Introduction
Elective waiting lists for surgery in England are currently stratified based on a prioritisation system produced by the Academy of Royal Colleges and endorsed by the National Health Service (NHS). The system relies on healthcare professionals, typically a surgeon, to manually assign a priority code (P-code) to each patient listed for elective surgery within their domain (P1 (highest) - P4 (lowest)). Following assignment of a P-code, the patient is expected to undergo surgery within a stipulated time frame, for example, the assigned code P3 means the patient must undergo surgery within 3 months. Stepping outside of these time frames means patients should be subjected to a harm review.1
The COVID-19 pandemic has widely disrupted the delivery of healthcare services, including elective surgery.2 3 As a result, the number of patients awaiting surgery has sharply risen, which is sometimes referred to as the ‘elective backlog’.4 The current method to prioritise patients is procedure-specific and simplified to allow rapid prioritisation. It is not designed to manage the priority within a group of P-coded patients. Yet, there will be those who deteriorate faster than others due to their pattern of comorbidities. There is a need to improve the accuracy of assessing patients listed for elective surgery and prioritise based on greater objectivity. As such, digital tools to improve this process have been proposed, such as the use of predictive algorithms and artificial intelligence. These could have a positive impact on identifying patients at greatest risk of harm from waiting for a procedure and thus improve clinical care and outcomes. With the electronic management of elective waiting lists, this potential may now be realised; however, active intelligent management with clinical decision support tools has not been reported outside of research settings.5 6
In the field of predicting the risk of mortality and complications in surgery, the Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) scoring system is one of the most established and widely accepted constructs.7 It has been iterated over time and been shown to be highly accurate at predicting adverse outcomes and death in a range of surgical procedures across specialities.8 The variables used to power POSSUM include routinely collected demographic and clinical parameters, which can be found in online supplemental file 1.
The Patient Tacking List (PTL) tool (C2-Ai(c)) is a clinical decision support tool based on the POSSUM Score that can help prioritise patients on an elective waiting list. The tool combines POSSUM, the planned surgical procedure details and time on the waiting list, and applies these to a referential dataset to produce a ‘matrix score’. The matrix score represents the difference in mortality and complication rate between the procedure being done electively versus waiting for the patient to decompensate and present as an emergency. Matrix scores range between 4 and 100, with a higher score corresponding to a greater risk of a poor outcome if the procedure is not done electively. Users of the tool can visualise their waiting list on a bespoke user-interface, which orders patients based on matrix scores, with options to filter based on demographics, clinical specialty and procedure type. These visualisations give a risk-stratified overview of patients awaiting surgery allowing better informed surgical planning.
The PTL tool has the potential to improve the accuracy of elective surgical risk-stratification and support clinical prioritisation. Furthermore, by including objective measures of risk, it may reduce variation, and thus improve equity in patient care. However, no objective measure of the accuracy of the tool to predict mortality and complications has previously been undertaken.
This report describes the implementation of the PTL tool as a pilot solution at three NHS trusts. The tool was used to analyse patients listed for elective surgery and produce matrix scores alongside current standard practice.
