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Background: Precision health is a rapidly developing field, largely driven by the development of artificial intelligence (AI)–related solutions. AI facilitates complex analysis of numerous health data risk assessment, early detection of disease, and initiation of timely preventative health interventions that can be highly tailored to the individual. Despite such promise, ethical concerns arising from the rapid development and use of AI-related technologies have led to development of national and international frameworks to address responsible use of AI. Objective: We aimed to address research gaps and provide new knowledge regarding (1) examples of existing AI applications and what role they play regarding precision health, (2) what salient features can be used to categorize them, (3) what evidence exists for their effects on precision health outcomes, (4) how do these AI applications comply with established ethical and responsible framework, and (5) how these AI applications address equity and social determinants of health (SDOH). Methods: This protocol delineates a state-of-the-art literature review of novel AI-based applications in precision health. Published and unpublished studies were retrieved from 6 electronic databases. Articles included in this study were from the inception of the databases to January 2023. The review will encompass applications that use AI as a primary or supporting system or method when primarily applied for precision health purposes in human populations. It includes any geographical location or setting, including the internet, community-based, and acute or clinical settings, reporting clinical, behavioral, and psychosocial outcomes, including detection-, diagnosis-, promotion-, prevention-, management-, and treatment-related outcomes. Results: This is step 1 toward a full state-of-the-art literature review with data analyses, results, and discussion of findings, which will also be published. The anticipated consequences on equity from the perspective of SDOH will be analyzed. Keyword cluster relationships and analyses will be visualized to indicate which research foci are leading the development of the field and where research gaps exist. Results will be presented based on the data analysis plan that includes primary analyses, visualization of sources, and secondary analyses. Implications for future research and person-centered public health will be discussed. Conclusions: Results from the review will potentially guide the continued development of AI applications, future research in reducing the knowledge gaps, and improvement of practice related to precision health. New insights regarding examples of existing AI applications, their salient features, their role regarding precision health, and the existing evidence that exists for their effects on precision health outcomes will be demonstrated. Additionally, a demonstration of how existing AI applications address equity and SDOH and comply with established ethical and responsible frameworks will be provided. 

Background: Older adult care organizations face challenges today due to high personnel turnover and pandemic-related obstacles in conducting training and competence development programs in a time-sensitive and fit-for-purpose manner. Digital microlearning is a method that attempts to meet these challenges by more quickly adapting to the educational needs of organizations and individual employees in terms of time, place, urgency, and retention capacity more than the traditional competency development methods. Objective: This study aimed to determine if and how an app-based digital microlearning intervention can meet older adult care organizations' personnel competency development needs in terms of knowledge retention and work performance. Methods: This study assessed the use of a digital microlearning app, which was at the testing stage in the design thinking model among managerial (n=4) and operational (n=22) employees within 3 older adult care organizations. The app was used to conduct predetermined competency development courses for the staff. Baseline measurements included participants' previous training and competency development methods and participation, as well as perceived needs in terms of time, design, and channel. They then were introduced to and used a digital microlearning app to conduct 2 courses on one or more digital devices, schedules, and locations of their own choice during a period of similar to 1 month. The digital app and course content, perceived knowledge retention, and work performance and satisfaction were individually assessed via survey upon completion. The survey was complemented with 4 semistructured focus group interviews, which allowed participants (in total 16 individuals: 6 managerial-administrative employees and 10 operational employees) to describe their experiences with the app and its potential usefulness within their organizations. Results: The proposed advantages of the digital microlearning app were largely confirmed by the participants' perceptions, particularly regarding the ease of use and accessibility, and efficiency and timeliness of knowledge delivery. Assessments were more positive among younger or less experienced employees with more diverse backgrounds. Participants expressed a positive inclination toward using the app, and suggestions provided regarding its potential development and broader use suggested a positive view of digitalization in general. Conclusions: Our results show that app-based digital microlearning appears to be an appropriate new method for providing personnel competency development within the older adult care setting. Its implementation in a larger sample can potentially provide more detailed insights regarding its intended effects.

