AI maturity in health care: An overview of 10 OECD countries.

BACKGROUND: Artificial Intelligence (AI) and its applications in health care are on the agenda of policymakers around the world, but a major challenge remains, namely, to set policies that will ensure wide acceptance and capture the value of AI while mitigating associated risks. OBJECTIVE: This study aims to provide an overview of how OECD countries strategize about how to integrate AI into health care and to determine their actual level of AI maturity. METHODS: A scan of government-based AI strategies and initiatives adopted in 10 proactive OECD countries was conducted. Available documentation was analyzed, using the Broadband Commission for Sustainable Development's roadmap to AI maturity as a conceptual framework. RESULTS: The findings reveal that most selected OECD countries are at the Emerging stage (Level 2) of AI in health maturity. Despite considerable funding and a variety of approaches to the development of an AI in health supporting ecosystem, only the United Kingdom and United States have reached the highest level of maturity, an integrated and collaborative AI in health ecosystem (Level 3). CONCLUSION: Despite policymakers looking for opportunities to expedite efforts related to AI, there is no one-size-fits-all approach to ensure the sustainable development and safe use of AI in health. The principles of equifinality and mindfulness must thus guide policymaking in the development of AI in health care.

Introducing the "AI Language Models in Health Care" Section: Actionable Strategies for Targeted and Wide-Scale Deployment.

The realm of health care is on the cusp of a significant technological leap, courtesy of the advancements in artificial intelligence (AI) language models, but ensuring the ethical design, deployment, and use of these technologies is imperative to truly realize their potential in improving health care delivery and promoting human well-being and safety. Indeed, these models have demonstrated remarkable prowess in generating humanlike text, evidenced by a growing body of research and real-world applications. This capability paves the way for enhanced patient engagement, clinical decision support, and a plethora of other applications that were once considered beyond reach. However, the journey from potential to real-world application is laden with challenges ranging from ensuring reliability and transparency to navigating a complex regulatory landscape. There is still a need for comprehensive evaluation and rigorous validation to ensure that these models are reliable, transparent, and ethically sound. This editorial introduces the new section, titled "AI Language Models in Health Care." This section seeks to create a platform for academics, practitioners, and innovators to share their insights, research findings, and real-world applications of AI language models in health care. The aim is to foster a community that is not only excited about the possibilities but also critically engaged with the ethical, practical, and regulatory challenges that lie ahead.

Revolutionizing nursing education through Ai integration: A reflection on the disruptive impact of ChatGPT.

Artificial intelligence (AI) is driving global change. An AI language model like ChatGPT could revolutionize the delivery of nursing education in the future. ChatGPT is an AI-enabled text generator that has garnered significant attention due to its ability to engage in conversations and answer questions. Nurse educators play a crucial role in preparing nursing students for a technology-integrated healthcare system, and the emergence of ChatGPT presents both opportunities and challenges. While the technology has limitations and potential biases, it also has the potential to benefit students by facilitating learning, improving digital literacy, and encouraging critical thinking about AI integration in healthcare. Nurse educators can incorporate ChatGPT into their curriculum through formative or summative assessments and should prioritize faculty development to understand and use AI technologies effectively. Collaboration between educational institutions, regulatory bodies, and educators is crucial to establish provincial and national competencies and frameworks that reflect the increasing importance of AI in nursing education and practice. It is paramount that nurses and nurse educators be open to AI-enabled innovations as well as continue to critically think about their potential value to advance the profession so nurses are better prepared to lead the digital future.

A Medicoeconomic Evaluation of a Telehealth Platform for Elective Outpatient Surgeries: Protocol for a Randomized Controlled Trial.

