Anwar Hussain

Unmet social needs could increase risk of costly surgical complications by 2-3x in some circumstances. Assessing and addressing SDOH factors is thus becoming mandatory under a variety of VBC payment models and episodic bundled payments. RevelAi Health's use of LLM-powered voice AI for specialty care management, social needs screening and closed loop social referrals with integration with Findhelp is delightfully seamless and groundbreaking at the same time - with far reaching applications. Very proud to share this simple demo and contribute to this work pioneered by Christian Pean M.D., M.S. and Hadi Javeed.

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Great insights from David Bell, Ph.D. on the challenges of #AI in #healthcare documentation. The complexity and nuance unique to each healthcare market and the fact healthcare typically has few rote tasks but a lot of paperwork, makes it critical that #AItools be developed by experts in the industry with deep understanding of the most appropriate problems for AI enhanced tools to solve.

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The defining factor isn't the size of the company or startup, but the vision behind it. Take OpenAI, for instance. Until last year, unless you were deeply embedded in the ML world as a computer scientist/researcher, or enthusiast, you might not have heard of OpenAI. Meanwhile, a giant like Google was dominating the scene. However, a few years back, OpenAI was just another small startup. Now, it's rapidly evolving into a major player in big tech. Why and how? It comes down to having a long-term vision, a clear roadmap, patience, and solid execution (i.e., talents). Even in the context of healthcare AI, where (small) startups face many challenges (including the ones mentioned), the principles of vision and strategy apply. While merging with big tech might seem like an option for some, if a startup truly believes in its vision and has a well-defined path to achieve it, it should forge ahead independently. The landscape is evolving, and like OpenAI, today’s small startups with clear, visionary goals could be tomorrow's industry leaders. #starup #founders #openai #google #healthcare

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As the discourse shifts from models to compound AI systems / agents, we need better AI benchmarks to evaluate multi-modal and multi-step task performance, especially in healthcare and life sciences. When we wrote the first paper demonstrating ChatGPT's performance on the USMLE, we chose the US Medical Licensing Exam as a benchmark for accessibility, speed, and ease. This benchmark was never intended to represent AI model performance on real-world clinical tasks. Today, I still see so many research teams and startups using benchmarks (like the USMLE) that are ill-fitted for assessing the true clinical or scientific performance and utility of the models they are developing for real-world contexts. Benchmark development may be seen as a "less sexy" area of research, but it is of paramount importance. Years after the rise of the transformer, we still lack adequate benchmarks for so many single-step tasks in biomedicine. With compound AI systems (i.e., architectures that integrate multiple AI models to perform complex tasks) emerging, we need new benchmarks for agentic behaviors. I'd argue that developing an agent with novel capabilities without at least proposing a companion benchmark (if an industry standard does not yet exist) may hinder the adoption of said agent, especially for high-stakes workflows. Designing more benchmarks that capture/simulate real-world clinical and scientific workflows will help us mitigate the major discrepancies observed between in silico and in vivo performance and better support safe + effective deployment of AI in biomedicine. There are already brilliant people focused here, and we need more. DMs are open if you're researching or working in this area of multi-step/multi-modal benchmarking in healthcare and life sciences! #healthcare #ai #artificialintelligence #generativeai

