From accelerating medical research to improving diagnostics, AI and, in particular, Generative AI (GAI) has had a transformative impact on healthcare. However, perhaps the greatest gift AI has given the healthcare industry is time.

While it was once thought that AI was going to take the human interaction out of medicine, as it turns out, quite the opposite is true. GAI tools are allowing doctors to spend much more quality time with their patients. GAI can streamline workflows for a better patient experience and work-life balance for providers. GAI has given patients more face-to-face interaction with their providers in the exam room and helps ensure their questions and concerns are heard.

To be sure, AI is dramatically speeding up drug trials and other research, but equally as important, it is giving back time to practitioners while still ensuring that the day-to-day tasks, like scheduling, notetaking, record sharing, and billing, can be efficiently completed. In fact, we could say that instead of making healthcare delivered by cold, emotionless machines as was once feared, AI is putting the heart back into healthcare and putting people first.

According to recent studies, over 80% of patients say the biggest problem with their healthcare experience is poor communication, particularly in the exam room.

Providers admit the problems and have always said it was one of time. They must spend endless hours filling out forms, patient notes, writing prescriptions, etc. All of which takes quality time away from patient interaction. In fact, one of the most pressing topics impacting healthcare conversations today is physician burnout. It is found that providers spend 5 hours a day performing administrative tasks.

But GAI is changing all of that. Thanks to Large Language Models (LLMs), GAI tools are streamlining all of these tedious and time-consuming processes, allowing doctors to focus more on care.

For example, Solutions like PRISMA, the healthcare industry’s first health information search engine, use AI to help formulate care plans based on the most relevant patient information while still providing the option to see greater patient detail when needed.

GAI tools have removed the need for notetaking during an appointment. With ambient listening, providers can record the appointment and review a conversation draft summary. The summary easily integrates into the patient’s progress note and recommends the next steps. This removes the device barrier, freeing the provider to spend more ‘stethoscope time’ with patients, meaning additional time for the physical examination with the additional time-savings and reduced administrative workload.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

We understand the profoundly transformative impact that GAI can have on health providers at the point of care. We are dedicated to giving doctors as well as health administrators back critical time by building GAI-LLM models specific to their organization’s unique targets and 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 Digital Health 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.

In the Age of AI, data is the common language of business. Achieving data literacy in your organization is a key to success and requires a shared mindset, language, and skills.

Data literacy — the ability to read, write, and communicate data in context — is fast becoming a requirement for all employees in forward-looking organizations.

IDC, a global provider of market intelligence, forecasts a ten-fold increase in worldwide data by 2025. Increasingly, data-driven organizations will produce data-literate employees who contribute more to their roles and help businesses sharpen their competitive edge in an aggressive global economy.

In the current environment where AI and Generative AI tools can be key to getting a leg up on the competition, company leaders should prioritize data literacy for employees across departments and at all levels within their organization.

A data literacy program should start with a shared mindset, language, and skills, said Valerie Logan, CEO and founder of consultancy The Data Lodge. “It’s an intentional commitment to upskilling your workforce and culture” and an opportunity to grow and amplify an understanding of artificial intelligence in the organization, she said.

Speaking at the 2024 International Chief Data Officer and Information Quality Symposium, Logan and Veronica Vilski, content and engagement director at The Data Lodge, shared insights on what organizations can do today to help their employees become data literate.

The pair offered six steps to launching a successful data literacy program for any type of organization:

1. Develop a clear, compelling case for change.
2. Launch and sustain a practical program foundation with targeted pilots.
3. Amplify and spotlight success stories.
4. Connect, support, and inspire communities who might feel isolated.
5. Leverage and connect data culture work and training resources across the organization.
6. Deliver lasting data culture benefits.

A data literacy program is also a place to grow and amplify an understanding of artificial intelligence in an organization; Logan said, “If you have had a data literacy program in place and you have not included AI in the scope of it from the start, you did not know what you were solving for, and you missed the mark.”

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

Like these two tech leaders at BigRio, we share their belief in the transformative impact that AI is having on all industries and how successful AI implementation goes hand in hand with data literacy.

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.

And we are delivering two Gen AI Workshops in September, 2024, one at Dallas and another in Boston:

and

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book

It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

There has been a boon in the acceptance and implementation of AI and, more recently, Generative AI (GAI) in healthcare. While much of AI revolution in healthcare is being driven by a need to streamline operations it is also largely being propelled by billions and billons of dollars being invested in AI strictly for healthcare.

For example, in recent weeks, Spring Health’s $3.3 billion valuation, CytoReason’s $80 million funding round, and the AWS-GE HealthCare collaboration all highlight the growing confidence in AI’s potential to transform mental health services, accelerate drug discovery, and revolutionize patient care delivery.

Let’s take a closer look at these three recent major investments in AI for healthcare.

A $100M Boost in AI Tech for Mental Health

Spring Health, a New York-based mental health platform, recently secured a $100 million Series E funding round, pushing its valuation to a lofty $3.3 billion. The company’s platform uses artificial intelligence to match patients with appropriate care providers and treatment plans.

This latest financial injection, led by Generation Investment Management, underscores the growing appetite for innovative mental health solutions by the investment community and by corporate CEOs. Its “Precision Mental Healthcare” approach aims to reduce the time it takes for patients to find effective treatment, a selling point that has resonated with employers and investors.

The company boasts a client roster that includes tech giant Microsoft, retailer Target, and financial powerhouse J.P. Morgan Chase.

An $80M Boost for AI Drug Discovery

CytoReason has raised $80 million for its AI-powered platform for disease modeling and drug discovery. The startup, founded in 2016, has caught the eye of industry giants, with Nvidia, Pfizer, OurCrowd, and Thermo Fisher Scientific all chipping in to fuel its growth.

CytoReason launched an AI platform that creates computational disease models, giving researchers a powerful tool to predict and develop new therapeutics. By simulating human diseases at the cellular level, the platform supposedly allows scientists to observe how potential treatments interact with the body, potentially fast-tracking drugs to patients.

The fresh injection of millions in capital will be used to expand the application of CytoReason’s computational models and grow its proprietary database of molecular and clinical data. The company is also planning to expand to the U.S., with plans to open an office in Cambridge, Massachusetts, later this year.

AWS and GE HealthCare Partner

Industry powerhouses, such as AWS and GE HealthCare, have announced a collaboration to harness AI to improve patient care. The partnership aims to develop AI models and applications that enhance the standard of care in the healthcare sector.

Currently, nearly a third of all digital information comes from the healthcare sector, yet 97% of this data is inaccessible to physicians due to its unstructured nature and confinement in data silos, according to the companies. AWS and GE HealthCare’s partnership seeks to unlock this data using AI foundation models (FMs) and innovative applications.

GE Healthcare plans to train and deploy clinical FMs on AWS’ machine learning and generative AI technologies. These tools are designed to help healthcare providers improve existing protocols and workflows and develop new approaches to patient care.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

We completely understand the investment community’s excitement about the transformative impact of AI, and particularly, GAI applications like those that will come out of the partnership between GE and AWS.

To this end, we have launched an AI Studio specifically for US-based Healthcare 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.

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.

And, we are offering Gen AI Workshops for Healthcare, on-site, in-person, 4 hours, 30 participants at $5000.00 : https://bigr.io/genai-workshops-for-healthcare-providers/

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.

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.