Reliability Evaluation for Continuous-Wave Functional Near-Infrared Spectroscopy Systems: Comprehensive Testing from Bench Characterization to Human Test.

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace.

Institutional Priority-Setting for Novel Drugs and Therapeutics: A Qualitative Systematic Review.

BACKGROUND: There is a lack of guidance on approaches to formulary management and funding for high-cost drugs and therapeutics by individual healthcare institutions. The objective of this review was to assess institutional approaches to resource allocation for such therapeutics, with a particular focus on paediatric and rare disease populations. METHODS: A search of Embase and MEDLINE was conducted for studies relevant to decision-making for off-formulary, high-cost drugs and therapeutics. Abstracts were evaluated for inclusion based on the Simple Multiple-Attribute Rating Techniques (SMART) criteria. A framework of 30 topics across 4 categories was used to guide data extraction and was based on findings from the initial abstract review and previous health technology assessment (HTA) publications. Reflexive thematic analysis was conducted using QSR NVivo 12 software. RESULTS: A total of 168 studies were included for analysis. Only 4 (2%) focused on paediatrics, while 21 (12%) centred on adults and the remainder (85%) did not specify. Thirty-two (19%) studies discussed the importance of high-cost therapeutics and 34 (23%) focused on rare/orphan drugs. Five themes were identified as being relevant to institutional decision-making for high-cost therapeutics: institutional strategy, substantive criteria, procedural considerations, guiding principles and frameworks, and operational activities. Each of these themes encompassed several sub-themes and was complemented by a sixth category specific to paediatrics and rare diseases. CONCLUSION: The rising cost of novel drugs and therapeutics underscores the need for robust, evidence-based, and ethically defensible decision-making processes for health technology funding, particularly at the hospital level. Our study highlights practices and themes to aid decision-makers in thinking critically about institutional, substantive, procedural, and operational considerations in support of legitimate decisions about institutional funding of high-cost drugs and therapeutics, as well as opportunities and challenges that exist for paediatric and rare disease populations.

Give Due Consideration .

The new EU Regulation on health technology assessment (HTAR) provides for joint clinical assessments (JCA) of health technologies at EU level. When Member States carry out health technology assessments (HTA) at the national level, they shall give due consideration to the results of a JCA and comply with other obligations of the Regulation. This article aims to clarify what these obligations mean for the Member States and whether JCA results have to be considered outside a national health technology assessment as well. In this context, the question of which processes qualify as 'national HTA' and which requirements need to be fulfilled to trigger the obligations under Article 13 HTAR are discussed in more detail in this paper.

Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study.

BACKGROUND: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.

Mitigating Health-Related Uncertainties During Pregnancy: The Role of Smart Health Monitoring Technologies.

Pregnancy is a time filled with uncertainties, which can be challenging and lead to fear or anxiety for expectant parents. Health monitoring technologies that allow monitoring of the vital signs of both the mother and fetus offer a way to address health-related uncertainties. But are smart health monitoring technologies (SHMTs) actually an effective means to reduce uncertainties during pregnancy, or do they have the opposite effect? Using conceptual reasoning and phenomenological approaches grounded in existing literature, this Viewpoint explores the effects of SHMTs on health-related uncertainties during pregnancy. The argument posits that while SHMTs can alleviate some health-related uncertainties, they may also create new ones. This is particularly the case when the abundance of vital data overwhelms pregnant persons, leads to false-positive diagnoses, or raises concerns about the accuracy and analysis of data. Consequently, it is concluded that the use of SHMTs is not a cure-all for overcoming health-related uncertainties during pregnancy. Since the use of such monitoring technologies can introduce new uncertainties, it is important to carefully consider where and for what purpose they are used, use them sparingly, and promote a pragmatic approach to uncertainties.Using conceptual reasoning and phenomenological approaches grounded in existing literature, the effects of SHMTs on health-related uncertainties during pregnancy are explored. The argument posits that while SHMTs can alleviate some health-related uncertainties, they may also create new ones. This is particularly the case when the abundance of vital data overwhelms pregnant persons, leads to false-positive diagnoses, or raises concerns about the accuracy and analysis of data. Consequently, it is concluded that the use of SHMTs is not a cure-all for overcoming health-related uncertainties during pregnancy. Since the use of such monitoring technologies can introduce new uncertainties, it is important to carefully consider where and for what purpose they are used, use them sparingly, and promote a pragmatic approach to uncertainties.

Combining user-centered design and behavioral theory to enhance health technologies: A personas-based approach for a primary-care based multifactorial falls risk assessment tool.

