搜索结果：Health technology assessment (Winchester, England)

This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA). We explore their applications in four critical areas, evidence synthesis, evidence generation, clinical trials and economic modeling: (1) Evidence synthesis: Generative AI has the potential to assist in automating literature reviews and meta-analyses by proposing search terms, screening abstracts, and extracting data with notable accuracy; (2) Evidence generation: These models can potentially facilitate automating the process and analyze the increasingly available large collections of real-world data (RWD), including unstructured clinical notes and imaging, enhancing the speed and quality of real-world evidence (RWE) generation; (3) Clinical trials: Generative AI can be used to optimize trial design, improve patient matching, and manage trial data more efficiently; and (4) Economic modeling: Generative AI can also aid in the development of health economic models, from conceptualization to validation, thus streamlining the overall HTA process. Despite their promise, these technologies, wh

This research paper presents a meta-analysis of the multifaceted role of technology in mental health. The pervasive influence of technology on daily lives necessitates a deep understanding of its impact on mental health services. This study synthesizes literature covering Behavioral Intervention Technologies (BITs), digital mental health interventions during COVID-19, young men's attitudes toward mental health technologies, technology-based interventions for university students, and the applicability of mobile health technologies for individuals with serious mental illnesses. BITs are recognized for their potential to provide evidence-based interventions for mental health conditions, especially anxiety disorders. The COVID-19 pandemic acted as a catalyst for the adoption of digital mental health services, underscoring their crucial role in providing accessible and quality care; however, their efficacy needs to be reinforced by workforce training, high-quality evidence, and digital equity. A nuanced understanding of young men's attitudes toward mental health is imperative for devising effective online services. Technology-based interventions for university students are promising, al

Artificial intelligence (AI) is increasingly explored across deep brain stimulation (DBS) for movement disorders, yet whether current systems are approaching deployment remains unclear. To characterise their scope, validation maturity, and translational readiness, we systematically evaluated 239 peer-reviewed studies published between 2000 and 2025, assessing AI methods, validation practices, and barriers constraining clinical translation. Research was dominated by Parkinson's disease and subthalamic nucleus targeting, with limited coverage of other disorders and targets. Most studies reported encouraging internal performance; however, external validation was rare, evaluations remained predominantly retrospective and single-centre, and more than one-quarter involved small-sample, high-dimensional datasets with elevated overfitting risk. Technology readiness assessment revealed that most systems remain at early-to-intermediate translational stages, constrained more by limited validation than by algorithmic inadequacy, compounded by the biological heterogeneity and dynamic complexity inherent to DBS. Nevertheless, emerging external and prospective studies suggest a field moving towar

Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through

Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics. For a chatbot which responds to millions of online users including minors, we argue that it should have a healthy mental tendency in order to avoid the negative psychological impact on them. In this paper, we establish several mental health assessment dimensions for chatbots (depression, anxiety, alcohol addiction, empathy) and introduce the questionnaire-based mental health assessment methods. We conduct assessments on some well-known open-domain chatbots and find that there are severe mental health issues for all these chatbots. We consider that it is due to the neglect of the mental health risks during the dataset building and the model training procedures. We expect to attract researchers' attention to the serious mental health problems of chatbots and improve the chatbots' ability in positive emotional interaction.

The growing demand for home healthcare calls for tools that can support care delivery. In this study, we explore automatic health assessment from voice using real-world home care visit data, leveraging the diverse patient information it contains. First, we utilize Large Language Models (LLMs) to integrate Subjective, Objective, Assessment, and Plan (SOAP) notes derived from unstructured audio transcripts and structured vital signs into a holistic illness score that reflects a patient's overall health. This compact representation facilitates cross-visit health status comparisons and downstream analysis. Next, we design a multi-stage preprocessing pipeline to extract short speech segments from target speakers in home care recordings for acoustic analysis. We then employ an Audio Language Model (ALM) to produce plain-language descriptions of vocal biomarkers and examine their association with individuals' health status. Our experimental results benchmark both commercial and open-source LLMs in estimating illness scores, demonstrating their alignment with actual clinical outcomes, and revealing that SOAP notes are substantially more informative than vital signs. Building on the illness

Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue.

