The Potential Application of Large Language Models in Pharmaceutical Supply Chain Management

Abstract

The pharmaceutical supply chain is growing increasingly intricate, with various challenges arising in drug inventory management, procurement, distribution, and dispensing processes. Issues such as shortage mitigation, inconsistency in regulatory compliance, cost control, cold-chain storage, adaptation to technologic advances, and secure information sharing all pose significant difficulties to pharmaceutical supply chain management.1 Consequently, this adds immense pressure on health system pharmacies to enhance their arsenal of available tools.2Pediatric hospitals in particular encounter unique challenges relating to pharmaceutical and supply shortages, which can cause delays in vital patient procedures, alterations in care protocols, and even unexpected changes in care locations.Historically, supply chain operations have been improved by using physical automation technologies and methodologies such as robotic process automation. Efficient operations use business intelligence to drive informed decisions regarding sourcing, pricing, and patient care, which can be facilitated either by internal teams or through vendor-provided solutions.3 While this approach does mitigate several problems, it rarely results in holistic problem resolution—like a chronic illness, these issues wax and wane.Addressing these supply chain challenges is crucial to ensure that patients receive the most optimal care available.3 Emerging technologies like large language models (LLMs) and generative artificial intelligence (AI) present new opportunities to address these supply chain challenges. LLMs can be used for knowledge management, data analysis, and process automation, contributing to the ongoing digitalization of supply chains, in ways that are difficult to predict, but worthwhile considering.4LLMs are a type of deep-learning artificial neural network that can perform various natural language processing (NLP) tasks, such as recognizing, translating, predicting, and generating text. However, unlike traditional NLP models, LLMs can capture long-range dependencies and contextual information within the entire prompt by using what are called self-attention mechanisms, instead of word by word. Self-attention allows the model to focus on the most relevant parts of the input and output, and how they relate to each other.5 This self-attention is based on a mathematical model called a transformer, which uses many layers of neural networks to process the input and output text sequences. The transformer consists of an encoder that converts the input text into numerical representations called tokens, and a decoder to convert the results back into text. These LLM transformers are trained to predict the next best word, or missing words, based on many examples of natural language from massive amounts of data, often from diverse sources and domains, to learn the patterns and structures of natural language. LLM transformers have billions of parameters, which are the mathematical weights the model learns during training and uses to make predictions during inference. Hence, an already trained transformer model that is used to generate text is called a generative pretrained transformer. LLMs have achieved remarkable results in various NLP benchmarks and applications, such as question answering, text summarization, text generation, and sentiment analysis. Training a transformer requires massive amounts of compute power,6 so these pretrained transformers can also be adapted to specific tasks or domains by fine-tuning, which is the process of updating the parameters of a pretrained model by using a smaller and more relevant dataset or even using reinforcement learning from human feedback of results.7LLMs have proven to be a disruptive technology, and there is a lot of interest in leveraging LLMs across many domains to perform different tasks, and as such, they have the potential to affect health care in many ways. For example, LLMs can help pharmacists and clinicians with