Healthcare Leaders Worldwide Say GAI Will Solve Staffing and Financial Pressures

The recently released “Philips’ Future Health Index 2024” has found that the majority of healthcare leaders worldwide are turning to Generative AI (GAI) to improve patient care.

A recent report based on the input of thousands of healthcare leaders worldwide has found that the vast majority of them are turning to Generative AI (GAI) technologies to address the significant financial and operational pressures caused by global staff shortages.

The Philips’ Future Health Index 2024 report was based on research among 3,000 healthcare leaders across 14 countries. It found that 85% of healthcare leaders are either investing in or planning to invest in GAI in an effort to streamline processes and reduce delays in patient care.

This heightened focus on GAI comes as nearly two-thirds (66%) of healthcare leaders report heightened burnout, stress, and mental health issues within their workforce, leading to a deterioration in work-life balance and reduced morale.

According to the report, use of AI for clinical decision support is already being utilized for in-hospital patient monitoring by 43% of respondents, with further investments planned over the next three years. Generative AI specifically has caught the attention of healthcare leaders in the past year, since its rapid emergence into the public domain. They recognize the benefits that generative AI could bring to patient care by unlocking new efficiencies and insights from patient data.

“Our research shows that 85% of healthcare leaders across the surveyed countries are already investing (29%) or plan to invest (56%) in generative AI within the next three years,” the report stated.

Finally, like many similar reports, the Phillip’s researchers found that, while there remains widespread excitement about the possibilities of AI in healthcare, there is also a shared recognition that it needs to be implemented in a responsible way to avoid unintended consequences.

Almost 9 in 10 healthcare leaders (87%) are concerned about the possibility of data bias in AI applications widening existing disparities in health outcomes. To address this risk, healthcare leaders say it is important to make AI more transparent and interpretable for healthcare professionals and offer continuous training and education in AI.

To amass the report, Philips, the Amsterdam-based health-tech giant commissioned the market research firm GemSeek to survey and interview a worldwide sampling of healthcare leaders. The researchers drew responses from around 200 participants each in Australia, Brazil, China, India, Indonesia, Italy, Japan, the Netherlands, Poland, Saudi Arabia, Singapore, South Africa, the United Kingdom and the United States.

You can read the entire report by following this link.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

We share the researchers’ conclusions of the transformative impact GAI and LLMs can have on healthcare. We also share their concerns about the inaccuracies that can sometimes be generated by commercial software like GPT-4.

Much like the findings in the Phillip’s paper, a recent report produced by the AMA pointed out many challenges remain before the medical industry can embrace GAI on a major scale. Most of the concerns the AMA identified are a problem 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 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 in 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 and organizations 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 use Odyssey Accelerator 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 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.

Study Finds That GAI and EHRs Can Work Well Together – But Only With Human Oversight

The results of a Mass General study have found that Generative AI (GAI) when combined with existing electronic medical records (EHR) solutions can do a good job at reducing physicians’ workloads and improving patient education — but also that these tools still have limitations that require human oversight.

The Mass General Brigham researchers set out to learn more about the efficacy of large language models (LLMs) when used to draft responses to patient messages in the HER.

For the study, the researchers used OpenAI’s GPT-4 large language model to produce 100 different hypothetical questions from patients with cancer.

The researchers had GPT-4 answer these questions, as well as six radiation oncologists who responded manually. Then, the research team provided those same six physicians with the GPT-4-generated responses, which they were asked to review and edit.

The oncologists could not tell whether GPT-4 or a human physician had written the responses — and in nearly a third of cases, they believed that a GPT-4-generated response had been written by a physician.

The study showed that physicians usually wrote shorter responses than GPT-4. The large language model’s responses were longer because they usually included more educational information for patients — but at the same time, these responses were also less direct and instructional, the researchers noted.

Overall, the physicians reported that using a large language model to help draft their patient message responses was helpful in reducing their workload and associated burnout. They deemed GPT-4-generated responses to be safe in 82% of cases and acceptable to send with no further editing in 58% of cases.

While these are impressive and mostly positive results, the researchers were also quick to point out that LLMs can be subject to “hallucinations” and dangerous without a human in the loop. The study found that 7% of GPT-4-produced responses could pose a risk to the patient if left unedited. Most of the time, this is because the GPT-4-generated response has an “inaccurate conveyance of the urgency with which the patient should come into the clinic or be seen by a doctor,” said Dr. Danielle Bitterman, who is an author of the study and Mass General Brigham radiation oncologist.