Previous studies have shown that health information technologies have a positive impact on health systems. Electronic health record (EHR) systems are one of the most promising applications, demonstrating a positive effect in high income countries. On the other hand, robust evidence for low and middle income countries is still spare. The aim of this study is to initiate a carefully planned nationwide EHR system in Vietnam by assessing the core readiness. The assessment structure is mainly based on previous research, which recommends a readiness assessment prior to to an EHR system implementation. To collect data, participant observation, document analysis and an in-depth interview were used. This study has revealed new insights into the current situation on EHR in Vietnam. The Ministry of Health is currently working on improving the conditions for future implementation of a Vietnamese EHR system. There are issues with the current way of handling health records. These issues are encouraging the Ministry of Health to work on identifying the next steps for an EHR system implementation. The integration of an EHR system with current systems seems to be challenging as most systems are com

This paper highlights the design philosophy and architecture of the Health Guardian, a platform developed by the IBM Digital Health team to accelerate discoveries of new digital biomarkers and development of digital health technologies. The Health Guardian allows for rapid translation of artificial intelligence (AI) research into cloud-based microservices that can be tested with data from clinical cohorts to understand disease and enable early prevention. The platform can be connected to mobile applications, wearables, or Internet of things (IoT) devices to collect health-related data into a secure database. When the analytics are created, the researchers can containerize and deploy their code on the cloud using pre-defined templates, and validate the models using the data collected from one or more sensing devices. The Health Guardian platform currently supports time-series, text, audio, and video inputs with 70+ analytic capabilities and is used for non-commercial scientific research. We provide an example of the Alzheimer's disease (AD) assessment microservice which uses AI methods to extract linguistic features from audio recordings to evaluate an individual's mini-mental state

Electronic Health Record (EHR) has become an essential tool in the healthcare ecosystem, providing authorized clinicians with patients' health-related information for better treatment. While most developed countries are taking advantage of EHRs to improve their healthcare system, it remains challenging in developing countries to support clinical decision-making and public health using a computerized patient healthcare information system. This paper proposes a novel EHR architecture suitable for developing countries--an architecture that fosters inclusion and provides solutions tailored to all social classes and socioeconomic statuses. Our architecture foresees an internet-free (offline) solution to allow medical transactions between healthcare organizations, and the storage of EHRs in geographically underserved and rural areas. Moreover, we discuss how artificial intelligence can leverage anonymous health-related information to enable better public health policy and surveillance.

BACKGROUND: Although health technology assessment (HTA) systems base their decision making process either on economic evaluations or comparative clinical benefit assessment, a central aim of recent approaches to value measurement, including value based assessment and pricing, points towards the incorporation of supplementary evidence and criteria that capture additional dimensions of value. OBJECTIVE: To study the practices, processes and policies of value-assessment for new medicines across eight European countries and the role of HTA beyond economic evaluation and clinical benefit assessment. METHODS: A systematic (peer review and grey) literature review was conducted using an analytical framework examining: (1) 'Responsibilities and structure of HTA agencies'; (2) 'Evidence and evaluation criteria considered in HTAs'; (3) 'Methods and techniques applied in HTAs'; and (4) 'Outcomes and implementation of HTAs'. Study countries were France, Germany, England, Sweden, Italy, Netherlands, Poland and Spain. Evidence from the literature was validated and updated through two rounds of feedback involving primary data collection from national experts. RESULTS: All countries assess similar types of evidence; however, the specific criteria/endpoints used, their level of provision and requirement, and the way they are incorporated (e.g. explicitly vs. implicitly) varies across countries, with their relative importance remaining generally unknown. Incorporation of additional 'social value judgements' (beyond clinical benefit assessment) and economic evaluation could help explain heterogeneity in coverage recommendations and decision-making. CONCLUSION: More comprehensive and systematic assessment procedures characterised by increased transparency, in terms of selection of evaluation criteria, their importance and intensity of use, could lead to more rational evidence-based decision-making, possibly improving efficiency in resource allocation, while also raising public confidence and fairness.

