Qualitative evidence synthesis informing our understanding of people's perceptions and experiences of targeted digital communication.
Health communication is an area where changing technologies, particularly digital technologies, have a growing role to play in delivering and exchanging health information between individuals, communities, health systems, and governments.[1] Such innovation has the potential to strengthen health systems and services, with substantial investments in digital health already taking place, particularly in low‐ and middle‐income countries. Communication using mobile phones is an important way of contacting individual people and the public more generally to deliver and exchange health information. Such technologies are used increasingly in this capacity, but poor planning and short‐term projects may be limiting their potential for health improvement. The assumption that mobile devices will solve problems that other forms of communication have not is also prevalent. In this context, understanding people's views and experiences may lead to firmer knowledge on which to build better programs. A qualitative evidence synthesis by Heather Ames and colleagues on clients' perceptions and experiences of targeted digital communication focuses on a particular type of messaging – targeted messages from health services delivered to particular group(s) via mobile devices, in this case looking at communicating with pregnant women and parents of young children, and with adults and teenagers about sexual health and family planning.[2] These areas of reproductive, maternal, newborn, child, and adolescent health (RMNCAH) are where important gains have been made worldwide, but there remains room for improvement. Ames and colleagues sought to examine and understand people's perceptions and experiences of using digital targeted client communication. This might include communication in different formats and with a range of purposes related to RMNCAH – for example, receiving text message reminders to take medicines (e.g. HIV medicines) or go to appointments (such as childhood vaccination appointments), or phone calls offering information or education (such as about breastfeeding or childhood illnesses), support (e.g. providing encouragement to change behaviours) or advice (such as advising about local healthcare services). These communication strategies have the potential to improve health outcomes by communicating with people or by supporting behaviour change. However, changing people's health behaviours to a significant and meaningful degree is notoriously challenging and seldom very effective across the board. There are a multitude of systematic reviews of interventions aiming to change behaviours of both patients and providers, with the overall objective of improving health outcomes – many of which show little or no average effects across groups of people.[3] This evidence synthesis is therefore important as it may help to understand why communicating with people around their health might (or might not) change behaviours and improve consequent health outcomes. By examining the experiences and perspectives of those receiving the interventions, this qualitative evidence synthesis allows us to better understand the interventions' acceptability and usefulness, barriers to their uptake, and factors to be considered when planning implementation. The synthesis looked at 35 studies from countries around the world, focussing on communication related to RMNCAH. Of the 35 studies, 16 were from high‐income countries, mainly the United States, and 19 were from low‐ or middle‐income countries, mainly African countries. Many of the studies presented hypothetical scenarios. The findings from the synthesis are mixed and give us a more nuanced picture of the role of targeted digital communication. People receiving targeted digital communications from health services often liked and valued these contacts, feeling supported and connected by them. However, some also reported problems with the use of these technologies, which may represent barriers to their use. These included practical or technical barriers like poor network or Internet access, as well as cost, language, technical literacy, reading or issues around confidentiality, especially where personal health conditions were involved. Access to mobile phones may also be a barrier, particularly for women and adolescents who may have to share or borrow a phone or who have access controlled by others. In such situations it may be difficult to receive communications or to maintain privacy of content. The synthesis also shows that people's experiences of these interventions are influenced by factors such as the timing of messages, their frequency and content, and their trust in the sender. Identifying key features of such communications by the people who use them might therefore help to inform future choices about how and when such messaging is used. The authors used their knowledge from 25 separate findings to list ten implications for practice. This section of the review is hugely valuable, making a practical contribution to assist governments and public health agencies wishing to develop or improve their delivery of digital health. The implications serve as a list of points to consider, including issues of access (seven different aspects are considered), privacy and confidentiality, reliability, credibility and trust, and responsiveness to the needs and preferences of users. In this way, qualitative evidence is building a picture of how to better communicate with people about health. For example, an earlier 2017 Cochrane qualitative evidence synthesis by Ames, Glenton and Lewin on parents' and informal caregivers' views and experiences of communication about routine childhood vaccination provides ample evidence that may help program managers to deliver or plan communication interventions in ways that are responsive to and acceptable to parents.[4] The qualitative synthesis method, therefore, puts a spotlight on how people's experiences of health and health care in the context of their lives may lead to the design of better interventions, as well as to experimental studies which take more account of the diversity that exists in people's attitudes and decision‐making experiences.[5] In the case of this qualitative evidence synthesis by Ames and colleagues, the method pulled together a substantial body of research (35 data‐rich studies were sampled from 48 studies identified, with the high‐to‐moderate confidence in the evidence for 13 of the synthesized findings). The evidence from this review can inform the development of interventions, and the design of trials and their implementation. While waiting for such new trials or trial evidence on effects to emerge, decision‐makers can build their programs on the highly informative base developed by this review. This qualitative evidence synthesis, alongside other reviews, has informed development by the World Health Organization of its first guideline for using digital technologies for health systems strengthening,[1, 6] part of a comprehensive program of work to better understand and support implementation of such new technologies.
