How LLMs Can Streamline Clinical Operations
Healthcare leaders are turning to AI to combat rising costs and clinician shortages
Large Language Models, or LLMs, are on the healthcare CTO and health leaders' wish lists these days. If you're reading this, it’s likely that you’ve been considering its functionality for your own clinical operations too. Living under the web of AI tools, LLMs are positioned to streamline many of the inefficient and time-consuming tasks beholden to clinicians while introducing new functionality to improve overall patient experiences.
That was a lot of fancy words in a small paragraph, basically, LLMs have the ability to make a clinician’s job easier in a few key areas like clinical documentation, decision support and multi-language communication. Let’s dive right in.
Understanding LLMs and their role in healthcare
Having a basic understanding of what advanced LLMs are and what they do is a great place to start if you’re thinking of incorporating them into your AI toolbox.
Essentially, an advanced language learning model (LLM) in healthcare refers to a sophisticated computational system designed to comprehend, analyze and generate human language with a high level of precision and contextual understanding. Utilizing artificial intelligence (AI) and machine learning techniques, particularly deep learning, these models are tailored to process and interpret healthcare-related language data in a manner that closely mirrors human linguistic capabilities.
Here are some key features and functionalities of advanced LLMs:
Natural Language Processing (NLP): Advanced LLMs shine in NLP, allowing them to interpret and understand healthcare-related language intricacies beyond basic syntactic and semantic analysis. They can discern nuances, grasp context and even analyze the sentiment in written or spoken medical communication.
Contextual awareness: Unlike traditional language models, advanced LLMs consider the context in which medical terms and phrases appear. This contextual awareness enables more accurate and contextually relevant responses or analyses in healthcare settings.
Deep learning architectures: These models often employ deep neural networks, such as recurrent neural networks (RNNs) or transformer architectures like BERT (Bidirectional Encoder Representations from Transformers). Deep learning allows the model to learn intricate representations of healthcare language, capturing complex patterns and dependencies.
Adaptability and continuous learning: Advanced LLMs are trainable and adaptable to evolving healthcare contexts. They can be fine-tuned for specific healthcare domains or tasks, allowing them to learn from new medical data and continuously enhance their language understanding capabilities.
Generative capabilities: Some advanced LLMs possess generative capabilities, enabling them to create coherent and contextually relevant language output. This is particularly valuable for tasks like medical note completion, generating clinical documentation or supporting creative medical writing.
Multimodal processing: Beyond textual information, advanced LLMs may incorporate multimodal processing to handle diverse healthcare data types, including medical images, audio recordings and video, in conjunction with language. This facilitates a more comprehensive understanding of healthcare information across different modalities.
Transfer learning for healthcare domains: Many advanced LLMs are pre-trained on extensive healthcare language datasets, allowing them to learn general patterns related to medical terminology and communication. This pre-training is often followed by fine-tuning on specific healthcare tasks or domains, ensuring efficiency and accuracy in healthcare-related language understanding.
Real-time interaction in healthcare settings: Advanced LLMs can effectively handle real-time interactions and responses, making them well-suited for applications such as healthcare chatbots, virtual assistants and customer support systems within the medical field.
How LLMs can be applied to clinician operations
Alright, now that we have a better understanding of what LLMs are and where they shine, it’s time to give you what you came for. There are a couple different ways LLMs can be incorporated into a clinician’s workflow to offer a helping hand.
Clinical documentation
There are two ways LLMs can help clinicians and healthcare providers streamline their clinical documentation; medical transcriptions and EHR charting.
Transcribing
For transcriptions, LLMs can improve speed and efficiency by potentially reducing the time and effort required for manual transcription. This can be utilized in a healthcare setting where scribes are low or if a transcription is needed suddenly. The AI tool can tap into its vast amount of data to navigate diverse accents and styles to ensure accurate note taking. It can also recognize and understand complex medical terms, enabling consistent terminology.
EHR documentation
AI-driven LLMs have the opportunity to dramatically improve the functionality of EHRs for providers and patients. According to an editorial in the National Library of Medicine, AI models like LLMs can simplify and automate many complex tasks, leading to enhanced productivity and improved healthcare outcomes.