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AI powered Remote Patient Monitoring As healthcare costs continue to rise, patients are exploring new healthcare delivery models that could offer higher quality of care at lower prices in the comfort of their homes. And Healthcare Providers are increasingly adopting technologies like Remote Patient Monitoring (RPM), to deliver these models. RPM entails using digital devices to monitor a patient's health outside a traditional clinical setting and transmitting the data to cloud for remote access by their Doctors. This enables patients to be provided with continuum-of-care for treatment and recovery in the comfort of their home while being constantly under their Doctor’s supervision. RPM systems collect significant amounts of personal health data 24/7 and in real-time and enables providers to detect and address problems as soon as they arise. Additionally, by applying AI and Machine Learning to this data, Doctors can also receive early warnings for potential problems along with valuable and actionable insights for delivering personalized & data-driven care. This guarantees positive outcomes and also reduces chances of hospital readmissions. RPM also enables delivery of high quality care to rural & underserved communities and to populations most in need of an effective alternative healthcare service. It enables them to provide comprehensive health screens locally and leverage city based Doctors and Specialists to receive high quality last mile care. RPM works closely with the caregiver teams and arms them with this real-time, accurate data on patient health, enabling them to prioritize patients based on their needs. Apps allow patients to report symptoms, receive medication reminders, and conduct a video consultation with their doctor. RPM is already moving to the forefront of the industry, and given current and projected future trends, it clearly has significant upside potential for the coming years. RHEMOS Health pioneered RPM in India. Today, we use a combination of revolutionary new radar sensors with non-intrusive technologies, discrete monitoring devices & wearables for continuous tracking of Heart rate, Respiratory rate, Sleep quality, ECG, BP, SpO2, Temp, and other health & mobility parameters. We can also do 30 Blood Tests, Urine test, Auscultations, Lung function & FHR/UA monitoring using RPM to deliver the highest quality of care remotely. If you are a Hospital, Home care or Elder care company or a Healthcare Technology Platform looking to offer or adopt India's Most Comprehensive RPM, talk to us: https://lnkd.in/dWHqZsJZ +91-9867791312 #healthtech #medicaldevices #digitalhealth #telemedicine #wearabletech #medtech #seniorcare #eldercare #agetech

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AI in healthcare is transforming the landscape—revolutionizing everything from patient care to operational efficiency. The conversations at Becker’s Health IT + Digital Health + RCM Meeting highlight the tangible impact AI has on precision medicine, predictive analytics, and enhancing the patient experience. Being at this conference, it’s clear that we're not just exploring possibilities but actively shaping a more intelligent and responsive healthcare system. It's exciting to witness how AI will soon become integral to every facet of healthcare. #AIinHealthcare #HealthTech #DigitalInnovation #HealthcareTransformation #beckersannualHITRCM #beckershealth

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📢 Great evening at the #DigitalHealthSummit from Swiss InsurTech Hub and McKinsey & Company. Together with leaders and shapers from many top companies, who discussed how Digital will make the difference in transforming #healthcare and funding systems. Key will be to provide more personalized care while pushing hard to bringing costs down at the same time. #Digitalhealth and particularly #AI can contribute a great deal to reach both goals, without compromising. 💡

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One key reason why healthcare technologies like AI, telemedicine, and software haven't structurally lowered costs or broadened access for the entire system after decades of experimentation (excluding pure financial engineering of payer contracts) is the limited reimagining of care delivery itself, with asynchronous telemedicine as a notable exception. In the same way that digitizing physical paper failed to exploit the capabilities of a new computing medium for word processing, hosting physician-patient conversations over video does not transform the actual delivery of care. In telemedicine, many of the inputs (e.g., clinician time) and therefore cost structures of in-person care persist, which is why we often see fee-for-service telemedicine companies struggle to achieve economies of scale (i.e., growing revenues non-linearly while growing costs linearly). Until we deconstruct the components of a medical appointment – from intake and triage, to chart review, history gathering, physical examination, diagnosis, treatment, and follow-up – we will continue to be underwhelmed by the disconnect between the transformative potential of these technologies and real-world patient/system impact. I’m optimistic that the emergence of reliable and performant medical AI agents will urge us to perform this deconstruction and redesign the fundamental building blocks of a “medical appointment.”

