Approaches to data mapping through the years
Over the years, clinical informaticists have used the best technology and techniques available to normalize this data and unlock its value for informed decision making, quality improvement, and enhanced patient care. The approaches for mapping lab data have evolved from manual, rule-based approaches to more automated machine learning models.
Early 2010s - Clinical Rules
Some of the first efforts in mapping complex data like labs were curated set of rules built by studying how humans approach the problem. Rules could be built using logic implicit to LOINC that included synonyms, word replacements, and unit conversions. This approach resulted in high accuracy rates, and was easy for mappers to understand the logic, but isn’t scalable and doesn’t learn from past results.
Late 2010s - Deep learning models
As AI and machine learning technology matured, the maps built in a clinical rules model could be leveraged to train a deep learning model to predict each of the six components that helped automate the effort. This improved accuracy and coverage across datasets. However, it is unable to generalize for unknown data, which presented challenges during the COVID pandemic when brand new LOINC codes and data were being introduced at a rapid pace, still requiring human intervention to map data and train the model.
This brings us to the current day. The latest development in technology for mapping lab data is Large Language Models (LLMs).
Unveiling the Power of Large Language Models
What are Large Language Models (LLMs)?
Large Language Models (LLMs) are a type of AI that can recognize and analyze text. They are based on transformers, a type of neural network architecture, that’s scalable and allows the LLM to be trained on vast amounts of data.
Integrating Large Language Models into the lab mapping workflow
Although it's a great step forward in terms of efficiency, a Large Language Model by itself is not enough to accurately map a complex clinical dataset like LOINC codes. LLMs offer a higher launching point compared to previous technologies, but they still need to be fine-tuned and have more clinical information imparted to bring them to a really usable and effective mapping algorithm. It’s when a LLM is combined with a foundation of terminology expertise, clinically accurate maps, and curated LOINC synonyms, that we’re able to see improvements in both speed and accuracy of mapping.
The call to action for healthcare organizations
For healthcare organizations, the call to action is not just to invest in technology but in transformation. This means putting resources into both new tools and the team. For informatics professionals and data scientists, the future looks promising. We have access to more powerful tools, but moving forward, we need a mix of respect for clinical accuracy, a willingness to explore new ways of doing things, and adaptability to navigate the changing healthcare data landscape.
In a landscape marked by data complexity , the strategic adoption of AI-driven lab mapping solutions provides a path to the forefront of data-driven healthcare transformation. Our terminology experts are here to help - reach out today to learn more!
