Cancer risk tool rolled out to over 4,000 GP practices
Over 4,000 practices using EMIS Web can now access a cancer ‘symptom checker’ which helps GPs to identify patients at risk of having some form of cancer who need to undergo investigations.
The online QCancer tool – developed by the team of researchers behind QRISK– has been integrated into EMIS Web, so that GPs can see if a patient’s signs and symptoms indicate their risk of an existing, as yet undiagnosed, cancer is above a certain level.
The tool was developed using the QResearch database including over 900 UK GP practices, and has now been evaluated using the THIN database, which researchers said showed it have very good levels of accuracy at identifying patients with as yet undiagnosed cancer.
The threshold for ‘high risk’ is currently set at a 5% chance of having any cancer, although this level could be changed, if for example NICE goes ahead with plans to lower the typical risk threshold for referral for suspected cancer to a positive predictive value of 3% for individual tumour sites.
The symptom checker is being developed into a full Cancer Decision Support (CDS) tool with help from Macmillan Cancer Support, which will be rolled out next month.
The CDS includes two extra features, a ‘batch processor’ that allows practices to score their entire patient population and review case notes of those identified at high risk, and an ‘alert’ that will flag up high-risk patients as the GP adds information to patient records.
Lead researcher Professor Julia Hippisley-Cox, from the University of Nottingham, told Pulse the symptom checker will help GPs carry out earlier, more targeted investigations of symptomatic patients.
Professor Hippisley-Cox said: ‘We are grateful for all the EMIS GPs who contribute to QResearch enabling [the] creating of tools such as these and for EMIS for integrating it into EMIS Web for the benefit of patients.
‘Early diagnosis of cancer remains a significant clinical challenge and we hope that this tool assist GPs in the consultation to identify those most at risk.’