For example, clinicians can dictate their notes that are transcribed real time by the AI model. It can then also be used to extract important information from clinical narratives and summarize crucial clinical information from a patient's record using NLP. Another benefit to integrating LLM tech into your EHR is its ability to create custom patient communication and education. Healthcare professionals can leverage this technology to create personalized care plans and ensure patients are well-informed.
Multilingual patient care
Thanks to LLMs natural talent as an NLP, they can help clinicians break down the language barrier between patients. One way they can do this is to provide real-time language translation services, enabling clinicians to communicate with patients who speak languages different from their own. These language capabilities could reduce miscommunication by providing accurate translations and interpretations.
Clinical decision making
The ability of LLMs to analyze and accurately interpret data can aid healthcare professionals in their clinical decision making. They can offer possible treatment plans based on patient information and data as well as interpret images and scans to offer potential diagnosis. Instead of having to navigate large groups of patient data manually, providers can also use LMMs to find patterns or anomalies in community health data in order to predict/track potential outbreaks or pandemics.
It’s important to note that AI-tools like LLMs should always be used in tandem with clinician oversight. While they continue to innovate, clinical decisions should not be based solely on a LLMs observations. It is a tool, not a licensed medical professional.
Common challenges and solutions
When discussing the challenges or concerns that face AI models like the large language models, most center around data/patient privacy and security.
Patient privacy and security
As LLMs process vast amounts of sensitive healthcare information, the main concern is ensuring patient confidentiality and complying with stringent privacy regulations, like the Health Insurance Portability and Accountability Act (HIPAA). Patients trust healthcare providers to safeguard their personal and medical data, and any compromise in the security of LLMs could lead to unauthorized access or data breaches, jeopardizing patient privacy.
How can we avoid this? Healthcare organizations need to implement strong encryption protocols, access controls and audit trails to fortify the security infrastructure surrounding LLMs. This would prevent unauthorized disclosures and preserve the integrity of patient information.
Ethical use of LLMs
Beyond the immediate privacy risks, the ethical use of LLMs confronts the potential biases embedded in the models. Training data used to develop LLMs may inadvertently contain biases, and if left unaddressed, these biases could perpetuate disparities in healthcare outcomes. Ensuring fairness and impartiality in LLMs is crucial to prevent discrimination based on factors such as language or cultural background.
Additionally, healthcare providers must continue to advocate for the transparency of data usage policies, educate patients about the role of LLMs in their care, obtain informed consent to mitigate concerns surrounding privacy and build and maintain patient trust in the adoption of these advanced language technologies.
LLMs being used in healthcare today
LLMs sound good in theory, but how are they being utilized? There are already some notable tech giants and healthcare companies working together to incorporate AI-models into their workflows.
Microsoft Corp. and Epic
In April of 2023, Microsoft Corp. and Epic announced an expanded partnership to integrate generative AI. As part of the initiative, they are leveraging the Azure OpenAI Service in conjunction with Epic's EHR software. The goal of this collaboration is to deliver a comprehensive array of generative AI-powered solutions to increase productivity, enhance patient care and improve financial integrity of health systems globally according to Microsoft’s press release.
Some of the initial solutions being developed through this initiative include auto-drafting message responses for prominent health institutions like UC San Diego Health, UW Health and Stanford Health Care. The two organizations are also developing another feature that involves the development of natural language queries in Epic’s self-service reporting tool, SlicerDicer.
While we don’t yet know for sure what fruit these tools will bear, it is clear healthcare leaders are looking to AI as a potential solution to large administrative costs and clinician shortages. According to Mckinsey & Company, nearly a quarter of U.S. national health expenditure goes toward administrative costs, which could be reduced through technology. Additionally, the U.S Department of Health and Human Services predicts that there will be a nationwide shortage of 90,000 physicians by 2025. As a result, AI has emerged as a potential solution to these healthcare hurdles.
Powering innovative virtual healthcare solutions
There is still a lot unknown about healthcare AI and what it might bring to the future of the industry. But there is no doubt that artificial intelligence will be an integral part of patient care in the future.
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