Reimagining Healthcare Operations with Voice Agents and Agentic AI: Key Takeaways from the June 2025 Webinar

Voice agents and Agentic AI are no longer futuristic technologies—they’re transforming healthcare operations today. In a recent webinar hosted by BigRio and Damo Consulting, industry leaders Ritu M. Uberoy, Rohit Mahajan, and moderator Helen Kotchoubey shed light on how these technologies are redefining patient engagement, clinical workflows, and enterprise operations across healthcare and pharma.

Brief Overview

The session kicked off with an introduction to Agentic AI and Voice AI agents, technologies that use large language models (LLMs) to create intelligent, context-aware AI agents capable of handling complex tasks with minimal human input. The speakers discussed real-world use cases, ranging from appointment scheduling and prescription refills to patient triage and clinical trial recruitment.

The highlight? A live demo of voice agents conducting natural, responsive conversations—including one that scheduled a patient appointment and another that refilled a prescription using patient records.

What participants said:

Great Webinar! I loved the attention to detail, clear explanations, and slides that were explained well. I just received my first AI cert, and this helped me understand more of what I learned.

I really liked how the webinar shared real-time examples of voice AGI in action-like assisting doctors with patient history in hospitals, or helping pharma reps quickly access drug data. (Excellent)

Great presentation, easy to understand, especially for those like me with limited knowledge on these technologies.

I am amazed where the technology is heading. There is so much progress that I would have not known.

I liked the real world use cases and summary of what voice ai agents can do “today” vs where we were 16 months ago.

I appreciated how Ritu started with the fundamentals-breaking down the components and foundations behind how Agentic works. The BIGRIO approach was especially insightful.

Top Q&A Takeaways

Here are the most insightful audience questions and responses from the webinar:

  1. What Makes Agentic Voice AI Different from Traditional IVRs or Siri?Agentic AI voice agents go far beyond rule-based systems or assistants like Siri. Unlike IVRs, they:
  • Understand natural language, intent, and emotion
  • Remember context from earlier in the conversation
  • Perform autonomous tasks, including API calls to EMRs or CRMsThis enables them to manage multi-step interactions—like scheduling an appointment or requesting a prescription refill—without human handholding.
  1. How Do These Voice Agents Handle Accents, Noise, or Unclear SpeechWhile modern speech recognition models have improved dramatically—thanks to training on diverse datasets—agents still face limitations. When speech is indecipherable or noisy, the system automatically escalates to a human agent. This “fallback” mechanism ensures continuity and patient safety.
  1. Is Patient Data Secure?Yes. Security and compliance are built into the architecture:
  • Platforms must be HIPAA and SOC 2 compliant
  • Data remains within geographical boundaries (e.g., US-based data must stay within the US)
  • Business Associate Agreements (BAAs) are standard for healthcare clients
    Governance and cloud security best practices are non-negotiable.
  1. How Long Does It Take to Deploy a Voice Agent PilotSurprisingly fast. BigRio and Damo shared a framework where pilots can be:
  • Designed and launched in 8–12 weeks
  • Supported by pre-built tools, workflows, and templates
  • Using a discovery-phase approach, they identify high-impact use cases and rapidly prototype working agents.
  1. Do Organizations Need to Build In-House or Use SaaSIt depends on digital maturity. Many healthcare providers prefer working with a managed service provider to handle data integration, system interoperability, and ongoing agent training. BigRio provides full-stack support—from defining the use case to deploying enterprise-grade solutions.
  1. What Are the Limitations of Agentic AI TodayDespite rapid advancement, current limitations include:
  • Handling ambiguous or highly emotional situations
  • Real-time memory constraints
  • Challenges with edge cases that don’t follow standard scripts

But thanks to real-time learning loops and adaptive memory, these limitations are narrowing down fast.

Why Voice Agents Matter Now

The demand for 24/7 engagement, operational efficiency, and personalized care is accelerating adoption. Voice agents:

  • Scale easily across languages and geographies
  • Offer empathetic, always-available support
  • Dramatically reduce call center volume
  • Drive measurable ROI—66% conversion rates in real-world pilots

As Helen Kotchoubey aptly put it: “This is not just a UI shift. It’s a shift in capability.”

Ready to Explore Voice Agents?

If you’re evaluating use cases for AI in your organization, consider starting with a focused pilot—such as appointment scheduling, prescription refills, or clinical trial outreach.

Contact BigRio or Damo to learn more about how they can help you design and deploy voice agent solutions at scale.

