Epidemiology Data Collection Methods

5 key developments this month in Wearable Devices supporting Digital Health ranging from current innovations to exciting future breakthroughs. And I made it all the way through without mentioning AI… until now. Oops! >> 🔘Movano Health has received FDA 510(k) clearance for its EvieMED Ring, a wearable that tracks metrics like blood oxygen, heart rate, mood, sleep, and activity. This approval enables the company to expand into remote patient monitoring, clinical trials, and post-trial management, with upcoming collaborations including a pilot study with a major payor and a clinical trial at MIT 🔘ŌURA has launched Symptom Radar, a new feature for its smart rings that analyzes heart rate, temperature, and breathing patterns to detect early signs of respiratory illness before symptoms fully develop. While it doesn’t diagnose specific conditions, it provides an “illness warning light” so users can prioritize rest and potentially recover more quickly 🔘A temporary scalp tattoo made from conductive polymers can measure brain activity without bulky electrodes or gels simplifying EEG recordings and reducing patient discomfort. Printed directly onto the head, it currently works well on bald or buzz-cut scalps, and future modifications, like specialized nozzles or robotic 'fingers', may enable use with longer hair 🔘Researchers have developed a wearable ultrasound patch that continuously and non-invasively monitors blood pressure, showing accuracy comparable to clinical devices in tests. The soft skin patch sensor could offer a simpler, more reliable alternative to traditional cuffs and invasive arterial lines, with future plans for large-scale trials and wireless, battery-powered versions 🔘According to researchers, a new generation of wearable sensors will continuously track biochemical markers such as hydration levels, electrolytes, inflammatory signals, and even viruses, from bodily fluids like sweat, saliva, tears, and breath. By providing minimally invasive data and alerting users to subtle health changes before they become critical, these devices could accelerate diagnosis, improve patient monitoring, and reduce discomfort (see image) 👇Links to related articles in comments #DigitalHealth #Wearables

As the #healthcare industry continues to explore the transformative potential of large language models (#LLMs), one area that remains critical yet underleveraged is the role of #standardized #ontologies such as SNOMED International, LOINC, and #RxNorm. While #LLM excel at parsing unstructured clinical narratives, they often generate outputs with high variability—making #interoperability and reproducibility a challenge. That’s where standardized medical ontologies come in. By applying these coding systems as a normalization layer on top of LLM-generated outputs, we can enhance semantic consistency, data reliability, and #EHR integration. These ontologies can help bridge the gap between free text and structured data—unlocking the full potential of LLMs in clinical decision support, population health, and quality reporting. Of course, these ontologies are not without limitations—but their foundational role in standardizing terminology and reducing downstream ambiguity cannot be overstated. SNOMED CT and other standards offer a roadmap toward safer, more interoperable #AI in healthcare. #Healthinformatics #ClinicalInformatics #dataanalytics #Data

3 Healthcare AI papers I'm reviewing today 1. 📚AI in Medicine: Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions https://lnkd.in/eNV5CDSp Medical Multimodal Foundation Models (MMFMs) combine diverse data types (imaging, text, labs) to improve diagnosis, treatment planning, and precision medicine. Recent advances in large datasets and multimodal architectures (vision-only and vision-language) enable strong generalization across tasks like segmentation, classification, and clinical report generation. Key opportunities lie in holistic integration of multi-organ/multimodal data, but challenges remain in optimizing representations and scaling real-world clinical adoption. 2. 📚 BMJ Dig Health & AI - Optimising large language models for clinical information extraction: a benchmarking study in the context of ulcerative colitis research https://lnkd.in/erXhhX9k This study compared open-source and closed-source LLMs for extracting the Mayo Endoscopic Subscore from colonoscopy reports. It found that QLoRA fine-tuning improves open-source performance significantly, but GPT-4o with prompt engineering still outperforms them by 5–11% and is more cost-effective. Overall, GPT-4o is the most efficient option today, while QLoRA-optimized open-source models are viable fallbacks, though both leave room for improvement in instruction following. 3. 📚 JAMIA Open - Generative artificial intelligence for automated data extraction from unstructured medical text https://lnkd.in/ekfy8-VX A GenAI pipeline using an open-source LLM was developed to extract structured data from right heart catheterization notes with built-in guardrails and a retry mechanism. It achieved high performance (99% precision, 85% recall, 91.5% F1, 90% accuracy), with missed values as the main error and hallucinations extremely rare (<0.01%). The study shows LLM pipelines can reliably mine unstructured clinical data, improving research efficiency and clinical applications.