You can read the full study, which was recently published in Lancet Digital Health, by clicking here.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

We share Mass General Brigham’s researchers’ conclusions of the transformative impact GAI and LLMs can have on healthcare. We also share their concerns about the inaccuracies that can sometimes be generated by commercial software like GPT-4.

A recent report produced by the AMA pointed out many challenges remain before the medical industry could embrace GAI on a major scale. Most of the concerns the AMA identified are a problem 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 for your healthcare organization’s unique targets and needs? You can, with BigRio’s Help!

Creating a large language model from scratch requires extensive resources, the expertise of AI developers and data scientists, the MLOps team, and computational power. It involves training the model on massive datasets, fine-tuning it through multiple iterations, and optimizing its performance. This process demands substantial time, expertise, and computational resources, including high-performance hardware and storage systems. The good news is that the BigRio team can offer you all of the above and more!

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.

And, BigRio has deep expertise in EMR/EHR systems integration including EPIC and Salesforce integrations with a variety of other systems.

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.

Why Organizations Still Struggle With GAI Adoption

Despite the explosion of Generative AI (GAI) tools for both personal as well as business use many companies remain in the dark over how to implement and use GAI.

ChatGPT was certainly the launching pad for the current explosion of Generative AI (GAI) tools for both personal as well as business use, and yet, more than a year after the launch of ChatGPT, companies are still facing the same question when they first considered the technology: How do they actually go about putting it into business use? Many companies have simply discovered that generative AI tools like LLMs, while impressive, aren’t exactly “plug and play.”

While the “fear of missing out” fervor of the GenAI craze has seemingly calmed, many companies are still facing the same questions they were when it started: How can they take advantage of the promised cost savings and substantial efficiency gains that GenAI allegedly offers? How do they actually go about putting it into business use?

According to the Harvard Business Review, many companies are still grappling with how to implement machine learning and “traditional” AI into the business to enhance data analytics and improve efficiency, let alone take on the additional complexities of GAI and LLM solutions. GAI can do a lot of things that traditional AI cannot and vice versa.

ChatGPT may be able to write a 5,000-word report in no time, but it cannot, for example, currently, do many other tasks, like extracting and classifying driver’s license data, that traditional AI can do easily in a matter of seconds. As such, companies need to think deeply about which business cases might be appropriate to find the benefits of GenAI. Navigating through traditional AI is like steaming through choppy waters with a state-of-the-art but somewhat cumbersome vessel, and GenAI adds more tonnage, power, and an even more turbulent sea. A company still unsteady with the former will, of course, struggle with the latter.

The Business Review also points out that many companies continue to struggle with the long term implications of GAI. such as the long-term costs and the impacts of current and future regulation — are still uncertain.

Link to the article: https://hbr.org/2024/02/your-organization-isnt-designed-to-work-with-genai

How BigRio Helps Bring LLM and Advanced AI Solutions to All Industries

As a company dedicated to helping companies implement GAI solutions we have seen first hand all of the issues that the Harvard Business Review has pointed out – and even more challenges. The primary challenge is to think through on how to build a Gen AI infrastructure that everone in the business can use across functions like HR, Legal, Finance, Sales, Marketing etc.. then identify the developers of the GAI tools that fit the needs of your business. Now, you can opt to utilize existing models developed by big tech companies like Microsoft, Google, or OpenAI – or you could building your own.

Of course, it is much easier to use an off-the-shelf LLM solution; however, while these “open source” GAI/LLM solutions like ChatGPT have gained significant attention across various fields they are limited by their need to be non-specific in scope and ability. Your business is unique shouldn’t GAI solution be as well?

What if you could build a “Custom” GAI model for your organization’s unique targets, domain and needs from the ground up? You can, with BigRio’s Help!

Creating a custom large language model from scratch requires extensive resources, the expertise of AI developers and data scientists, the MLOps team, and computational power. It involves training the model on massive datasets, fine-tuning it through multiple iterations, and optimizing its performance. This process demands substantial time, expertise, and computational resources, including high-performance hardware and storage systems. The good news is that the BigRio team can offer you all of the above and more!

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.

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.

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.

Kid Teaches GAI How Humans Learn Language!