INTRODUCTION: Multifactorial falls risk assessment tools (FRATs) can be an effective falls prevention method for older adults, but are often underutilized by health care professionals (HCPs). This study aims to enhance the use and implementation of multifactorial FRATs by combining behavioral theory with the user-centered design (UCD) method of personas construction. Specifically, the study aimed to (1) construct personas that are based on external (i.e., needs, preferences) and intrinsic user characteristics (i.e., behavioral determinants); and (2) use these insights to inform requirements for optimizing an existing Dutch multifactorial FRAT (i.e., the 'Valanalyse'). METHODS: Survey data from HCPs (n = 31) was used to construct personas of the 'Valanalyse.' To examine differences between clusters on 68 clustering variables, a multivariate cluster analysis technique with non-parametric analyses and computational methods was used. The aggregated external and intrinsic user characteristics of personas were used to inform key design and implementation requirements for the 'Valanalyse,' respectively, whereby intrinsic user characteristics were matched with appropriate behavior change techniques to guide implementation. RESULTS: Significant differences between clusters were observed in 20 clustering variables (e.g., behavioral beliefs, situations for use). These variables were used to construct six personas representing users of each cluster. Together, the six personas helped operationalize four key design requirements (e.g., guide treatment-related decision making) and 14 implementation strategies (e.g., planning coping responses) for optimizing the 'Valanalyse' in Dutch geriatric, primary care settings. CONCLUSION: The findings suggest that theory- and evidence-based personas that encompass both external and intrinsic user characteristics are a useful method for understanding how the use and implementation of multifactorial FRATs can be optimized with and for HCPs, providing important implications for developers and eHealth interventions with regards to encouraging technology adoption.

SurvInt: a simple tool to obtain precise parametric survival extrapolations.

BACKGROUND: Economic evaluation of emerging health technologies is mandated by agencies such as the National Institute for Health and Care Excellence (NICE) to ensure their cost is proportional to their benefit. To avoid bias, NICE stipulate that the benefit of a treatment is assessed across the lifetime of the patient population, which can be many decades. Unfortunately, follow-up from a clinical trial will not usually cover the required period and the observed follow-up will require extrapolation. For survival data this is often done by selecting a preferred model from a set of candidate parametric models. This approach is limited in that the choice of model is restricted to those originally fitted. What if none of the models are consistent with clinical prediction or external data? METHOD/RESULTS: This paper introduces SurvInt, a tool that estimates the parameters of common parametric survival models which interpolate key survival time co-ordinates specified by the user, which could come from external trials, real world data or expert clinical opinion. This is achieved by solving simultaneous equations based on the survival functions of the parametric models. The application of SurvInt is shown through two examples where traditional parametric modelling did not produce models that were consistent with external data or clinical opinion. Additional features include model averaging, mixture cure models, background mortality, piecewise modelling, restricted mean survival time estimation and probabilistic sensitivity analysis. CONCLUSIONS: SurvInt allows precise parametric survival models to be estimated and carried forward into economic models. It provides access to extrapolations that are consistent with multiple data sources such as observed data and clinical predictions, opening the door to precise exploration of regions of uncertainty/disagreement. SurvInt could avoid the need for post-hoc adjustments for complications such as treatment switching, which are often applied to obtain a plausible survival model but at the cost of introducing additional uncertainty. Phase III clinical trials are not designed with extrapolation in mind, and so it is sensible to consider alternative approaches to predict future survival that incorporate external information.

Editorial for the Special Issue "Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare".

The integration of sensor technology in healthcare has become crucial for disease diagnosis and treatment [...].

The Evolving Regulatory Paradigm of AI in MedTech: A Review of Perspectives and Where We Are Today.

Artificial intelligence (AI)-enabled technologies in the MedTech sector hold the promise to transform healthcare delivery by improving access, quality, and outcomes. As the regulatory contours of these technologies are being defined, there is a notable lack of literature on the key stakeholders such as the organizations and interest groups that have a significant input in shaping the regulatory framework. This article explores the perspectives and contributions of these stakeholders in shaping the regulatory paradigm of AI-enabled medical technologies. The formation of an AI regulatory framework requires the convergence of ethical, regulatory, technical, societal, and practical considerations. These multiple perspectives contribute to the various dimensions of an evolving regulatory paradigm. From the global governance guidelines set by the World Health Organization (WHO) to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.

Building a Competency Framework to Integrate Inter-disciplinary Precision Medicine Capabilities into the Medical Technology and Pharmaceutical Industry.

