Effects of Digital Health Technologies

**Contributing to the Future of Dermatologic Care with AI and Digital Innovations** 🌐 (MAYO Clinic Proceedings: Digital Health) I am thrilled to have contributed to the article "Skin and Digital – The 2024 Narrative," published in Mayo Clinic Proceedings: Digital Health. This piece explores the transformative impact of digital technologies on dermatology, highlighting both opportunities and challenges. **Key Points:** 1. **Global Burden of Skin Diseases**: Over 3 billion individuals are affected by skin diseases, posing significant public health challenges worldwide. Disparities in access to dermatologic care and misinformation are major issues that need addressing. 2. **Role of Digital Technologies**: Innovations like AI, teledermatology, and large language models are bridging access gaps in dermatologic care, particularly in low-income and middle-income countries. Practical applications and case studies demonstrate their impact. 3. **AI and Teledermatology**: AI enhances personalized treatment and predictive analytics, while teledermatology reduces patient travel and improves access to care. These technologies come with risks and require careful implementation. 4. **Combating Misinformation**: Misinformation in skin health is prevalent. Effective digital tools and global collaboration among dermatologists are essential to counteract this and promote accurate information. 5. **Multimodal Large Language Models (MLLMs)**: Tools like ChatGPT-4 are revolutionizing medical education and practice. However, a balance between technology and human expertise is crucial for optimal patient care. 6. **Climate Change and Dermatology**: Digital health technologies help mitigate the environmental impact of healthcare by reducing patient travel and promoting sustainable practices. 7. **Future Directions**: "Radical dermatology" emphasizes proactive, predictive, and patient-centered care. Integrating digital innovations aims to address current issues and anticipate future needs. Read the full article to delve deeper into how digital innovations are shaping the future of dermatologic care: #DigitalHealth #Dermatology #AIinHealthcare #Teledermatology #HealthcareInnovation #Misinformation #ClimateChange #DRGPT Reference:  VOLUME 2, ISSUE 3, P322-330, SEPTEMBER 2024

Digital Health in Health Assessment & Medical Diagnostics Global Startups Landscape 2.5Q 2024 is evolving rapidly, driven by AI-powered innovations across various medical fields. AI-Driven Diagnostics: AI is at the forefront of medical diagnostics. Startups like Aiberry (mental health) and Aidoc (cardiovascular health) are using AI to analyze data in real-time, improving early diagnosis and decision-making. These technologies offer non-invasive, faster, and more accurate assessments than traditional methods. Medical Imaging and Radiology: AI-powered imaging is a key area, with startups like Aidence (lung cancer screening) and Paige (digital pathology) leading the way in enhancing radiological diagnostics. These companies are pushing the boundaries of precision medicine, improving early detection and workflow efficiencies for radiologists and pathologists alike. Portable and Wearable Devices: Portable and wearable diagnostic tools are gaining prominence, exemplified by Butterfly Network, Inc. (handheld ultrasound) and Hyperfine, Inc. (portable MRI). These startups are making high-quality medical imaging more accessible, especially in underserved regions. Predictive and Personalized Medicine: Companies like Cardiosense (cardiovascular health) and Freenome (cancer detection) are leveraging multi-sensor devices and AI to predict disease onset, providing personalized treatment recommendations. This shift toward predictive healthcare is reshaping patient care, enabling more proactive intervention strategies. Voice and Speech Biomarkers: In mental health, companies like Sonde Health, Inc. and Kintsugi are innovating by using voice technology to detect signs of depression and anxiety, proving the versatility of AI in mental health diagnostics and offering real-time mental health assessments. Women’s Health: LEVY Health (endocrine disorders and fertility), Sonio (prenatal ultrasound), and Nevia bio (early disease detection) are advancing women’s health diagnostics, focusing on reproductive and prenatal health through AI-powered decision support platforms. Cross-Specialty Diagnostics: Startups such as Viz.ai and PathAI provide cross-specialty diagnostic tools, focusing on synchronizing care in fields like neurology and pathology. Viz.ai facilitates faster stroke care with its AI-driven platform, whereas PathAI uses AI to enhance diagnostic accuracy in pathology, especially in cancer diagnostics. Global startups in this space are attracting significant investments, with companies like Aidoc raising substantial funds to expand their platforms to more conditions and regions. Achieving CE marking and FDA clearances, as seen with companies like Ultromics, is essential for global expansion and validation. #DigitalHealth #Healthcare #Assessment #Medical #Diagnostics #AIinHealthcare 