Ryan R
，Hill S
《Cochrane Database of Systematic Reviews》
Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review.
Recent healthcare advancements highlight the potential of Artificial Intelligence (AI) - and especially, among its subfields, Machine Learning (ML) - in enhancing Breast Cancer (BC) clinical care, leading to improved patient outcomes and increased radiologists' efficiency. While medical imaging techniques have significantly contributed to BC detection and diagnosis, their synergy with AI algorithms has consistently demonstrated superior diagnostic accuracy, reduced False Positives (FPs), and enabled personalized treatment strategies. Despite the burgeoning enthusiasm for leveraging AI for early and effective BC clinical care, its widespread integration into clinical practice is yet to be realized, and the evaluation of AI-based health technologies in terms of health and economic outcomes remains an ongoing endeavor.
This scoping review aims to investigate AI (and especially ML) applications that have been implemented and evaluated across diverse clinical tasks or decisions in breast imaging and to explore the current state of evidence concerning the assessment of AI-based technologies for BC clinical care within the context of Health Technology Assessment (HTA).
We conducted a systematic literature search following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist in PubMed and Scopus to identify relevant studies on AI (and particularly ML) applications in BC detection and diagnosis. We limited our search to studies published from January 2015 to October 2023. The Minimum Information about CLinical Artificial Intelligence Modeling (MI-CLAIM) checklist was used to assess the quality of AI algorithms development, evaluation, and reporting quality in the reviewed articles. The HTA Core Model® was also used to analyze the comprehensiveness, robustness, and reliability of the reported results and evidence in AI-systems' evaluations to ensure rigorous assessment of AI systems' utility and cost-effectiveness in clinical practice.
Of the 1652 initially identified articles, 104 were deemed eligible for inclusion in the review. Most studies examined the clinical effectiveness of AI-based systems (78.84%, n= 82), with one study focusing on safety in clinical settings, and 13.46% (n=14) focusing on patients' benefits. Of the studies, 31.73% (n=33) were ethically approved to be carried out in clinical practice, whereas 25% (n=26) evaluated AI systems legally approved for clinical use. Notably, none of the studies addressed the organizational implications of AI systems in clinical practice. Of the 104 studies, only two of them focused on cost-effectiveness analysis, and were analyzed separately. The average percentage scores for the first 102 AI-based studies' quality assessment based on the MI-CLAIM checklist criteria were 84.12%, 83.92%, 83.98%, 74.51%, and 14.7% for study design, data and optimization, model performance, model examination, and reproducibility, respectively. Notably, 20.59% (n=21) of these studies relied on large-scale representative real-world breast screening datasets, with only 10.78% (n =11) studies demonstrating the robustness and generalizability of the evaluated AI systems.
In bridging the gap between cutting-edge developments and seamless integration of AI systems into clinical workflows, persistent challenges encompass data quality and availability, ethical and legal considerations, robustness and trustworthiness, scalability, and alignment with existing radiologists' workflow. These hurdles impede the synthesis of comprehensive, robust, and reliable evidence to substantiate these systems' clinical utility, relevance, and cost-effectiveness in real-world clinical workflows. Consequently, evaluating AI-based health technologies through established HTA methodologies becomes complicated. We also highlight potential significant influences on AI systems' effectiveness of various factors, such as operational dynamics, organizational structure, the application context of AI systems, and practices in breast screening or examination reading of AI support tools in radiology. Furthermore, we emphasize substantial reciprocal influences on decision-making processes between AI systems and radiologists. Thus, we advocate for an adapted assessment framework specifically designed to address these potential influences on AI systems' effectiveness, mainly addressing system-level transformative implications for AI systems rather than focusing solely on technical performance and task-level evaluations.
Uwimana A
，Gnecco G
，Riccaboni M
《-》
ResearchGate
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钛学术
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