A group of New York University Researchers has used a neural network and a single baby to train a Generative AI model to acquire language more like humans do!

A single baby has been able to teach Generative AI (GAI) how humans learn language!

Generative AI solutions like ChatGPT that leverage Large Language Models to communicate more like humans have been revolutionizing all industries, not the least of which is healthcare. It takes millions of data points to train these GAI applications to “speak” as humans do, and even then, they often still fall short in understanding the nuances of human language.

Children, on the other hand, have access to only a tiny fraction of that data, yet by age three, they’re communicating in quite sophisticated ways. This prompted a team of researchers at New York University to wonder if AI could learn like a baby. What could an AI model do when given a far smaller data set—the sights and sounds experienced by a single child learning to talk?

For this experiment, the researchers relied on 61 hours of video from a helmet camera worn by a child who lives near Adelaide, Australia. After feeding that data into the AI model, it managed to match words to the objects they represent. “There’s enough data even in this blip of the child’s experience that it can do genuine word learning,” says Brenden Lake, a computational cognitive scientist at New York University and an author of the study. This work, published in Science Today, not only provides insights into how babies learn but could also lead to better AI models.

The child, Sam, wore the helmet cam on and off from the time he was about six months old until he was speaking rather fluently at two. The camera captured the things Sam looked at and paid attention to during about 1% of his waking hours. It recorded Sam’s two cats, his parents, his crib and toys, his house, his meals, and much more. “This data set was totally unique,” Lake says. “It’s the best window we’ve ever had into what a single child has access to.”

To train their AI model, Lake, and his colleagues used 600,000 video frames paired with the phrases that were spoken by Sam’s parents or other people in the room when the image was captured—37,500 “utterances” in all. Sometimes, the words and objects matched. Sometimes they didn’t. For example, in one still, Sam looks at a shape sorter, and a parent says, “You like the string.” In another, an adult hand covers some blocks, and a parent says, “You want the blocks too.”

The team gave the model two cues. When objects and words occur together, that’s a sign that they might be linked. But when an object and a word don’t occur together, that’s a sign they likely aren’t a match. “So we have this sort of pulling together and pushing apart that occurs within the model,” says Wai Keen Vong, a computational cognitive scientist at New York University and an author of the study. “Then the hope is that there are enough instances in the data where when the parent is saying the word ‘ball,’ the kid is seeing a ball,” he says.

Matching words to the objects they represent may seem like a simple task, but it’s not. To give you a sense of the scope of the problem, imagine the living room of a family with young children. It has all the normal living room furniture but also kid clutter. The floor is littered with toys. Crayons are scattered across the coffee table. There’s a snack cup on the windowsill and laundry on a chair. If a toddler hears the word “ball,” it could refer to a ball. But it could also refer to any other toy, or the couch, or a pair of pants, or the shape of an object, or its color, or the time of day. “There’s an infinite number of possible meanings for any word,” Lake says.

AI models that can pick up some of the ways in which humans learn language might be far more efficient at learning; they might act more like humans and less like “a lumbering statistical engine for pattern matching,” as the linguist Noam Chomsky and his colleagues once described large language models like ChatGPT.

Beyond that, creating models that can learn more like children will not only improve AI but help researchers better understand human learning and development, which could have major implications for treating learning disorders such as autism.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

We share the belief with the NYU researchers in the transformative power of GAI, particularly in the areas of medical research and the delivery of healthcare. But, when it comes to leveraging GAI and LLMs for healthcare, there are two primary approaches: building your own model like these researchers did or utilizing existing models developed by big tech companies like GPT.

Of course, it is much easier to use an off-the-shelf LLM solution; however, while these “open source” GAI/LLM solutions like ChatGPT have gained significant attention across various fields, including healthcare, they are limited by their need to be non-specific in scope and ability.

What if you could build an LLM model for your healthcare organization’s unique targets and needs? You can, with BigRio’s Help!

Creating a large language model from scratch requires extensive resources, the expertise of AI developers and data scientists, the MLOps team, and computational power. It involves training the model on massive datasets, fine-tuning it through multiple iterations, and optimizing its performance. This process demands substantial time, expertise, and computational resources, including high-performance hardware and storage systems. The good news is that the BigRio team can offer you all of the above and more!

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.

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.

Another article you might find interesting: https://bigr.io/transforming-the-healthcare-industry-with-large-language-models/

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.

