China.

METHODS

A discrete choice experiment (DCE) was conducted to assess parental preferences for a hypothetical childhood obesity prevention programme attributes. Recruitment occurred between 20th October 2022 and 30th December 2022, using snowball sampling facilitated through social media platforms. Eligibility criteria were limited to parents with at least one child aged between 5 and 17 years old. Relevant attributes of the childhood obesity prevention programme were identified through a literature review and expert consultation. The study encompassed six attributes, and the coefficient of these different attributes was analysed using multinomial logit models (MNL) and latent class models (LCM).

RESULTS

This study, involving 631 participants, demonstrates that in prioritizing attributes of childhood obesity prevention programs, parents place the greatest importance on additional costs (32.36%). This is followed by daily sleep duration (18.42%) and dietary choices (16.49%). A preference for a 9-hour sleep duration is evident (Odds Ratio [OR]: 1.291; 95% Confidence Interval [CI]: 1.186-1.406; p < 0.05, reference: 7 h), as well as a tendency towards high-protein diets over low-fat ones (OR: 1.114; 95% CI: 1.034-1.200; p < 0.05, reference: low-fat diet). School-based exercise is favoured over fitness centres (OR: 0.837; 95% CI: 0.785-0.893; p < 0.001, reference: school-based). A latent class model (LCM) identifies two distinct groups: one preferring school-based exercise, 8-hour sleep, and minimal additional expenses; the other favouring 9-hour sleep and willingness to invest an additional RMB200 for weight control. Both groups prefer high-protein diets and early eating schedules.

CONCLUSIONS

Understanding parental preferences and concerns is vital for crafting effective public health policies aligned with UN SDGs and the SDH framework. Key elements include promoting balanced diets, ensuring safe exercise spaces, and fostering parental engagement. Collaboration among policymakers, educators, and parents is essential to mitigate childhood obesity.

Preferences for attributes of an artificial intelligence-based risk assessment tool for HIV and sexually transmitted infections: a discrete choice experiment

INTRODUCTION

Early detection and treatment of HIV and sexually transmitted infections (STIs) are crucial for effective control. We previously developed MySTIRisk, an artificial intelligence-based risk tool that predicts the risk of HIV and STIs. We examined the attributes that encourage potential users to use it.

METHODS

Between January and March 2024, we sent text message invitations to the Melbourne Sexual Health Centre (MSHC) attendees to participate in an online survey. We also advertised the survey on social media, the clinic's website, and posters in affiliated general practice clinics. This anonymous survey used a discrete choice experiment (DCE) to examine which MySTIRisk attributes would encourage potential users. We analysed the data using random parameters logit (RPL) and latent class analysis (LCA) models.

RESULTS

The median age of 415 participants was 31 years (interquartile range, 26-38 years), with a minority of participants identifying as straight or heterosexual (31.8%, n = 132). The choice to use MySTIRisk was most influenced by two attributes: cost and accuracy, followed by the availability of a pathology request form, level of anonymity, speed of receiving results, and whether the tool was a web or mobile application. LCA revealed two classes: "The Precisionists" (66.0% of respondents), who demanded high accuracy and "The Economists" (34.0% of respondents), who prioritised low cost. Simulations predicted a high uptake (97.7%) for a tool designed with the most preferred attribute levels, contrasting with lower uptake (22.3%) for the least preferred design.

CONCLUSIONS

Participants were more likely to use MySTIRisk if it was free, highly accurate, and could send pathology request forms. Tailoring the tool to distinct user segments could enhance its uptake and effectiveness in promoting early detection and prevention of HIV and STIs.

Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods

OBJECTIVE

With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance.

MATERIALS AND METHODS

We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations.

RESULTS

Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI.

DISCUSSION

Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation.

CONCLUSION

This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.

Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review

Purpose

Artificial intelligence (AI) technology is being rapidly adopted into many different branches of medicine. Although research has started to highlight the impact of AI on health care, the focus on patient perspectives of AI is scarce. This scoping review aimed to explore the literature on adult patients' perspectives on the use of an array of AI technologies in the health care setting for design and deployment.

Methods

This scoping review followed Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR). To evaluate patient perspectives, we conducted a comprehensive literature search using eight interdisciplinary electronic databases, including grey literature. Articles published from 2015 to 2022 that focused on patient views regarding AI technology in health care were included. Thematic analysis was performed on the extracted articles.

Results

Of the 10,571 imported studies, 37 articles were included and extracted. From the 33 peer-reviewed and 4 grey literature articles, the following themes on AI emerged: (i) Patient attitudes, (ii) Influences on patient attitudes, (iii) Considerations for design, and (iv) Considerations for use.

Conclusions

Patients are key stakeholders essential to the uptake of AI in health care. The findings indicate that patients' needs and expectations are not fully considered in the application of AI in health care. Therefore, there is a need for patient voices in the development of AI in health care.

“What are the roles and competencies of doctors in the artificial intelligence implementation society?: A qualitative analysis through physician interview” (Preprint)

BACKGROUND

Artificial intelligence (AI) is a term used to describe the use of computers and technology to emulate human intelligence mechanisms. Although AI is known to affect health services, the impact of information provided by AI on the patient-physician relationship in actual practice is unclear.

OBJECTIVE

The purpose of this study is to investigate the effect of introducing AI functions into the medical field on the role of the physician or physician-patient relationship, as well as potential concerns in the AI era.