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AI's dominance in venture capital is significantly impacting medtech fundraising. With AI/ML investments reaching $77B and medtech investments down 20% from their peak, roughly half of all VC dollars are now chasing AI. This shift has led to fewer life science exits, making it harder for medtech companies to secure funding. It's crucial to assess whether your innovation is indispensable and adjust your strategy accordingly. Source: Spencer Jones PitchBook #fundraising #ai #medtech #VC #MergersandAquisitions

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AI in healthcare isn't just a trend - it's a necessity we're building at Innovaccer. At the Dubai AI & Web3 Festival, I'll be sharing our real-world experiences: the breakthroughs, the setbacks, and the ethical dilemmas we face as the tech industry. The truth is, implementing AI in healthcare is complex. We've seen it dramatically improve patient outcomes, but we've also encountered challenges with data privacy and equitable access. I'm looking forward to honest discussions about these issues. If you're at the festival, let's cut through the hype together. I'm eager to hear about your experiences - good and bad - with AI in your field. What's working? What isn't? What keeps you up at night? No sales pitch, no silver bullets - just real talk about the future of AI in healthcare and beyond. Hope to see you there. #TechinInnovation #Innovaccer #AI #PowerofAI #FutureTech #Healthcare #Healthtech

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Have you read the latest voice/Agentic AI reports from some of the top VCs in the world? Whether you follow Andreessen Horowitz, Lightspeed, or Bessemer Venture Partners you would see that: 1- Voice AI is transforming how businesses and customers interact 2- Healthcare is one of the most promising verticals for disruption 3- The companies that do best at Healthcare are verticalized and focused uniquely on healthcare (Vertical AI) 4- Hyro is a market leader in Healthcare! For additional reading, links in the first comment, or feel free to reach out! Thank you for the mention - Lisa Han Olivia Moore Libbie Frost Michael Droesch Aia Sarycheva Alex Kayyal !! #conversationalai #voiceai #responsibleai

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The future of health tech is shaped by key trends promising to redefine healthcare delivery and patient experiences. One pivotal trend is the increasing integration of artificial intelligence (AI) in clinical settings. AI algorithms enhance diagnostic accuracy, predict patient outcomes, and personalize treatment plans. This technology's ability to analyze vast datasets quickly and accurately can transform how healthcare providers make decisions and manage patient care. Explore how AI is revolutionizing healthcare with enhanced diagnostics and personalized treatment plans. Discover the future of patient care in the era of health tech innovation in our latest article - https://buff.ly/4b9TTAH #healthtech #investing #AIwellness #digitalhealth #ecommerce #investmentstrategies #wellnessindustry #AItechnology Subscribe to the Evolving HealthTech newsletter here: https://buff.ly/44pGPE0

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Cracking the Code: Overcoming Venture Capital Challenges in Healthcare Substack:https://lnkd.in/eeE83ry3 Youtube: https://lnkd.in/e6pKBXZF In this video, we discuss the complex challenges healthcare startups face when dealing with venture capital. From conflicting expectations to ever-changing investment criteria, we dive into the issues entrepreneurs encounter and offer insights for success. #HealthcareStartups #VentureCapitalChallenges #StartupInvestment #Entrepreneurship #HealthcareInnovation #BusinessGrowth #InvestmentCriteria #EntrepreneurAdvice #HealthcareIndustry #StartupSuccess

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10 must-haves for an empathetic and trustworthy healthcare conversational agent Conversational agents (CAs) in healthcare settings can be a scalable way of delivering personalized health services to people, and more offerings like Kore.ai, Wysa, Woebot Health, Balm.ai, and outbound.AI are emerging across different healthcare channels. They have potential to improve access to quality health information and supportive services when designed and implemented thoughtfully. Here are some factors to achieve an optimal healthcare CA:  1. Empathetic understanding: Utilizing NLP/NLU, CAs must accurately interpret patient inputs and provide relevant, compassionate responses.  2. Evidence-based knowledge: Dialogue should align with current best practices from scientific literature and clinical guidelines, mirroring experienced clinicians.  3. Emergency response: Adequate safeguards are needed to address medical/psychological crises by providing hotlines, initial advice, or escalating to a provider when warranted.  4. User-centric design: Have an intuitive interface, accessible to users across all age groups, literacy levels, and degrees of technical ability.  5. Multimodal accessibility: Support multiple languages along with voice, text, and other input/output modes to maximize comprehension across diverse populations.  6. Personalization: Learn from patient data and preferences to tailor the conversational experience.  7. Cultural adaptation: Contextualize information and communication style to local cultural norms for better relatability.  8. Ethics and privacy: Stringent safeguards must protect users' sensitive personal health information in compliance with data laws.  9. Data balance: Collect only the information required to personalize the intervention while respecting users' privacy boundaries. 10. Health integration: Seamlessly collect data from users and connected devices to enable monitoring, feedback, reminders, and active reporting for engaged patient care.