The Combination of Wearable Tech and AI is Transforming Medicine!

The Director of the Cedars-Sinai Center for Surgical Innovation and Engineering shares his thoughts on how AI and wearable technologies are transforming medicine.

The combination of wearable tech like Fitbits and Smartwatches and the Internet of Things with AI is poised to have a major transformative effect on medicine and healthcare.

Imagine if you will a scenario in which such a device on your wrist tracks not only your step count, but also your blood sugar, heart rate, blood pressure and respiration. Then, the watch automatically sends a personalized health snapshot to your physician, alerting them to early signs of disease. According to according to Joseph Schwab, MD, director of the Cedars-Sinai Center for Surgical Innovation and Engineering, that world is not far off, and thanks to AI, much of it is already here. Schwab is leading innovative research into wearable healthcare technologies. He had this to say in a recent interview in Cedars-Sinai Discoveries Magazine.

“Our focus area is wearable devices,” says Dr. Schwab. “Consumer wearables on the market are essentially motion trackers. They may have an accelerometer or gyroscope that can simply measure your position or motion to track steps and other data. What we’re doing is different in that our devices are sending energy—in the form of light, electrical energy, and sound—into the tissues, and we can measure that energy as it leaves the tissue, and we can deduce things based on how the energy was affected by the tissue.”

Where Does AI Come Into the Equation?

Schwab explains where AI comes into the mix and how it is the real game-changer. “The sensors on our wearable devices receive an incredible amount of data from the energy after it has traveled through the tissue, which requires advanced computing power to interpret. At its core, AI is just that—very advanced mathematics and computer programming. We utilize AI to interpret the data captured and correlate it to clinical problems.”

He continues, “Separate from our wearable technologies; we are also able to use AI to make predictions on a smaller scale for use in clinical practice, such as interpreting electronic health data. For instance, a patient may be considering a procedure that has a 5% risk of complication for the whole population; however, using AI to interpret their personal health information, we may learn that their individual complication risk is closer to 25%. This could heavily impact their decision-making. Giving more precise predictions like this is a form of personalized medicine.”

The technologies that Schwab and his team are developing can truly benefit everyone across the healthcare spectrum. Patients whose health data is analyzed could receive more personalized care. They could be directed to more precise tests to get an accurate diagnosis and personalized treatments, for example, and may ultimately have better outcomes.

There’s even the potential for a positive impact on healthcare payers and insurance companies by making the right treatment decisions and thus reducing health expenditures. There are so many opportunities and benefits.

How BigRio Helps Bring Advanced AI Solutions to Healthcare

At BigRio, we share Dr. Schwab’s vision of the transformative power of AI in healthcare. In fact, improving disease detection and diagnostics is the area where AI is making one of the technology’s biggest impacts. We pride ourselves on being a facilitator and incubator for such advances in leveraging AI to improve diagnostics.

In fact, we have launched an AI Studio specifically for US-based Healthcare startups with AI 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 excels in overcoming such initial hurdles, which present nearly insurmountable obstacles to a startup operation.

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.

Rohit Mahajan is a Managing Partner with BigRio. He has a 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.

New Study Shows LLM Tool GPT-3.5 and 4 Do Very Well in Clinical Reasoning

A new study confirms that GPT-3.5 and 4 excel in clinical reasoning and furthers the case for the use of LLMs in healthcare.

In a recent study published in NPJ Digital Medicine, researchers developed diagnostic reasoning prompts to investigate whether large language models (LLMs) could simulate diagnostic clinical reasons. The study found that with the right prompts, GPT-3.5 and 4 did quite well at the task.

In this particular study, one of the latest of its kind, researchers assessed diagnostic reasoning by GPT-3.5 and GPT-4 for open-ended-type clinical questions, hypothesizing that GPT models could outperform conventional chain-of-thought (CoT) prompting with diagnostic reasoning prompts.

The team used the revised MedQA United States Medical Licensing Exam (USMLE) dataset and the New England Journal of Medicine (NEJM) case series to compare conventional CoT prompting with various diagnostic logic prompts modeled after the cognitive procedures of forming differential diagnosis, analytical reasoning, Bayesian inferences, and intuitive reasoning.

