NLP for optimisation of endoscopic resources

CSC Lead: Agathe

The current COVID pandemic has lengthened the waiting list for endoscopic surveillance for premalignant disease such that some patients may develop malignancy whilst waiting for endoscopy. Similarly, by booking follow up endoscopy too early for patients, scarce healthcare resources are being used inappropriately. This risk could be offset by a rigorous guideline based assessment of patients on the waiting list for assessment so that patients who have been planned to be endoscoped too early, or inappropriately can be planned more appropriately. To do this endoscopy and pathology free text reports need to be analysed for patients on the waiting list. The decision about the timing of follow up endoscopy depends on the natural language information contained within both sets of reports. Because of the number of patients, the aim of the current study is to automate the analysis using natural language processing.

Clinical lead: Sebastian Zeki

Project Plan
1.Meeting of all persons involved to determine AI specifications.

2. Setting technical and system requirements for AI model.

3. Dataset curation (retrospective).

4. Model training

5. Model testing

6. Implementation

7. Audit

References