Medical appointments and COVID-19: Research reveals difficulties in arranging medical/doctor appointments during the Coronavirus crisis

A press release:

New research ⁽¹⁾ from Zegami, an Oxford based medical image analysis platform, reveals that 15% of people say they have a known medical condition that requires treatment or proper diagnosis, but they have not been able to go to hospital to get this because of the Coronavirus crisis.

In terms of the conditions these people have, 9% say they have a heart problem, and 3% say they have cancer.

The findings reveal that overall since the Coronavirus crisis started, 43% of people say they have tried to make an appointment to see their GP or doctor, 40% said they got one quickly, but 26% said they couldn’t get one.   Some 19% said they had to wait a few days for one, and 16% said they waited over a week to see a doctor.

Zegami, which has recently developed a system to analyse large numbers of mammograms and identify abnormalities, which is the first stage of breast cancer screening, says its research reveals that  5% of the adult population say they or a member of their family have had treatment for cancer delayed or cancelled during the Coronavirus crisis, with 53% saying this made them feel more anxious.  One in three (33%) said it made it more difficult to carry on with everyday life, and 31% said it made them depressed.

Roger Noble, CEO and founder of Zegami said: “The NHS has done a brilliant job during the crisis under very difficult circumstances.  However, if you have a medical condition you need to liaise closely with your doctor regarding your treatment, and if you suspect you may have developed something, you should seek medical advice.”

Zegami’s newly developed system for analysing large numbers of mammograms and identify abnormalities uses the recently announced Medical Imaging Server for DICOM from Microsoft.  The system also allows scientists to develop Machine Learning models to automate this analysis, making it faster and more accurate.

Initial mammogram data for the system has been sourced from The Cancer Imaging Archive (TCIA)  and consists of 3486 DICOM (Digital Imaging and Communications in Medicine) images, which while anonymised, includes pathology data, allowing this to be factored into the analysis.  One example of the analysis is a map of the “Calcification” breast cancer abnormalities – the system highlights regions distinctly containing examples with little light areas, which are typically the benign (without a callback) instances.