They investigated whether LLMs could mimic clinical reasoning skills using specialized prompts, combining clinical expertise with advanced prompting techniques. The study found that GPT-4 prompts could mimic the clinical reasoning of clinicians without compromising diagnostic accuracy, which is crucial to assessing the accuracy of LLM responses, thereby enhancing their trustworthiness for patient care. The approach can help overcome the black box limitations of LLMs, bringing them closer to safe and effective use in medicine.

GPT-3.5 accurately responded to 46% of assessment questions by standard CoT prompting and 31% by zero-shot-type non-chain-of-thought prompting. Of prompts associated with clinical diagnostic reasoning, GPT-3.5 performed the best with intuitive-type reasonings (48% versus 46%).

The study’s findings showed that GPT-3.5 and GPT-4 have improved reasoning abilities but still have some issues with accuracy when compared to conventional CoT reasoning. GPT-4 performed similarly with conventional and intuitive-type reasoning chain-of-thought prompts but worse with analytical and differential diagnosis prompts. Bayesian inferences and chain-of-thought prompting also showed worse performance compared to classical CoT.

Yet despite some limitations, overall, the researchers concluded, “We find that GPT-4 can be prompted to mimic the common clinical reasoning processes of clinicians without sacrificing diagnostic accuracy. This is significant because an LLM that can imitate clinical reasoning to provide an interpretable rationale offers physicians a means to evaluate whether an LLM’s response is likely correct and can be trusted for patient care. Prompting methods that use diagnostic reasoning have the potential to mitigate the “black box” limitations of LLMs, bringing them one step closer to safe and effective use in medicine.”

You can read the entire study here: https://www.nature.com/articles/s41746-024-01010-1

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

At BigRio, we are excited to see this kind of real-world evidence that helps make the case for LLMs in medicine, albeit with proper safeguards and human oversight in place. When it comes to leveraging GAI and LLMs for healthcare, there are two primary approaches: building your own model or utilizing existing public models developed by big tech companies like OpenAI.

Of course, it is much easier to use an off-the-shelf LLM solution; however, while these “open source” GAI/LLM solutions like ChatGPT have gained significant attention across various fields, including healthcare, they are limited by their need to be non-specific in scope and ability.

What if you could build an LLM model for your healthcare organization’s unique targets and needs? You can, with BigRio’s Help!

Creating a large language model from scratch requires extensive resources, the expertise of AI developers and data scientists, the MLOps team, and computational power. It involves training the model on massive datasets, fine-tuning it through multiple iterations, and optimizing its performance. This process demands substantial time, expertise, and computational resources, including high-performance hardware and storage systems. The good news is that the BigRio team can offer you all of the above and more!

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.

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.

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.

Could AI Be Used to Quash the Opioid Epidemic?

Opioid addiction and opioid-involved deaths have been on the rise in America over the past few decades, so much so that the Centers for Disease Control have rightly labeled it an “epidemic.” One researcher has thought of a novel approach to solve the opioid crisis, and it involves leveraging the power of AI to find alternative painkillers that can be just as effective but non-addictive.

The National Institute on Drug Abuse has awarded $1.5 million over five years to researcher Benjamin Brown of the Vanderbilt Center for Addiction Research and the Center for Applied Artificial Intelligence in Protein Dynamics. Brown developing an AI solution to analyze billions of potential opioid drugs to reveal detailed insights into how they interact with key proteins. Brown says that he views the opioid problem on a molecular level. Painkillers used legitimately in medicine, such as oxycodone, are highly addictive, but a better understanding of how their molecules interact with proteins in the body could lead to the formulation of nonaddictive alternatives.

Brown says he is focusing his research on “Mu-opioid receptors,” which are signaling proteins in the central nervous system that bind with opioids. These receptors modulate pain, stress, mood, and other functions. Drugs that target these receptors are among the most powerful analgesics, but they also are the most addictive.

Brown’s AI platform models drug-protein interactions in a way that accounts for their dynamic physical movements. These movements called “conformational changes,” can occur in milliseconds and make a big difference in how a protein behaves and binds or interacts with a small molecule drug.

By using AI to model this motion, the algorithms can more effectively predict how tightly proteins and drugs will interact and the effects of this interplay. This information is used to screen billions of potential drugs-an unprecedented scale for highly dynamic proteins-or design new ones with properties that lead to fewer addictive side effects.

How Damo Helps Bring Advanced AI Solutions to Healthcare

We share Ben Brown’s belief in the transformative power of AI, particularly in the areas of medical research and drug development.