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We may not agree with all, but Vinod Khosla's insights on AI reveal crucial possibilities we face: ➕ Job Displacement: Automation could eliminate up to 80% of current jobs, leading to significant economic challenges. ➕ Healthcare Transformation: AI can revolutionize patient care by enhancing diagnostics and enabling personalized treatments, potentially saving millions of lives. ➖ Widening Inequality: If unchecked, AI might deepen the wealth gap, benefiting a select few while leaving many behind. ➖ Ethical Concerns: Risks include authoritarian surveillance, systemic biases, and the spread of misinformation. ➕ Collaboration is Key: The focus should be on AI augmenting human abilities, not replacing them. ➕ Regulatory Frameworks: There’s an urgent need for ethical guidelines to govern AI development responsibly. Future of AI needs both caution and optimism.

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GPT-4 and OpenAI o1: Transforming Diagnosis and Patient Care in Healthcare AI continues to revolutionize healthcare, with OpenAI’s GPT-4 and the new o1 series leading the charge. Here’s how these cutting-edge language models are shaping the future of medical practice: 1. Enhanced Diagnostic Accuracy With improved capabilities, GPT-4 and o1 assist in making more accurate diagnoses by analyzing complex patient histories, medical jargon, and rare conditions. The o1-preview model excels in reasoning, providing precise diagnoses and transparent explanations for its conclusions. 2. Personalized Treatment Plans By integrating vast amounts of medical literature with patient data, these models help create personalized treatment plans, anticipate potential complications, and recommend tailored interventions based on individual risk factors. 3. Streamlined Clinical Documentation Both models significantly improve clinical coding, accurately mapping patient data to ICD-10 and SNOMED codes. The o1-preview model enhances the extraction of clinical insights, leading to more precise diagnoses and condition identification. 4. Ongoing Medical Education As educational tools, GPT-4 and o1 support continuous medical learning by summarizing complex research, explaining intricate medical concepts, and offering insights into evolving treatment strategies. 5. Data Extraction and Analysis The o1-preview model sets a new standard for extracting and analyzing clinical data, offering more nuanced clinical context than previous iterations, ultimately aiding in better patient care. While these advancements are groundbreaking, it’s essential to remember that AI models are tools to augment, not replace, human expertise. Healthcare professionals must remain critical of AI outputs and understand their limitations. As o1-preview continues to evolve, with features like web browsing and file uploads still in development, responsible integration is key. The future of AI in healthcare is exciting—but how do we ensure it enhances care while preserving the human touch? Join the conversation! How do you see AI impacting your practice? #HealthcareAI #GPT4InHealthcare #MedicalInnovation #AIInMedicine #OpenAIo1