Selecting the right monitoring level in Remote Patient Monitoring (RPM) systems for e-healthcare is crucial for balancing patient outcomes, various resources, and patient's quality of life. A prior work has used one-dimensional health representations, but patient health is inherently multidimensional and typically consists of many measurable physiological factors. In this paper, we introduce a multidimensional health state model within the RPM framework and use dynamic programming to study optimal monitoring strategies. Our analysis reveals that the optimal control is characterized by switching curves (for two-dimensional health states) or switching hyper-surfaces (in general): patients switch to intensive monitoring when health measurements cross a specific multidimensional surface. We further study how the optimal switching curve varies for different medical conditions and model parameters. This finding of the optimal control structure provides actionable insights for clinicians and aids in resource planning. The tunable modeling framework enhances the applicability and effectiveness of RPM services across various medical conditions.

Artificial intelligence (AI) has shown great promise in revolutionizing the field of digital health by improving disease diagnosis, treatment, and prevention. This paper describes the Health Guardian platform, a non-commercial, scientific research-based platform developed by the IBM Digital Health team to rapidly translate AI research into cloud-based microservices. The platform can collect health-related data from various digital devices, including wearables and mobile applications. Its flexible architecture supports microservices that accept diverse data types such as text, audio, and video, expanding the range of digital health assessments and enabling holistic health evaluations by capturing voice, facial, and motion bio-signals. These microservices can be deployed to a clinical cohort specified through the Clinical Task Manager (CTM). The CTM then collects multi-modal, clinical data that can iteratively improve the accuracy of AI predictive models, discover new disease mechanisms, or identify novel biomarkers. This paper highlights three microservices with different input data types, including a text-based microservice for depression assessment, a video-based microservice for

This chapter discusses the existing and future use of robotics and intelligent sensing technology in mental health care. While the use of this technology is nascent in mental health care, it represents a potentially useful tool in the practitioner's toolbox. The goal of this chapter is to provide a brief overview of the field, discuss the recent use of robotics technology in mental health care practice, explore some of the design issues and ethical issues of using robots in this space, and finally to explore the potential of emerging technology.

We present a technical case study on the Privacy-Enhancing Technologies (PETs) for Public Health Challenge, a collaborative effort to safely leverage sensitive private sector data for social impact, specifically pandemic management. The project utilized Differential Privacy (DP) to create realistic, privacy-preserved synthetic financial transaction data, which was then combined with public health and mobility datasets. This approach successfully addressed the critical hurdle of sharing sensitive financial information for research and policy. The analysis demonstrated that this synthetic, DP-protected data possesses significant spatial-temporal and predictive power for public health. Key outcomes include the development of six reusable tools and frameworks supporting diagnostic nowcasting (e.g., Hotspot Detection, Pandemic Adherence Monitoring) and predictive forecasting (e.g., Mobility Analysis, Contact Matrix Estimation) for epidemiological decision-making. The study provides best practices for advancing data sharing in a privacy-compliant manner.