INTRODUCTION: Integration of precision medicine (PM) competencies across the Medical Technology and Pharmaceutical industry is critical to enable industry professionals to understand and develop the skills needed to navigate the opportunities arising from rapid scientific and technological innovation in PM. Our objective was to identify the key competency domains required by industry professionals to enable them to upskill themselves in PM-related aspects of their roles. METHODS: A desktop research review of current literature, curriculum, and healthcare trends identified a core set of domains and subdomains related to PM competencies that were consistent across multiple disciplines and competency frameworks. A survey was used to confirm the applicability of these domains to the cross-functional and multi-disciplinary work practices of industry professionals. Companies were requested to trial the domains to determine their relevance in practice and feedback was obtained. RESULTS: Four PM-relevant domains were identified from the literature review: medical science and technology; translational and clinical application; governance and regulation and professional practice. Survey results refined these domains, and case studies within companies confirmed the potential for this framework to be used as an adjunct to current role specific competency frameworks to provide a specific focus on needed PM capabilities. CONCLUSION: The framework was well accepted by local industry as a supplement to role specific competency frameworks to provide a structure on how to integrate new and evolving technologies into their current workforce development planning and build a continuous learning and cross-disciplinary mindset.

Assessment of the Barriers and Enablers of the Use of mHealth Systems in Sub-Saharan Africa According to the Perceptions of Patients, Physicians, and Health Care Executives in Ethiopia: Qualitative Study.

BACKGROUND: Digital technologies are increasingly being used to deliver health care services and promote public health. Mobile wireless technologies or mobile health (mHealth) technologies are particularly relevant owing to their ease of use, broad reach, and wide acceptance. Unlike developed countries, Sub-Saharan Africa experiences more challenges and obstacles when it comes to deploying, using, and expanding mHealth systems. In addition to barriers, there are enabling factors that could be exploited for the design, implementation, and scaling up of mHealth systems. Sub-Saharan Africa may require tailored solutions that address the specific challenges facing the region. OBJECTIVE: The overall aim of this study was to identify the barriers and enablers for using mHealth systems in Sub-Saharan Africa from the perspectives of patients, physicians, and health care executives. METHODS: Multi-level and multi-actor in-depth semistructured interviews were employed to qualitatively explore the barriers and enablers of the use of mHealth systems. Data were collected from patients, physicians, and health care executives. The interviews were audio recorded, transcribed verbatim, translated, and coded. Thematic analysis methodology was adopted, and NVivo software was used for the data analysis. RESULTS: Through this rigorous study, a total of 137 determinants were identified. Of these determinants, 68 were identified as barriers and 69 were identified as enablers. Perceived barriers in patients included lack of awareness about mHealth systems and language barriers. Perceived enablers in patients included need for automated tools for health monitoring and an increasing literacy level of the society. According to physicians, barriers included lack of available digital health systems in the local context and concern about patients' mHealth capabilities, while enablers included the perceived usefulness in reducing workload and improving health care service quality, as well as the availability of mobile devices and the internet. As perceived by health care executives, barriers included competing priorities alongside digitalization in the health sector and lack of interoperability and complete digitalization of implemented digital health systems, while enablers included the perceived usefulness of digitalization for the survival of the highly overloaded health care system and the abundance of educated manpower specializing in technology. CONCLUSIONS: mHealth systems in Sub-Saharan Africa are hindered and facilitated by various factors. Common barriers and enablers were identified by patients, physicians, and health care executives. To promote uptake, all relevant stakeholders must actively mitigate the barriers. This study identified a promising outlook for mHealth in Sub-Saharan Africa, despite the present barriers. Opportunities exist for successful integration into health care systems, and a user-centered design is crucial for maximum uptake.

Codesign of health technology interventions to support best-practice perioperative care and surgical waitlist management.