However, Damo Consulting recognizes that digital maturity varies widely across enterprises and technology solution providers. We are proud to help clients develop digital transformation roadmaps that can be implemented with informed technology choices to meet organizational objectives.

We bring deep industry knowledge, market insights, and technology skills to help develop and implement enterprise digital roadmaps. The companies that we have recognized in the past with the DigiM Award and will do again with the 2023 nominees and recipients represent those that are leading the way with best-in-class programs for digital health, technology-led innovation, and organizational governance models to drive the healthcare industry’s transformation to a digital future.

Damo Consulting focuses exclusively on the healthcare market with a strong understanding of the provider and payer space. We make it our mission to explore, understand, and evaluate the most pressing issues at the intersection of healthcare and technology, specifically in the context of digital transformation in healthcare.

Rohit Mahajan is a Managing Partner with Damo Consulting. 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.

Since 2012, Damo Consulting has been working with leading healthcare enterprises on technology strategy and digital transformation. The firm has worked with some of the leading technology firms and emerging health IT companies to transform their brands and accelerate growth. For more information, visit Damo Consulting.

Can AI Help Deliver on the Promise of Value-Based Care in Medicine?

To say that 2023 was a profoundly transformative year for AI in healthcare would be an understatement. Most significantly, 2023 was the year when, for the first time, generative AI (GAI) tools became widely available to businesses and consumers, and their impact and rate of adoption in healthcare and drug discovery have been unprecedented. Now, as GAI becomes more and more ubiquitous in medicine, will AI prove to be the launching pad for the much-talked-about Value-Based Care (VBC) model long sought after by the healthcare industry?

VBC is a macro trend that is moving U.S. healthcare from a system driven by volume, i.e., a “fee-for-service” model, to one driven by patient outcomes and care quality. VBC emphasizes and incentivizes overall health but also poses administrative, clinical, and financial challenges for healthcare payers, self-funded employers, and providers.

However, GAI is poised to change all that and perhaps finally deliver on VBC’s promise. AI has tremendous potential to address the technology challenges of VBC and empower healthcare’s transition from volume to value.

AI technology—with the ability to aggregate massive volumes of data, uncover analytic insights, streamline repetitive tasks, minimize errors, and improve care quality—is already improving our ability to offer better care at a lower cost, which will lead to a successful VBC future. This is made even more possible when you add the power and potential of GAI and Large Language Model (LLM) solutions into the model. Such applications are quite capable of improving efficiency and reducing administrative burdens for clinical and non-clinical healthcare professionals. As GAI and LLM-driven tools become more sophisticated, they can take over repetitive and time-consuming tasks, such as manual data entry and reporting. That frees up human resources to focus on patients’ and members’ needs – the cornerstone of VBC.

Bessemer Business Partners, in its recently published 2024 Healthcare and Life Sciences Predictions, stated it believes that “This year, we predict we’ll see continued iteration in VBC models focused on specialties like cardiology, neurology, nephrology, and oncology. Innovation will be propelled by evolving payment models and newly approved high-cost therapeutics and diagnostics. For example, we expect to see new companies rise to meet the need to diagnose patients with early-onset Alzheimer’s and other neurodegenerative diseases after a new wave of neurodegenerative drugs like Leqembi are widely available.”

However, as with much we have written about AI and healthcare, for VBC to live up to its potential, all of this must be approached with serious consideration of transparency and ethics. The stakes are extremely high in healthcare, so there must be accountability to ensure patient and member safety. In fact, Bessemer included on its list of predictions that “It’s conceivable that the next largest healthcare AI startup could be a compliance-focused platform for monitoring privacy, data, and model assets in the wild.”

How Damo Helps Bring Advanced AI Solutions to Healthcare

We share the belief in the transformative power of GAI, particularly in the areas of medical research and the delivery of healthcare.

However, Damo Consulting recognizes that digital maturity varies widely across enterprises and technology solution providers. We are proud to help clients develop digital transformation roadmaps that can be implemented with informed technology choices to meet organizational objectives.

We bring deep industry knowledge, market insights, and technology skills to help develop and implement enterprise digital roadmaps. The companies that we have recognized in the past with the DigiM Award and will do again with the 2023 nominees and recipients represent those that are leading the way with best-in-class programs for digital health, technology-led innovation, and organizational governance models to drive the healthcare industry’s transformation to a digital future.

