In November, President Biden signed a new Executive Order – titled “Safe, Secure, and Trustworthy Artificial Intelligence” – that promises to introduce new national AI regulations that focus on safety and responsibility in the use of this revolutionary new technology. Fast on the heels of this high-profile EO, the Biden Administration has already started the process of writing actual standards for the safe use of generative AI by announcing that the U.S. Department of Commerce. In late December, the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) announced a deadline of February 2, 2023, to receive input from the public on federal guidance for testing and safeguarding AI systems.

This represents significant interest by the federal government on AI. The question is: What does this mean for healthcare?

New standards, for one. By including healthcare in this broad new EO, the Biden Administration is signaling very clearly that health systems should expect new safety, security, and equity standards for AI very soon.

More broadly, the Biden EO reflects a transformative moment for the entire healthcare industry. The use of AI has brought us to a before-and-after moment. We have now entered the era of “Ethical AI” – a period of healthcare technology where our use of AI must be matched by our commitment to patient care and comport with the new standards being established by the government. Our ability to meld these influences will determine the degree to which the healthcare industry will benefit from the revolutionary potential offered by the use of AI.

Privacy and cyber security are two areas of concern cited by healthcare leaders in any discussion of AI. Is there an approach that aligns with the EO’s mission to enhance privacy while still enabling clinical efficiency? Can we protect patients’ most valuable information – such as electronic personal health information (ePHI), which is a primary target for cybercriminals – and still provide top-quality care?

Let’s take a closer look to find out.

The EO and NIST

To fully grasp the origins of President Biden’s EO, it’s important to understand the NIST-AI Risk Management Framework (RMF) AI-100-1. As stipulated within the National Artificial Intelligence Initiative Act of 2020 (P.L. 116-283), the RMF aims to serve as a resource to organizations utilizing AI systems and hopefully help manage the many risks of AI and promote trustworthy and responsible development and use of AI systems. 

For healthcare, the EO and RMF provide a useful dual framework for enhancing privacy when using AI. The standards outlined in NIST AI-100-1 provide guidance for getting visibility into the life of ePHI. These steps include: meticulously tracking where it resides, learning how it’s transmitted, and maintaining a detailed log of access details. The EO and NIST both encourage cutting-edge encryption methods and secure data transmission to safeguard patient information. This means healthcare leaders should be placing privacy at the forefront of our AI solutions.

A Blueprint for Executive Assurance

To navigate the complexities of AI integration with confidence, healthcare executives can use the EO and NIST frameworks to vet AI tools that help them ensure compliance while delivering reliable performance. The following are traits to look for in AI solutions designed to promote privacy and cyber protection:

• Valid and Reliable: AI solutions should guarantee the validity and reliability of healthcare insights, providing executives with a foundation of trust in decision-making.
• Safe: By prioritizing patient safety, AI applications should minimize risks and ensure that safety protocols evolve with the dynamic healthcare landscape.
• Secure and Resilient: Robust security measures fortify healthcare data, guaranteeing integrity and confidentiality. AI systems should adapt to emerging threats, enhancing resilience and securing data transmission within health systems.
• Accountable and Transparent: AI models should be designed for executive understanding, providing detailed logs of ePHI access for transparency and accountability.
• Explainable and Interpretable: Recognizing the need for interpretability, AI models should be deliberately designed to be explainable, allowing executives to confidently interpret AI-generated insights.
• Privacy-Enhanced: The EO’s commitment to privacy requires visibility into ePHI, actively managed access, and transmission in alignment with NIST AI-100-1 standards.
• Fair: Actively addressing biases, AI models should promote fair treatment across diverse patient populations, fostering healthcare equity and inclusivity.

Elevating Executive Assurance through Ethical AI

Health systems create a strategic advantage by deploying AI tools for adaptability and compliance. One significant area where AI can be leveraged for adaptability is in privacy-promoting solutions where AI is not only more resilient at adapting to evolving regulations and changing uses for patient data. This puts healthcare executives in a position where compliance does not become a mere checkbox but an ongoing commitment to ensuring they can navigate regulatory changes and new ways of using their most critical data assets.