OBJECTIVES: This project aimed to determine where health technology can support best-practice perioperative care for patients waiting for surgery. METHODS: An exploratory codesign process used personas and journey mapping in three interprofessional workshops to identify key challenges in perioperative care across four health districts in Sydney, Australia. Through participatory methodology, the research inquiry directly involved perioperative clinicians. In three facilitated workshops, clinician and patient participants codesigned potential digital interventions to support perioperative pathways. Workshop output was coded and thematically analysed, using design principles. RESULTS: Codesign workshops, involving 51 participants, were conducted October to November 2022. Participants designed seven patient personas, with consumer representatives confirming acceptability and diversity. Interprofessional team members and consumers mapped key clinical moments, feelings and barriers for each persona during a hypothetical perioperative journey. Six key themes were identified: 'preventative care', 'personalised care', 'integrated communication', 'shared decision-making', 'care transitions' and 'partnership'. Twenty potential solutions were proposed, with top priorities a digital dashboard and virtual care coordination. DISCUSSION: Our findings emphasise the importance of interprofessional collaboration, patient and family engagement and supporting health technology infrastructure. Through user-based codesign, participants identified potential opportunities where health technology could improve system efficiencies and enhance care quality for patients waiting for surgical procedures. The codesign approach embedded users in the development of locally-driven, contextually oriented policies to address current perioperative service challenges, such as prolonged waiting times and care fragmentation. CONCLUSION: Health technology innovation provides opportunities to improve perioperative care and integrate clinical information. Future research will prototype priority solutions for further implementation and evaluation.

mHealth Apps for the Self-Management of Low Back Pain: Systematic Search in App Stores and Content Analysis.

BACKGROUND: With the rapid development of mobile health (mHealth) technology, many health apps have been introduced to the commercial market for people with back pain conditions. However, little is known about their content, quality, approaches to care for low back pain (LBP), and associated risks of use. OBJECTIVE: The aims of this research were to (1) identify apps for the self-management of LBP currently on the market and (2) assess their quality, intervention content, theoretical approaches, and risk-related approaches. METHODS: The UK iTunes and Google Play stores were initially searched for apps related to the self-management of LBP in May 2022. A repeat search in June 2023 was conducted to ensure that any relevant new apps developed in the last year were incorporated into the review. A total of 3 keywords recommended by the Cochrane Back and Neck Group were used to search apps "low back pain," "back pain," and "lumbago." The quality of the apps was assessed by using the 5-point Mobile App Rating Scale (MARS). RESULTS: A total of 69 apps (25 iOS and 44 Android) met the inclusion criteria. These LBP self-management apps mainly provide recommendations on muscle stretching (n=51, 73.9%), muscle strengthening (n=42, 60.9%), core stability exercises (n=32, 46.4%), yoga (n=19, 27.5%), and information about LBP mechanisms (n=17, 24.6%). Most interventions (n=14, 78%) are consistent with the recommendations in the National Institute for Health and Care Excellence (NICE) guidelines. The mean (SD) MARS overall score of included apps was 2.4 (0.44) out of a possible 5 points. The functionality dimension was associated with the highest score (3.0), whereas the engagement and information dimension resulted in the lowest score (2.1). Regarding theoretical and risk-related approaches, 18 (26.1%) of the 69 apps reported the rate of intervention progression, 11 (15.9%) reported safety checks, only 1 (1.4%) reported personalization of care, and none reported the theoretical care model or the age group targeted. CONCLUSIONS: mHealth apps are potentially promising alternatives to help people manage their LBP; however, most of the LBP self-management apps were of poor quality and did not report the theoretical approaches to care and their associated risks. Although nearly all apps reviewed included a component of care listed in the NICE guidelines, the model of care delivery or embracement of care principles such as the application of a biopsychosocial model was unclear.

Access in all areas? A round up of developments in market access and health technology assessment: part 3.

In this latest update, we explore some of the key updates in market access over recent months including the UK's voluntary scheme for branded medicines pricing, access and growth (VPAG), the first drugs funded by the Innovative Medicines Fund in the UK and the Direct Access Scheme in France, and, finally, the new Institute for Clinical and Economic Review (ICER) value assessment framework in the USA.

The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities / La revolución de la inteligencia artificial en la atención primaria: desafíos, dilemas y oportunidades

Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare.(AU)

La inteligencia artificial (IA) puede ser una herramienta de gran valor para la atención primaria (AP), ya que, entre otras cosas, puede ayudar a los profesionales de la salud a mejorar la precisión en los diagnósticos, la gestión de enfermedades crónicas y la eficiencia general del cuidado que proporcionan. Es importante subrayar que la IA no debe ser vista como una herramienta de sustitución, sino como una ayuda para los profesionales de la AP. Aunque la IA es capaz de procesar grandes volúmenes de datos y generar predicciones precisas, no puede reemplazar la destreza y la experiencia de los profesionales en la toma de decisiones clínicas. La IA todavía requiere la interpretación y el juicio clínico de un profesional de la salud capacitado, y no puede ofrecer la empatía y el apoyo emocional que a menudo se requiere en el ámbito sanitario.(AU)