Stay tuned for future webinars.

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AI Agents Provide Powerful Assistance for Clinical Decision Making in Cancer Treatment

Rohit Mahajan

We have written a lot on these pages about how Agentic AI is transforming healthcare from patient compliance and diagnostic standpoint. New research is now showing the role that AI Agents can play in helping doctor with clinical decisions, in this case specifically regarding treatment plans for cancer patients.

Researchers at the Else Kröner Fresenius Center (EKFZ) for Digital Health at TUD Dresden University of Technology, in collaboration with partners from Germany, the UK, and the USA, have developed and validated an autonomous AI Agent capable of supporting clinical decision-making in oncology. The findings were published in the journal Nature Cancer. In the future, AI agents could assist healthcare professionals in navigating complex medical data and supporting informed, personalized treatment decisions for cancer patients.

Clinical decision-making in oncology is challenging and requires the analysis of various data types – from medical imaging and genetic information to patient records and treatment guidelines. To effectively support medical practice, AI models must be capable of processing multimodal data and have reasoning and problem-solving capabilities that resemble those of humans.

o build an autonomous AI agent for precision medicine, the researchers enhanced the large language model GPT-4 with several digital tools – including radiology report generation from MRI and CT scans, medical image analysis, prediction of genetic alterations directly from histopathology slides, and search functions across platforms such as PubMed, Google and OncoKB. To ensure that decisions were grounded in current medical knowledge, the model was given access to around 6,800 documents compiled from official oncology guidelines and clinical resources.

The autonomous AI agents successfully tested on realistic, simulated patient cases. According to the researchers, the AI agent reached correct clinical conclusions in 91% of cases and accurately cited relevant oncology guidelines in over 75% of its responses. Importantly, the use of specialized tools and medical information retrieval significantly improved the model’s performance. As a result, so-called “hallucinations” – seemingly plausible but incorrect statements – were significantly reduced. This improvement is particularly important in the sensitive area of healthcare.

 

“AI tools are designed to support medical professionals, freeing up valuable time for patient care,” says Dyke Ferber, first author of the publication. “They could help in daily decision-making processes and support doctors to stay updated on the latest treatment recommendations, contributing to the identification of optimal personalized care for cancer patients.”

You can access the full study by clicking on this link.

How BigRio Promotes Innovation in Agent AI 

At BigRio, we share Dr Ferber’s belief in the transformative impact Agentic AI is having on healthcare. Yet, we also recognize the challenges ahead and the need for education and training in the responsible implementation and use of these agents and copilots/ tools. To this end, we have launched a Gen AI Center of Excellence and hold ongoing Gen AI Workshops. 

Our Gen AI Center of Excellence focuses on working with industry leaders and innovators to create custom tools that safely augment human intellect, allowing people to know more, do more, and create more than ever before.

BigRio’s Gen AI Workshops are designed to help healthcare professionals meet the challenges of Gen AI and Agent AI implementation. We provide customized onsite, in-person healthcare-focused learning and ideation workshops to accelerate your Gen AI journey regardless of your current state of readiness. 

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity. BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery and all industries. We have recently been focusing our efforts on supporting startups and developing our own solutions that use GAI and Agent AI to boost productivity and transform core functions and customer service while innovating at top speed.

Agentic AI Will Shift Medicine From “Sick Care” to “Well Care!”

Rohit Mahajan

For decades, even modern healthcare has been for the most part “transactional,” we get sick we see a doctor. AI, and in particular Agentic AI is transforming healthcare into a system that is less transactional and more continuous. In other words, Agentic AI may bring about a fundamental paradigm shift in medicine from “sick care” to “well care.”

Dr Adil Khan, CEO, Tulu Health explains.

“Our [AI] agents don’t just assist—they build trust, guide patients, and enable smoother transitions into actual care. Hospitals now see more patients follow through with appointments, procedures, and treatment plans, often above industry conversion benchmarks. Most importantly, they’re witnessing clear ROI within weeks. It’s not just a tech upgrade; it’s a tangible transformation in how care is initiated and sustained.”

Dr. Khan made those comments in a recent interview with Express Healthcare.

In the same sit down he went on to say, “Agentic AI will shift healthcare from being transactional to continuous. Today, you seek care only when you’re sick. In the near future, intelligent agents will proactively remind you to take medication, interpret diagnostics, flag warning signs, and follow up after discharge—all autonomously.