Sharing this impressive #RPM study published by Stanford University and Johannes Ferstad. The paper explores ways to improve digital health interventions (DHIs) for managing chronic conditions, focusing on remote patient monitoring (RPM) for youth with #T1D. Key Takeaways: 🔎 Problem Addressed: Existing RPM technologies encounter challenges such as limited effectiveness, a high workload for clinicians, and a lack of interpretability, which hinder their adoption. 💡 Solution Proposed: The authors developed a machine learning-based pipeline to create explainable treatment policies by integrating clinician-informed data into RPM workflows. This allows for targeted, effective interventions. 📊 Results: ✔️ Clinician-informed data representations led to better treatment outcomes than black-box machine learning approaches. ✔️ These representations improved efficiency and interpretability, aligning interventions with clinical best practices. 🌎 Real-World Impact: The study used RPM data to enhance glycemic control in youth with T1D by prioritizing patients who need immediate intervention, emphasizing collaboration between clinicians and machine learning researchers for effective healthcare solutions. 🩺 Broader Implications: The study's methodology can extend to other areas of digital health, enhancing personalized care with transparency and clinical relevance. #digitalhealth #HealthcareResearch #healthcare #healthtech #telehealth

Digital therapeutics can improve symptom control in Parkinson's disease >> 🧠A study using PD Neurotechnology’ telemonitoring device ‘PDMonitor’ demonstrated significant improvements in managing Parkinson's symptoms 🧠The time patients spent in the "Off" state, when medication effects wore off, reduced from 36.2% to 20.3% 🧠Nearly half (44.4%) of patients reported improvement, while 37% experienced stable symptoms which is impressive given the usual progression of the disease 🧠Patient satisfaction with medication effectiveness rose significantly, with "very satisfied" responses increasing from 7.7% to 23.1% 💬It’s yet another interesting example of technology + data being used to optimise medication delivery, personalize treatments and improve the patient experience 👇Link to article and study in comments #digitalhealth #DTx

Digital medicine - or implementing tech in general - will cause ripple effects in our system. Some are helpful, some uncover issues we didn't realize we had that we can fix. Others might be less so. It can increase disparity or show us where we have deeper problems than we realized. Then there are ones that are less clear. This reminds me of an example from a few years ago. When we were building #teletriage in 2016, it didn't register that when you have attending #emergencymedicine physicians putting in initial orders, that means our residents are not. In emergency medicine, the ability to identify critically ill patients, interpret clinical cues, and promptly issue necessary orders is a fundamental skill. With the implementation of tele-triage, we inadvertently restricted the opportunities for residents to practice and learn these skills. (Although to be fair, it did register to our residency director who was concerned about this). This scenario highlights a common tendency – the eagerness to employ technology to address problems without fully grasping or contemplating all the potential consequences. Especially those that are not immediate obvious or because the problem you're solving is acutely more important. Teletriage solved the problem we wanted it to. But it also might have caused some others. Here are some other ways i've seen this happen. As #AI is the next hot topic, this is another place where these discussions are important. There are many people warning of what might happen without thinking through what we are doing. While i'd never advocate for not using #technology, it's worthwhile looking back at some other examples to move forward. #digitalhealth #telemedicine #telehealth #teletriage #medicine #futureofmedicine #futureofhealth #meded #medicaleducation #em #aiforhealth #rpm #vr

