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Background: Alternative means of blood sampling continue to grow due to the scarcity of phlebotomists and the need for person-centered care. It is crucial to consistently support these alternative blood sampling innovations with scientific evidence to guarantee the quality of care, especially when implementing for instance Lateral Upper-arm Blood Collection (LUBC) for non-trained professionals at home. Knowledge gaps remain in how to quantify imprecision introduced by the collection method and its impact on clinical use., Methods: We developed the Six Sigma analysis for alternative Sampling methods, including sampling method-induced imprecision, accuracy and precision, called σ 3 -methodology. We performed a two-step verification using σ 3 -methodology for LUBC TAP® II, which included fourteen routine clinical analytes. We used venipuncture as the gold standard., Results: The biggest source of imprecision for all analytes was sampling method-induced imprecision, which, for the first time, was quantified, resulting in a varying effect on the clinical usability.TAP® II showed acceptable analytical performance for ALP, bilirubin, HDL-cholesterol, CRP and sodium. Unacceptable analytical performance was found for ALT, AST, cholesterol, creatinine, GGT, HbA1c, potassium, LDH and triglycerides., Conclusion: Alternative blood sampling innovations hold promise for advancing diagnostic care, aiming to deliver accessible decentralised sample collection at home that does not require phlebotomist involvement. However, sampling method-induced imprecision should not be overlooked in the performance assessment to guarantee responsible development that will contribute to the success and desirable societal impact of alternative sampling technologies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Paediatric critical care nurses face challenges in promptly detecting patient deterioration and delivering high-quality care, especially in low-resource settings (LRS). Patient monitors equipped with data-driven algorithms that monitor and integrate clinical data can optimise scarce resources (e.g. trained staff) offering solutions to these challenges. Poor algorithm output design and workflow integration, however, are important factors hindering successful implementation. This study aims to explore nurses' perspectives to inform the development of a data-driven algorithm and user-friendly interface for future integration into a continuous vital signs monitoring system for critical care in LRS., Methods: Human-centred design methods, including contextual inquiry, semi-structured interviews, prototyping and co-design sessions, were carried out at the high-dependency units of Queen Elizabeth Central Hospital and Zomba Central Hospital in Malawi between March and July 2023. Triangulating these methods, we identified what algorithm could assist nurses and used co-creation methods to design a user interface prototype. Data were analysed using qualitative content analysis., Results: Workflow observations demonstrated the effects of personnel shortages and limited monitor equipment for vital signs monitoring. Interviews identified four themes: workload and workflow, patient prioritisation, interaction with guardians, and perspectives on data-driven algorithms. The interviews emphasised the advantages of predictive algorithms in anticipating patient deterioration, underlining the need to integrate the algorithm's output, the (constant) monitoring data, and the patient's present clinical condition. Nurses preferred a scoring system represented with familiar scales and colour codes. During co-design sessions, trust, usability and context specificity were emphasised as requirements for these algorithms. Four prototype components were examined, with nurses favouring scores represented by colour codes and visual representations of score changes., Conclusions: Nurses in the LRS studied, perceived that data-driven algorithms, especially for predicting patient deterioration, could improve the provision of critical care. This can be achieved by translating nurses' perspectives into design strategies, as has been carried out in this study. The lessons learned were summarised as actionable pre-implementation recommendations for the development and implementation of data-driven algorithms in LRS., Competing Interests: Declarations. Ethics approval and consent to participate: The study was approved by the College of Medicine Research and Ethics Committee (COMREC) at the University of Malawi on October 6th, 2022 (P.01/22/3552). The study adhered to the principles outlined in the Declaration of Helsinki and followed the guidelines set by COMREC for Health Research. Written informed consent was obtained, and nurses signed the informed consent form in English or Chichewa. Consent for publication: Not applicable. Competing interests: Two of the authors, LdM and BB, are affiliated with GOAL 3, a social enterprise. The remaining authors declare no competing interests., (© 2024. The Author(s).)

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.

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.

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.

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.

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., (© 2022. The Author(s).)

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., (©Anna V Silven, Annelieke H J Petrus, María Villalobos-Quesada, Ebru Dirikgil, Carlijn R Oerlemans, Cyril P Landstra, Hileen Boosman, Hendrikus J A van Os, Marco H Blanker, Roderick W Treskes, Tobias N Bonten, Niels H Chavannes, Douwe E Atsma, Y K Onno Teng. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.09.2020.)

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., (© 2018 John Wiley & Sons Ltd.)