BACKGROUND: The number of elective outpatient surgeries in Canada has increased markedly in the last 10 years. However, unanticipated cancellations on the day of surgery and adverse postoperative events are frequent. Modern technologies have been shown to be of great help in the medical field in improving patient care. Thus, it is likely that dedicated technologies could also significantly improve surgical outpatients' pathways. Therefore, the department of anesthesiology at the University of Montreal Medical Center, in collaboration with LeoMed, a telemedicine platform, has developed a telehealth solution to offer more efficient perioperative support and follow-up for patients undergoing ambulatory surgery. OBJECTIVE: The objective is to evaluate the medicoeconomic benefit of a dedicated perioperative telehealth platform for patients undergoing day surgery. Our hypothesis is that this dedicated telecare solution will allow more efficient patient care, which will reduce all types of medical costs related to day surgery pathways. METHODS: This study is a single-center, single-blinded, 2-group randomized controlled trial. One thousand patients aged over 18 years with internet access who are scheduled to undergo ambulatory surgery will be enrolled and randomized either to follow a perioperative path that includes a patient-tailored perioperative digital app via the LeoMed telecare platform for 1 month or to follow the standard of care, which does not offer personalized digital support. The primary outcome will be to evaluate the cost-effectiveness of the telecare platform, assessing direct costs from factors such as unanticipated cancellations on the day of surgery due to preoperative instructions not being followed, calls to the local health information line, calls to the provincial health information line, emergency department consultations, unplanned readmissions, or medical visits for problems related to the surgical procedure within the first 30 days after the intervention. The secondary outcome will be to evaluate cost utility using a questionnaire assessing quality-adjusted life years. A blinded independent research team will analyze outcomes. All data will be analyzed according to the intention-to-treat principle. A sample size of 500 subjects in each group was calculated to detect a 21% reduction in postoperative complications with a power of 90%. This study has been approved by the ethics board of Centre hospitalier de l'Université de Montréal (University of Montreal Health Centre). No employee of LeoMed was involved in the study conception, and none will be involved in either data collection or analysis. RESULTS: Results of this trial will be useful to determine the economic benefit of a telecare platform specifically developed for surgical outpatient pathways. CONCLUSIONS: We believe that the deployment of a dedicated perioperative telehealth app will lead to better patient care and fewer postoperative complications, which will lower all types of costs related to surgical outpatient care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04948632; https://ClinicalTrials.gov/ct2/show/NCT04948632. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44006.

Nature and perceived benefits of patient-initiated consultations in community pharmacies: A population survey.

Background: The role of community pharmacists in enhancing patient care has received increased attention. However, there is a paucity of literature on the nature, frequency, and perceived impacts of patient-initiated consultations in community pharmacies. Objectives: We aim to describe the profile of patients seeking advice from community pharmacists as well as the nature and impact of those consultations. Methods: A survey was conducted with Quebec adults who had consulted a pharmacist in the previous four weeks. Data was collected in 2017 and 1104 agreed to participate (25.3%). Of those, 93 were withdrawn due to incomplete data and 98 failed to meet the inclusion criteria. Sample representativeness was ensured by quota sampling (gender, age) after stratification by region. Results: Among the 913 respondents, 46% had consulted a pharmacist more than once during the four weeks prior to the survey. Individuals with a university degree consulted less often than those without (1.97 vs. 2.17 times; t = 2.0; p < .05) and participants with one or several chronic diseases consulted more frequently than those having no chronic disease (2.18 vs. 1.94 times; t = 5.7; p < .05). Older adults (55+) consulted more often for themselves compared to younger (18-34) and middle-aged (35-54) adults (1.53 vs. 1.31 vs. 1.44 times; F = 4.0; p < .05). Concerning the consultations, 58% were related to medications and 33% to health problems. In terms of impacts, 81% of consultations were perceived to have prevented the use of other healthcare resources. Patient satisfaction with their consultations was high with an average score of 8.75 on a 10-point scale (SD = 1.63). Conclusions: Findings reveal that the reasons for consulting a community pharmacist are diverse, most being related to medications or health issues. Patients reported that pharmacists were able to manage most consultations without referring them to other health care resources or professionals, and their satisfaction with their consultation was high. MeSH terms: Community pharmacy; counselling; patient satisfaction; primary health care; surveys and questionnaires.

Medical students' intention to integrate digital health into their medical practice: A pre-peri COVID-19 survey study in Canada.

