Digital tools for youth health promotion: principles, policies and practices in sub-Saharan Africa.

Although digital health promotion (DHP) technologies for young people are increasingly available in low- and middle-income countries (LMICs), there has been insufficient research investigating whether existing ethical and policy frameworks are adequate to address the challenges and promote the technological opportunities in these settings. In an effort to fill this gap and as part of a larger research project, in November 2022, we conducted a workshop in Cape Town, South Africa, entitled 'Unlocking the Potential of Digital Health Promotion for Young People in Low- and Middle-Income Countries'. The workshop brought together 25 experts from the areas of digital health ethics, youth health and engagement, health policy and promotion and technology development, predominantly from sub-Saharan Africa (SSA), to explore their views on the ethics and governance and potential policy pathways of DHP for young people in LMICs. Using the World Café method, participants contributed their views on (i) the advantages and barriers associated with DHP for youth in LMICs, (ii) the availability and relevance of ethical and regulatory frameworks for DHP and (iii) the translation of ethical principles into policies and implementation practices required by these policies, within the context of SSA. Our thematic analysis of the ensuing discussion revealed a willingness to foster such technologies if they prove safe, do not exacerbate inequalities, put youth at the center and are subject to appropriate oversight. In addition, our work has led to the potential translation of fundamental ethical principles into the form of a policy roadmap for ethically aligned DHP for youth in SSA.

The value of standards for health datasets in artificial intelligence-based applications.

Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative).

Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation.

The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.

Determining the unmet needs of patients with intracranial meningioma-a qualitative assessment.

BACKGROUND: Meningiomas are the most common primary benign brain neoplasms, but despite their commonality, the supportive needs of this patient population have been overlooked. The aim of this study is to identify unmet needs of meningioma patients, caregivers, and health care providers. METHODS: We adopted a patient-centered approach by using qualitative interviewing with patients diagnosed with a meningioma who have undergone treatment in the last 10 years since the date of their interview. Informal caregivers (family and/or friends) of the patient population and health care providers who are normally involved in the management and care of meningioma patients were also interviewed. Interview transcripts were subjected to thematic analysis. RESULTS: Of the 50 participants interviewed, there were 30 patients, 12 caregivers, and 8 health care professionals. Thematic analysis revealed 4 overarching themes: (1) access to targeted postoperative care, (2) financial struggles for patients and their families, (3) lack of information specific to meningiomas and postsurgical management, and (4) lack of psychosocial support. CONCLUSION: This study identified supportive needs specific to the meningioma patient population, which predominantly falls within the postoperative phase. The postoperative journey of this patient population could potentially extend to the rest of the patient's life, which necessitates resources and information directed to support postoperative recovery and management. The development of directly relevant supportive resources that support meningioma patients in their postoperative recovery is necessary to improve the health-related quality of life in this patient population.

Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning.

Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. We suggest that taking a patient safety and quality improvement approach to bias can support the quantification of bias-related effects on ML. Drawing from the ethical principles underpinning these approaches, we argue that patient safety and quality improvement lenses support the quantification of relevant performance metrics, in order to minimize harm while promoting accountability, justice, and transparency. We identify specific methods for operationalizing these principles with the goal of attending to bias to support better decision making in light of controllable and uncontrollable factors.

Ambiguous identities of drugs and people: A scoping review of opioid-related stigma.

BACKGROUND: Human beings have long consumed opiates and opioids for pleasure and as a treatment for numerous ailments, most notably pain. North America is currently in the grips of a crisis of opioid-related overdoses, and stigma is considered a major driver of the harms. While it is well established that substance use in general is highly stigmatized, stigma is a complex concept and opioid-related stigma is not well understood. A lack of clarity on opioid-related stigma has practice and policy implications in terms of understanding the sources of opioid stigma, how it manifests in various contexts, its impact on affected groups, and the development of effective strategies to redress it. METHODS: We performed a scoping review of the academic literature to develop a typology of opioid-related stigma. A charting process identified the type, agent, and recipient of stigma as well as the methodology and substances considered. RESULTS: Our search yielded 8,543 articles, from which 49 were included in the analysis. Based on the findings, we developed a typology of four main themes: (1) interpersonal and structural stigma toward people accessing opioid agonist therapy (OAT); (2) stigma related to opioids for the treatment of chronic pain; (3) stigma in healthcare settings; and (4) self-stigma. CONCLUSION: How opioid-stigma is (re)produced depends on the context of opioid use, the social identity and networks of the person who is consuming the opioid, and what type of opioid is being consumed, including medically-sanctioned forms of treatment. Opioid-related stigma permeates intrapersonal, interpersonal, structural, and societal levels, and people who consume opioids are marginalized at all levels. Our review describes our typology of stigma and illuminates multi-level considerations for reducing opioid-related stigma in healthcare settings.