Learn about research on AI that is providing decision support to hospitals across multiple EHR systems and is using data transfers in predictive models.
It’s beyond doubt that data is increasingly important in healthcare, but there is also a strong sense that doctors themselves are not very keen on it. I wrote last year, for instance, about a study exploring how doctors felt when patients brought their own data into consultations.
This perhaps goes some way to explaining a finding from a second paper, published earlier this year, suggesting that doctors themselves are often the main barriers against the digitization of patient records.
A pair of papers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) aim to help doctors make better use of the digital information they’re presented with.
The first paper documents a machine-learning-based approach called ICU Intervene, which uses data from ICU to provide doctors and nurses with the best treatment given a range of symptoms. In addition to providing suggestions, it also explains its reasoning to provide a level of accountability that is so important in healthcare.
The aim of the project was to provide actionable insights that can make a profound difference to the health outcomes of the patients on the ICU ward.
The system provides hourly predictions for five different interventions that aim to cover a variety of critical care needs, be that breathing assistance of the improvement of cardiovascular function.
Each hour, the system pulls out data that allow for vital signs to be represented alongside the patient notes and other data points. The data is presented to staff in a way that clearly shows how far from the „ideal“ (average) the patient is.
Through testing, the system outperformed existing systems at predicting the correct intervention, and was particularly strong at predicting the need for vasopressors, which are used to tighten blood vessels, and therefore raise blood pressure. The team hopes to continue developing the system so that it can support individualized care and advanced reasoning for the treatments it recommends.
The second paper documents a system known as „EHR Model Transfer,“ which is designed to facilitate the use of predictive models based upon an electronic health record system, even if it’s trained on data from a completely different system.
This is a crucial piece of work, as most machine learning models require data to be stored in a consistent manner. The carousel many hospitals have with constantly changing EHR systems significantly hinders attempts to utilize machine learning.
This means successful data transfer is a big thing. EHR Model Transfer works using natural language processing to identify key clinical concepts that may be encoded different across different systems. These can then be mapped to a number of clinical concepts.
The system is particularly useful when data needs to transfer between different systems, such as when patients move hospitals, but doctors still need to derive insights from that data.
The system was tested according to its ability to predict both the mortality risk of the patient and their need for a prolonged stay. The system was trained on one EHR platform and then tested on a different one. It managed to outperform existing systems that only had to work on a single EHR system.
Both systems were trained using data from the critical care database MIMIC, which contains data from around 40,000 critical care patients.