He said he sees this being particularly true in a country like India, where doctor-patient ratios are stretched. “[Here in India], this will be a game-changer. AI agents won’t replace doctors—they’ll extend their capacity. We believe India is uniquely positioned to lead this shift at a population scale, showcasing how AI can enable inclusive, scalable, and always-on care.”

Tulu Health is a healthtech and fintech startup founded by experts from AIIMS, IIT, and Stanford University. In April of 2025 it launched an AI Agent Platform that provides modular AI agents tailored for both healthcare providers and patients, delivered through widely used platforms like WhatsApp and hospital websites.

How BigRio Promotes Innovation in Agent AI

At BigRio, we share Dr. Khan’s belief in the transformative impact Agentic AI is having on healthcare. Yet, we also recognize the challenges ahead and the need for education and training in the responsible implementation and use of these agents and copilots/ tools. To this end, we have launched a Gen AI Center of Excellence and hold ongoing Gen AI Workshops.

Our Gen AI Center of Excellence focuses on working with industry leaders and innovators to create custom tools that safely augment human intellect, allowing people to know more, do more, and create more than ever before.

BigRio’s Gen AI Workshops are designed to help healthcare professionals meet the challenges of Gen AI and Agent AI implementation. We provide customized onsite, in-person healthcare-focused learning and ideation workshops to accelerate your Gen AI journey regardless of your current state of readiness.

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity. BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery and all industries. We have recently been focusing our efforts on supporting startups and developing our own solutions that use GAI and Agent AI to boost productivity and transform core functions and customer service while innovating at top speed.

Join us for our next informative Webinar: Agentic AI and Voice Agents in Healthcare and Pharma to be held on June 25. Click here to register.

ElevenLabs Introduces Advanced Features for Building Voice Agents

Rohit Mahajan

ElevenLabs a pioneer in “text to speech” technology and Generative AI has announced a significant advancement in conversational AI technology with the introduction of a new multimodal system for AI “voice agents.” According to a company press releases, this cutting-edge development enables AI voice agents to process both voice and text inputs concurrently, enhancing the fluidity and effectiveness of user interactions.

“By enabling agents to process both text and voice, we empower users to choose the input method best suited to the information they need to convey. This hybrid approach allows for smoother, more robust conversations. Users can speak naturally and then, when precision is paramount or typing is more convenient, seamlessly switch to text input within the same interaction,” the release said.

The press release went on to explain the advantages of “multimodal interaction,” which include: 

  • Increased Interaction Accuracy: Users can enter complex information via text, reducing transcription errors.
  • Enhanced User Experience: The flexibility of input methods makes interactions feel more natural and less restrictive.
  • Improved Task Completion Rates: Minimizes errors and user frustration, leading to more successful outcomes.
  • Natural Conversational Flow: Allows for smooth transitions between input types, mirroring human interaction patterns.

Finally, concluding, “We believe that text+voice multimodality will significantly enhance the capabilities and user experience of Conversational AI. We look forward to seeing how our users leverage this powerful new feature.”

How BigRio Promotes Innovation in Agentic AI Development

At BigRio, we share the vision of innovators like ElevenLabs in the transformative power of voice agents. In fact, we now offer Voice Agent Development and Implementation as one of our core services. Like these other trend-setters we recognize how voice technology is reshaping healthcare and other industries by enabling faster, more natural interactions between customers and the organizations they patronize. At BigRio, we develop advanced, AI-powered voice agents that help any type of company boost efficiency, improve accessibility, and deliver more personalized customer experiences—while staying fully compliant with privacy concerns and regulatory standards.

Whether you’re looking to automate scheduling, improve call center responsiveness, or support remote monitoring, BigRio can help you implement a voice strategy that drives better outcomes.

We also continue to offer online Gen AI Workshops and Webinars that are now focused on Agentic AI and the impact of Voice Agents. Please join us for our next informative Webinar: Agentic AI and Voice Agents in Healthcare and Pharma to be held on June 25. Click here to register.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my first book, Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint. My soon-to-be-released second book will focus on GAI and the impact of Agentic AI on healthcare.

Rohit Mahajan is a Managing Partner with BigRio. He has particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

Optimizing Patient Experience: Insights from Kristen Jacobsen

In the rapidly evolving landscape of healthcare, patient experience has emerged as a crucial factor in driving better health outcomes. At a recent session during Becker’s Digital Innovation + Patient Experience + Marketing Virtual Event, Kristen Jacobsen, Vice President of Marketing and Product Management at RevSpring, shared valuable insights on how healthcare organizations can optimize patient engagement. Her discussion highlighted the significance of a patient-centric approach, the role of technology, and strategies for overcoming engagement challenges.

