Paper by Miao Zhang, Salman Rahman, Vishwali Mhasawade and Rumi Chunara: “…New data sources and AI methods for extracting information are increasingly abundant and relevant to decision-making across societal applications. A notable example is street view imagery, available in over 100 countries, and purported to inform built environment interventions (e.g., adding sidewalks) for community health outcomes. However, biases can arise when decision-making does not account for data robustness or relies on spurious correlations. To investigate this risk, we analyzed 2.02 million Google Street View (GSV) images alongside health, demographic, and socioeconomic data from New York City. Findings demonstrate robustness challenges; built environment characteristics inferred from GSV labels at the intracity level often do not align with ground truth. Moreover, as average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, intervention on features measured by GSV would be misestimated without proper model specification and consideration of this mediation mechanism. Using a causal framework accounting for these mediators, we determined that intervening by improving 10% of samples in the two lowest tertiles of physical inactivity would lead to a 4.17 (95% CI 3.84–4.55) or 17.2 (95% CI 14.4–21.3) times greater decrease in the prevalence of obesity or diabetes, respectively, compared to the same proportional intervention on the number of crosswalks by census tract. This study highlights critical issues of robustness and model specification in using emergent data sources, showing the data may not measure what is intended, and ignoring mediators can result in biased intervention effect estimates…(More)”
Collaboration in Healthcare: Implications of Data Sharing for Secondary Use in the European Union
Paper by Fanni Kertesz: “The European healthcare sector is transforming toward patient-centred and value-based healthcare delivery. The European Health Data Space (EHDS) Regulation aims to unlock the potential of health data by establishing a single market for its primary and secondary use. This paper examines the legal challenges associated with the secondary use of health data within the EHDS and offers recommendations for improvement. Key issues include the compatibility between the EHDS and the General Data Protection Regulation (GDPR), barriers to cross-border data sharing, and intellectual property concerns. Resolving these challenges is essential for realising the full potential of health data and advancing healthcare research and innovation within the EU…(More)”.
Using internet search data as part of medical research
Blog by Susan Thomas and Matthew Thompson: “…In the UK, almost 50 million health-related searches are made using Google per year. Globally there are 100s of millions of health-related searches every day. And, of course, people are doing these searches in real-time, looking for answers to their concerns in the moment. It’s also possible that, even if people aren’t noticing and searching about changes to their health, their behaviour is changing. Maybe they are searching more at night because they are having difficulty sleeping or maybe they are spending more (or less) time online. Maybe an individual’s search history could actually be really useful for researchers. This realisation has led medical researchers to start to explore whether individuals’ online search activity could help provide those subtle, almost unnoticeable signals that point to the beginning of a serious illness.
Our recent review found 23 studies have been published so far that have done exactly this. These studies suggest that online search activity among people later diagnosed with a variety of conditions ranging from pancreatic cancer and stroke to mood disorders, was different to people who did not have one of these conditions.
One of these studies was published by researchers at Imperial College London, who used online search activity to identify signals of women with gynaecological malignancies. They found that women with malignant (e.g. ovarian cancer) and benign conditions had different search patterns, up to two months prior to a GP referral.
Pause for a moment, and think about what this could mean. Ovarian cancer is one of the most devastating cancers women get. It’s desperately hard to detect early – and yet there are signals of this cancer visible in women’s internet searches months before diagnosis?…(More)”.
Relational ethics in health care automation
Paper by Frances Shaw and Anthony McCosker: “Despite the transformative potential of automation and clinical decision support technology in health care, there is growing urgency for more nuanced approaches to ethics. Relational ethics is an approach that can guide the responsible use of a range of automated decision-making systems including the use of generative artificial intelligence and large language models as they affect health care relationships.
There is an urgent need for sector-wide training and scrutiny regarding the effects of automation using relational ethics touchstones, such as patient-centred health care, informed consent, patient autonomy, shared decision-making, empathy and the politics of care.
The purpose of this review is to offer a provocation for health care practitioners, managers and policy makers to consider the use automated tools in practice settings and examine how these tools might affect relationships and hence care outcomes…(More)”.
Community consent: neither a ceiling nor a floor
Article by Jasmine McNealy: “The 23andMe breach and the Golden State Killer case are two of the more “flashy” cases, but questions of consent, especially the consent of all of those affected by biodata collection and analysis in more mundane or routine health and medical research projects, are just as important. The communities of people affected have expectations about their privacy and the possible impacts of inferences that could be made about them in data processing systems. Researchers must, then, acquire community consent when attempting to work with networked biodata.
Several benefits of community consent exist, especially for marginalized and vulnerable populations. These benefits include:
- Ensuring that information about the research project spreads throughout the community,
- Removing potential barriers that might be created by resistance from community members,
- Alleviating the possible concerns of individuals about the perspectives of community leaders, and
- Allowing the recruitment of participants using methods most salient to the community.
