Aditya Deshpande

I am grateful to the Forbes Tech Council for featuring me in their latest expert panel on “Innovative Data Management.” AI is unlocking vast human potential in health plan operations by streamlining manual tasks, and provider data management serves as a compelling case study for this evolution. The downstream impacts of AI’s utilization in areas like roster automation, mining provider legal contracts and improving directory accuracy are profound and hold significant implications for the entire healthcare industry. As roster ingestion becomes fully automated and directory accuracy improves, we are witnessing firsthand how AI creates space for humans to engage in higher-order thinking. Generative AI is only going to accelerate this trend, and I am proud that HiLabs is part of that innovation

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The future of healthtech is poised to revolutionize the global healthcare landscape, transforming how we diagnose, treat, and manage diseases. With advancements in artificial intelligence, machine learning, and data analytics, healthtech is driving personalized medicine to new heights. AI algorithms can now analyze vast datasets to identify patterns and predict patient outcomes with unprecedented accuracy, enabling more tailored and effective treatments. Telemedicine and remote patient monitoring are reshaping patient-doctor interactions, making healthcare more accessible, especially in underserved regions. The integration of wearable devices and IoT in healthcare allows for continuous monitoring of vital signs, providing real-time data that can preemptively identify health issues and facilitate timely interventions. Genomics and biotechnology are also at the forefront of healthtech innovations. CRISPR and other gene-editing technologies promise to eradicate genetic disorders and pave the way for advanced therapies. Personalized medicine, driven by genetic profiling, ensures that treatments are specifically designed for an individual's genetic makeup, enhancing efficacy and reducing adverse effects. Blockchain technology is revolutionizing healthcare data management by ensuring secure, immutable, and transparent patient records. This enhances data integrity and fosters patient trust. Additionally, augmented reality (AR) and virtual reality (VR) are being leveraged for medical training and patient treatment, providing immersive and effective educational tools and therapeutic interventions. However, the rapid evolution of healthtech presents several challenges. Data privacy and security are paramount, given the sensitive nature of health information. Regulatory frameworks must evolve to keep pace with technological advancements, ensuring patient safety without stifling innovation. Interoperability of systems remains a significant hurdle, as seamless integration across different healthcare platforms is crucial for effective data sharing and patient care. The future of healthtech holds immense potential for improving global health outcomes, making healthcare more efficient, accessible, and personalized. The continuous collaboration between technology developers, healthcare providers, and regulators will be essential in navigating these challenges and realizing the full potential of healthtech innovations. As we move forward, a critical question remains: How can we best balance the rapid advancement of healthtech with the need for robust data privacy and security measures to protect patient information? #HealthTech #DigitalHealth #FutureOfHealthcare

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UnitedHealth testing 500 AI use cases UnitedHealth Group is running around 500 use-case applications for AI across the organization, CEO Andrew Witty told investors. Speaking at a conference on May 29, Mr. Witty said some of the company's AI efforts are "low-hanging" opportunities aimed at speeding up administrative processes. "We have a very strict internal and external government board around all of what we do in AI," Mr. Witty said. "We're focused on making sure there is a human in the loop anytime you get close to what you would characterize as clinical." The Major Question for all-"Does anybody trust anything Optum or UHC ever says?" Nothing gets in the way of profits and payouts to its C suite or shareholders. It disposes of employees when it gets in the way of profits. #UHC, #unitedhealthcare, #profitsfirst, #AIreducingcosts, #layoffs https://lnkd.in/etMcDqE9