Objective: We aimed to explore the factors that influence medical students' intention to integrate dHealth technologies in their practice and analyze the influence of the COVID-19 pandemic on their perceptions and intention. Methods: We conducted a two-phased survey study at the University of Montreal's medical school in Canada. The study population consisted of 1367 medical students. The survey questionnaire was administered in two phases, that is, an initial survey (t0) in February 2020, before the Covid-19 pandemic, and a replication survey (t1) in January 2021, during the pandemic. Component-based structural equation modeling (SEM) was used to test seven research hypotheses. Results: A total of 184 students responded to the survey at t0 (13%), whereas 138 responded to the survey at t1 (10%). Findings reveal that students, especially those who are in their preclinical years, had little occasion to experiment with dHealth technologies during their degree. This lack of exposure may explain why a vast majority felt that dHealth should be integrated into medical education. Most respondents declared an intention to integrate dHealth, including AI-based tools, into their future medical practice. One of the most salient differences observed between t0 and t1 brings telemedicine to the forefront of medical education. SEM results confirm the explanatory power of the proposed research model. Conclusions: The present study unveils the specific dHealth technologies that could be integrated into existing medical curricula. Formal training would increase students' competencies with these technologies which, in turn, could ease their adoption and effective use in their practice.

Nurse practitioners' involvement and experience with AI-based health technologies: A systematic review.

BACKGROUND: Artificial intelligence (AI) is emerging in healthcare in various forms, including AI-based clinical decision support systems, machine learning, computer vision, natural language processing, big data analytics and AI-enhanced robotics. Given their potential impact on clinical processes and decision-making, AI-based health technologies (AIHT) are now seen to have a transformative effect on the nursing and medical professions, and on advanced nursing practice in particular. AIMS: While nurse practitioners (NPs) are increasingly called upon to play a crucial role in improving the healthcare provided to the population, little is known about the nature, extent and outcomes of their involvement and experience with AIHT. This study's research objectives are twofold. First, it aims to characterize NPs' involvement and experience with AIHT in terms of the functional and clinical attributes of the AIHT-based systems and applications that have emerged in advanced nursing care settings, and of the clinical tasks of NPs targeted for support by these systems and applications. Second, it aims to characterize this involvement and experience with AIHT in terms of its expected impacts on the clinical activities and performance of NPs, and of its potential outcomes for NPs' patients and for the general population. METHOD: We thus contribute to advanced practice nursing research by carrying out an initial evaluation of the role played by NPs in the emergence of these technologies, by means of a systematic review of the literature. FINDINGS: This review demonstrates that NPs, acting alone or in collaboration with physicians and other healthcare professionals, participate in the development and evaluation of various AI-based decision-making and predictive tools in primary, hospital and emergency care settings. This participation involves NPs as diagnostic and therapeutic experts whose clinical activities, decision-making and performance can be significantly impacted by their adoption and assimilation of AIHT.

Assimilation of Medical Appointment Scheduling Systems and Their Impact on the Accessibility of Primary Care: Mixed Methods Study.

