Service Providers Need to Prioritize Security, Efficiency, and Transparency when Leveraging Language Models (LLMs)
It's no surprise that artificial intelligence (AI) is generating buzz in the healthcare sector. This innovative technology has the potential to revolutionize healthcare, from boosting administrative efficiency to advancements in pharmaceutical discovery.
Among the most significant impacts AI is already making in the clinical setting is the support of overwhelmed doctors. With medical knowledge doubling every 72 days, it's a daunting task for physicians to keep up with updated guidelines. Luckily, AI and large language models (LLMs) can step up to the plate, digesting enormous amounts of data from different sources and providing clear, concise insights for doctors to consider when making patient-care decisions.
However, a recent McKinsey study revealed that many healthcare providers harbor concerns about AI risks, including potential HIPAA violations and concerns about the training data sources of LLMs. This vulnerability increases with public LLMs such as ChatGPT, as they are not HIPAA-compliant and allow multiple customers to share the same resources.
One solution healthcare organizations might consider is training their own LLMs on patient data, but this approach can be costly, time-consuming, and requires specialized expertise. Additionally, it could lead to model "lock-in" and hinder the adoption of new, more powerful LLMs. Finally, it can be difficult to trace the source of a model's recommendations, raising questions about reliability.
Flexibility, Transparency, and Control with RAG
To overcome these obstacles, healthcare organizations need more than just private and secure AI enclaves. They require flexibility to work with various LLMs, transparency to trace recommendations back to their sources, and full control over those sources and data.
By implementing Retrieval-Augmented Generation (RAG), providers can achieve these benefits and more. This approach enhances private LLMs with multiple knowledge sources chosen by the healthcare organization. These sources can encompass electronic health records, clinical guidelines, and other types of data. RAG instructs an LLM to retrieve pertinent information from these sources without exposing data from the sources to the outside world.
In a practical scenario, let's say a patient is experiencing dizziness and can't get an appointment with an ear, nose, and throat specialist in a reasonable timeframe. This patient visits their general practitioner for help. Instead of making an educated guess based on the patient's symptoms, the GP can type a simple query into their laptop and receive personalized, up-to-date treatment recommendations based on the latest clinical practice guidelines issued by the American Academy of Otolaryngology-Head and Neck Surgery.
RAG offers more benefits than just improved care for clinicians. It enables the tracing of answers back to information sources, which facilitates verification of the accuracy of information provided and creates audit trails if necessary. Furthermore, it eliminates the need for model retraining and fine-tuning, as organizations can simply introduce new data into their existing LLMs as needed. Finally, healthcare providers can swap out LLMs as new models are introduced, avoiding model lock-in.
Improving RAG Implementation
LLMs can tackle challenges beyond clinical decision support. For instance, they can solve data interoperability problems caused by poorly written or malformed APIs. In this case, APIs serve as important tools for handling insurance claims but often result in rejections due to a lack of understanding. Utilizing LLMs and RAG, healthcare providers can submit data to an API supported by an LLM service, and the LLM translates the data into the expected format, thereby minimizing rejection rates and reducing costs associated with pursuing claims denials.
As we move into 2025, the generative AI hype cycle in healthcare will peak, and its use cases will become clearer. Now is the perfect time for healthcare organizations to assess how to best utilize LLMs for maximum effectiveness. Building in-house LLMs may not be the most cost-effective solution, as healthcare institutions strive to cut costs amid rising financial pressures. A combination of open-source LLMs and the RAG methodology offers a more financially viable option while enabling clinicians to provide patients with targeted and accurate care. Through the use of AI, we can transform the healthcare landscape, making it more accessible, efficient, and patient-centered.
- In the realm of health-and-wellness, artificial intelligence (AI) and large language models (LLMs) have the potential to address medical-conditions, not just in pharmaceutical discovery, but also in the clinical setting by supporting overwhelmed doctors with up-to-date guidelines, thanks to Retrieval-Augmented Generation (RAG) technology.
- As technology advances and AI becomes more integrated into the healthcare sector, the use of open-source LLMs in conjunction with RAG methodology can enable healthcare providers to optimize their resources, ensuring improved care for patients while addressing interoperability issues caused by poorly-written APIs, thus streamlining processes and cutting costs.
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