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💡📈 Building a Strong Business Case for GenAI Investment Few Healthcare and Life Sciences organizations have begun adopting GenAI across their operations, and are already seeing tangible benefits (https://shorturl.at/fgrPZ). Initial reported use cases are focused on efficiently extracting and searching for relevant information within notes, reports, and other documents, and creating personal employee productivity tasks (https://shorturl.at/beqR1). Adoption of GenAI is particularly high in the areas of operations, research, and administrative tasks. 🙄 While GenAI adoption is surging, many use cases continue to mimic the user experience of ChatGPT. It's almost as if the industry is being constrained by the first mover, rather than exploring the full potential of these powerful AI models. Still, most firms continue to struggle to convince stakeholders to invest in GenAI. As leaders, you need to be able to showcase its potential impact on your organization and balance implementation costs against the cost of maintaining the status quo. Here are key points to consider: 1. 💰 Cost Comparison: Analyze the upfront and ongoing costs of implementing GenAI versus maintaining current processes, highlighting potential long-term savings and benefits. 2. 📊 Return on Investment (ROI): Calculate the overall financial return from implementing GenAI, considering factors like cost savings, productivity gains, and revenue increases. Use this data to justify the investment. 3. 🏆 Value-Driven Metrics: Focus on metrics that demonstrate GenAI's impact on business value, such as increased customer satisfaction, improved decision-making, and enhanced product/service quality. 4. 📲 Scalability and Flexibility: Evaluate GenAI's ability to scale alongside your business and adapt to changing needs, enabling future growth and cost savings through automation and efficiency gains. 5. 🛡 Risk Mitigation: Assess potential risks and challenges associated with GenAI implementation, such as integration issues or data privacy concerns. Develop strategies to mitigate these risks and ensure a successful rollout. Five-Step Framework for Building a GenAI Business Case: 🕵‍♀️ Identify Opportunities: Pinpoint areas that can benefit from GenAI, focusing on tasks or processes that are time-consuming, repetitive, or require creative input. 📊 Estimate Impact: Quantify the potential benefits of GenAI, using key metrics to support your estimates. 🚧 Assess Risks and Challenges: Identify potential risks and challenges and develop strategies to mitigate them. 🗺 Develop a Roadmap: Create a detailed implementation roadmap that outlines the steps required to integrate GenAI into your organization. 🔍 Monitor and Evaluate: Measure the success of your GenAI implementation using key metrics and success indicators, adjusting your strategy as needed. 💬 Share your experiences on building a business case for GenAI! 📢 Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #aws

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The same way that vertical SaaS companies shipped POS (point of sale) devices to SMBs in order to drive payments revenue, healthcare infrastructure platforms will distribute AI wearables to independent practices in order to drive more clinical services. This is Telehealth 3.0 which no longer involves just a computer or a phone screen. Now with AI-powered technology, care delivery infrastructure becomes a true companion that moves beyond the old monolithic EHR environment and now into portable devices that handle everything clinical and operations for physicians and clinicians providing care delivery services. It will initially kick off with fractional micro practices who build or hire an AI companion (custom AI agents) that are powered by wearables and other devices, but quickly expand to larger group practices and enterprise organizations. There are millions of physicians and clinicians currently practicing in the United States. What do you think happens when we give each one of them an AI companion? ----- Hi, I'm Dr. Mario Amaro, Founder and CEO Ease, and I talk about all things starting, running, and growing a private practice with AI. Follow me, if you are planning to start one or are currently figuring out creative ways to grow one. #PrivatePractice #SelfEmployment #HealthcareOnLinkedin #HeyEase #IndependentTogether #Fintech #Infrastructure #AIinHealthcare #CareDelivery

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At LateralCare, we envision a future where healthcare organizations can seamlessly integrate advanced technologies and human expertise to optimize productivity and accuracy. alai, our flagship solution, is designed to revolutionize RCM through the power of generative AI. alai is not just another software; it is a comprehensive, easy-to-adopt solution that breaks down silos and unifies systems and processes. By leveraging the latest AI technology, alai ensures that healthcare organizations can streamline their revenue cycles, reduce inefficiencies, and ultimately, recognize revenue more effectively and promptly. Are you ready to transform your Revenue Cycle Management and embrace the future of healthcare? Visit www.lateralcare.com to learn more about alai or set up a call with our team today. #alaibyLateralCare #RevenueCycleManagement #Healthcare #GenAi #Revenueintegrity #Revenuerecognition #ContractManagement #LateralCare

