AI-powered algorithm for categorising patients based on their perioperative risk, enabling appropriate pathway planning and optimisation in elective surgeries.
Perioperative patient complexity assessment
Patient risk stratification for elective surgery
Graveyard
This project has been removed to the graveyard. Along with stakeholder resource constraints, an exploratory data analysis demonstrated that the data for this project was too complex and too varied in missingness and meaningfulness to proceed at this point in time. There are currently no plans to revisit this project at a later date.
Clinical lead: Ramai Santhirpala
Project Overview The process for determining patient suitability for elective surgery is currently fragmented and occurs close to the procedure date, which leads to cancellations and delays for high-risk patients.
Perioperative patient complexity assessment aims to develop a Machine Learning algorithm that categorises patients into three groups based on their perioperative risk. The algorithm will gather patient data from various sources, enabling appropriate pathway planning and optimisation. Ultimately, it will reduce cancellations and delays for high-risk patients. Additionally, low-risk patients will experience reduced waiting times as they can be fast-tracked to a non-admitted model of care, such as a high volume low complexity hub or day case setting.
The initial platform will cover an initial pilot at GSTT in Gastrointestinal Medicine and Surgery, and Orthopaedics, with a view to scaling in other specialties and Trusts if successful. The algorithm will be tested on approximately 200 patients across the two surgical specialties. The number of patients will be defined based on feasibility and validity of a three-month pilot study.