Shifting to a Patient-Centric Healthcare Approach

Traditionally, healthcare has been provider-driven, with decisions largely made by medical professionals. However, Kristen emphasized the shift towards a patient-centered model where individuals are empowered to take an active role in their healthcare journey. This approach prioritizes:

  • Personalization: Tailoring healthcare services to individual patient needs enhances satisfaction and adherence to treatment plans.
  • Accessibility: Ensuring healthcare services are easy to navigate fosters trust and engagement, ultimately improving health outcomes.

Technology as a Catalyst for Patient Engagement

Technology has revolutionized patient-provider interactions, making healthcare more accessible and efficient. Kristen highlighted several key digital tools that enhance engagement:

  • Mobile Health Apps & Patient Portals: These platforms enable seamless communication, appointment scheduling, and access to medical records.
  • Telemedicine: Virtual consultations have removed geographical barriers, providing greater convenience for patients.
  • Artificial Intelligence & Data Analytics: AI-driven insights allow for personalized recommendations and proactive interventions, helping patients stay on track with their healthcare needs.

Challenges in Patient Engagement

While digital advancements offer immense benefits, barriers to patient engagement still exist. Kristen outlined key challenges and potential solutions:

  • Digital Literacy Gaps: Some patients struggle to navigate digital tools. Solution: Implement educational programs that enhance digital health literacy.
  • Disparities in Access to Technology: Socioeconomic factors may limit access to essential healthcare tools. Solution: Develop community-driven initiatives to bridge the digital divide.
  • Resistance to New Healthcare Models: Patients may be hesitant to adopt new engagement methods. Solution: Enhance user experience and provide clear, consistent communication on benefits.

Best Practices for Enhancing Patient Engagement

Kristen provided actionable strategies that healthcare providers can implement to improve patient engagement:

  • Open Communication: Creating an environment where patients feel heard strengthens trust and adherence to care plans.
  • Multi-Channel Engagement: Leveraging SMS, email, and in-app notifications ensures continuous interaction and treatment adherence.
  • Behavioral Insights & Incentives: Encouraging participation in health programs through tailored incentives can drive proactive health management.

Conclusion: The Future of Patient Engagement

Kristen Jacobsen’s discussion underscored that the future of patient engagement lies at the intersection of technology, personalized care, and proactive communication. Healthcare organizations must adapt to these evolving dynamics to enhance patient experiences, improve health outcomes, and build long-term trust.

Recommendations for Healthcare Organizations

  1. Invest in User-Friendly Digital Platforms: Ensure seamless patient-provider interactions.
  2. Leverage AI-Driven Insights: Offer personalized healthcare recommendations to enhance engagement.
  3. Address Accessibility Concerns: Develop targeted initiatives to bridge the digital divide.
  4. Foster Collaboration: Encourage partnerships between providers, patients, and technology developers to refine engagement strategies.

By embracing these strategies, healthcare providers can create meaningful patient experiences that drive better health outcomes and long-term relationships. The key lies in making engagement seamless, personalized, and accessible to all.

AI Agents Raise New Possibilities for GAI and New Concerns

The latest generation of “AI agents” have transformed the way businesses operate and interact with customers; however, they have also raised some new ethical concerns. AI agents may be the next big thing in Generative AI tools; however, they also may present new challenges and concerns for users.

To date, GAI tools like ChatGPT have become remarkably good at being prompted by humans to create text, images, videos, and music, but they’re not all that good at doing things for us. That’s where “AI agents” come in.

A recent MIT newsletter says to think of AI agents as AI models “with a script and a purpose.” The piece went on to describe the two types of AI agents currently being developed and deployed.

The first, called tool-based agents, can be coached using natural human language -instead of any kind of coding – to complete digital tasks for us. Anthropic released one such agent in October, the first from a major AI model-maker. It can translate instructions, i.e., “fill in this form for me,” into actions on someone’s computer, moving the cursor to open a web browser, navigating to find data on relevant pages, and filling in a form using that data. Salesforce has released its own agent, too, and OpenAI reportedly plans to release one in January.