But community consent does not replace individual consent and limits exist for both community and individual consent. Therefore, within the context of a biorepository, understanding whether community consent might be a ceiling or a floor requires examining governance and autonomy…(More)”.
Precision public health in the era of genomics and big data
Paper by Megan C. Roberts et al: “Precision public health (PPH) considers the interplay between genetics, lifestyle and the environment to improve disease prevention, diagnosis and treatment on a population level—thereby delivering the right interventions to the right populations at the right time. In this Review, we explore the concept of PPH as the next generation of public health. We discuss the historical context of using individual-level data in public health interventions and examine recent advancements in how data from human and pathogen genomics and social, behavioral and environmental research, as well as artificial intelligence, have transformed public health. Real-world examples of PPH are discussed, emphasizing how these approaches are becoming a mainstay in public health, as well as outstanding challenges in their development, implementation and sustainability. Data sciences, ethical, legal and social implications research, capacity building, equity research and implementation science will have a crucial role in realizing the potential for ‘precision’ to enhance traditional public health approaches…(More)”.
AI: a transformative force in maternal healthcare
Article by Afifa Waheed: “Artificial intelligence (AI) and robotics have enormous potential in healthcare and are quickly shifting the landscape – emerging as a transformative force. They offer a new dimension to the way healthcare professionals approach disease diagnosis, treatment and monitoring. AI is being used in healthcare to help diagnose patients, for drug discovery and development, to improve physician-patient communication, to transcribe voluminous medical documents, and to analyse genomics and genetics. Labs are conducting research work faster than ever before, work that otherwise would have taken decades without the assistance of AI. AI-driven research in life sciences has included applications looking to address broad-based areas, such as diabetes, cancer, chronic kidney disease and maternal health.
In addition to increasing the knowledge of access to postnatal and neonatal care, AI can predict the risk of adverse events in antenatal and postnatal women and their neonatal care. It can be trained to identify those at risk of adverse events, by using patients’ health information such as nutrition status, age, existing health conditions and lifestyle factors.
AI can further be used to improve access to women located in rural areas with a lack of trained professionals – AI-enabled ultrasound can assist front-line workers with image interpretation for a comprehensive set of obstetrics measurements, increasing quality access to early foetal ultrasound scans. The use of AI assistants and chatbots can also improve pregnant mothers’ experience by helping them find available physicians, schedule appointments and even answer some patient questions…
Many healthcare professionals I have spoken to emphasised that pre-existing conditions such as high blood pressure that leads to preeclampsia, iron deficiency, cardiovascular disease, age-related issues for those over 35, various other existing health conditions, and failure in the progress of labour that might lead to Caesarean (C-section), could all cause maternal deaths. Training AI models to detect these diseases early on and accurately for women could prove to be beneficial. AI algorithms can leverage advanced algorithms, machine learning (ML) techniques, and predictive models to enhance decision-making, optimise healthcare delivery, and ultimately improve patient outcomes in foeto-maternal health…(More)”.
Exploring Digital Biomarkers for Depression Using Mobile Technology
Paper by Yuezhou Zhang et al: “With the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms.
We found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams).
Leveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues…(More)”
Are We Ready for the Next Pandemic? Navigating the First and Last Mile Challenges in Data Utilization
Blog by Stefaan Verhulst, Daniela Paolotti, Ciro Cattuto and Alessandro Vespignani:
“Public health officials from around the world are gathering this week in Geneva for a weeklong meeting of the 77th World Health Assembly. A key question they are examining is: Are we ready for the next pandemic? As we have written elsewhere, regarding access to and re-use of data, particularly non-traditional data, for pandemic preparedness and response: we are not. Below, we list ten recommendations to advance access to and reuse of non-traditional data for pandemics, drawing on input from a high-level workshop, held in Brussels, within the context of the ESCAPE program…(More)”
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How this mental health care app is using generative AI to improve its chatbot
Interview by Daniela Dib: “Andrea Campos struggled with depression for years before founding Yana, a mental health care app, in 2017. The app’s chatbot provides users emotional companionship in Spanish. Although she was reluctant at first, Campos began using generative artificial intelligence for the Yana chatbot after ChatGPT launched in 2022. Yana, which recently launched its English-language version, has 15 million users, and is available in Latin America and the U.S.
This interview has been edited for clarity and brevity.
How has your product evolved since you introduced generative AI to it?
At first, we didn’t use generative AI because we believed it was far from ready for mental health support. We designed and guardrailed our chatbot’s responses with decision trees. But when ChatGPT launched and we saw what it could do, it wasn’t a question of whether to use generative AI or not, but how soon — we’d fall behind otherwise. It’s been a challenge because everyone quickly began developing with generative AI, but our advantage was that, having operated our chatbot for a while, we had gathered over 2 billion data points that have been invaluable for our app’s fine-tuning. One thing is clear: It’s crucial to have a model tailored to the specific needs of our product…(More)”.