Responding to the Biden EO and the need for privacy protections requires healthcare executives to navigate directly at the intersection of healthcare and AI. By deploying the right AI systems, these organizations can achieve unparalleled visibility into ePHI, secure data transmission, and adhere to NIST AI-100-1 standards. This positions them to lead confidently in a digital era where technology aligns seamlessly with the principles of patient-centric care and regulatory excellence.

About David Ting

David Ting, Tausight Founder & CTO, was the co-founder and former CTO of Imprivata and a former appointee to the U.S. Department of Health and Human Services Health Care Industry Cybersecurity Task Force. David has more than twenty years of experience developing identity and security solutions for government and enterprise environments. David holds twenty-two US patents, with additional pending.

While at Imprivata, Ting built the technology behind the OneSign solution used extensively in healthcare. As director, he oversaw Imprivata’s evolution from a venture backed startup to a public company and subsequent private acquisition in 2016. Ting has more than twenty years of experience developing identity and security solutions for government and enterprise environments. In 2016, he was appointed by the U.S. Department of Health and Human Services to the Health Care Industry Cybersecurity Task Force, authorized under the Cybersecurity Information Sharing Act of 2015. Ting helped draft the recommendations for securing healthcare in the Cybersecurity Task Force Report submitted to Congress in 2017. 

With many economic hurdles this year including rising costs and inflation, the healthcare industry has had a challenging backdrop in which to operate. In a sector dedicated to improving and saving lives, healthcare has so much to gain by adopting new AI and machine learning (ML) technologies. The World Economic Forum sums up the transformative potential of AI and ML in this setting: from propelling the development of drugs and vaccines to improving medical diagnosis and treatment, it can virtually be applied to any stage of the value chain, boosting efficiencies across the overall healthcare system. 

Like in all organizations today, leading with data and building it into a formal strategy should no longer be viewed as an adjunct to an existing healthcare business model – digital and data must be considered part of it. There are certainly more hoops to jump through in this industry than many others when it comes to privacy, security and governing rules. However, if you look at other highly regulated industries like finance, you can see significant strides by taking a gradual approach with great care. McKinsey’s analysis of data from the banking sector shows that digital and AI transformations created bottom-line value. Healthcare can achieve similar results by applying technology thoughtfully and with purpose.

Digital modernization in healthcare

Healthcare is an industry with great digital promise, but one that has traditionally been hamstrung by its largely legacy IT systems and data practices. This is compounded by IT staff shortages and budget constraints. According to Gartner, top items for increased investment in healthcare include cybersecurity, business intelligence/data analytics and cloud platforms. However, without the supporting IT staff many healthcare systems lag in technological advancement.

Modernizing processes and technology in healthcare is also difficult because the margin of error in healthcare is slim to none, as it many times involves life-or-death scenarios. This means that experimentation and trialling technology is not a common or widely viable option. Additionally, with the immediate and growing financial demands surrounding patient care delivery, executives aren’t always willing to allocate budget to new initiatives where the use case is not crystal clear. This means that adoption of any new technology must be easy to implement, extremely reliable and provide fast results immediately.  

How AI and ML can strengthen healthcare organizations

For an industry built on patient experience, the promise of what AI and ML can deliver is significant. Healthcare systems have an enormous amount of personalized data that could be compared or contrasted with the vast amounts of external studies to design more effective treatments and staffing. The only way this data becomes useful, however, is if a solid data strategy is in place to harness it for action. AI and ML can help immensely in this area. Healthcare companies can better manage patient care by forecasting patient admissions and readmissions and leveraging insights to design precision medicine and preventative strategies, not to mention the operational efficiencies.

Exploring “what-if” scenarios is at the heart of making predictions, and is one of the most impactful ways that AI and ML are being applied in modern medicine today. It reduces provider burden and clinical variation by leveraging statistical techniques that learn from large amounts of training data. For example, when looking to improve patient care, AI and ML can help predict what kind of treatment plan will be most successful, based on their unique characteristics and situation as compared with other patients. In the case of assessing the urgency of care, it can identify gaps in medical history and predict which patients need care first. Appalachian Regional Healthcare (ARH) saw this firsthand, utilizing tailored automated ML and cloud solutions to better identify at-risk patients and encourage them to keep appointments. There are numerous other use cases on the care side and of course on the operational side – such as reducing time-consuming administrative tasks from physicians, like documenting appointment notes and summaries as just one example.

