Empowering healthcare providers with generative AI

Empowering healthcare providers with generative AI

Microsoft’s Sally Ann Frank shares how large-scale organisations and life sciences startups are using new technology and large language models to decipher healthcare data and improve patient experiences  

Guest contributor |

Artificial intelligence has taken the central spotlight for technology providers, and 2023 marks the season of generative AI innovation. The surge in interest surrounding generative AI within the healthcare domain surpasses the typical allure of novelty.  

According to a July 2023 article by McKinsey & Company, generative AI represents “a meaningful new tool that can help unlock a piece of the unrealised $1 trillion of improvement potential present in the healthcare industry.” From automating pre-authorisation letters to summarising patient notes, generative AI is being progressively integrated into healthcare services to offer improved experiences for both patients and medical professionals. Although concerns about the safety and ethics of using generative AI in healthcare and life sciences persist, organisations are finding ways the technology can enhance healthcare without compromising the quality of care of the privacy of patients.  

On a large scale, Microsoft is working with healthcare software company Epic to alleviate clinician administrative burden by integrating Azure Open AI into its electronic medical records software. This synergy empowers clinicians and staff to interact with health data in a conversational and intuitive way, thus responding more effectively to patient messages. Additionally, through Nuance DAX Express, Microsoft has embedded AI-powered clinical documentation capabilities into Epic workflows, further alleviating administrative workloads that contribute to burnout, while broadening patient care access and enhancing healthcare outcomes. 

At the other end of the spectrum, smaller, younger companies are also pivotal in fuelling the widespread interest and adoption of generative AI. Pangaea Data, a life sciences technology firm, addresses clinical challenges by providing a product platform that combines medical expertise with innovative AI to emulate a clinician’s manual tasks. Pangaea demonstrated the potential and limitations of large language models (LLMs) by conducting two analyses using ChatGPT and GPT-4: one to find patients with specific diseases using a real-world large electronic health record database, and the other for assisting healthcare workers in prospectively evaluating chronic obstructive pulmonary disease (COPD) patients throughout their disease progression.  

Pangaea revealed that GPT-4 performs effectively on diverse tasks concerning various diseases, such as COPD, chronic kidney disease, Cancer Cachexia, herpes simplex virus infections and primary biliary cholangitis. To generate more coherent and extensive text, Pangaea employed prompting techniques such as chain-of-thought, which incorporate clinical knowledge of diseases and instruct LLMs to produce intermediate reasoning. It also utilised few-shot prompting, enabling GPT-4 to learn from a few examples without requiring extensive training data. Pangaea demonstrated GPT-4’s capability to attain performance levels as high as 96 per cent F1 scores for disease classification, and the potential of providing assistance to clinicians in the prospective evaluation of patients. 

Another company, ScienceIO, is on a mission to develop language models to decipher healthcare data. Its solution transforms medical text into enriched data to enhance patient care. With the application programming interface, users can dissect medical records and pinpoint essential details like medications, treatments and procedures. Additionally, ScienceIO ensures data security by excluding sensitive healthcare information. The foundation behind ScienceIO’s work is its healthcare-specific language models, which are trained using high-quality biomedical and clinical data to avoid potential biases and bad practices that may seep in from web data, posing risks to healthcare applications. ScienceIO recently unveiled its Embeddings API, which helps developers to construct search products that uncover critical patient information. 

Beyond aiding patient care, startups also leverage large language models to understand and improve patient experiences and engagement. Hyro, a provider of conversational AI healthcare solutions, empowers enterprises to automate workflows and interactions across their most valuable platforms, services, and channels – including call centres, websites and mobile applications. Using a plug-and-play approach alongside conversational intelligence, Hyro delivers omnichannel analytics, including engagement metrics, trending topics and knowledge gaps that offer industry-leading control and optimisation. Its new GPT-powered assistant, Spot, is trusted by the world’s leading health systems. It generates customised responses to queries while navigating patients to relevant pages for deeper exploration.  

Once clinicians have engaged with patients, CommerceAI can help them to monitor their sentiment via generative AI. CommerceAI’s latest offering, auto-MATE, is a generative AI tool catering to diverse industries, including healthcare, by processing unstructured data such as contact centre calls, Teams meetings or telemedicine recordings to extract structured insights and subsequently automate workflows. This tool streamlines provider operations, elevates patient care and enhances overall healthcare outcomes. CommerceAI even tailors the auto-MATE tool specifically for pharmaceutical companies to support activities ranging from drug discovery to regulatory compliance. 

While the potential of generative AI in healthcare is immense, there is still much to learn and safeguards to be developed. The key principle in successfully adopting LLMs is to begin with low-risk, high-impact scenarios such as administrative tasks, with all findings undergoing clinician adjudication. As an industry, we should work together to solve the challenges of data privacy, algorithmic biases and interpretability to ensure the safe and responsible deployment of LLMs in patient care.  

Sally Ann Frank is worldwide lead for health and life sciences at Microsoft for Startups 

This article was originally published in the Autumn 2023 issue of Technology Record. To get future issues delivered directly to your inbox, sign up for a free subscription

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