How AI could solve the problem of patients lost to follow up

New technology is being developed at UCLH which could help ensure all patients receive the right follow up care after their appointment.

The software, called CogStack, uses a type of artificial intelligence called natural language processing (NLP) to interpret free text notes found in a patient’s record.

The software is being developed by a team from the BRC-supported Clinical and Research Informatics Unit, in conjunction with the Gastrointestinal (GI) Clinic at UCLH.

The challenge

The GI clinic identified a problem where, occasionally, the ‘next clinical action’ is not recorded in a patient’s record. These follow up actions include referrals onto other services, requests for bloods and imaging procedures, and should be created by the clinician or clinical support staff upon the completion of a patient consultation.

In a small number of cases, due to human error, these orders are not placed and after a period of time the patients can become lost to follow up.

The solution

Where next clinical actions have not been recorded in a patient’s record, it is usually possible to infer what orders should have been created for a patient, since a patient’s next clinical steps are almost always documented by the clinician in the patient’s notes.

This is what CogStack is designed to do – through use of NLP. By interpreting free text found in a patient’s medical record, it can highlight who the clinic may need to follow up with and how.

The CogStack system makes use of a trained machine learning model (BERT model) that has been trained to detect various intents/orders related to the GI clinic. The model has been pre-trained in a supervised training session where GI operational staff trained the model to accurately detect various intents [1].

The UCLH CogStack team have developed an alerting dashboard that would allow GI operational staff to act on what CogStack identifies as a follow up action it believes should have been created for a patient based on what it has picked up on in the patient’s notes.

The next stage of the research is to trial the system in the GI clinic.