A smarter, simpler healthcare setting for all

Everyone involved in the healthcare system – from patients to providers to payers – will benefit from the data-driven insights generated by AI and ML models. The results can guide business practices, policies and field operations, thus creating a smarter and simpler setting for all. When applied in a safe and controlled way, AI and ML technologies can cost-effectively target valuable use cases where both patient care and operations can quickly see vast improvements. 

About Nick Magnuson

Nick Magnuson is the Head of AI at Qlik, executing the organization’s AI strategy, solution development, and innovation. Nick joined the company through the acquisition of Big Squid, where he was the firm’s CEO, and has previously held various executive roles in the field of machine learning and predictive analytics.

What You Should Know:

– Artisight, Inc., a pioneer in AI-Driven smart hospital technology, announced today that it has raised $42M in an oversubscribed Series B funding round.

– The Series B funding will allow Artisight to double its headcount, fuel the development of new AI capabilities, and solidify its position as the leading smart hospital platform.

Smart Hospital Platform: Revolutionizing Care Through AI and Collaboration

Developed by clinicians for clinicians, Artisight’s platform seamlessly blends IoT sensors, deep learning, and open integration to streamline patient care. Imagine a hospital equipped with Artisight’s superpowers:

– Virtual nursing: AI co-pilots assist nurses, freeing up their time for direct patient interaction.

– HIPAA-compliant capabilities: Computer vision, voice recognition, vital sign monitoring, indoor positioning, and actionable analytics reports empower informed decisions.

– Reduced documentation and coordination burden: Clinicians can focus on what matters most – their patients.

– Improved patient outcomes and satisfaction: Artisight’s data-driven insights lead to better care and happier patients.

Results Speak Louder Than Words

Artisight’s impact is not just theoretical. Look no further than Northwestern Medicine, a prestigious healthcare system, for proof:

– 52% reduction in nursing overtime: Artisight empowers nurses, maximizing their efficiency.

– 89% reduction in falls: AI co-pilots keep patients safe and prevent accidents.

– 76% reduction in nursing turnover: Happy nurses stay, creating a stable and positive environment.

– Record-high patient and nurse satisfaction: The Artisight difference is evident in every smile.

“This funding is a game-changer,” says Artisight co-founder and CEO, Dr. Andrew Gostine. “We’ll double our headcount, develop new solutions, and support the nearly 200 hospitals implementing Artisight. We’re the only true ambient intelligence platform, transforming care from clinics to operating rooms.”

What You Should Know:

– TruLite Health, a pioneer in health equity solutions, and Mayo Clinic, a global leader in medical care, today announced a groundbreaking collaboration to address disparities in medical outcomes and costs for diverse patient populations.

– This partnership marks a significant step forward in the fight against clinical bias, a pervasive issue that disproportionately impacts the health of Black, Latinx, and other minority communities.

Quantifying the Impact of Bias

Clinical bias has a staggering financial and human cost. A TruLite study revealed that individuals with one of seven chronic illnesses who are Black or Latinx incur approximately $5,300 more in annual healthcare expenses compared to their White counterparts. Truity™ aims to tackle this disparity head-on by ensuring all patients receive equitable care regardless of their background.

Truity™: AI Championing Fairness in Healthcare

TruLite’s revolutionary software, Truity™, identifies and mitigates clinical bias within electronic health records (EHRs). The platform analyzes patient data and suggests personalized interventions for clinicians, care teams, and patients themselves. These interventions address social and behavioral factors that often contribute to unequal healthcare outcomes, particularly for Black and Latinx populations.

Mayo Clinic: A Powerhouse Partner

Mayo Clinic brings its vast expertise and resources to the table. Their physicians will provide valuable feedback on Truity™, helping refine the user experience and workflow to maximize its effectiveness in clinical settings. Additionally, Mayo Clinic has a financial interest in the technology, demonstrating its commitment to advancing health equity solutions.

This partnership goes beyond software:

• Virtual health equity coaching: TruLite offers virtual coaching programs to empower patients and advocate for their healthcare needs.
• Partnership with Morehouse School of Medicine: TruLite collaborates with Morehouse to address the specific healthcare challenges faced by Black communities.