The other type of agent is called a “simulation agent,” and you can think of these as AI models designed to behave like human beings. The first people to work on creating these agents were social science researchers. They wanted to conduct studies that would be expensive, impractical, or unethical to do with real human subjects, so they used AI to simulate subjects instead. This trend particularly picked up with the publication of an oft-cited 2023 paper by Joon Sung Park, a PhD candidate at Stanford, and colleagues called “Generative Agents: Interactive Simulacra of Human Behavior.”

Recently, Park and his team published a new paper on arXiv called “Generative Agent Simulations of 1,000 People.” In this work, researchers had 1,000 people participate in two-hour interviews with an AI. Shortly after, the team was able to create simulation agents that replicated each participant’s values and preferences with stunning accuracy.

There are two really important developments here. First, it’s clear that leading AI companies think it’s no longer good enough to build dazzling generative AI tools; they now have to build agents that can accomplish things for people. Second, it’s getting easier than ever to get such AI agents to mimic the behaviors, attitudes, and personalities of real people. What were once two distinct types of agents—simulation agents and tool-based agents—could soon become one thing: AI models that can not only mimic your personality but go out and act on your behalf.

Dangers and Ethical Concerns

If such tools become cheap and easy to build, it will raise lots of new ethical concerns, but two in particular stand out. The first is that these agents could create even more personal and even more harmful deepfakes. Image generation tools have already made it simple to create videos using a single image of a person, but this crisis will only deepen if it’s easy to replicate someone’s voice, preferences, and personality as well.

The second is the fundamental question of whether we deserve to know whether we’re talking to an agent or a human. If you complete an interview with an AI and submit samples of your voice to create an agent that sounds and responds like you, are your friends or coworkers entitled to know when they’re talking to it and not to you? On the other side, if you ring your cell service provider or doctor’s office and a cheery customer service agent answers the line, are you entitled to know whether you’re talking to an AI?

Implementing AI agents can result in substantial cost savings for businesses. By reducing labor costs and optimizing resource allocation, organizations can achieve greater operational efficiency. AI agents also minimize the risk of human error, which can lead to costly mistakes. Industries such as manufacturing, logistics, and customer support have witnessed significant cost reductions through the integration of AI agents.

However, it is essential to consider the potential disadvantages and ethical challenges associated with their implementation.

How BigRio Helps Bring Advanced and Responsible AI Solutions to Healthcare

Personally, I could not agree more with the concerns about the proliferation of AI agents raised by MIT. This is why, at BigRio, we are dedicated to providing solutions and supporting AI startups that focus on the ethical and responsible use of AI.

Now that we are focusing our efforts company wide on GAI and LLM solutions, we are committed to the practice of building AI solutions that have clear and transparent rules on how they process data and use it to provide your company with the insights you need to be more productive from an ethical, responsible, and legal point of view.

AI startups face numerous challenges when it comes to demonstrating their value proposition, not the least of which is responsible AI (RAI). This is particularly true when it comes to advanced GAI solutions for pharma and healthcare – an area that BigRio focuses on. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs and particular challenges of GAI adoption in the healthcare field.

You can read much more about how AI is redefining business in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint, but it also discusses the impact of AI on all operations and businesses.

Key Takeaways from Digital Health Counsel 2024 AI Summit

The Digital Health Counsel 2024 AI Summit recently took place in Seattle, WA. Attendees of the two-day event left with an undeniable realization of the profound impact that Generative AI (GAI) and data-powered innovation are having on healthcare.

Organized by Ogden Murphy Wallace and sponsored by Fenwick, a leading law firm for the tech and life sciences industries, the summit was a fantastic opportunity to connect and exchange ideas with fellow lawyers, in-house professionals, and AI industry leaders leading the way in this exciting space.

Here are a few key takeaways from the event, according to a Fenwick press release.

AI governance is taking center stage. AI governance is becoming an increasingly critical issue across industries, particularly in healthcare, as new legislation, rulemaking, conventions, case law, etc., continue to flesh out this fast-developing space. The EU’s AI Act has entered into force and is gradually entering effect alongside a host of state-specific legislation in the United States, including 17 AI bills recently signed by California Gov. Gavin Newsom covering a gamut of issues, including disclosure, transparency, and digital likenesses. But beyond these new laws, it’s important to keep track of various emerging AI governance frameworks, such as the National Institute of Standards and Technology’s AI Risk Management Framework, which is increasingly being featured in AI-specific legislation.

Don’t fear the regulator. While the FDA has not yet authorized any generative AI-based medical devices or issued formal rules specifically about GAI, the agency is actively assessing the technology and various regulatory strategies. However, digital health startups should not be intimidated; instead, they should employ best practices for engaging with the FDA and maintain open lines of communication to help minimize their risk. Given the unique aspects of generative AI, post-market performance monitoring will likely be an important regulatory tool.

