Clinical documentation plays a multi-faceted role in the world of value-based healthcare, which by default means it’s a mission-critical aspect for providers. For instance, accurate documentation is critically vital to support full and fair reimbursement. For patients, on the other hand, documentation is key to recording their medical history, current diagnosis, and ongoing treatment plan. And like any mission-critical system, it can create a heavy burden for clinicians and medical staff. According to a report by JAMA Internal Medicine, physicians spend nearly two hours each day after office hours to complete clinical documentation.
The issue of burnout has been a major talking point in the last decade when it comes to clinical documentation. While there have been several technological advances, especially in medical transcription, that have helped alleviate some of that burden, none have held the promise that large language models (LLMs) bring to the table.
The Prospect of LLMs in Clinical Documentation
LLMs were launched into an exponential trajectory of popularity with the launch of OpenAI’s ChatGPT, which brought the foundational models to the forefront of commercial application. These models excel at processing and manipulating natural language, which makes them great at generating, summarizing, classifying and extracting content in text form. By virtue of these capabilities, LLMs have emerged as the single most transformative technology in the field of clinical documentation.
Content synthesis and summarization
LLMs are capable of synthesizing data from numerous sources to generate contextual drafts for speedier collaboration between parties (for instance, between payers and hospitals). For instance, LLMs can streamline the creation of clinical documents by distilling ambient transcripts into a clearly distinguished Subjective Objective Assessment Plan (SOAP) format, converting large volumes of unstructured data into a universal, structured, systematic format.
On the other hand, using LLMs to optimize medical dialogue summarization can provide both physicians and patients with concise points that are easy to understand and retain. Similarly, LLMs can be employed to respond efficiently to patient queries, offering quick and accurate answers to questions regarding diagnoses, treatments, or medications, thereby saving time for healthcare providers while keeping patients well-informed.
Predictive health analytics
When exploring the possibility of LLMs in clinical documentation, predictive analysis of patient data emerges as a high-impact area. By effectively analyzing patient data, LLMs can highlight patients who stand a higher risk of certain conditions, allowing providers to plan more efficient treatment plans.
Improved value-based outcome
Their ability to extract and process unstructured data allows LLMs to provide unprecedented support to clinical decision-making tools and predictive models, based on nothing but natural language inputs. This includes providing data-driven insights into the potential risks and benefits of different treatment paths and informing balanced clinical decisions.
From an administrative perspective, they can automate the coding of medical procedures and services with superhuman accuracy, identifying and correcting coding errors and facilitating improved efficiency in billing processes. This would, in turn, lead to a significant reduction in denied claims and financial penalties.
The Risks and Road Ahead
For as long as LLMs have enjoyed the limelight, there has been a growing concern in the shadow: reliability of models. LLMs are pre-trained on a vast pool of data. However, the purpose of this training data is to decode human language, not use it as a knowledge repository.
The lack of reliability of LLMs takes many forms, but the one that is talked about is hallucination. This happens when an LLM that is faced with a question it does not know the answer to start generating an “educated” guess that may include false information or made-up facts.
There are a few other reliability issues with LLMs like:
- Sycophancy: The LLM generates responses that conform to the user’s beliefs rather than the factually correct information
- Potentially risky behavior: The LLM tries to manipulate corresponding systems and human users through its responses (read how GPT-4 convinced a human to fill a CAPTCHA for it)
- Overconfidence: The LLM repeatedly generates incorrect responses due to overdependence on training data.
Proper guardrails can, however, ensure the model’s accuracy, which includes undertaking traditional validation exercises, developing a process to flag high-risk errors in a clinical setting, and always keeping a “human in the loop”. Moreover, while LLMs are more or less reliable when it comes to reductive tasks like analyzing data, summarization, categorization, and extraction), the risks are much higher for generative tasks.
Despite their shortcomings, LLMs have a disruptive potential that can not only transform medical documentation but also elevate the quality-of-care outcomes with greater efficiency. However, the application of this technology calls for thorough testing before it is implemented by care providers. Altysys, a technology expert partner, helps care providers rapidly prototype and implement LLMs across healthcare use cases to mitigate rising clinician burnout and derive the greatest possible value. Get in touch with us today and start reaping the benefits of LLMs in your medical documentation workflow.