BACKGROUND: The COVID-19 pandemic has prompted the adoption of digital health technologies to maximize the accessibility of medical care in primary care settings. Medical appointment scheduling (MAS) systems are among the most essential technologies. Prior studies on MAS systems have taken either a user-oriented perspective, focusing on perceived outcomes such as patient satisfaction, or a technical perspective, focusing on optimizing medical scheduling algorithms. Less attention has been given to the extent to which family medicine practices have assimilated these systems into their daily operations and achieved impacts. OBJECTIVE: This study aimed to fill this gap and provide answers to the following questions: (1) to what extent have primary care practices assimilated MAS systems into their daily operations? (2) what are the impacts of assimilating MAS systems on the accessibility and availability of primary care? and (3) what are the organizational and managerial factors associated with greater assimilation of MAS systems in family medicine clinics? METHODS: A survey study targeting all family medicine clinics in Quebec, Canada, was conducted. The questionnaire was addressed to the individual responsible for managing medical schedules and appointments at these clinics. Following basic descriptive statistics, component-based structural equation modeling was used to empirically explore the causal paths implied in the conceptual framework. A cluster analysis was also performed to complement the causal analysis. As a final step, 6 experts in MAS systems were interviewed. Qualitative data were then coded and extracted using standard content analysis methods. RESULTS: A total of 70 valid questionnaires were collected and analyzed. A large majority of the surveyed clinics had implemented MAS systems, with an average use of 1 or 2 functionalities, mainly "automated appointment confirmation and reminders" and "online appointment confirmation, modification, or cancellation by the patient." More extensive use of MAS systems appears to contribute to improved availability of medical care in these clinics, notwithstanding the effect of their application of advanced access principles. Also, greater integration of MAS systems into the clinic's electronic medical record system led to more extensive use. Our study further indicated that smaller clinics were less likely to undertake such integration and therefore showed less availability of medical care for their patients. Finally, our findings indicated that those clinics that showed a greater adoption rate and that used the provincial MAS system tended to be the highest-performing ones in terms of accessibility and availability of care. CONCLUSIONS: The main contribution of this study lies in the empirical demonstration that greater integration and assimilation of MAS systems in family medicine clinics lead to greater accessibility and availability of care for their patients and the general population. Valuable insight has also been provided on how to identify the clinics that would benefit most from such digital health solutions.

Motivational profiles, accelerometer-derived physical activity, and acute diabetes-related symptoms in adults with type 2 diabetes.

BACKGROUND: Using self-determination theory, the objective of this study was to examine, over a one-month period, how physical activity (PA) motivation would influence accelerometer-derived PA behavior, and ultimately, acute diabetes-related symptoms burden among adults with type 2 diabetes (T2D adults). Using both a person and variable-centered approach, this objective was attained by means of: 1) investigating the indirect effect of PA participation on the relationship between PA motivation and acute diabetes-related symptom burden and 2) examining whether participants who met PA recommendations (i.e., 150 min of moderate-to-vigorous PA per week) would experience less acute diabetes-related symptom burden over a one-month period. METHODS: A two-wave prospective longitudinal design was used. At time 1, participants completed a questionnaire assessing their PA motivation and were asked to wear an ActiGraph GT3x accelerometer for four consecutive weeks. At time 2, they completed a short questionnaire assessing their acute diabetes-related symptoms (i.e., symptoms related to fatigue, cognitive distress, hyperglycemia, and hypoglycemia). The final sample includes 165 adults (89 or 53.61% women) aged from 26 to 75 years (M = 62.05, SD = 8.75) with T2D, which provided at least 21 valid days of accelerometer-derived data. RESULTS: First, results of a path analysis demonstrated that over a one-month period, the average number of minutes spent practicing moderate to vigorous PA per week mediated the relationship between intrinsic and external PA motivation and the level of burden associated with the following diabetes-related symptoms: fatigue, cognitive distress, and hyperglycemia. In addition, results of covariance analyses showed that participants meeting PA recommendations also reported significantly less burden associated with these three symptoms over a month period. Then, the existence of four motivational profiles (Self-Determined, High Introjected, Low Motivation, and Non-Self-Determined) was confirmed using a k-means analysis. Results of covariance and chi-square analyses further showed, respectively, that compared to other motivational profiles, the Self-Determined profile was associated with a higher score on weekly PA participation and meeting PA recommendations. CONCLUSIONS: The results highlight the importance of promoting autonomous motives for PA participation among T2D adults. They also suggest that T2D adults meeting PA recommendations experience less acute diabetes-related symptoms burden, which further support the importance of their PA motivation.

Comparing three-dimensional serial optical coherence tomography histology to MRI imaging in the entire mouse brain.