🔍 The Epic EHR Wonderland: A Tale of High Hopes and Lingering Dissatisfaction Since the rollout of Epic EHR, funded by the HITECH Act of 2009, healthcare providers have faced mixed experiences. A 2022 study from Finland and Denmark revealed that 32% of users were dissatisfied with Epic, citing poor usability despite improved technical performance. Strikingly, only a small fraction of physicians (4.7%) and nurses (7.3%) found accessing patient information easy, with even fewer agreeing that it improved patient care. Meanwhile, a 2019 US study showed that while modern EHRs enhanced certain process measures, they made no significant impact on patient outcomes. Worse, the emotional toll on healthcare workers is substantial. A 2020 study found that physicians working after hours on EHR tasks were 4.8 to 12.5 times more likely to experience emotional exhaustion. As we continue to navigate the balance between technology and care, it’s critical to re-examine whether systems like Epic are truly serving healthcare providers and patients. #HealthcareIT #EpicEHR #PhysicianBurnout #HealthcareInnovation #DigitalHealth

How Do We Evaluate Digital Tools and AI? That’s an area where we need much more discussion. I’ve been a critic of standards that require nearly 100% accuracy compared to usual care. That’s because usual care is not 100% accurate. Lately though, I’ve been wondering if we are asking the wrong questions when it comes to health tech. Should we be comparing AI and other tech tools to doctors — or to no care at all? Here’s what we should consider: With provider shortages, many people are left waiting weeks, if not months, for appointments — or, worse, they receive no care at all. While chatbots have limitations and aren’t replacements for a doctor’s nuanced expertise, they can offer valuable support when traditional care is out of reach. Imagine someone struggling with postpartum depression who can’t see a mental health professional soon; a chatbot could provide resources and a listening ear, even helping them understand when to seek urgent care. For a patient managing diabetes, a chatbot could assist with insulin dose calculations when their provider isn’t available. Or consider someone dealing with anxiety who can access techniques to help manage symptoms in real time, providing relief that may otherwise have been delayed. Of course, it depends on the condition and the situation. But let’s not dismiss it outright. By reframing our expectations, we can see digital tools that fill critical gaps, especially in underserved areas. They might help with triage, answer basic health questions, and even provide peace of mind in moments when real-life providers aren’t available. As we continue to improve these technologies, the goal shouldn’t be perfection but rather how well they serve when options are limited. What do you think — should our measure of success be based on their performance against doctors or against the reality of going without care altogether? #HealthcareTech #AI #DigitalHealth #AccessToCare

💻 Can Digital Health Democratize Healthcare? 🌍 Digital health technologies have been hailed as the future of patient empowerment, offering tools to improve access, autonomy, and health outcomes. But are they living up to the promise? Recent research highlights critical challenges: ❌ Algorithmic Bias: Skewed datasets and AI often exclude marginalized populations. ❌ Health Inequity: Disparities persist in diagnostic accuracy and outcomes for women and non-white populations. ❌ Responsibility Shift: Patients are burdened with self-management while systemic inequities remain unaddressed. The road to true democratization must prioritize health justice, ensuring inclusivity, fairness, and equity in digital healthcare innovation. We need to move beyond access and focus on systemic transformation.💡 #DigitalHealth #HealthcareInnovation #HealthEquity #AIinHealthcare

A searing analysis published today by Peterson Health Technology Institute (PHTI) that I expect will create ripples across the industry finds that "digital diabetes management solutions ... do not deliver meaningful clinical benefits, and they increase healthcare spending relative to usual care." Tools assessed for the analysis include those from companies such as Omada Health, Vida Health, Glooko, Onduo by Verily and Teladoc's Livongo.

Medicare patients of colour are 33% more likely to get readmitted after surgery than their white counterparts. This number shows a huge disparity in the quality of treatment both these ethnicities get. And with AI, this polarization will only get deeper and wider. Here’s why: ➤ 1. Unequal access to technology People with low incomes will face issues while getting access to technological devices that more affluent people can easily get. This will create fewer options to get advanced medical care for the less fortunate. ➤ 2. Poor internet infrastructure in rural areas 60% of rural counties experience high rates of chronic illness. However, due to inadequate broadband connection, there will be fewer data points to feed AI algorithms. So, these people will stay underrepresented. ➤ 3. Lack of digital literacy An NCBI report shows only 51.8% of health professionals possess the necessary digital literacy level to use technology. This means nearly 50% of patients (who will consult these professionals) won’t get access to advanced treatments. If we want to make healthcare universal, the time to address these issues and find their solutions is NOW. What measures according to you can we take to reduce these inequities? #healthcare #healthtech #equality