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I caught up with Nikhil Krishnan of Out-Of-Pocket fame to get his take on the current state of the healthcare technology landscape. Up first: his current thoughts on what's exciting and where there are opportunities for AI-driven solutions. First half of our Q&A below 👇 — 🟩 Q: When it comes to AI, what area are you most excited about? Patient-facing solutions. The AI admin and back-office space has quickly become saturated, and incumbents will always have a distribution advantage here. Which is why I’m actually surprised there aren’t more players entering the patient-facing side. I think the traditional healthcare companies are too nervous to actually go into that patient-facing area. And it's also a regulatory gray area, so if you actually get traction, you can probably help shape how this stuff ultimately gets regulated. So right now, I think the greatest opportunities are for patient-facing AI tools. 🟩 Q: Where specifically do you see whitespace to develop patient-facing solutions? I think there’s still a lot of opportunity on the patient-facing automation side. A great example actually is automated lab results. There has been an issue for the last 3 years that lab results automatically get released to patients, and they see something's abnormal. The patient is trying to figure out what’s going on and what they've read. Part of this is explaining the result, which is something Elaborate is working on, and in some cases, actually, it's really useful for the patient to get that notice immediately with follow up steps, like scheduling a diagnostic. Another area is AI that can autonomously make workflow changes within a local system. Viz.ai is working on this automated workflow piece in the context of situations like stroke care coordination and hypertrophic cardiomyopathy. This requires not only automation in terms of being able to understand the context of multiple datasets, but also make the requisite workflow changes (e.g. escalating to the right specialist). These are particularly useful in time-sensitive areas. 🟩 Q: Any other AI whitespaces? Monitoring of AI models. It's one of the areas that's still unknown in terms of how do you give peace of mind to your buyer? It becomes really hard to actually evaluate and audit a lot of these AI algorithms without some form of data exhaust. I feel like one of the things that will happen is you'll see some form of certifying entity emerge that can give a gold standard to hospitals. 🟩 Q: What else are you thinking about as it pertains to AI? I do think that pricing is one of the more interesting components here. For example, in AI scribing, how you think about pricing changes a lot depending on if you’ve got a human in the loop or not. Because adding a human layer goes from near zero marginal cost to cost scaling linearly. One of the ways that companies will be able to differentiate themselves — especially from incumbents — is the pricing model.

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💡 📖 Demystifying AI Decisions in Healthcare and Life Sciences: The Power of Explainable AI As healthcare and life sciences organizations increasingly adopt AI, ensuring trust and transparency in AI-driven decisions becomes crucial. Explainable AI (XAI) - a powerful approach that enables stakeholders to understand how AI models make predictions and reach conclusions. A recent research paper by Stephanie Baker and Wei Xiang (2023) explores how explainability creates trustworthy and socially responsible artificial intelligence. To learn more, access the article here: https://lnkd.in/e6TE4ADR Key benefits of XAI for healthcare and life sciences organizations include: 1. 🤝 Trust Building: XAI helps build trust in AI systems by providing visibility into the decision-making process, leading to increased confidence among patients, clinicians, and regulators. 2. 😇 Ethical AI Adoption: By ensuring AI decisions are explainable, XAI facilitates ethical AI usage, minimizing unintended consequences and potential biases. 3. ⚖ Regulatory Compliance: XAI enables organizations to demonstrate compliance with evolving regulations and guidelines, such as GDPR and FDA recommendations, which emphasize transparency and interpretability. Implementing XAI requires collaboration between data scientists, clinicians, and regulatory experts to develop interpretable models and user-friendly explanations. As generative AI becomes more mainstream within healthcare and life sciences organizations, business and technology leaders would benefit by analyzing which LLMs (prompts, design patterns, and platforms) assist with XAI, and if the outcomes of their applications aligns with their corporate Responsible AI policy. As always, learn by doing - talk to your technology and strategy partners to work with you on a POC (proof of concept) that aims to prove XAI that aligns with your responsible AI policy. 💬 Share your strategies for integrating XAI and promoting transparency in AI-driven healthcare and life sciences solutions below! #ExplainableAI #XAI #Healthcare #LifeSciences #Trust #Transparency #EthicalAI #GenerativeAI #HealthcareInnovation #pharmaceutical #biopharma #biotech #aws #AmazonBedrock #ChangeManagement #innovation #genai #lifesciences #patientcare #ai #clinicalresearch #clinicaltrial #partnership #aiadoption #coe #ai #customersuccess 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7

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The adoption of blockchain technology has the potential to revolutionize the field of clinical trials, offering a new level of transparency and accountability. By utilizing blockchain-based platforms, we can securely store trial data in unalterable and permanent ledgers, granting stakeholders immediate access to the progress and results of the trials. This increased transparency not only builds trust and credibility, but also simplifies regulatory compliance and promotes effortless data sharing between researchers and regulatory entities. #BlockchainInClinicalTrials #Blockchain #TransparentTrials #MedicalInnovation #HealthTechRevolution #BlockchainTransparency #ClinicalResearch #RegulatoryCompliance #DataSharing #HealthcareInnovation #DigitalHealth

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I came across the concept of reusing health data… it’s of such value. Reusing health data in healthcare enhances personalized care, speeds up diagnoses, and improves coordination among providers, benefiting patients with faster treatment and empowerment through access to their health information. Securely storing and sharing patient data using interoperable systems and standards improves care delivery, outcomes, research, and cost savings in healthcare. Data security measures like encryption, access controls, anonymization, firewalls, audits, and compliance with regulations protect patient information, ensuring confidentiality, integrity, and trust within the healthcare ecosystem.