An automated serial histology setup combining optical coherence tomography (OCT) imaging with vibratome sectioning was used to image eight wild type mouse brains. The datasets resulted in thousands of volumetric tiles resolved at a voxel size of (4.9×4.9×6.5) µm3 stitched back together to give a three-dimensional map of the brain from which a template OCT brain was obtained. To assess deformation caused by tissue sectioning, reconstruction algorithms, and fixation, OCT datasets were compared to both in vivo and ex vivo magnetic resonance imaging (MRI) imaging. The OCT brain template yielded a highly detailed map of the brain structure, with a high contrast in white matter fiber bundles and was highly resemblant to the in vivo MRI template. Brain labeling using the Allen brain framework showed little variation in regional brain volume among imaging modalities with no statistical differences. The high correspondence between the OCT template brain and its in vivo counterpart demonstrates the potential of whole brain histology to validate in vivo imaging.

Self-regulation resources and physical activity participation among adults with type 2 diabetes.

Physical activity plays a crucial role in the prevention and treatment of type 2 diabetes. Therefore, it is important to understand why so few adults with type 2 diabetes regularly engage in physical activity. The role of self-regulation in the context of health-related behavior adherence, especially in terms of physical activity engagement and adherence, has largely been reviewed based on the strength energy model. Building on this line of research, the aim of this theoretical work was to highlight how self-regulation and ego depletion can influence the lower rate of physical activity participation among adults with type 2 diabetes, compared to adults from the general population.

Serial optical coherence scanning reveals an association between cardiac function and the heart architecture in the aging rodent heart.

Normal aging is accompanied by structural changes in the heart architecture. To explore this remodeling, we used a serial optical coherence tomography scanner to image entire mouse hearts at micron scale resolution. Ex vivo hearts of 7 young (4 months) and 5 old (24 months) C57BL/6 mice were acquired with the imaging platform. OCT of the myocardium revealed myofiber orientation changing linearly from the endocardium to the epicardium. In old mice, this rate of change was lower when compared to young mice while the average volume of old mice hearts was significantly larger (p<0.05). Myocardial wall thickening was also accompanied by extracellular spacing in the endocardium, resulting in a lower OCT attenuation coefficient in old mice endocardium (p<0.05). Prior to serial sectioning, cardiac function of the same hearts was imaged in vivo using MRI and revealed a reduced ejection fraction with aging. The use of a serial optical coherence tomography scanner allows new insight into fine age-related changes of the heart associated with changes in heart function.

Whole mouse brain imaging using optical coherence tomography: reconstruction, normalization, segmentation, and comparison with diffusion MRI.

An automated massive histology setup combined with an optical coherence tomography (OCT) microscope was used to image a total of [Formula: see text] whole mouse brains. Each acquisition generated a dataset of thousands of OCT volumetric tiles at a sampling resolution of [Formula: see text]. This paper describes techniques for reconstruction and segmentation of the sliced brains. In addition to the measured OCT optical reflectivity, a single scattering photon model was used to compute the attenuation coefficients within each tissue slice. Average mouse brain templates were generated for both the OCT reflectivity and attenuation contrasts and were used with an [Formula: see text]-tissue segmentation algorithm. To better understand the brain tissue OCT contrast origin, one of the mouse brains was acquired using dMRI and coregistered to its corresponding assembled brain. Our results indicate that the optical reflectivity in a fiber bundle varies with its orientation, its fiber density, and the number of fiber orientations it contains. The OCT mouse brain template generation and coregistration to dMRI data demonstrate the potential of this massive histology technique to pursue cross-sectional, multimodal, and multisubject investigations of small animal brains.

Validating Intravascular Imaging with Serial Optical Coherence Tomography and Confocal Fluorescence Microscopy.

Atherosclerotic cardiovascular diseases are characterized by the formation of a plaque in the arterial wall. Intravascular ultrasound (IVUS) provides high-resolution images allowing delineation of atherosclerotic plaques. When combined with near infrared fluorescence (NIRF), the plaque can also be studied at a molecular level with a large variety of biomarkers. In this work, we present a system enabling automated volumetric histology imaging of excised aortas that can spatially correlate results with combined IVUS/NIRF imaging of lipid-rich atheroma in cholesterol-fed rabbits. Pullbacks in the rabbit aortas were performed with a dual modality IVUS/NIRF catheter developed by our group. Ex vivo three-dimensional (3D) histology was performed combining optical coherence tomography (OCT) and confocal fluorescence microscopy, providing high-resolution anatomical and molecular information, respectively, to validate in vivo findings. The microscope was combined with a serial slicer allowing for the imaging of the whole vessel automatically. Colocalization of in vivo and ex vivo results is demonstrated. Slices can then be recovered to be tested in conventional histology.