“This collaboration with Mayo Clinic represents a critical milestone in the fight for health equity,” says Dr. Alan Roga, Founder and CEO of TruLite. “Mayo Clinic’s expertise and commitment to patient care, combined with TruLite’s innovative technology, will allow us to reach a wider audience and make a real difference in people’s lives.”

What You Should Know:

– Debiopharm, a leading independent biopharmaceutical company, and ThinkingNodeLife.ai (TNL), a pioneer in AI-powered drug discovery, today announced a groundbreaking partnership to propel the development of a cutting-edge cancer drug.

– This strategic partnership combines Debiopharm’s deep pharmaceutical expertise with TNL’s revolutionary AI Digital Cells Lab platform, unlocking new frontiers in cancer treatment innovation.

Unlocking the Hidden Potential of a Promising Drug

The partnership focuses on unlocking the full potential of one of Debiopharm’s promising cancer drugs. By leveraging TNL’s AI-powered platform, the collaboration will explore:

• Expanding Indications: TNL’s platform will identify potential new applications for the drug, extending its reach and impact on cancer patients.
• Combination Therapy Optimization: Utilizing AI digital knockdowns, the team will identify synergistic combinations with other drugs in development, maximizing the therapeutic efficacy of Debiopharm’s cancer drug.
• Mechanism of Action Validation: TNL’s digital cell clones will provide a deeper understanding of the drug’s mechanism of action, ensuring targeted and effective treatment strategies.
• Comparative Analysis: Digital cell clones will enable side-by-side comparisons with existing cancer treatments, providing valuable insights for optimal drug selection and development.
• Biomarker Discovery: The partnership will focus on identifying predictive biomarkers that can guide patient selection and treatment personalization, ultimately improving patient outcomes.

“This partnership represents a significant leap forward in cancer drug development,” said Bertrand Ducrey, CEO of Debiopharm. “By combining our expertise with TNL’s innovative AI platform, we are confident in unlocking the full potential of this promising drug and bringing it to even more patients in need.”

What You Should Know:

– Innovaccer, a healthcare data platform, unveils Sara Scribe, an AI assistant poised to revolutionize healthcare documentation.

– Sara Scribe, also known as InScribe, transcends the limitations of standard dictation software. This innovative tool uses advanced AI to not only transcribe doctor-patient conversations in real-time but also analyze them and deliver actionable insights, streamlining workflows and freeing up valuable clinician time.

Beyond Transcription: Clinical Intelligence at Your Fingertips

Sara Scribe is more than just a transcriptionist; it’s a clinical advisor. It analyzes conversations and leverages EHR and claims data to identify:

– Quality gaps: Potential areas for improvement in patient care.

– Coding gaps: Opportunities to optimize reimbursement through accurate coding.

– Potential diagnoses: Early flags for possible health conditions, enabling proactive care.

This real-time guidance empowers clinicians to make informed decisions, optimize care delivery, and improve patient outcomes.

Two Modes to Fit Every Need

Innovaccer caters to both large and small healthcare providers with two distinct InScribe modalities:

Enterprise: Seamlessly integrated with Innovaccer’s existing Point-of-Care assistant, InNote, this version elevates user experience with ambient documentation and clinical insights drawn from comprehensive EHR and claims data.

Individual: Designed for independent practitioners and smaller groups, this standalone web-based tool boasts an intuitive mobile companion app. Users can sign up and begin using InScribe within minutes, making it accessible and convenient.

“InScribe isn’t just a transcription tool—it’s a game-changer in the AI assistant space. It not only documents the conversation but also nudges the provider with real-time clinical insights during a visit,” Abhinav Shashank, Cofounder and CEO, Innovaccer. “We understand burnout is at an all-time high. With InScribe, we aim to reduce the administrative and cognitive load of clinicians and redirect their time back to patients.”

What You Should Know:

– Mayo Clinic Platformv and Techcyte have joined forces to revolutionize the field of pathology by creating a digital pathology platform. This platform aims to pioneer the next generation of pathology services through an open, AI-driven solution, enhancing treatment, efficiency, and accuracy globally.

– By enabling healthcare organizations to economically digitize their pathology practices, the platform integrates innovative technical and AI workflow solutions.

