title: Early diagnostic suggestions improve accuracy of GPs: a randomised controlled trial using computer-simulated patients.
abstract: BACKGROUND
  Designers of computerised diagnostic support systems (CDSSs) expect physicians to notice when they need advice and enter into the CDSS all information that they have gathered about the patient. The poor use of CDSSs and the tendency not to follow advice once a leading diagnosis emerges would question this expectation.
  
  
  OBJECTIVE
  To determine whether providing GPs with diagnoses to consider before they start testing hypotheses improves accuracy.
  
  
  METHODS
  Mixed factorial design, where 297 GPs diagnosed nine patient cases, differing in difficulty, in one of three experimental conditions: control, early support, or late support.
  
  
  METHODS
  Data were collected over the internet. After reading some initial information about the patient and the reason for encounter, GPs requested further information for diagnosis and management. Those receiving early support were shown a list of possible diagnoses before gathering further information. In late support, GPs first gave a diagnosis and were then shown which other diagnoses they could still not discount.
  
  
  RESULTS
  Early support significantly improved diagnostic accuracy over control (odds ratio [OR] 1.31; 95% confidence interval [95%CI] = 1.03 to 1.66, P = 0.027), while late support did not (OR 1.10; 95% CI = 0.88 to 1.37). An absolute improvement of 6% with early support was obtained. There was no significant interaction with case difficulty and no effect of GP experience on accuracy. No differences in information search were detected between experimental conditions.
  
  
  CONCLUSIONS
  Reminding GPs of diagnoses to consider before they start testing hypotheses can improve diagnostic accuracy irrespective of case difficulty, without lengthening information search.
authors.affiliation: Department of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
authors.forename: Olga
authors.initials: O
authors.lastname: Kostopoulou
authors.affiliation: Department of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
authors.forename: Andrea
authors.initials: A
authors.lastname: Rosen
authors.affiliation: Department of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: Round
authors.affiliation: Department of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
authors.forename: Ellen
authors.initials: E
authors.lastname: Wright
authors.affiliation: Department of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
authors.forename: Abdel
authors.initials: A
authors.lastname: Douiri
authors.affiliation: Department of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
authors.forename: Brendan
authors.initials: B
authors.lastname: Delaney
mesh_terms: D015746:Abdominal Pain / Q000175:diagnosis*; D000328:Adult; D002637:Chest Pain / Q000175:diagnosis*; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods / Q000592:standards; D003937:Diagnosis, Differential; D003951:Diagnostic Errors / Q000517:prevention & control*; D004417:Dyspnea / Q000175:diagnosis*; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008297:Male; D020380:Needs Assessment; D058996:Quality Improvement; D006113:United Kingdom
keywords: clinical decision support systems; decision making; diagnosis; diagnostic errors