Background: Health and welfare technologies (HWTs) are interventions that aim at maintaining or promoting health, well-being, quality of life, and increasing efficiency in the service delivery system of welfare, social, and health care services, while improving the working conditions of the staff. Health and social care must be evidence-based according to national policy, but there are indications that evidence for HWT effectiveness is lacking in related Swedish municipal work processes. Objective: This study aimed to investigate whether the evidence is used when Swedish municipalities procure, implement, and evaluate HWT, and if so, the kinds of evidence and the manner of their use. The study also aimed to identify if municipalities currently receive adequate support in using evidence for HWT, and if not, what support is desired. Methods: An explanatory sequential mixed methods design was used with quantitative surveys and subsequent semistructured interviews with officials in 5 nationally designated “model” municipalities regarding HWT implementation and use. Results: In the past 12 months, 4 of 5 municipalities had required some form of evidence during procurement processes, but the frequency of this varied and often consisted of references from other municipalities instead of other objective sources. Formulating requirements or requests for evidence during procurement was viewed as difficult, and gathered evidence was often only assessed by procurement administration personnel. In total, 2 of 5 municipalities used an established process for the implementation of HWT, and 3 of 5 had a plan for structured follow-up, but the use and dissemination of evidence within these were varying and often weakly integrated. Standardized processes for follow-up and evaluation across municipalities did not exist, and those processes used by individual municipalities were described as inadequate and difficult to follow. Most municipalities desired support for using evidence when procuring, establishing evaluation frameworks for, and following up effectiveness of HWT, while all municipalities suggested tools or methods for this kind of support. Conclusions: Structured use of evidence in procurement, implementation, and evaluation of HWT is inconsistent among municipalities, and internal and external dissemination of evidence for effectiveness is rare. This may establish a legacy of ineffective HWT in municipal settings. The results suggest that existing national agency guidance is not sufficient to meet current needs. New, more effective types of support to increase the use of evidence in critical phases of municipal procurement and implementation of HWT are recommended.

Background: Artificial Intelligence (AI) has great potential to transform health systems to improve the quality of healthcare services. However, AI is still new in Tanzania, and there is limited knowledge about the application of AI technology in the Tanzanian health sector.Objectives: This study aims to explore the current status, challenges, and opportunities for AI application in the health system in Tanzania. Methods: A scoping review was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis Extensions for Scoping Review (PRISMA-ScR). We searched different electronic databases such as PubMed, Embase, African Journal Online, and Google Scholar.Results: Eighteen (18) studies met the inclusion criteria out of 2,017 studies from different electronic databases and known AI-related project websites. Amongst AI-driven solutions, the studies mostly used machine learning (ML) and deep learning for various purposes, including prediction and diagnosis of diseases and vaccine stock optimisation. The most commonly used algorithms were conventional machine learning, including Random Forest and Neural network, Naive Bayes K-Nearest Neighbour and Logistic regression. Conclusions: This review shows that AI-based innovations may have a role in improving health service delivery, including early outbreak prediction and detection, disease diagnosis and treatment, and efficient management of healthcare resources in Tanzania. Our results indicate the need for developing national AI policies and regulatory frameworks for adopting responsible and ethical AI solutions in the health sector in accordance with the World Health Organisation (WHO) guidance on ethics and governance of AI for health.

Background: Health and welfare technologies (HWT) are increasingly procured and implemented by public providers in Swedish municipalities, but it remains unclear if and how evidence for these technologies’ effectiveness is used in both processes. The aim of this study was to investigate the use of evidence in Swedish municipal public sector procurement and implementation of HWT. Methods: A telephone survey of 197 municipalities was conducted with questions regarding the use of evidence in both processes, as well as eventual support needs regarding its use. Standard definitions of HWT and evidence were provided prior to the survey. Response frequencies and percentage proportions were calculated per question. Lambda (Λ) values with corresponding significance values were calculated for associations between responses to selected questions and the size and type of municipality, with values of 0.01 to 0.19 designated as weak associations, 0.20 to 0.39 as moderate, and 0.40 and above as strong. Results: Sixty-four municipalities completed the entire survey. Consistent use of evidence for effectiveness of HWT occurred in less than half of respondents’ municipal public procurement processes. Two-thirds of municipalities did not have an established model or process for implementation of HWT that used evidence in any manner. More than three quarters of municipalities lacked a systematic plan for follow-up and evaluation of effectiveness of implemented HWT, and of those that did less than half followed their plan consistently. Most municipalities expressed the need for support in using evidence in HWT-related processes but did not consider evidence and systematic evaluation to be prioritized. Conclusions: Weaknesses and gaps in using evidence in procurement and implementation processes may create a legacy of sub-optimal implementation of HWT in Swedish municipal health- and social care services, and lost opportunities for real-world evidence generation. There was a clear indication of the need for unified national guidance for using and generating evidence in key HWT-related municipal processes and implementation. Such guidance needs to be developed and effectively communicated.