Transform Global Practice of Pathology

The collaborative effort emphasizes patient-centric innovation and brings together diverse stakeholders, including pathologists, LIS vendors, whole slide scanner manufacturers, AI providers, and more. The platform, an extension of Techcyte’s clinical pathology platform, unifies clinical and anatomic pathology into a comprehensive solution, marking a significant advancement in diagnostic practices.

“Mayo Clinic puts the needs of the patient first. That inspires us to innovate, discovering new cures and testing new care models across the many domains of medicine. Our collaboration with Techcyte will produce a global digital pathology platform, bringing together privacy-protected data sources, solution developers, and caregivers,” says John Halamka, M.D., president of Mayo Clinic Platform.

What You Should Know:

– Care Continuity, a leading provider of data-driven patient navigation solutions, has secured $10M in Series A-3 funding, fueling its mission to streamline healthcare journeys and optimize network utilization. The investment, co-led by Empactful Capital & Viewside Capital Partners with participation from Flat Creek Capital and Loop 289 Limited Partners, empowers Care Continuity to further enhance its platform and reach, impacting both providers and patients across the healthcare ecosystem.

– The funding will fuel the integration of AI capabilities to further personalize care journeys, improve outreach efficiency, and connect more patients with the right care at the right time.

Enhancing the Care Journey

• Patient-Centered Navigation: Care Continuity’s expertise lies in guiding patients through complex healthcare networks, ensuring they access the right care at the right time. This reduces patient leakage and strengthens network utilization.
• AI-Powered Solutions: The funding empowers Care Continuity to integrate advanced AI capabilities into its platform. This enhances patient reach, improves efficiency, and personalizes the navigation experience.
• Data-Driven Insights: Beyond patient navigation, Care Continuity offers sophisticated analytics and digital tools. These valuable resources help payers, ACOs, and healthcare providers engage their members and optimize network performance.

Expansion Plans

The company plans to invest in expanding its team, focusing on sales, marketing, technology, data science, and advisory services, propelling its growth trajectory. In addition, Care Continuity will utilize the investment to expand its platform features, offering even more comprehensive and targeted navigation solutions for various patient needs and healthcare settings.

This funding allows us to empower both providers and patients at a critical juncture. We’ll enhance our platform and leverage AI to connect more patients to the right care within their networks, driving efficiency and positive health outcomes,” said Brad Prugh, Care Continuity CEO.

What You Should Know:

– Amgen, a global leader in biotechnology, is embarking on a groundbreaking venture: Project Freyja.

– The new initiative will harness the power of an NVIDIA DGX SuperPOD, christened after the Norse goddess of prediction, to accelerate drug discovery and development through AI-powered insights from one of the world’s largest human datasets.

Advance Drug Discovery Using NVIDIA DGX SuperPOD-Powered Insight

• Human Diversity Atlas: Freyja will map the landscape of human genetic variation, creating an invaluable atlas for identifying drug targets and disease-specific biomarkers. This will pave the way for personalized medicine, tailoring treatments to individual patients based on their unique genetic makeup.
• Precision Medicine Revolution: Equipped with Freyja’s AI capabilities, Amgen researchers will develop cutting-edge precision medicine models, potentially enabling the creation of individualized therapies for a multitude of serious diseases.
• Diagnostics Made Easy: By identifying novel biomarkers, Freyja will empower the development of accurate and efficient diagnostics for monitoring disease progression and response to treatment.

Unleashing the Power of Data

• DGX SuperPOD: A powerhouse of computing, the SuperPOD boasts 248 NVIDIA H100 Tensor Core GPUs, allowing Amgen to train state-of-the-art AI models in days, not months. This dramatically accelerates the pace of data analysis and discovery.
• 200 Petabytes of Insights: Freyja will draw upon deCODE genetics’ vast library of deidentified human data, exceeding 200 petabytes, encompassing nearly 3 million individuals. This unparalleled resource provides a unique window into human diversity and its role in health and disease.

“Freyja embodies our commitment to leveraging the convergence of technology and biotechnology. By combining our world-class human data with NVIDIA’s cutting-edge AI technology, we are poised to unlock a new era of drug discovery,” said David M. Reese, Amgen’s Chief Technology Officer.

