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Importance: The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. Objectives: To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. Evidence Review: PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. Findings: The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). Conclusions and Relevance: The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

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BACKGROUND: Diagnostics are increasingly shifting to patients' home environment, facilitated by new digital technologies. Digital diagnostics (diagnostic services enabled by digital technologies) can be a tool to better respond to the challenges faced by primary care systems while aligning with patients' and healthcare professionals' needs. However, it needs to be clarified how to determine the success of these interventions. OBJECTIVES: We aim to provide practical guidance to facilitate the adequate development and implementation of digital diagnostics. STRATEGY: Here, we propose the quadruple aim (better patient experiences, health outcomes and professional satisfaction at lower costs) as a framework to determine the contribution of digital diagnostics in primary care. Using this framework, we critically analyse the advantages and challenges of digital diagnostics in primary care using scientific literature and relevant casuistry. RESULTS: Two use cases address the development process and implementation in the Netherlands: a patient portal for reporting laboratory results and digital diagnostics as part of hybrid care, respectively. The third use case addresses digital diagnostics for sexually transmitted diseases from an international perspective. CONCLUSIONS: We conclude that although evidence is gathering, the often-expected value of digital diagnostics needs adequate scientific evidence. We propose striving for evidence-based 'responsible digital diagnostics' (sustainable, ethically acceptable, and socially desirable digital diagnostics). Finally, we provide a set of conditions necessary to achieve it. The analysis and actionable guidance provided can improve the chance of success of digital diagnostics interventions and overall, the positive impact of this rapidly developing field.

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BACKGROUND: eHealth offers opportunities to improve health and healthcare systems and overcome primary care challenges in low-resource settings (LRS). LRS has been typically associated with low- and middle-income countries (LMIC), but they can be found in high-income countries (HIC) when human, physical or financial resources are constrained. Adopting a concept of LRS that applies to LMIC and HIC can facilitate knowledge interchange between eHealth initiatives while improving healthcare provision for socioeconomically disadvantaged groups across the globe. OBJECTIVES: To outline the contributions and challenges of eHealth in low-resource primary care settings. STRATEGY: We adopt a socio-ecological understanding of LRS, making LRS relevant to LMIC and HIC. To assess the potential of eHealth in primary care settings, we discuss four case studies according to the WHO 'building blocks for strengthening healthcare systems'. RESULTS AND DISCUSSION: The case studies illustrate eHealth's potential to improve the provision of healthcare by i) improving the delivery of healthcare (using AI-generated chats); ii) supporting the workforce (using telemedicine platforms); iii) strengthening the healthcare information system (through patient-centred healthcare information systems), and iv) improving system-related elements of healthcare (through a mobile health financing platform). Nevertheless, we found that development and implementation are hindered by user-related, technical, financial, regulatory and evaluation challenges. We formulated six recommendations to help anticipate or overcome these challenges: 1) evaluate eHealth's appropriateness, 2) know the end users, 3) establish evaluation methods, 4) prioritise the human component, 5) profit from collaborations, ensure sustainable financing and local ownership, 6) and contextualise and evaluate the implementation strategies.

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Background: Biobanks form key research support infrastructures that ensure the highest sample quality for scientific research. Their activity must align closely and proportionally to the interests of researchers, donors, and society. Informed consent (IC) is a central tool to guarantee the protection of donors' rights and interests. Aim: This study aimed to analyze the challenges of obtaining IC for biobanking in clinical settings and ways to improve this process. Methods: Biobank Bellvitge University Hospital HUB-ICO-IDIBELL in Barcelona received 8671 IC forms between 2017 and 2020. The mistakes that caused IC forms to be rejected by the Biobank were analyzed. In addition, interventions aimed at physicians to improve the IC process were evaluated through a calculation of the relative risk (RR). Finally, physicians who submitted samples to the Biobank, most of whom are involved in research activities, were surveyed about the barriers to collecting IC and how to improve this process. Results: During 2017-2020, 19.6% of IC forms were rejected. The most relevant cause of rejection was the use of outdated IC forms, followed by missing patient information or mistakes having been made by the physician. Evaluation of the rejection rates before and after interventions to improve the IC process suggests significant improvement (27.7% before interventions (January 2017-May 2018) compared to 9.6% after interventions (February-December 2020), RR 0.4 95% CI 0.34-0.47; p < 0.0001). According to the physicians, the most important barrier to collecting IC is the time constraint, and they consider digitalization as a viable solution. Conclusions: Our research offers a view of the less well-understood practical challenges that physicians and biobanks face when collecting IC in clinical settings. It suggests that, despite multiple challenges, continuous monitoring, training, and information programs for physicians are key to optimizing the IC process in clinical settings.