Motives for Participation in Physical Activity and Observance of Physical Activity Recommendations among Adults with Type 2 Diabetes.

OBJECTIVES: Drawing on self-determination theory, this study examined how motivation to engage in physical activity (PA) influences the observance of PA recommendations by adults with type 2 diabetes. METHODS: This study used a cross-sectional design. In total, 365 adults with type 2 diabetes (181 men and 184 women, mean age = 60.39 years; SD=6.47) completed a questionnaire assessing their motivations and behaviours regarding PA. PA behaviour was assessed by means of the weekly practice of moderate-to-vigorous PA (MVPA) during leisure time. A symptom checklist was also used to assess diabetes-related symptoms experienced over the past month. RESULTS: Results of a hierarchic binary logistic regression revealed that participants accumulating at least 150 minutes per week of MVPA during their leisure time were more likely to display higher scores on intrinsic and identified motives, whereas participants not accumulating 150 minutes per week of MVPA during their leisure time were more likely to present higher scores on external regulation and amotivation. In addition, results of a multivariate analysis of covariance (MANCOVA) showed that participants accumulating at least 150 minutes per week of MVPA during their leisure time experienced less fatigue and fewer hyperglycemic symptoms. CONCLUSIONS: These findings suggest that adults who observe PA recommendations for type 2 diabetes possess higher levels of autonomous motives to practise PA. Results further highlight the importance of motives for engaging in PA by adults with type 2 diabetes in terms of the positive relationship between observing PA recommendations and experiencing less fatigue and fewer hyperglycemic symptoms.

Optogenetic Tools for Confined Stimulation in Deep Brain Structures.

Optogenetics has emerged in the past decade as a technique to modulate brain activity with cell-type specificity and with high temporal resolution. Among the challenges associated with this technique is the difficulty to target a spatially restricted neuron population. Indeed, light absorption and scattering in biological tissues make it difficult to illuminate a minute volume, especially in the deep brain, without the use of optical fibers to guide light. This work describes the design and the in vivo application of a side-firing optical fiber adequate for delivering light to specific regions within a brain subcortical structure.

Repetitive and retinotopically restricted activation of the dorsal lateral geniculate nucleus with optogenetics.

Optogenetics allows the control of cellular activity using focused delivery of light pulses. In neuroscience, optogenetic protocols have been shown to efficiently inhibit or stimulate neuronal activity with a high temporal resolution. Among the technical challenges associated with the use of optogenetics, one is the ability to target a spatially specific population of neurons in a given brain structure. To address this issue, we developed a side-illuminating optical fiber capable of delivering light to specific sites in a target nucleus with added flexibility through rotation and translation of the fiber and by varying the output light power. The designed optical fiber was tested in vivo in visual structures of ChR2-expressing transgenic mice. To assess the spatial extent of neuronal activity modulation, we took advantage of the hallmark of the visual system: its retinotopic organization. Indeed, the relative position of ganglion cells in the retina is transposed in the cellular topography of both the dorsal lateral geniculate nucleus (LGN) in the thalamus and the primary visual cortex (V1). The optical fiber was inserted in the LGN and by rotating it with a motor, it was possible to sequentially activate different neuronal populations within this structure. The activation of V1 neurons by LGN projections was recorded using intrinsic optical imaging. Increasing light intensity (from 1.4 to 8.9 mW/mm²) led to increasing activation surfaces in V1. Optogenetic stimulation of the LGN at different translational and rotational positions was associated with different activation maps in V1. The position and/or orientation of the fiber inevitably varied across experiments, thus limiting the capacity to pool data. With the optogenetic design presented here, we demonstrate for the first time a transitory and spatially-concise activation of a deep neuronal structure. The optogenetic design presented here thus opens a promising avenue for studying the function of deep brain structures.