What You Should Know:

– In a decisive move towards faster and more efficient drug development, QuantHealth, a leading AI-powered clinical trial design company, has secured a strategic investment of $17M.

– The new capital, fueled by Accenture Ventures alongside a prominent CRO and additional investors, bolsters QuantHealth’s total Series A funding and propels its mission to revolutionize clinical trials through AI simulations.

Saving Millions with 86% Predictive Accuracy

QuantHealth’s proprietary AI technology boasts an impressive 86% accuracy rate in predicting clinical trial outcomes on the binary endpoint metric. This remarkable feat is powered by a massive dataset of 350 million patients, vast biomedical knowledge graphs, and comprehensive clinical trial data. This potent combination allows QuantHealth’s platform to simulate thousands of protocol variations, uncovering the optimal design for success.

Reducing Risk, Expediting Development

The pharmaceutical industry faces a daunting reality: clinical trials are expensive and fraught with uncertainty. With a staggering cost exceeding $1 billion for a single new drug, failure rates hover around 90%, draining $50 billion annually. QuantHealth’s platform tackles this challenge head-on by enabling R&D teams to:

– Lower risks: By simulating trials at scale, QuantHealth identifies potential pitfalls before they arise, minimizing costly failures.

– Expedite development: Identifying the optimal trial design upfront streamlines the entire process, bringing life-saving treatments to patients faster.

– Optimize drug development: QuantHealth’s platform ensures the right drug reaches the right population through precise matching, maximizing success rates.

Orr Inbar, co-founder and CEO of QuantHealth, sees the Accenture Ventures partnership as a catalyst for global expansion: “Accenture’s expertise in data and AI will be invaluable as we scale our platform and reach pharmaceutical and biotech companies worldwide.” This global reach will ultimately translate to faster development of effective treatments for patients in need.

What You Should Know:

– In a move poised to reshape the landscape of precision care, GE HealthCare (Nasdaq: GEHC) has announced the acquisition of MIM Software, a provider of medical imaging analysis and AI solutions.

– This strategic acquisition strengthens GE HealthCare’s commitment to developing smart, interconnected devices and digital solutions that empower clinicians to deliver more precise, personalized care across various specialties.

Fueling Innovation in Precision Care

Recognizing the transformative power of digital innovation in healthcare, GE HealthCare is relentlessly pursuing advancements in smart devices aligned with specific disease states. This dedication aligns perfectly with MIM Software’s expertise in AI-driven imaging analysis, offering a suite of solutions spanning radiation oncology, molecular radiotherapy, diagnostic imaging, and urology.

MIM Software’s Robust Portfolio

– Multimodal integration: Seamlessly combining diagnostic images from various modalities into treatment plans.

– Automation and efficiency: Reducing repetitive tasks and manual interventions through automation.

– Advanced processing: Quantitating and processing diagnostic imaging and nuclear medicine data to assess treatment response.

– Theranostics platform: Facilitating imaging and dosimetry for Theranostic applications.

Integrating for Enhanced Precision:

GE Healthcare Integration Plans for MIM Software

By integrating MIM Software’s solutions into its advanced visualization offerings, GE HealthCare aims to revolutionize care delivery. This includes:

– AI-powered segmentation and contouring: Streamlining treatment planning workflows.

– Enhanced dosimetry analysis: Optimizing treatment delivery for improved patient outcomes.

– Expanded reach: Driving advancements in radiology, molecular imaging, and radiation oncology.

“This acquisition strengthens our position as a leading provider of integrated imaging systems and advanced digital workflows. We are committed to transforming patient care through precision medicine across multiple specialties,” said Peter Arduini, President & CEO, GE HealthCare.

What You Should Know:

– Deloitte, a global leader in professional services, has unveiled Atlas AI, a groundbreaking addition to its Quartz AI™ suite, marking a significant leap forward in life sciences and healthcare (LSHC).

– Built on the NVIDIA AI and NVIDIA Omniverse™ platforms, Atlas AI empowers scientists with novel tools to expedite drug discovery and bring life-saving treatments to market faster.

Harnessing the Power of Generative AI

Atlas AI boasts a unique arsenal of AI technologies:

– Generative AI models: Made accessible through BioNeMo, these models facilitate the generation of new drug candidates with enhanced properties.