Wearable bioelectronics can already measure the exposure to sun, basic vital signs, sweat and hydration, but here is another step towards progress: monitoring the flux of vapors through the skin! "𝑇ℎ𝑒 𝑐𝑜𝑚𝑝𝑎𝑐𝑡, 𝑤𝑖𝑟𝑒𝑙𝑒𝑠𝑠 𝑑𝑒𝑣𝑖𝑐𝑒 𝑖𝑠 𝑡ℎ𝑒 𝑓𝑖𝑟𝑠𝑡 𝑤𝑒𝑎𝑟𝑎𝑏𝑙𝑒 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑎𝑏𝑙𝑒 𝑡𝑜 𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑜𝑢𝑠𝑙𝑦 𝑎𝑛𝑑 𝑝𝑟𝑒𝑐𝑖𝑠𝑒𝑙𝑦 𝑚𝑒𝑎𝑠𝑢𝑟𝑒 𝑤𝑎𝑡𝑒𝑟 𝑣𝑎𝑝𝑜𝑟, 𝑣𝑜𝑙𝑎𝑡𝑖𝑙𝑒 𝑜𝑟𝑔𝑎𝑛𝑖𝑐 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑𝑠, 𝑎𝑛𝑑 𝑐𝑎𝑟𝑏𝑜𝑛 𝑑𝑖𝑜𝑥𝑖𝑑𝑒 𝑓𝑙𝑢𝑥𝑒𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑠𝑘𝑖𝑛 𝑖𝑛 𝑟𝑒𝑎𝑙 𝑡𝑖𝑚𝑒.  𝐵𝑒𝑐𝑎𝑢𝑠𝑒 𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑠 𝑖𝑛 𝑡ℎ𝑒𝑠𝑒 𝑓𝑎𝑐𝑡𝑜𝑟𝑠 𝑎𝑟𝑒 𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑒𝑑 𝑤𝑖𝑡ℎ 𝑖𝑛𝑓𝑒𝑐𝑡𝑖𝑜𝑛 𝑎𝑛𝑑 𝑑𝑒𝑙𝑎𝑦𝑒𝑑 ℎ𝑒𝑎𝑙𝑖𝑛𝑔, 𝐹𝑙𝑎𝑣𝑖𝑛 𝑛𝑜𝑡𝑒𝑠 𝑡ℎ𝑎𝑡 𝑡ℎ𝑖𝑠 𝑘𝑖𝑛𝑑 𝑜𝑓 𝑤𝑖𝑟𝑒𝑙𝑒𝑠𝑠 𝑚𝑜𝑛𝑖𝑡𝑜𝑟𝑖𝑛𝑔 “𝑐𝑜𝑢𝑙𝑑 𝑔𝑖𝑣𝑒 𝑐𝑙𝑖𝑛𝑖𝑐𝑖𝑎𝑛𝑠 𝑎 𝑛𝑒𝑤 𝑡𝑜𝑜𝑙 𝑡𝑜 𝑢𝑛𝑑𝑒𝑟𝑠𝑡𝑎𝑛𝑑 𝑡ℎ𝑒 𝑝𝑟𝑜𝑝𝑒𝑟𝑡𝑖𝑒𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑠𝑘𝑖𝑛.” It could mean a new way of monitoring health through a device worn on the skin. Maybe this would finally allow us to study the long-term effects of exposure to environmental hazards like wildfires or chemical fumes on skin function and overall health. Source: https://lnkd.in/e3zCpaH5

'Advances in biomonitoring technologies for women’s health' article, published in Nature Magazine, review addresses the long-standing bias in biomedical research and healthcare toward male populations, which has resulted in women (and transgender individuals) being underrepresented in studies, diagnostic norms, and device design. The review explores applications of wearables and biosensors across multiple domains of women’s health, including fertility, pregnancy and maternal health, hormonal monitoring, vaginal infections, gynecologic and breast cancers, and osteoporosis. 📌 For example, devices that track basal body temperature, sweat biomarkers, or hormonal shifts can help with ovulation tracking and fertility. 📌 In pregnancy, smart textiles, abdominal sensors, and wearable ECG/uterine contraction monitors are being developed to continuously monitor maternal and fetal biomarkers. 📌 On the diagnostic side, innovations in point-of-care assays and microfluidic devices are being adapted to detect vaginal pathogens (e.g. via pH, enzymatic markers, or nucleic acid amplification) and early signals of gynecologic cancers (liquid biopsy, micro-exosome capture, multifunctional immunosensors). The authors argue that this gap contributes to delays in diagnosis, suboptimal treatments, and systemic inequities in women’s health. They survey emerging technologies—especially wearable sensors, point-of-care diagnostics, and AI/ML tools—that can help close that gap by enabling continuous, non-invasive biomonitoring tailored to female physiology. However, the authors underscore significant barriers and challenges to adoption. Many of the devices are still in prototype or small-scale testing stages and lack validation in diverse, large populations, especially in low-resource settings. Usability, user compliance, comfort, data interpretation, cost, and integration with clinical workflows are major hurdles. In addition, socioeconomic and digital divides—such as access to internet, smartphones, and health literacy—can limit uptake among marginalized groups. The review also discusses how AI and machine learning could amplify the impact of biomonitoring by improving predictive accuracy and pattern recognition, though models must be trained on more balanced, representative datasets to avoid reinforcing bias. Find out more via link 🔗 https://lnkd.in/d-xh9R6m #femtech #womenshealth #innovation #biomonitoring #biomarkers