METHODS

We conducted focus group interviews in Tokyo's suburbs with physicians recruited through snowball sampling. The interviews were conducted in accordance with the questions listed in the interview guide. A verbatim transcript recording of all interviews was qualitatively analyzed using content analysis by all authors. Similarly, extracted code was grouped into subcategories, categories, and then core categories. We continued interviewing, analyzing, and discussing until we reached data saturation. In addition, we shared the results with all interviewees and confirmed the content to ensure the credibility of the analysis results.

RESULTS

A total of 9 participants who belonged to various clinical departments in the 3 groups were interviewed. The same interviewers conducted the interview as the moderator each time. The average group interview time for the 3 groups was 102 minutes. Content saturation and theme development were achieved with the 3 groups. We identified three core categories: (1) functions expected to be replaced by AI, (2) functions still expected of human physicians, and (3) concerns about the medical field in the AI era. We also summarized the roles of physicians and patients, as well as the changes in the clinical environment in the age of AI. Some of the current functions of the physician were primarily replaced by AI functions, while others were inherited as the functions of the physician. In addition, "functions extended by AI" obtained by processing massive amounts of data will emerge, and a new role for physicians will be created to deal with them. Accordingly, the importance of physician functions, such as responsibility and commitment based on values, will increase, which will simultaneously increase the expectations of the patients that physicians will perform these functions.

CONCLUSIONS

We presented our findings on how the medical processes of physicians and patients will change as AI technology is fully implemented. Promoting interdisciplinary discussions on how to overcome the challenges is essential, referring to the discussions being conducted in other fields.

Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics

PROBLEM

Policymakers must decide on interventions to control the pandemic. These decisions are driven by weighing the risks and benefits of various non-pharmaceutical intervention alternatives. Due to the nature of the pandemic, these decisions are not based on sufficient evidence regarding the effects, nor are decision-makers informed about the willingness of populations to accept the economic and health risks associated with different policy options. This empirical study seeks to reduce uncertainty by measuring population preferences for non-pharmaceutical interventions.

METHODS

An online-based discrete choice experiment (DCE) was conducted to elicit population preferences. Respondents were asked to choose between three pandemic scenarios with different interventions and impacts of the Corona pandemic. In addition, Best-worst scaling (BWS) was used to analyze the impact of the duration of individual interventions on people's acceptance. The marginal rate of substitution was applied to estimate willingness-to-accept (WTA) for each intervention and effect by risk of infection.

RESULTS

Data from 3006 respondents were included in the analysis. The DCE showed, economic effect of non-pharmaceutical measures had a large impact on choice decisions for or against specific lockdown scenarios. Individual income decreases had the most impact. Excess mortality and individual risk of infection were also important factors influencing choice decisions. Curfews, contact restrictions, facility closures, personal data transmissions, and mandatory masking in public had a lesser impact. However, significant standard deviations in the random parameter logit model (RPL) indicated heterogeneities in the study population. The BWS results showed that short-term restrictions were more likely to be accepted than long-term restrictions. According to WTA estimates, people would be willing to accept a greater risk of infection to avoid loss of income.

DISCUSSION

The results can be used to determine which consequences of pandemic measures would be more severe for the population. For example, the results show that citizens want to limit the decline in individual income during pandemic measures. Participation in preference studies can also inform citizens about potential tradeoffs that decision-makers face in current and future decisions during a pandemic. Knowledge of the population's preferences will help inform decisions that consider people's perspectives and expectations for the future. Survey results can inform decision-makers about the extent to which the population is willing to accept certain lockdown measures, such as curfews, contact restrictions, lockdowns, or mandatory masks.

Applications of discrete choice experiments in COVID-19 research: Disparity in survey qualities between health and transport fields

Published choice experiments linked to various aspects of the COVID-19 pandemic are analysed in a rapid review. The aim is to (i) document the diversity of topics as well as their temporal and geographical patterns of emergence, (ii) compare various elements of design quality across different sectors of applied economics, and (iii) identify potential signs of convergent validity across findings of comparable experiments. Of the N = 43 published choice experiments during the first two years of the pandemic, the majority identifies with health applications (n = 30), followed by transport-related applications (n = 10). Nearly 100,000 people across the world responded to pandemic-related discrete choice surveys. Within health applications, while the dominant theme, up until June 2020, was lockdown relaxation and tracing measures, the focus shifted abruptly to vaccine preference since then. Geographical origins of the health surveys were not diverse. Nearly 50% of all health surveys were conducted in only three countries, namely US, China and The Netherlands. Health applications exhibited stronger pre-testing and larger sample sizes compared to transport applications. Limited signs of convergent validity were identifiable. Within some applications, issues of temporal instability as well as hypothetical bias attributable to social desirability, protest response or policy consequentiality seemed likely to have affected the findings. Nevertheless, very few of the experiments implemented measures of hypothetical bias mitigation and those were limited to health studies. Our main conclusion is that swift administration of pandemic-related choice experiments has overall resulted in certain degrees of compromise in study quality, but this has been more so the case in relation to transport topics than health topics.

The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study

BACKGROUND

Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer.

OBJECTIVE

The purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images.

METHODS

We conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed.

RESULTS

We analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results.

CONCLUSIONS

For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation.

TRIAL REGISTRATION

PROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223.

Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review

BACKGROUND

Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care.

OBJECTIVE

This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care.

METHODS

A systematic scoping review was conducted according to the 5-stage framework by Arksey and O'Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted.

RESULTS

Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study.

CONCLUSIONS

The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.

Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic

Background and aims

Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart.

Methods

We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure.

Results

AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed.

Conclusions

Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.