Workforce Crisis in Radiology in the UK and the Strategies to Deal With It: Is Artificial Intelligence the Saviour?

The die-hards, negotiators and migrants: Portraits of doctors' career pathways through specialisation

INTRODUCTION

Global workforce shortages in medical specialties strain healthcare systems, jeopardising patient outcomes. Enhancing recruitment strategies by supporting professional identity (PI) development may be one way to address this workforce gap-yet little research has explored this topic. The goal of the current study was to explore specialty-specific recruitment through considering PI. As proposed causes of workforce shortages in anatomical pathology (AP) bear similarities to many other specialties, this study uses the field of AP as a model for specialist PI development and asks: (1) why, how and when do doctors choose to pursue AP training and (2) what can be learned from this for recruitment to AP and other specialties?

METHODS

A qualitative research approach was undertaken using narrative inquiry. Interviews with junior doctors interested in AP, AP registrars and AP consultants from Australia and New Zealand were interpreted as stories via 're-storying'. Narrative synthesis of participants' collective stories identified chronological key events (i.e. 'turning points') in choosing AP.

RESULTS

Narrative synthesis resulted in identification of three portraits entering medical specialist training: (1) die-hards, deciding upon initial exposure; (2) negotiators, choosing after comparing specialties; and (3) migrants, seeking to move away from non-pathology specialties. The negotiators and migrants cemented their decision to pursue AP as a postgraduate doctor, whereas the die-hards made this decision during medical school.

CONCLUSIONS

Given the similarities in portrait traits between AP and other specialties across the literature, our results suggest ways to support specialty recruitment using PI development. We propose a medical specialist recruitment framework to support the PI development of doctors with die-hard, negotiator and migrant traits. Use of this framework could enhance current specialty-specific recruitment approaches, particularly in fields challenged by workforce shortages.

Urgent Direct Access to Diagnostic Services for General Practitioners: Bridging the Gap in Cancer Diagnosis

Urgent direct access to diagnostic services for general practitioners (GPs) is a new pathway to capture any cancer diagnoses that may have been missed due to vague symptom presentations. Hence, GPs should look out for the key symptoms mentioned by NHS England that should prompt urgent direct access referrals for chest X-ray (CXR), computed tomography (CT) chest, MRI brain, ultrasound (US) abdomen and pelvis, and CT abdomen and pelvis. By implementing this approach, we can significantly reduce the time to diagnosis, while minimizing the number of visits to GP and specialist appointments prior to initiating investigations. However, the use of this pathway can only improve if access to diagnostic scans is improved. This needs to be done by ensuring all GPs in the country have access to directly request MRI brains, CT chest, abdomen, and pelvis. Further research into the impact of the urgent direct access pathway as well as investigating the number of GPs without access to these vital diagnostic services is required to fully improve and measure the progress of this referral pathway.

A retrospective audit of an artificial intelligence software for the detection of intracranial haemorrhage used by a teleradiology company in the United Kingdom

Objectives

Artificial intelligence (AI) algorithms have the potential to assist radiologists in the reporting of head computed tomography (CT) scans. We investigated the performance of an AI-based software device used in a large teleradiology practice for intracranial haemorrhage (ICH) detection.

Methods

A randomly selected subset of all non-contrast CT head (NCCTH) scans from patients aged ≥18 years referred for urgent teleradiology reporting from 44 different hospitals within the United Kingdom over a 4-month period was considered for this evaluation. Thirty auditing radiologists evaluated the NCCTH scans and the AI output retrospectively. Agreement between AI and auditing radiologists is reported along with failure analysis.

Results

A total of 1315 NCCTH scans from as many distinct patients (median age, 73 years [IQR 53-84]; 696 [52.9%] females) were evaluated. One hundred twelve (8.5%) scans had ICH. Overall agreement, positive percent agreement, negative percent agreement, and Gwet's AC1 of AI with radiologists were found to be 93.5% (95% CI, 92.1-94.8), 85.7% (77.8-91.6), 94.3% (92.8-95.5) and 0.92 (0.90-0.94), respectively, in detecting ICH. 9 out of 16 false negative outcomes were due to missed subarachnoid haemorrhages and these were predominantly subtle haemorrhages. The most common reason for false positive results was due to motion artefacts.

Conclusions

AI demonstrated very good agreement with the radiologists in the detection of ICH.

Advances in knowledge

Real-world evaluation of an AI-based CT head interpretation device is reported. Knowledge of scenarios where false negative and false positive results are possible will help reporting radiologists.