Last week Biolinq raised US$100 million to push its intradermal glucose patch through final regulatory gates. The sensor sits just beneath the skin, flashes a gentle light when your blood glucose drifts off-course. Additionally it may layer in sleep and activity data on the same device. While I cannot speak to the effectiveness of the device, this is an early glimpse of a biowearable future where we get to choose to wear single devices in multiple points throughout our body. And if you think continuous metabolic tracking is only for people currently living with diabetes, here’s the bigger picture: While a continuous glucose monitor (CGM) isn't necessary for everyone, the effects of glucose dysregulation can start well before it shows up on your annual health screening. So it's important to work with a wearable-informed doctor to assess whether you can benefit from wearing a CGM. Here are some takeaways for CGMs: 1. If you try a CGM, focus on patterns—not single spikes. 2. Capture lifestyle context: track your meals (including alcohol!), sleep, workouts, stress etc. 3. Review the trends with that wearable-informed doctor. I mean it. Not just any doctor. Someone who understands what do with the data and how to adjust your lifestyle accordingly. Wearables, in general, will be crucial for our preventative health-focused future. The questions are which ones and how do we use them to our advantage at different stages of our lives. Photo: Biolinq

🚀 𝐖𝐞𝐚𝐫𝐚𝐛𝐥𝐞𝐬 + 𝐀𝐈: 𝐓𝐡𝐞 𝐆𝐚𝐦𝐞 𝐂𝐡𝐚𝐧𝐠𝐞𝐫 𝐟𝐨𝐫 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞! 💡 The healthcare industry is at a crossroads. With rising costs, an aging population, and an increasing demand for patient empowerment, the traditional healthcare model is being pushed to its limits. One of the most transformative innovations? 𝐓𝐡𝐞 𝐟𝐮𝐬𝐢𝐨𝐧 𝐨𝐟 𝐖𝐞𝐚𝐫𝐚𝐛𝐥𝐞 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐚𝐧𝐝 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞. 🔹 60% of health outcomes are linked to lifestyle choices. 🔹 The majority of patient data (91%) remains unstructured and underutilized. 🔹 AI has the power to transform raw data into 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 - leading to proactive, 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞. 🏥 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐈𝐦𝐩𝐚𝐜𝐭: ✔️ Patients discharged from UK hospitals now wear AI-powered bracelets that monitor vitals 24/7. ✔️ Hospital readmissions are dropping, ER visits are reduced, and long-term treatment adherence has skyrocketed to 96% (vs. the industry average of 50%). ✔️ AI + wearables mean 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐛𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐞𝐯𝐞𝐧 𝐤𝐧𝐨𝐰 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐢𝐭! The future of healthcare isn’t just about new technology - it’s about a 𝐜𝐮𝐥𝐭𝐮𝐫𝐚𝐥 𝐬𝐡𝐢𝐟𝐭. A shift toward 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬, 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝, 𝐚𝐧𝐝 𝐝𝐞𝐦𝐨𝐜𝐫𝐚𝐭𝐢𝐳𝐞𝐝 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞. 💡 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫? For too long, healthcare has relied on a 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 - treating patients after they show symptoms. With AI-driven wearables, we can shift toward preventive and predictive care, reducing hospital burden and improving quality of life. We now have an incredible opportunity 𝐞𝐦𝐩𝐨𝐰𝐞𝐫 𝐩𝐚𝐭𝐢𝐞𝐧𝐭𝐬, 𝐫𝐞𝐝𝐮𝐜𝐞 𝐜𝐨𝐬𝐭𝐬, 𝐚𝐧𝐝 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬. Let’s embrace the revolution. What are your thoughts? Are wearables + AI the breakthrough we need? Drop your insights below! ⬇️ #DigitalHealth #Wearables #AI #HealthcareInnovation #FutureOfHealthcare