Prioritize empathetic and ethical AI. Accuracy is not enough. AI—particularly in the healthcare context—must operate ethically and from a place of empathy. This starts with mitigating bias from the underlying data used to train the AI or machine-learning algorithm. Not only does that encourage more equitable and empathetic service for end users, but it can also reduce the risk of hallucinations that pose broader technical problems.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

Like the key takeaways from the Seattle Summit, we understand the transformative impact that GAI is having on the healthcare industry. However, we also recognize that these kinds of moves toward greater use of GenAI-enabled technology must be done with care. In particular, the inherent issues of inaccuracy, bias, and hallucinations of commercially available GAI tools can be quite concerning in the healthcare arena.

In fact, a recent report produced by the AMA pointed out that many challenges remain before the medical industry embraces GAI on a major scale. Most of the concerns the AMA identified are problems when healthcare organizations and clinicians turn to “commercial off the shelf” GAI solutions such as those created by big tech companies like Google, Microsoft, or OpenAI.

But what if you could surmount privacy, bias, and other challenges by building a GAI-LLM model from the ground up for your healthcare organization’s unique targets and needs? You can, with BigRio’s Help!

BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery, originally in the field of diagnostics and research. We have recently been focusing our efforts on supporting startups and developing our own solutions that use LLMs and GAI to improve those areas of healthcare, as well as direct patient interactions, customer relationship management, EMR integration, and so much more.

We are proud to have recently launched our proprietary LLM-driven tool, Odyssey Accelerator. It is a first-of-its-kind solution that enables businesses of all sizes who want to use GAI to transform the way they search or query enterprise data, design workflows, derive enterprise analytics, report generation, dashboards, and more, to build a proof of concept before scaling it enterprise-wide.

Leading healthcare IT innovator Vivid Health partnered with BigRio to develop an LLM-driven end-to- end solution, that today can assess a patient’s status on any combination of more than 100 chronic conditions across sixteen specialties, generating personalized care plans at scale in under 30 seconds. That’s over 75 million possible care plan combinations in a fraction of the time it typically takes to pull together individualized plans. Its successful development and deployment stand as a testament to GAI’s potential to enhance the quality and efficiency of healthcare delivery.

Complimentary Gen AI Webinar: https://bigr.io/genai-webinar-december-2024/

You can read much more about how AI is redefining healthcare delivery and drug discovery in my book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

New Study Finds That Generative AI Can Track Cognitive Decline using EMRs!

A new study out of Boston suggests that combining large language models (LLMs) with traditional AI and deep learning models enhances accuracy in identifying early signs of cognitive decline, offering new hope for early diagnosis and effective treatment of dementia. The study recently published in in eBioMedicine evaluated the effectiveness of LLMs in identifying the signs of cognitive decay in electronic health records (EHRs). They also compared the performance of large language models with conventional AI models trained with domain-specific data.

Using the International Classification of Diseases, tenth revision, clinical modification (ICD-10-CM) criteria for Mild Cognitive Impairment (MCI), the researchers analyzed proprietary and open-source LLMs at Boston’s Mass General Brigham. They studied medical notes from four years before a 2019 MCI diagnosis among individuals 50 years and over.

The study dataset looked at nearly 5000 clinical note sections of about 2000 individuals, among whom 53% were female with a mean age of 76 years. Cognitive function keywords filtered the notes to develop study models. The testing dataset without keyword filtering comprised 2000 sections of clinical notes from about 1200 individuals, among whom 53% were female with a mean age of 77 years.

The researchers tested a proprietary GPT-4 LLM tool and an open-source LLM – Llama 2. The team found GPT-4 more accurate and efficient than Llama 2. GPT-4 highlighted dementia therapy options like Aricept and donepezil. It also detected diagnoses like mild neurocognitive disorders, major neurocognitive disorders, and vascular dementia better than previous models. GPT-4 addressed the emotional and psychological consequences of cognitive problems, such as anxiety, often disregarded by other models.

Combining the two, along with a traditionally trained AI model, into an ensemble model dramatically improved performance. The ensemble study model attained 90% precision, 94% recall, and a 92% F1 score, outperforming all individual study models regarding all performance metrics with statistically significant results.