– Knowledge representation and reasoning: This technology unlocks deeper data insights by connecting and interpreting scientific knowledge.

– Custom protein and chemoinformatics LLMs: These specialized language models analyze protein structures and predict drug-target interactions, paving the way for more precise drug design.

Beyond Drug Discovery

Atlas AI is just the beginning for Deloitte’s LSHC ambitions. Future iterations will incorporate additional applications like:

– Precision Medicine: Tailoring treatments to individual patients based on their unique genetic and molecular profiles.

– Voice of the Patient Insight: Capturing and analyzing patient feedback to improve treatment plans and healthcare outcomes.

Dan Ferrante, Deloitte’s AI leader for innovation and R&D, highlights the transformational potential of Atlas AI: “It represents the true power of Generative AI and LLMs, enabling deeper data insights and seamless human-machine collaboration for real-world impact. Through its no-code interface, Atlas AI puts powerful tools directly in the hands of scientists, saving valuable research time.”

Over the last year, the term “Generative AI” has exploded on the healthcare scene, with health and social systems trying to determine if and how they can incorporate the newest tech trend into their day-to-day practice to streamline and improve operations, and ultimately improve patient care.

But most organizations have decided to take a measured approach and see how the hype plays out – they have decided to proceed thoughtfully and with diligence before viewing Generative AI as the panacea to all things healthcare. I’ve been working in health tech for 15+ years, and I’ve yet to see any Generative AI applications exit the proof-of-concept phase and enter the production phase. Along similar lines, I’ve noticed that almost all articles in healthcare talk about “the promise of” Generative AI rather than “the results of” Generative AI. 

After all, when you consider the true definition of generative AI – the ability to generate language – we must ask ourselves: is this really helpful? Do clinicians really want a tool that can generate language on its own, which treads close to replacing their own clinical judgment? 

Remember what it is that clinicians really need: to receive the information needed to understand patients and improve the quality of care. Generative AI isn’t needed to accomplish this. 

Generative AI has two steps: The first is to identify the relevant historical data. The second is the generative part – to take that historical data and reconstruct it as a summary. The first part is accomplished through natural language processing to gather the relevant data. This technology is well-proven and I believe is 90% of what clinicians need.. The second part (using Generative AI to summarize this data) is the remaining 10%, and this is what has created all the hype. But this part is clearly not yet proven.

The distinction above between “creating new content” and “gathering relevant content” is an important one to make and I believe the latter is significantly more useful to clinicians because the more they are informed with relevant content, the more context they have to make decisions with the patient. 

In fact, the impact of a tool that can gather content has already been realized and has been proven in clinical settings across healthcare in the following ways:

• Helping clinicians identify critical behavioral and social gaps in care, where this content is completely contained in the unstructured data.
• Understanding relevant patient history so that the clinician can make the most informed decisions.
• Identifying risks, early warning signs and care quality issues across patient populations that may go unnoticed by the clinician.
• Understanding community-level trends that enable a health system to offer the right balance of services to the populations it serves.

I believe that the value of the above-proven use cases, especially about quality of care received, generally outweighs the value of the top prospective Generative AI use cases:

• Drafting of patient notes for clinicians to finalize
• Automating chatbot correspondence with patients
• Suggesting clinical care plans

I believe in 2024, the industry should begin to focus more heavily on the identification of actionable content instead of the creation of new content. Generative AI has become part of the culture, but let’s avoid getting caught in the hype and focus on how to deliver value today.

About Marty Elisco

Marty Elisco is the CEO of Augintel, a SaaS company that improves the ability of health and human services professionals to deliver care. Augintel builds natural language processing (NLP) software used by caseworkers, therapists and clinicians to identify key information buried in text in case management and medical records systems, giving them a deep understanding of the at-risk individuals and families they care for.

What You Should Know:

– In a groundbreaking collaboration, Aiforia, an AI company specializing in pathology solutions, and Mayo Clinic have announced the exclusive licensing of a revolutionary AI model that improves the prediction of colorectal cancer recurrence.

– The successful development and licensing of this AI model marks a significant milestone in the fight against colorectal cancer. As this technology becomes integrated into clinical practice, it has the potential to transform the landscape of cancer care, leading to improved patient outcomes, reduced treatment costs, and a more personalized approach to managing this prevalent disease.