Damo Consulting focuses exclusively on the healthcare market with a strong understanding of the provider and payer space. We make it our mission to explore, understand, and evaluate the most pressing issues at the intersection of healthcare and technology, specifically in the context of digital transformation in healthcare.

Rohit Mahajan is a Managing Partner with Damo Consulting. 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.

Since 2012, Damo Consulting has been working with leading healthcare enterprises on technology strategy and digital transformation. The firm has worked with some of the leading technology firms and emerging health IT companies to transform their brands and accelerate growth. For more information, visit Damo Consulting.

Canada Uses AI to Solve Healthcare Worker Shortage!

Canadian healthcare workers turn to AI for help amid a staffing crisis!

Many countries, including the US, are facing a healthcare staffing crisis, particularly of medical support staff like skilled nurses. This shortage has only gotten worse since the COVID-19 pandemic. Canadian healthcare officials are turning to AI as a novel way to solve the problem.

The flagship example is an AI-driven system implemented by St. Michael’s Hospital in Toronto known as  CHARTWatch. Doctors and hospital administrators describe it as an “AI-powered early-warning system,” and so since its inception, it has made a real difference and has saved many lives. Case in point, last year, during one of her shifts on the internal medicine unit, Yuna Lee received an alert on her phone from CHARTWatch, indicating that a patient in the ward was at high risk of dying or needing intensive care.

Dr. Lee, the division head of general internal medicine, checked on the woman and found nothing obviously amiss. She ordered extra blood tests just to be safe. The results revealed the patient’s liver enzymes were elevated, prompting Dr. Lee to call for an ultrasound of her liver.

As the patient was about to be transferred to the imaging department, she spiked a fever and developed pain in her abdomen – the first overt symptoms of what turned out to be an inflamed gallbladder. CHARTWatch, which was developed by St. Michael’s data science team and analyzes hundreds of points of patient data to produce hourly risk scores, had figured out something was seriously wrong before doctors or nurses did!

“That was very surprising,” Dr. Lee said. “It made me go, ‘Wow, CHARTWatch is amazing.’ ”

In the two years since CHARTWatch’s launch, St. Michael’s general internal medicine unit has experienced a nearly 36-per-cent reduction in the relative risk of death among non-palliative patients compared with the same period in the four previous years.

“I am absolutely convinced that advanced data analytics and artificial intelligence is going to transform health care as we know it,” said Tim Rutledge, the president and chief executive officer of Unity Health, the network that includes St. Michael’s, St. Joseph’s Health Centre, and Providence Healthcare. “If we can automate tasks that are now laborious, it allows our clinicians to spend more quality time interacting with patients.”

Rutledge’s assessment is exactly why Canadian healthcare officials believe AI can help alleviate the country’s staffing crisis by taking rote tasks such as writing clinical notes off the plates of overworked nurses and doctors.

Along with CHARTWatch, other AI solutions developed at St. Michael’s include a tool for assigning emergency department nurses to different posts, such as triaging patients or working in the ER’s resuscitation bay. That assigning task, which used to require hours of manual input on an Excel spreadsheet, is now done by an algorithm in less than 15 minutes.

Another tool analyzes patient information that triage nurses punch into their computers and uses the data to produce wait-time estimates that flash on a screen in the ER, cutting down on the number of times harried staff members are asked, “How long will it be?”

Yet another project synthesizes the electronic medical records of patients with multiple sclerosis into a concise, visual timeline that is particularly helpful for junior doctors who may only have a 10-minute window to prepare for an appointment. The model can summarize seven years’ worth of charts in less than two seconds.

As it is in the US, Generative AI and Large Language Model (LLM) solutions are poised to be the next big thing in Canada for healthcare. St. Michale’s in-house AI R&D lab is developing an LLM-powered medical chatbot and automated clinical note generator called “Clinical Camel.”

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

When it comes to leveraging GAI and LLMs for healthcare, there are two primary approaches: utilizing existing models developed by big tech companies like Google or taking the route of St. Michael’s in Canada and developing your own property LLM solutions.

Of course, it is much easier to use an off-the-shelf LLM solution; however, while these “open source” GAI/LLM solutions like ChatGPT have gained significant attention across various fields, including healthcare, they are limited by their need to be non-specific in scope and ability.