The researchers concluded that LLMs trained using general domains need additional development to improve clinical decision-making. Future studies should combine LLMs with more localized models, using medical information and domain expertise to improve model performance for specific tasks and experimenting with prompting and fine-tuning tactics.

You can read the full study entitled Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes by following this link.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

It’s interesting that the Boston researchers found that the LLM tools in the study worked best for healthcare analysis when they are proprietary, as opposed to “open source” like Lama 2. Furthermore, they found that even the proprietary tool in the study worked even better when it was customized specifically for the task at hand.

That kind of customization is the cornerstone of BigRio’s mission – to create LLM solutions that specifically target your healthcare organization’s unique needs. BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery, originally in the field of diagnostics and research. We have recently been focusing our efforts on supporting startups and developing our own solutions that use LLMs and GAI to improve those areas of healthcare, as well as direct patient interactions, customer relationship management, EMR integration, and so much more.

We are proud to have recently launched our proprietary LLM-driven tool, Odyssey Accelerator. It is a first-of-its-kind solution that enables businesses of all sizes who want to use GAI to transform the way they search or query enterprise data, design workflows, derive enterprise analytics, report generation, dashboards, and more, to build a proof of concept before scaling it enterprise-wide.

Leading healthcare IT innovator Vivid Health partnered with BigRio to develop an LLM-driven end-to- end solution, that today can assess a patient’s status on any combination of more than 100 chronic conditions across sixteen specialties, generating personalized care plans at scale in under 30 seconds. That’s over 75 million possible care plan combinations in a fraction of the time it typically takes to pull together individualized plans.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

Two Pioneers in Artificial Intelligence Receive Noble Prizes

Two pioneers of artificial intelligence — John Hopfield and Geoffrey Hinton — won the Nobel Prize in physics for helping create the building blocks of machine learning that is revolutionizing the way we work and live but also creates new threats for humanity.

Hinton, who is known as the godfather of artificial intelligence, is a citizen of Canada and Britain who works at the University of Toronto, and Hopfield is an American who is a professor emeritus at Princeton University.

“These two gentlemen were really the pioneers,” said Nobel physics committee member Mark Pearce.

The artificial neural networks — interconnected computer nodes inspired by neurons in the human brain — the researchers pioneered are used throughout science and medicine and “have also become part of our daily lives,” said Ellen Moons of the Nobel committee at the Royal Swedish Academy of Sciences.

Hopfield, 91, created an associative memory that can store and reconstruct images and other types of patterns in data.

“When you get systems that are rich enough in complexity and size, they can have properties which you can’t possibly intuit from the elementary particles you put in there,” he said in a press conference convened by Princeton. “You have to say that system contains some new physics.”

He echoed Hinton’s concerns, saying there was something unnerving about the unknown potential and limits of AI.

“One is accustomed to having technologies which are not singularly only good or only bad, but have capabilities in both directions,” he said.

The Royal Swedish Academy of Sciences said it awarded the prize to the two men because they used “tools from physics to develop methods that are the foundation of today’s powerful machine learning” that is “revolutionizing science, engineering and daily life.”

The award comes with a prize sum of 11 million Swedish crowns ($1.1 million), which is shared by the two winners.

British-born Hinton, 76, now professor emeritus at the University of Toronto, invented a method that can autonomously find properties in data and carry out tasks such as identifying specific elements in pictures, the academy said.

Asked about the concerns surrounding machine learning and other forms of artificial intelligence, Ellen Moons, chair of the Nobel Committee for Physics, said: “While machine learning has enormous benefits, its rapid development has also raised concerns about our future.

“Collectively, humans carry the responsibility for using this new technology in a safe and ethical way for the greatest benefit of humankind.”

The prizes have been awarded with a few interruptions since 1901. Outside the sometimes controversial choices for peace and literature, physics often makes the biggest splash among the prizes, with the list of past winners featuring scientific superstars such as Albert Einstein, Niels Bohr, and Enrico Fermi.

How BigRio Promotes Innovation in AI

We share the vision of these two AI pioneers and agree with the Noble Prize Committee’s recognition of the transformative impact of AI and now Generative AI (GAI)

To this end, we have launched an AI Studio specifically for US-based startups with GAI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:

  • Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.
  • Assistance in the development and testing of the MVP, Prototypes, and POCs.
  • Professional services for implementation and support of Pilot projects
  • Sales and Marketing support and potential client introductions.
  • Access to private capital sources.

BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery, originally in the field of diagnostics and research. We have recently been focusing our efforts on supporting startups and developing our own solutions that use LLMs and GAI to improve those areas of healthcare as well as in direct patient interactions and customer relationship management.