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BACKGROUND: Implementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal dilemmas. This study aimed to identify challenges regarding responsibility and liability when prescribing digital health in clinical practice. This was part of an overarching project aiming to explore the most pressing ethical and legal obstacles regarding the implementation and adoption of digital health in the Netherlands, and to propose actionable solutions. METHODS: A series of multidisciplinary focus groups with stakeholders who have relevant digital health expertise were analysed through thematic analysis. RESULTS: The emerging general theme was 'uncertainty regarding responsibilities' when adopting digital health. Key dilemmas take place in clinical settings and within the doctor-patient relationship ('professional digital health'). This context is particularly challenging because different stakeholders interact. In the absence of appropriate legal frameworks and codes of conduct tailored to digital health, physicians' responsibility is to be found in their general duty of care. In other words: to do what is best for patients (not causing harm and doing good). Professional organisations could take a leading role to provide more clarity with respect to physicians' responsibility, by developing guidance describing physicians' duty of care in the context of digital health, and to address the resulting responsibilities. CONCLUSIONS: Although legal frameworks governing medical practice describe core ethical principles, rights and obligations of physicians, they do not suffice to clarify their responsibilities in the setting of professional digital health. Here we present a series of recommendations to provide more clarity in this respect, offering the opportunity to improve quality of care and patients' health. The recommendations can be used as a starting point to develop professional guidance and have the potential to be adapted to other healthcare professionals and systems.

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Despite significant efforts, the COVID-19 pandemic has put enormous pressure on health care systems around the world, threatening the quality of patient care. Telemonitoring offers the opportunity to carefully monitor patients with a confirmed or suspected case of COVID-19 from home and allows for the timely identification of worsening symptoms. Additionally, it may decrease the number of hospital visits and admissions, thereby reducing the use of scarce resources, optimizing health care capacity, and minimizing the risk of viral transmission. In this paper, we present a COVID-19 telemonitoring care pathway developed at a tertiary care hospital in the Netherlands, which combined the monitoring of vital parameters with video consultations for adequate clinical assessment. Additionally, we report a series of medical, scientific, organizational, and ethical recommendations that may be used as a guide for the design and implementation of telemonitoring pathways for COVID-19 and other diseases worldwide.

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Nowadays, we participate in a knowledge-based society where a large part of our lives has been digitalised. Products, services, and professional, academic and personal activities have turned digital and with that change, it became possible to record and collect data about (almost) everything. Through research and innovation, this information has offered great opportunities to improve health and the health systems it depends on. The present study is interested in initiatives that use this kind of personal information, specifically, big data initiatives that serve as resources to support research and innovation, such as 'biobanks' or 'genome projects'. They will be further referred in this paper as 'health big data resources' or 'initiatives'. The main goal of this work is to identify a model of consent that would allow these initiatives to align with EU values, respect fundamental rights and meet the expectations of participants, data subjects and society. In the EU, consent responds to the different socioeconomic contexts, the national and EU policy (including the General Data Protection Regulation), and to the abandonment of the idea of absolute anonymisation. From the point of view of citizens, consent should respond to their expectations regarding health, well-being, health systems, science and technology. Because it is shaped by multiple factors, there are different models of consent used in health big data resources and there is a constant interest in improving them. The current tendency is that consent has shifted away from the model used in traditional clinical or biomedical research. Instead, big data resources are adopting models of broad or assumed consent. This analysis supports the current general idea that the traditional concept of informed consent is insufficient for supporting health big data resources. Moreover, it stresses that it is insufficient to simply move towards models of broad or assumed consent. The main purpose of this analysis is to show that health big data initiatives should aim towards a model of consent based on a representative governance system. Through this system, participants, data subjects and society will be able to influence the initiative's operations, communicate their will and retain a certain level of control over their personal data and the activities of the resource. In other words, it is necessary to strive for mechanisms to extend the traditional concept of consent through governance which would enable the exercise of autonomy of those implicated. Therefore, consent must become not only modifiable (in the sense that it should be allowed to change through time), but participative. For these models of consent to be successful, they must be grounded in a solid relationship of trust with participants, data subjects and society in general. Consequently, it is necessary to establish a permanent process of dialogue and public engagement with the goal of informing, shaping and directing the governance systems of big data resources for health