Of course, there are many advantages to building an LLM model for your healthcare organization’s unique targets and needs. But not every hospital has the luxury of an in-house AI R&D lab like the one that developed CHARTWatch; however, that’s where BigRio can Help!

Creating a large language model from scratch requires extensive resources, the expertise of AI developers and data scientists, the MLOps team, and computational power. It involves training the model on massive datasets, fine-tuning it through multiple iterations, and optimizing its performance. This process demands substantial time, expertise, and computational resources, including high-performance hardware and storage systems. The good news is that the BigRio team can offer you all of the above and more!

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.

NEW GAI WORKSHOP LAUNCHED:

https://www.damoconsulting.net/gai-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.

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.

What Will Quantum Computing Really Bring to AI in Healthcare?

The marriage between quantum computing and AI promises to be the next big thing in IT, probably no more so than how it will impact and advance AI applications for healthcare.

AI is already being used in many areas of healthcare, from analyzing medical images to developing personalized treatment plans for patients. While quantum computing is still in its infancy, it has the potential to solve complex problems much faster than classical computers. When these two technologies are combined as is beginning to be realized, the possibilities are endless, particularly in healthcare delivery and drug research.

The revolution is already beginning. For example, Moderna and IBM recently announced a partnership that will explore various use cases of quantum computing within the life sciences, particularly mRNA medicine design.

As part of the collaboration, both organizations will employ MoLFormer, an AI-based foundation model, to predict the properties of molecules and gain insights into the characteristics of potential mRNA medicines.

What Makes Quantum Computers So Special?

Quantum computing operates by substituting classical computing’s bits with quantum bits, commonly referred to as “qubits.” Unlike bits, which can only store binary values of 0 or 1, qubits can exist as a superposition of both 0 and 1 simultaneously. This is made possible through a phenomenon in quantum mechanics called entanglement. This gives them computing power vastly superior to even today’s fastest supercomputers. Quantum computers’ ability to process vast amounts of data very quickly and very intuitively makes them particularly useful for advanced AI algorithms, particularly the kinds that are used in healthcare diagnostics and medical research where analyzing vast amounts of data for often minute details are required.

Let’s drill down on what quantum computing and AI when combined, can mean for healthcare delivery and for drug discovery.

Healthcare Delivery

Quantum computing can be used to process large amounts of data from medical records, electronic health records, and other sources to provide personalized healthcare services. The use of AI and quantum computing in healthcare delivery can also help in developing predictive models that can forecast the likelihood of certain diseases or health conditions. This information can help healthcare providers take proactive measures to prevent or treat diseases early, thus reducing the overall cost of healthcare and improving patient outcomes.

Additionally, AI can be used to develop chatbots that can provide immediate responses to patients’ queries and concerns. Chatbots can help reduce the workload on healthcare providers and provide quick and efficient responses to patients’ questions, thus improving patient satisfaction and overall healthcare experience.

Drug Research

As evidenced by the announced partnership between Moderna and IBM, drug discovery and pharmaceutical research is where quantum computing and AI is posed to make their most significant leap.

The development of new drugs is a complex and time-consuming process that can take years or even decades to complete. However, the combination of AI and quantum computing can significantly speed up this process. Quantum computing can be used to simulate chemical reactions and predict the properties of new molecules, making it possible to identify promising drug candidates faster.

AI can then be used to analyze the vast amounts of data generated by quantum computing simulations to identify the most promising drug candidates for further testing. This approach can significantly reduce the time and cost of drug development and bring new treatments to market that much faster.

The combination of AI and quantum computing has the potential to revolutionize healthcare delivery and drug research. It can help healthcare providers deliver personalized healthcare services, develop predictive models, and improve patient outcomes. It can also significantly speed up the drug development process and bring new treatments to the market much faster. While there are challenges that need to be addressed, the possibilities are endless, and the future of healthcare looks promising with the integration of these two technologies.

How Big Rio Can Help

Quantum computing is still very much an emerging technology with large-scale and practical applications still a way off. However, the technology is steadily graduating from the lab and heading for the marketplace. In 2019, Google announced that it had achieved “quantum supremacy.”

Much like the partnership between Moderna and IBM BigRio has partnered with our sister company, Citadel Discovery, to use AI to advance drug discovery. Citadel was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

You can read much more about how AI is redefining 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, with a particular emphasis on drug discovery and Pharma research.

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.