AI Model

The AI model, meticulously trained on extensive datasets, analyzes digitized tissue samples to identify 15 key histological features. By combining this data with two additional clinical parameters, it generates a personalized colorectal cancer recurrence risk score for each patient. This score empowers physicians to make informed treatment decisions, potentially paving the way for:

• Reduced reliance on unnecessary chemotherapy: By accurately identifying patients with a low risk of recurrence, the AI model can help avoid unnecessary exposure to the harmful side effects of chemotherapy. This not only improves patient quality of life but also reduces the financial burden on healthcare systems.
• Targeted therapy for high-risk patients: For those patients predicted to be at high risk of recurrence, the model can guide physicians towards more aggressive treatment options, potentially improving their chances of successful long-term outcomes.

Beyond the Numbers

The impact of this AI-driven approach extends far beyond mere cost savings and treatment optimization. It marks a significant step towards personalized medicine, where each patient receives a tailored treatment plan based on their unique biological and clinical profile. This shift empowers both patients and physicians, fostering a more collaborative and informed treatment journey.

A Collaborative Journey

The Aiforia-Mayo Clinic partnership extends far beyond the development of this single model. Their collaboration began in 2020 with the implementation of Aiforia’s AI-assisted image analysis software in Mayo Clinic’s pathology research. Today, over 70 Mayo Clinic pathologists utilize Aiforia’s tools to develop their own AI models for future clinical application, and more than 30 ongoing research projects leverage Aiforia technology. This ongoing collaboration highlights a deep commitment to continuous innovation and the translation of cutting-edge AI into tangible clinical benefits.

“This is the first example of an AI model developed with the Aiforia platform, which is used to assess the risk of cancer recurrence. It is also the first clinically relevant AI model developed with a customer and commercialized by Aiforia. The licensing agreement with the Mayo Clinic enables us to market and sell this AI model for colorectal cancer recurrence risk evaluation globally, says Jukka Tapaninen, the CEO of Aiforia. “This is an example of many similar AI model co-development projects, which are ongoing with the Mayo Clinic and other partners. These will enable us to significantly enrich our product portfolio.”

What You Should Know:

– Nabla, a Boston, MA-based ambient AI assistant for healthcare professionals, announced today the initial close of a $24M Series B funding round led by Cathay Innovation, with participation from ZEBOX Ventures.

– The latest round of funding brings Nabla’s total funding to over $43M and fuels its mission to revolutionize healthcare delivery by empowering clinicians and improving patient care.

Easing the Burden of EHRs:

Clinicians often cite managing the electronic health record (EHR) as their biggest stressor, adding hours of tedious documentation to their workload. Nabla tackles clinicians’ biggest pain point: the electronic health record (EHR). Clinical documentation takes up a staggering six hours per week, stealing valuable time from patient care. Nabla Copilot, powered by AI, tackles this challenge with:

• AI-powered note generation: Creates accurate and concise notes in real-time, reducing documentation time significantly.
• Medical coding recognition: Automatically identifies and codes diagnoses and procedures, streamlining billing and reimbursement.
• Seamless EHR integration: Works seamlessly with existing EHR platforms, minimizing disruption to workflows.

Nabla caters to both small practices and large health systems, partnering with EHR platforms for seamless integration and prioritizing privacy, accuracy, and speed. Their proprietary Large Language Models (LLMs) and Speech-to-Text (STT) technologies, developed by former Facebook AI Research engineers, ensure cutting-edge performance and security.

95% accuracy

Launched in March 2023, Nabla Copilot has experienced rapid growth with +20,000 providers who have already adopted the solution for its accessibility, accuracy, and speed, and have helped build Copilot to better serve the provider community’s needs. To date, more than 3 million consultations were conducted through Nabla Copilot annually. The solution boasts 95% accuracy in generated notes, requiring minimal edits and time savings of up to 50% in documentation tasks.

“This fundraise allows us to continue delivering on our vision to build the premier ambient AI assistant that will allow clinicians to experience the joy of practicing again,” says Alex Lebrun, Nabla’s co-founder and CEO. “Clinical documentation is just the beginning. Health systems are eager to leverage AI across the entire spectrum of healthcare, and we’re ready to be their partner.”