Academic research moves slowly—until it doesn't. At Northwestern, I faced a data nightmare: 15 separate longitudinal studies, 49,000+ individuals, different measurement instruments, inconsistent variable naming, and multiple institutions all trying to answer the same research questions about personality and health. Most teams would analyze their own data and call it done. That approach takes years and produces scattered, hard-to-compare findings. Instead, I built reproducible pipelines that harmonized all 15 datasets into unified workflows. The result? 400% improvement in research output. Here's what made the difference: ➡️ Version control from day one (Git for code, not just "analysis_final_v3_ACTUAL_final.R") ➡️ Modular code architecture—each analysis step as a function, tested independently ➡️ Automated data validation checks to catch inconsistencies early ➡️ Clear documentation that teams could actually follow ➡️ Standardized output formats so results could be systematically compared The lesson: I treated research operations like product development. When you build for scale and reproducibility instead of one-off analyses, you don't just move faster—you move better. This approach enabled our team to publish coordinated findings on how personality traits predict chronic disease risk across diverse populations. The methods we developed are now used by multi-institutional research networks. The mindset shift from "getting it done" to "building infrastructure" unlocked value that compounded across every subsequent analysis. Whether you're working with research data, product analytics, or user behavior datasets, the principle holds: invest in the pipeline, and the insights flow faster.

This new paper by Sergio Consoli et al explores how generative AI can transform unstructured outbreak data into structured, searchable knowledge. The team developed an epidemiological knowledge graph (eKG) using WHO Disease Outbreak News (DONs), applying an ensemble of large language models to extract details such as disease name, country, date, and number of cases or deaths. The researchers used open-source models including Mistral, Zephyr, and Meta-Llama to extract information from over 3,000 outbreak reports. They structured this data into a FAIR-compliant knowledge graph, linking it with biomedical and geographic ontologies. The resulting resource—comprising nearly 3,000 outbreak events—is now publicly accessible via SPARQL endpoints and visualization tools. This matters because many official outbreak reports remain locked in prose, making them difficult to analyze at scale. With eKG, public health professionals can conduct detailed, structured queries across decades of global outbreak data. This significantly improves our ability to track, analyze, and respond to emerging health threats. The big takeaway: AI can unlock the full value of legacy outbreak data by transforming it into structured, interoperable formats that support real-time analysis and response. This approach opens new possibilities for integrating informal sources like news and social media into formal disease surveillance systems, advancing global preparedness and early warning capabilities. https://lnkd.in/ePc54yvQ #GlobalHealth #PathogenSurveillance #HealthInnovation #PublicHealth

If you're researching human subjects, You're familiar with the sampling dilemma. Your sampling technique changes the future direction of your work. It enhances your methodology and improves your chances of acceptance. There are 5 main types of sampling techniques you can choose from ⤵ →  𝘗𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘚𝘢𝘮𝘱𝘭𝘪𝘯𝘨 Every member of the population has a known, non-zero chance of being selected. This method ensures that the sample is representative of the population, reducing the risk of bias. →  𝘙𝘢𝘯𝘥𝘰𝘮 𝘚𝘢𝘮𝘱𝘭𝘪𝘯𝘨 Random sampling is a type of probability sampling in which each member of the population has an equal chance of being selected. This method is the gold standard for ensuring a representative sample and minimizing sampling bias. →  𝘚𝘵𝘳𝘢𝘵𝘪𝘧𝘪𝘦𝘥 𝘚𝘢𝘮𝘱𝘭𝘪𝘯𝘨 This technique involves dividing the population into distinct subgroups or strata based on specific characteristics, such as age, gender, or income level. A sample is then drawn from each stratum to make sure that the sample reflects the diversity of the population. →  𝘚𝘺𝘴𝘵𝘦𝘮𝘢𝘵𝘪𝘤 𝘚𝘢𝘮𝘱𝘭𝘪𝘯𝘨 Researchers select every 𝘯th member of the population after a random starting point. This method is straightforward and easy to implement, which makes it a popular choice in large-scale surveys. But it assumes that the population is ordered in a way that does not introduce bias. →  𝘕𝘰𝘯-𝘱𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘚𝘢𝘮𝘱𝘭𝘪𝘯𝘨 Unlike probability sampling, not every member of the population has a known chance of being selected. This method incorporates techniques like convenience sampling, where participants are selected based on availability, and purposive sampling, where participants are chosen based on specific criteria. While quicker and easier to implement, non-probability sampling can introduce bias and limit generalizability. P.S. Have you ever received a journal rejection because of your sampling technique? ________________ 🔔 This is Dr. Samira Hosseini. Scholars who took my training published +2,000 articles in top-tier journals. Join my inner circle not to miss even one single bit of learning: https://lnkd.in/eVNSihCM