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It's unbelievable! Every day, I come across another new digital health startup that claims it is integrated with Epic, AthenaHealth, eClinicalWorks, Cerner, and many other EMRs. It took SOAP Health a year to integrate with athenahealth and they are an easy, friendly, and responsive partner. SOAP has achieved the highest level of integration with Athena where discrete data is fed into many discrete data fields, but not all, e.g., family medical history, physicians can directly access our physician portal within Athena, and Athena patient data can securely pre-populate patient fields. Now, SOAP, a member of the AthenaHealth Marketplace, is turning on new Athena customers in one week with one hour of training. Now that's integration! Many digital health companies claim they have an integration plan or have already done so when it's simply not true. Claiming you can do all that with multiple EMRs when you can't do it with any will fool no one. A word to the wise: don't promise what you can't deliver. Do the heavy lifting and then claim the truth. Otherwise, you will blow many opportunities when your actual level of interoperability, e.g., sending a pdf, is discovered. Digital health relationships, like most, are built on trust. As a veteran of digital health and health care in general, harken to my words when I say your would-be partners may trust, but they will also certainly verify. #interoperability #EMR #EHR #digitalhealth #emrintegration #truth

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AI in Healthcare is a hot topic. But what is the reality of it today vs a few years from now? This is what I wanted to dive into with our good friend Ankit Jain. This was a great conversation on AI and the Trough of Disillusionment. Trough of Disillusionment is a phase in the Gartner Hype Cycle that occurs when interest in a new technology wanes after initial hype and expectations are not met. Internet, cell phones, etc all went through this phase of failed ventures until the real use cases started to be come common place. It's hard to remember that the internet went through this. We had a wave in early 2000 and then it all broke when it wasn't being adopted as fast or just wasnt ready for real prime time. I think we are in this phase right now with AI being a hot topic and people can see the potential. Yet, full scale practical changing of my entire world or job isn't there yet (for most things). check it out... https://lnkd.in/gjg6w_m2

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WEEKLY HEALTH TECH UPDATE : QUICK READ ‼️ Stay ahead in the Health tech world, follow for regular updates. Google's Med-Gemini Shines in Medical Diagnostics with 91.1% accuracy. Google and DeepMind's recent leap in AI for healthcare brings a smile with its Med-Gemini model, a true medical marvel boasting a 91.1% accuracy in diagnostics! 🌟 Here's the quick scoop: - Multimodal Mastery: Born from the success of the Gemini series, Med-Gemini thrives on language prowess, long-context reasoning, and multimodal understanding. - Highly Specialized: Custom encoders and multimodal fine-tuning tailored specifically for medical applications mean Med-Gemini is not just smart; it's specialized. It's like having a dedicated AI medical resident on your team! - Benchmark Buster: Outshining its predecessors, Med-Gemini dazzled on 14 medical benchmarks, redefining state-of-the-art with superior performance across 25 varied tasks. - Practical Genius: Beyond benchmarks, it excels in real-world tasks like summarizing medical texts and crafting referral letters, sometimes even outpacing human experts. - Interactive Intelligence: Imagine a system that not only understands multimodal medical data but also engages in dialogues, seeking further info as needed—Med-Gemini does just that! Google's foray into specialized healthcare AI like Med-Gemini continues to set impressive benchmarks, potentially transforming the future interactions between healthcare providers and patients. It's not just about numbers; it's about real-world impact. Cheers to smarter AI making a healthier world! 😄

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The healthcare industry stands on the cusp of a revolution, driven by Artificial Intelligence (#AI). Here are the top trends that are reshaping healthcare : 1️⃣ Predictive Analytics 2️⃣ Personalized Medicine 3️⃣ Telemedicine 4️⃣ Radiology 5️⃣ Drug Development 6️⃣ Virtual Health Assistants 7️⃣ Smart EHRs 8️⃣ Robotic Surgery 9️⃣ Mental Health These trends are not just predictions, they are already unfolding and creating a new era of healthcare that is more efficient, personalized, and accessible. #HealthcareAI #DigitalHealth #Innovation #AIinHealthcare #FutureofHealthcare #AI

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How healthcare organizations solve revenue cycle management challenges quickly and easily with fast, iterative analysis #genai #selfserviceanalytics #caddi #chatwithyourdata, #dataanalytics #dvsum https://zurl.co/s56V

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