Call the Doctor: Are Patients Ready for Generative AI in Healthcare? Bain & Company

How to Regulate Generative AI in Health Care

NVIDIA has introduced Generative AI microservices aimed at advancing drug discovery, medical technology (MedTech), and digital health. These microservices, available through the NVIDIA AI Enterprise 5.0 software platform, offer a wide range of capabilities, including advanced imaging, natural language processing, and digital biology generation. GenAI tools typically rely on other AI approaches, like NLP and machine learning, to generate pieces of content that reflect the characteristics of the model’s training data.

The technology instills efficiency in drug discovery and paves the way for customized medical solutions. The shift to value-based care –transitioning from traditional fee-for-service models to payment structures that reward efficiency and outcomes– requires rethinking how care is delivered, with a focus on improving patient health while managing costs. However, despite its promise, the adoption of AI in healthcare raises significant ethical concerns.

One of the most powerful capabilities of generative AI in healthcare is offering tailored recommendations and individual support. These relate both to offering psychological and physical care assistance, like drug use instructions. External data is first encoded into vectors and stored in the vector database (where vectors are mathematical representations of various types of data in a high-dimensional space).

Predictive Analytics for Disease Progression

To determine whether there was a difference in responses before and after the intervention, Mann Whitney tests were performed. Existing products that have traditionally relied on manual point-and-click interfaces for data entry are now incorporating AI to enhance their functionality. Healthcare may not be the only sector with an identify crisis barreling towards an inevitable AI transformation, but we are certainly the only ones with human life on the line. Unless we get our house in order and start answering the tough questions about who we are and where we’re going – don’t be surprised if generative AI ingests all our insecurities for mass production and weaponizes our own identity crisis against us. The cousin you can’t stand is again comparing his life to yours because you went to the same high school 12 years ago. The uncle who barely got the invite thinks this is the dinner that will finally convince you to vote for his presidential candidate come November.

“And there are some amazing thoughts that are out there, but we have a large component of the healthcare market that just hasn’t had the governance in place to get the most out of AI.” Click the banner below to find out how infrastructure modernization increases healthcare agility. Hannah Nelson has been covering news related to health information technology and health data interoperability since 2020. Meditech is exploring future generative AI use cases for advanced directives, prior authorizations and claims processing.

The agency also finalized new, nonbinding guidance to streamline the process for approving modifications to these devices. Vickers also suggested that breakthroughs in AI-driven research, drug discovery and genomics could lead to significant advancements in precision medicine in 2025. MacTaggart recommended that organizations start with administrative use cases that present less risk to patients.

Josh Stageberg, Vice President of Product at SolarWinds, talks about the organizational successes and possible pitfalls that generative AI can bring, as well as other topics of discussion from the HIMSS AI in Healthcare Forum. Mayo Clinic and NVIDIA will massively accelerate the development of next-generation pathology foundation models. Generative AI is emerging as a pivotal force in healthcare, poised to reshape patient care, medical research and operations. This sophisticated technology, capable of learning from vast datasets to produce contextually relevant information, is enhancing processes and opening doors to new possibilities in medicine. In 2024, healthcare organizations increasingly explored potential use cases for advanced AI and analytics tools to improve care, streamline administrative workflows and increase efficiency. Generative AI, or GenAI, dominated much of the health IT conversation throughout the year, as it had in 2023.

Personalizing health care services

Generative artificial intelligence has the potential to be a helpful tool for healthcare organizations, especially when it comes to increasing efficiency in clinical and operational settings. However, many generative AI tools out there lack transparency regarding the data sets they’re trained on, which could lead to biases. The coming year will see increased interest inhealthcare organizations developing long-term AI strategies, optimizing workflows, managing risks, and ensuring responsible use of technology. While AI can’t replace human compassion and expertise in healthcare, it can support healthcare professionals, allowing them to focus more on people, not paperwork.

In healthcare, RNNs have the potential to bolster applications like clinical trial cohort selection. Artificial intelligence (AI) has the potential to significantly bolster these efforts, so much so that health systems are prioritizing AI initiatives this year. Additionally, industry leaders are recommending that healthcare organizations stay on top of AI governance, transparency, and collaboration moving forward. As healthcare organizations collect more and more digital health data, transforming that information to generate actionable insights has become crucial. While this study is the first of its kind in OT education, the findings align with broader recognition of AI’s potential to streamline clinical processes and enhance creativity in intervention planning as a complement to traditional practices.

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In the pilot group, 90% of the nurses rated the model as helpful enough to replace their current clinical documentation process for handoffs. For many, that will involve making informed strategy decisions about partnering with AI vendors. Both parties will need to vet each other’s protocols, priorities, and mutual interests before signing any contractual agreements. Organizations that have a clearly defined long-term AI strategy will have an easier time understanding which vendors their own vision aligns with. With a better understanding of the technology, a more concrete, actionable dialogue can take place.

For example, medical documentation (e.g., visit notes and medical summaries) has long burdened clinicians, leaving less time for patient interactions. However, the use of GenAI in the clinical setting also poses risks, creating potential liability for healthcare providers, which AB 3030 does little to dispel. California regulators noted in the Senate Floor Analyses of August 19, 2024, concerns that AI-generated content could be biased due to being trained on historically inaccurate data, leading to substandard care for certain patient groups.

From your perspective, what advancements in generative AI are currently having the most impact?

Some healthcare companies are wary and aren’t even willing to start discussions about the legal implications of AI. At IMO Health, we’ve made progress by working closely with our security and legal teams to navigate these uncertainties. In working with several pharmaceutical companies, we leverage our comprehensive terminology to identify patterns in their searches for rare diseases. By doing so, we can align these findings with clinical trials that are actively seeking participants who have specific, often hard-to-identify conditions. As part of the collaboration, NVIDIA has provided Arc Institute with expertise in large-scale model development; the NVIDIA BioNeMo platform running on NVIDIA DGX Cloud for easy-to-use, optimized training; and NVIDIA NIM microservices and Blueprints.