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A common question I get is, "what is healthtech?" There are many applications of healthtech (healthcare technology). But I want to highlight 4 "hot areas" of healthtech I believe are transforming patient care, informed by HealthTech2Care's experience. — 1. AI in Healthcare This is the #1 hot area. A recent Silicon Valley Bank report found that 1 in 4 dollars invested in healthcare are going towards companies leveraging AI. Healthcare organizations are adopting AI in different ways based on what they need most. Here are 3 areas where I see AI being adopted today: (A) Enhance Diagnostics AI quickly analyzes large amounts of medical data (e.g., digital images, PGHD). For example, AI-powered diagnostics detect cancer in tissue samples faster than a trained human eye. (B) Personalize Treatment & Coverage AI considers patient data to create personalized treatment plans. For example, personalized digital coaching for chronic disease management. Studies show that personalized post-acute treatment significantly enhances patient recovery rates and reduces costs. AI also analyzes complex benefits information in health insurance plans, simplifying your coverage options and making healthcare decisions easier. (C) Automate Care Coordination AI automates routine care coordination tasks, allowing healthcare staff to focus more on patient care. For example, AI Nurse chatbots assist patients with scheduling appointments, surgery prep, and managing long-term chronic conditions post-surgery. — 2. Contextualizing Patient Data Raw data is useless without context, especially when patient data is both structured (e.g., demographics, vitals) and unstructured (e.g., dictated notes, discharge summaries). Creating longitudinal views of patient data has long been challenging. However, advancements in interoperability standards like HL7 FHIR FOUNDATION’s Release 5 and Centers for Medicare & Medicaid Services interoperability initiatives, along with modern data solutions, are making an impact. Advanced platforms can now process thousands of incompatible data sources in seconds and run complex queries against billions of patient records in minutes. This helps providers make data-driven decisions that improve outcomes of entire patient populations. These solutions adhere to strict data privacy/security regulations (e.g., HIPAA, HITECH). — 3. Virtual Care/Health Healthcare providers are adopting virtual care at scale. Technologies like Telehealth and AI-driven health interventions are improving access and equity, particularly for at-risk patients and underserved populations. Telehealth includes clinical services provided remotely, such as RPM and CCM. The growth of virtual health is supported by evolving reimbursement policies (e.g., CMS 2024 Physician Fee Schedule) and a focus on generating meaningful data about its benefits. For example, patient-reported outcomes (e.g., controlling BP) can lead to improved Star ratings, resulting in higher reimbursement.

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The Morisky Medication Adherence Scale-8 (MMAS-8) is an ideal barometric measure for assessing medication adherence among patients due to its reliability and consistency over time. Connect with me for more information and if you want to integrate this tool within your own EHR or digital platform for RPM, telehealth or DTx. Your patients deserve to know their adherence score and an opportunity to improve it over time. 1. Validity and Reliability: The MMAS-8 has been extensively validated in various patient populations and healthcare settings, demonstrating strong psychometric properties. Its reliability makes it a dependable tool for consistently measuring medication adherence among patients over time. Healthcare providers can trust the accuracy and consistency of MMAS-8 scores when assessing adherence levels. 2. Comprehensive Assessment: The MMAS-8 assesses multiple dimensions of medication adherence, including forgetfulness, carelessness, and intentional non-adherence. By capturing these various aspects of adherence behavior, the scale provides a comprehensive understanding of patients' medication-taking patterns. This comprehensive assessment enables healthcare providers to identify specific barriers to adherence and tailor interventions accordingly. 3. Easy Administration and Interpretation: The MMAS-8 is very easy to administer and interpret, making it practical for use in clinical practice. The scale consists of eight simple questions that patients can quickly respond to, minimizing burden on both patients and healthcare professionals. Scoring and interpreting MMAS-8 results are straightforward, allowing for efficient assessment of medication adherence during routine clinical visits. 4. Longitudinal Monitoring: Due to its reliability over time, the MMAS-8 can be used for longitudinal monitoring of medication adherence among patients with chronic conditions. By administering the scale at multiple time points, healthcare providers can track changes in adherence behavior, evaluate the effectiveness of adherence interventions, and identify trends or patterns that may require intervention adjustment. This longitudinal approach facilitates proactive management of adherence-related issues and supports continuity of care. 5. Predictive Value: Research has shown that MMAS-8 scores are predictive of various clinical outcomes, including treatment response, disease progression, and healthcare utilization. Patients with high MMAS-8 scores (indicating good adherence) are more likely to achieve therapeutic goals and experience positive health outcomes compared to those with low scores. By using the MMAS-8 as a barometric measure, healthcare providers can identify patients at risk of poor outcomes early on and intervene to improve adherence and prevent complications.

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