Hoy en día participamos de una sociedad del conocimiento donde gran parte de nuestras vidas ha sido digitalizada. Esta transformación ha afectado productos y servicios y ha cambiado nuestras actividades profesionales, académicas y personales. La magnitud de la digitalización de nuestra sociedad ha permitido generar y almacenar datos de (casi) todo. A través de la investigación e innovación, esta información nos brinda grandes oportunidades para mejorar nuestra salud y los sistemas de salud de los que depende. El presente estudio está interesado en los proyectos de salud de datos masivos que utilizan este tipo de información personal y que son establecidos como facilitadores al servicio de la investigación e innovación. Puesto que llevan diferentes nombres, como 'biobancos' o 'proyectos genoma', se les referirá en este artículo como 'proyectos de salud de datos masivos' o 'iniciativas'. El objetivo principal de este estudio es identificar un modelo de consentimiento que le permita a estos proyectos funcionar de acuerdo con los valores de la UE, respetar los derechos humanos fundamentales y satisfacer las expectativas de los participantes, de los titulares de los datos y de la sociedad en general. Actualmente en la UE, los modelos de consentimiento utilizados para estas iniciativas responden a la transformación de los modelos socioeconómicos, a los cambios en las políticas nacionales y de la UE (incluyendo el nuevo Reglamento General de Protección de Datos), a los avances científicos y tecnológicos y al abandono de la idea de anonimización absoluta. Desde el punto de vista de los ciudadanos, el consentimiento debe responder a sus expectativas con respecto a su salud, bienestar, servicios sanitarios, ciencia y tecnología. Puesto que el consentimiento se ve influenciado por múltiples factores, diferentes modelos se proponen para proyectos de datos masivos en salud y hay un interés constante en mejorarlos. Hoy en día, los modelos de consentimiento utilizados en proyectos de salud de datos masivos se han alejado del modelo tradicionalmente utilizado en la investigación clínica o biomédica. En su lugar se adoptan modelos de consentimiento genérico o presunto

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INTRODUCTION: The paradigm shift to a knowledge-based economy has incremented the use of personal information applied to health-related activities, such as biomedical research, innovation, and commercial initiatives. The convergence of science, technology, communication and data technologies has given rise to the application of big data to health; for example through eHealth, human databases and biobanks. METHODS: In light of these changes, we enquire about the value of personal data and its appropriate use. In order to illustrate the complex ground on which big data applied to health develops, we analyse the current situation of the European Union and two cases: the Catalan VISC+/PADRIS and the UK Biobank, as perspectives. DISCUSSION AND CONCLUSIONS: Personal health-related data in the context of the European Union is being increasingly used for big data projects under diverse schemes. There, public and private sectors participate distinctively or jointly, pursuing very different goals which may conflict with individual rights, notably privacy. Given that, this paper advocates for stopping the unjustified accumulation and commercialisation of personal data, protecting the interests of citizens and building appropriate frameworks to govern big data projects for health. A core tool for achieving such goals is to develop consent mechanisms which allow truly informed but adaptable consent, conjugated with the engagement of donors, participants and society.

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