• By leveraging AI, healthcare professionals can focus more on patient care, improving overall efficiency and accuracy.
• Medical data analysis is a cornerstone of modern healthcare, and generative AI has the potential to revolutionize this field.
• Propose recommendations for integrating AI tools into OT curricula and suggest areas for further research based on the findings of this exploratory study.
• This lack of transparency not only undermines the trust of physicians and patients in the generated content but, more importantly, it may pose serious medical risks and ethical concerns.

The risks go down as technologies improve;2025 will see organizations measure which processes are ready to be improved via today’s technology against their own risk tolerance. The analysis is especially critical in healthcare, in which a high-risk gamble can truly be a matter of life and death. But there are certain problems that AI can solve without a mistake resulting in, for example, a patient being prescribed the wrong medication or dosage. Figuring out where to draw the line of risk tolerance will be a critical challenge for organizations in the year ahead.

Technology & Innovation

Organizations like the Centers for Medicare & Medicaid Services (CMS) and other clinical data standards groups have employed CQL to automate quality measure reporting and clinical decision support. These solutions include AI agents that can speed clinical trials by reducing administrative burden, AI models that learn from biology instruments to advance drug discovery and digital pathology, and physical AI robots for surgery, patient monitoring and operations. AI agents, AI instruments and AI robots will help address the $3 trillion of operations dedicated to supporting industry growth and create an AI factory opportunity in the hundreds of billions of dollars. Among the providers using conversational AI agents are Mercy Health, Baptist Health, and Intermountain Healthcare.

Experts: Healthcare AI Will Require Expensive Humans – Healthcare Innovation

Experts: Healthcare AI Will Require Expensive Humans.

Posted: Thu, 23 Jan 2025 19:19:40 GMT [source]

AI also automatically blurs patients’ identities to ensure the highest security and privacy standards. At the university’s Simulation Center, students receive feedback from professors after each fictional medical intervention. NY state plans to invest another $700,000 in 2024 to offer setups, maintenance, and device security

to patients under the care of the Office for the Aging. Without further ado, here are a few capabilities of AI in the field that I find the most exciting along with examples of groundbreaking healthcare AI companies. Using techniques like ML and text mining, NLP is often used to convert unstructured language into a structured format for analysis, translating from one language to another, summarizing information, or answering a user’s queries. Cognitive computing systems must be able to learn and adapt as inputs change, interact organically with users, ‘remember’ previous interactions to help define problems, and understand contextual elements to deliver the best possible answer based on available information.

How Generative AI Can Reduce Administrative Burden in Healthcare

She also suggested that organizations build their own knowledge bases and begin to provide their own data to large language models so that they can provide the right kinds of answers. While that won’t solve larger cultural issues or biases, it does help those engaging with the AI to know what data is involved and what data is missing. These health IT influencers are change-makers, innovators and compassionate leaders seeking to prepare the industry for emerging trends and improve patient care. According to a 2023 AMA survey, healthcare staff dedicate approximately 12 hours each week to completing prior authorizations, with some employees working solely on these tasks. “Doctors and nurses and other care team members all live in this world of all of these additional administrative tasks that take them away from their day-to-day work of focusing on patients and providing care,” Schlosser said.

In assisting clinical decision-making, RAG may provide the sources of information upon which the diagnoses are based, including clinical guidelines, medical evidence, and clinical cases. By categorizing queries into simple factual searches or multi-step reasoning processes, RAG can further clarify how different types of information contribute to a given recommendation, enhancing the transparency of its decision-making. Additionally, some research utilizes external medical knowledge graphs (such as the Unified Medical Language System) or self-construed knowledge graphs to enhance the diagnostic capabilities of models14,39. Based on a given query, the RAG system first identifies relevant nodes in the knowledge graph, such as diseases, symptoms, or medications, and then retrieves both direct relations and multi-hop paths connecting these nodes.

• For instance, when targeting different gender groups, RAG could retrieve research findings on their specific physiological patterns, common disease spectra, clinical manifestations, as well as related recommendations on clinical practice21,22,23.
• The cousin you can’t stand is again comparing his life to yours because you went to the same high school 12 years ago.
• It’s fast and accurate, which is why it is so good at spotting potential drug candidates and speeding up the drug discovery process.
• While GenAI’s potential in these areas is apparent, healthcare firms are cautious, prioritizing safer, less regulated applications in the near term to mitigate risks and maintain compliance.
• — and be able to give them real examples so that they understand how this is being used, where it’s being used and the power behind it.

Retrieval-augmented generation holds the potential to alleviate these issues and drive medical innovation from the perspectives of equity, reliability, and personalization. GenAI tools take a prompt provided by the user via text, images, videos, or other machine-readable inputs and use that prompt to generate new content. Generative AI models are trained on vast datasets to generate realistic responses to users’ prompts. In healthcare, NLP can sift through unstructured data, such as EHRs, to support a host of use cases. To date, the approach has supported the development of a patient-facing chatbot, helped detect bias in opioid misuse classifiers, and flagged contributing factors to patient safety events. The term typically refers to systems that simulate human reasoning and thought processes to augment human cognition.

In other areas, there’s going to be some work that probably needs to be done, a lot more work. For instance, in pathology, those departments, the images may not even have digital images; they may just be using old-school light microscopes. He implies that the benefits or value propositions of these AI solutions may need to be more compelling to drive widespread adoption in the market, leading to a lack of commercial viability. This deficit applies to both female-specific conditions and those affecting both sexes but manifests differently in women.