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity. For this reason, Damo and BigRio’s GenAI focus is on working with industry leaders and innovators to create custom tools that augment human intellect, allowing people to know more, do more, and create more than ever before. Customized Onsite, In-Person Healthcare-focused Learning & Ideation Workshops to Accelerate Your GenAI Journey: https://bigr.io/genai-workshops-for-healthcare-providers/

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

Pfizer Launches GAI-Driven App to Improve Access to Healthcare

Pfizer has launched a GAI-driven Direct-to-Consumer platform that it hopes will usher in a new era of healthcare accessibility.

Pharmaceutical giant Pfizer has launched an AI-driven “direct-to-consumer” healthcare platform to simplify access to healthcare information and services.

Nearly two in every three Americans (65%) say coordinating and managing healthcare is overwhelming and time-consuming, Pfizer said in a news release, citing a poll by the American Academy of Physician Associates. According to the same poll, 73% of them feel the healthcare system is not meeting their needs. Direct-to-consumer (DTC) healthcare models are designed to make healthcare more accessible, affordable, and convenient by reducing barriers to access, empowering patients, and simplifying the healthcare journey.

Such DTC solutions are only made possible through the use of Generative AI (GAI) and Large Language Model (LLM) technologies.

By leveraging GAI technology, the DTC healthcare model can play a critical role in addressing the growing doctor shortage in the U.S. — one projected to reach a deficit of 86,000 physicians by 2036.

While a drug company like Pfizer may not be the first name that comes to mind in providing a DTC healthcare solution, James Allen, vice president U.S. Channel Management and Partnerships, Primary Care at Pfizer, said that its efforts during the pandemic earned the company significant consumer trust, and this trust became the cornerstone of Pfizer’s strategic pivot to direct-to-consumer healthcare.

“We’ve recognized the responsibility that comes with this trust. It’s not just about COVID — it’s about other therapeutic areas where patients are looking for reliable guidance,” Allen said. “Each patient wants to retain the right to choose their care path. We provide options, whether that’s telehealth, seeing a doctor in person, or getting enough information to return to their regular provider.”

The platform known as “Pfizer For All” key focus is on providing patients with optionality, or multiple paths to care, by giving patients to access healthcare information, the ability to connect with healthcare providers, as well as enabling them to manage treatments more easily.

While it’s only been a few weeks since the launch, as data comes in, Pfizer expects to make adjustments to the platform, learning more about patient needs and how best to serve them. “We don’t yet know what all the feedback will look like, but we’re open to learning from it and evolving the platform accordingly,” said Allen. “We’re watching engagement closely — how patients interact with the site, where they continue their journey, and what needs they express that we aren’t meeting yet.”

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

As the U.S. faces a shortage of primary care physicians and specialists, like Pfizer, we see how improving access to healthcare via innovative use of GAI is becoming more pressing now than ever.

BigRio has long been a facilitator and developer in leveraging AI to improve healthcare delivery, originally in the field of diagnostics and research. We have recently been focusing our efforts on supporting startups and developing our own solutions that use LLMs and GAI to improve those areas of healthcare, as well as direct patient interactions, customer relationship management, EMR integration, and so much more.

We are proud to have recently launched our proprietary LLM-driven tool, Odyssey Accelerator. It is a first-of-its-kind solution that enables businesses of all sizes who want to use GAI to transform the way they search or query enterprise data, design workflows, derive enterprise analytics, report generation, dashboards, and more, to build a proof of concept before scaling it enterprise-wide.

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity. For this reason, BigRio’s GenAI focus is on working with industry leaders and innovators to create custom tools that augment human intellect, allowing people to know more, do more, and create more than ever before. Customized Onsite, In-Person Healthcare-focused Learning & Ideation Workshops to Accelerate Your GenAI Journey : https://bigr.io/genai-workshops-for-healthcare-providers/

Leading healthcare IT innovator Vivid Health partnered with BigRio to develop an LLM-driven end-to- end solution, that today can assess a patient’s status on any combination of more than 100 chronic conditions across sixteen specialties, generating personalized care plans at scale in under 30 seconds. That’s over 75 million possible care plan combinations in a fraction of the time it typically takes to pull together individualized plans.

Vivid Health’s GAI-driven care management platform is currently the most powerful on the market. Its successful development and deployment stand as a testament to GAI’s potential to enhance the quality and efficiency of healthcare delivery.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.