BACKGROUND: In older age, reduction in physical function can lead to loss of independence, the need for hospital and long-term nursing or residential home care, and premature death. Home-visiting programmes for older people, carried out by nurses and other health-care professionals (e.g. occupational therapists and physiotherapists), aim to positively affect health and functional status, and may promote independent functioning of older people. OBJECTIVE: The main research question addressed by this assessment is 'What is the clinical effectiveness and cost-effectiveness of home-based, nurse-led health promotion intervention for older people in the UK?' DATA SOURCES: A comprehensive literature search was undertaken across 12 different databases and research registries from the year 2001 onwards (including MEDLINE, MEDLINE in Process & Other Non-Indexed Citations, EMBASE, Science Citation Index Expanded, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, NHS Health Economic Evaluation Database, Health Technology Assessment Database, Database of Abstracts of Reviews of Effects, Cumulative Index to Nursing and Allied Health Literature). Published systematic reviews were also hand searched to identify other trials previously published. REVIEW METHODS: Potentially relevant studies were sifted by one reviewer, and inclusion decisions were agreed among the broader research team. The methodological quality of included studies was assessed using the Cochrane Risk of Bias tool. The results of included studies were synthesised using narrative and statistical methods. A separate systematic search was undertaken to identify existing health economic analyses of home-based, nurse-led health promotion programmes. Included studies were critically appraised using a published checklist. Owing to resource constraints, a de novo health economic model was not developed. RESULTS: Eleven studies were included in the systematic review of clinical effectiveness. There was considerable heterogeneity among the studies with respect to the nature of the intervention, the nurses delivering the programmes and the populations in which the interventions were assessed. Overall, the quality of the included studies was good: all but one of the included studies were judged to be at medium or low risk of bias. Meta-analysis of eight studies suggested a statistically significant mortality benefit for the home-based health promotion groups, whereas a meta-analysis of four studies suggested non-significant benefits in terms of fewer falls in the intervention groups than in the control groups. Positive outcomes for home-based, nurse-led health promotion interventions were also reported within individual studies across several other outcomes. Only three economic studies met the criteria for inclusion in the review of cost-effectiveness. This evidence base consists of one non-randomised cost minimisation analysis and two economic evaluations undertaken alongside randomised controlled trials. Two of these studies involved an intervention targeted specifically at patients with a known underlying incurable disease, whereas the third study examined the clinical effectiveness and cost-effectiveness of early discharge in patients with a range of conditions, including fractures, neurological conditions and cardiorespiratory conditions. Each study indicated some likelihood that home-based, nurse-led health promotion may offer cost savings to the NHS and associated sectors, such as social services. However, one study did not report any comparison of health outcomes and instead simply assumed equivalence between the intervention and comparator groups, whereas the other two studies suggested at best a negligible incremental benefit in terms of preference-based health-related quality-of-life measures. LIMITATIONS: The evidence base for clinical effectiveness is subject to considerable heterogeneity. The UK economic evidence base is limited to three studies. CONCLUSIONS: On the basis of the evidence included in this systematic review, home-based, nurse-led health promotion may offer clinical benefits across a number of important health dimensions. However, it is generally unclear from the available studies which components of this type of complex intervention contribute towards individual aspects of benefit for older people. Given the limitations of the current evidence base, it remains unclear whether or not home-based health promotion interventions offer good value for money for the NHS and associated sectors. Given the considerable uncertainties in the available evidence base, it is difficult to isolate the key areas in which future research would be valuable or the exact study design required. Although this report does not identify specific studies that should be undertaken, it does set out a number of key considerations for the design of future research in this area. STUDY REGISTRATION: PROSPERO number: CRD42012002133.

The Oregon Health Insurance Experiment (OHIE) offers a unique opportunity to examine the causal relationship between Medicaid coverage and happiness among low-income adults, using an experimental design. This study leverages data from comprehensive surveys conducted at 0 and 12 months post-treatment. Previous studies based on OHIE have shown that individuals receiving Medicaid exhibited a significant improvement in mental health compared to those who did not receive coverage. The primary objective is to explore how Medicaid coverage impacts happiness, specifically analyzing in which direction variations in healthcare spending significantly improve mental health: higher spending or lower spending after Medicaid. Utilizing instrumental variable (IV) regression, I conducted six separate regressions across subgroups categorized by expenditure levels and happiness ratings, and the results reveal distinct patterns. Enrolling in OHP has significantly decreased the probability of experiencing unhappiness, regardless of whether individuals had high or low medical spending. Additionally, it decreased the probability of being pretty happy and having high medical expenses, while increasing the

Nature access is increasingly recognised as a public health and equity imperative, yet cities lack standardised ways to measure who benefits from green infrastructure. We present the first national, building-level assessment of the 3-30-300 urban greening rule across England, integrating high-performance computing with open LiDAR and geospatial datasets. Our framework quantifies proximity, availability, and accessibility of greenery, linking each to socioeconomic deprivation through Gini-based inequality metrics. Results reveal that while most English residents meet the tree visibility criterion, only 0.1% of urban areas meet all three thresholds; canopy cover and park access sharply diverge along deprivation lines. Wealthier areas enjoy greater ambient greenness, whereas deprived urban cores often have better proximity to parks but lower vegetation density. The study offers a scalable computational blueprint for assessing nature equity and demonstrates that green accessibility represents a new dimension of socioeconomic inequality. These findings call for policy approaches that move beyond proximity metrics toward equitable, quality-based standards for nature access.

The ILC Technology Network (ITN) was established in 2022 by the ILC International Development Team, a subcommittee of the International Committee for Future Accelerators, to advance engineering studies toward the realisation of the International Linear Collider (ILC). While the ITN work packages focus on engineering activities for the ILC, their topics are also relevant to a broad range of accelerator applications in particle physics and beyond. These work packages are being carried out now by laboratories in Asia and Europe in close collaboration. This report summarises the current status of the ITN activities.