Literature DB >> 27651515

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

Jessica Sheringham1, Rachel Sequeira1, Jonathan Myles2, William Hamilton3, Joe McDonnell4, Judith Offman2, Stephen Duffy2, Rosalind Raine1.   

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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Entities:  

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

Mesh:

Year:  2016        PMID: 27651515     DOI: 10.1136/bmjqs-2016-005679

Source DB:  PubMed          Journal:  BMJ Qual Saf        ISSN: 2044-5415            Impact factor:   7.035


  9 in total

1.  Artificial intelligence and diagnosis in general practice.

Authors:  Nick Summerton; Martin Cansdale
Journal:  Br J Gen Pract       Date:  2019-07       Impact factor: 5.386

Review 2.  Comorbid chronic diseases and cancer diagnosis: disease-specific effects and underlying mechanisms.

Authors:  Cristina Renzi; Aradhna Kaushal; Jon Emery; Willie Hamilton; Richard D Neal; Bernard Rachet; Greg Rubin; Hardeep Singh; Fiona M Walter; Niek J de Wit; Georgios Lyratzopoulos
Journal:  Nat Rev Clin Oncol       Date:  2019-07-26       Impact factor: 66.675

3.  Patients' preferences for GP consultation for perceived cancer risk in primary care: a discrete choice experiment.

Authors:  Katriina L Whitaker; Alex Ghanouni; Yin Zhou; Georgios Lyratzopoulos; Stephen Morris
Journal:  Br J Gen Pract       Date:  2017-05-08       Impact factor: 5.386

4.  Using clinical simulation to study how to improve quality and safety in healthcare.

Authors:  Guillaume Lamé; Mary Dixon-Woods
Journal:  BMJ Simul Technol Enhanc Learn       Date:  2018-09-29

5.  Influence of doctor-patient conversations on behaviours of patients presenting to primary care with new or persistent symptoms: a video observation study.

Authors:  Dorothee Amelung; Katriina L Whitaker; Debby Lennard; Margaret Ogden; Jessica Sheringham; Yin Zhou; Fiona M Walter; Hardeep Singh; Charles Vincent; Georgia Black
Journal:  BMJ Qual Saf       Date:  2019-07-20       Impact factor: 7.418

Review 6.  The use of experimental vignette studies to identify drivers of variations in the delivery of health care: a scoping review.

Authors:  Jessica Sheringham; Isla Kuhn; Jenni Burt
Journal:  BMC Med Res Methodol       Date:  2021-04-22       Impact factor: 4.615

7.  Role of primary care physician factors on diagnostic testing and referral decisions for symptoms of possible cancer: a systematic review.

Authors:  Victoria Hardy; Adelaide Yue; Stephanie Archer; Samuel William David Merriel; Matthew Thompson; Jon Emery; Juliet Usher-Smith; Fiona M Walter
Journal:  BMJ Open       Date:  2022-01-24       Impact factor: 2.692

8.  Using Virtual Patients to Explore the Clinical Reasoning Skills of Medical Students: Mixed Methods Study.

Authors:  Ruth Plackett; Angelos P Kassianos; Jessica Timmis; Jessica Sheringham; Patricia Schartau; Maria Kambouri
Journal:  J Med Internet Res       Date:  2021-06-04       Impact factor: 5.428

9.  Online patient simulation training to improve clinical reasoning: a feasibility randomised controlled trial.

Authors:  Ruth Plackett; Angelos P Kassianos; Maria Kambouri; Natasha Kay; Sophie Mylan; Jenny Hopwood; Patricia Schartau; Shani Gray; Jessica Timmis; Sarah Bennett; Chris Valerio; Veena Rodrigues; Emily Player; Willie Hamilton; Rosalind Raine; Stephen Duffy; Jessica Sheringham
Journal:  BMC Med Educ       Date:  2020-07-31       Impact factor: 2.463

  9 in total

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