Variations in GPs' decisions to investigate suspected lung cancer: a factorial experiment using multimedia vignettes

Abstract

Introduction: Lung cancer survival is low and comparatively poor in the UK. Patients with symptoms suggestive of lung cancer commonly consult primary care, but it is unclear how general practitioners (GPs) distinguish which patients require further investigation. This study examined how patients' clinical and sociodemographic characteristics influence GPs' decisions to initiate lung cancer investigations.

Methods: A factorial experiment was conducted among a national sample of 227 English GPs using vignettes presented as simulated consultations. A multimedia-interactive website simulated key features of consultations using actors ('patients'). GP participants made management decisions online for six 'patients', whose sociodemographic characteristics systematically varied across three levels of cancer risk. In low-risk vignettes, investigation (ie, chest X-ray ordered, computerised tomography scan or respiratory consultant referral) was not indicated; in medium-risk vignettes, investigation could be appropriate; in high-risk vignettes, investigation was definitely indicated. Each 'patient' had two lung cancer-related symptoms: one volunteered and another elicited if GPs asked. Variations in investigation likelihood were examined using multilevel logistic regression.

Results: GPs decided to investigate lung cancer in 74% (1000/1348) of vignettes. Investigation likelihood did not increase with cancer risk. Investigations were more likely when GPs requested information on symptoms that 'patients' had but did not volunteer (adjusted OR (AOR)=3.18; 95% CI 2.27 to 4.70). However, GPs omitted to seek this information in 42% (570/1348) of cases. GPs were less likely to investigate older than younger 'patients' (AOR=0.52; 95% CI 0.39 to 0.7) and black 'patients' than white (AOR=0.68; 95% CI 0.48 to 0.95).

Conclusions: GPs were not more likely to investigate 'patients' with high-risk than low-risk cancer symptoms. Furthermore, they did not investigate everyone with the same symptoms equally. Insufficient data gathering could be responsible for missed opportunities in diagnosis.

Keywords: Diagnostic errors; General practice; Health services research; Primary care; Simulation.