Computerised Diagnostic Decision Support System for the Classification of Pre-Invasive Cervical Squamous Lesions.

Previous studies have revealed considerable interobserver and intraobserver variation in the histological classification of preinvasive cervical squamous lesions. The aim of the present study was to develop a decision support system (DSS) for the histological interpretation of these lesions. Knowledge and uncertainty were represented in the form of a Bayesian belief network that permitted the storage of diagnostic knowledge and, for a given case, the collection of evidence in a cumulative manner that provided a final probability for the possible diagnostic outcomes. The network comprised 8 diagnostic histological features (evidence nodes) that were each independently linked to the diagnosis (decision node) by a conditional probability matrix. Diagnostic outcomes comprised normal; koilocytosis; and cervical intraepithelial neoplasia (CIN) 1, CIN II, and CIN M. For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature. The system was designed to be interactive in that the histopathologist was prompted to enter evidence into the network via a specifically designed graphical user interface (i-Path Diagnostics, Belfast, Northern Ireland). Membership functions were used to derive the relative likelihoods for the alternative feature outcomes, the likelihood vector was entered into the network, and the updated diagnostic belief was computed for the diagnostic outcomes and displayed. A cumulative probability graph was generated throughout the diagnostic process and presented on screen. The network was tested on 50 cervical colposcopic biopsy specimens, comprising 10 cases each of normal, koilocytosis, CIN 1, CIN H, and CIN III. These had been preselected by a consultant gynecological pathologist. Using conventional morphological assessment, the cases were classified on 2 separate occasions by 2 consultant and 2 junior pathologists. The cases were also then classified using the DSS on 2 occasions by the 4 pathologists and by 2 medical students with no experience in cervical histology. Interobserver and intraobserver agreement using morphology and using the DSS was calculated with K statistics. Intraobserver reproducibility using conventional unaided diagnosis was reasonably good (kappa range, 0.688 to 0.861), but interobserver agreement was poor (kappa range, 0.347 to 0.747). Using the DSS improved overall reproducibility between individuals. Using the DSS, however, did not enhance the diagnostic performance of junior pathologists when comparing their DSS-based diagnosis against an experienced consultant. However, the generation of a cumulative probability graph also allowed a comparison of individual performance, how individual features were assessed in the same case, and how this contributed to diagnostic disagreement between individuals. Diagnostic features such as nuclear pleomorphism were shown to be particularly problematic and poorly reproducible. DSSs such as this therefore not only have a role to play in enhancing decision making but also in the study of diagnostic protocol, education, self-assessment, and quality control. (C) 2003 Elsevier Inc. All rights reserved.

Decision making in childhood asthma exacerbation – a clinical judgement analysis

Background: Clinical decisions which impact directly on patient safety and quality of care are made during acute asthma attacks by individual doctors on the basis of their knowledge and experience. These include administration of systemic corticosteroids (CS), oral antibiotics, and admission to hospital. Clinical judgement analysis provides a methodology for comparing decisions between practitioners with different training and experience, and improving decision making. Methods: Stepwise linear regression was used to select clinical cues based on visual analogue scale assessments of the propensity of 62 clinicians to prescribe a short course of oral CS (decision 1), a course of antibiotics (decision 2), and/or admit to hospital (decision 3) for 60 â??paperâ?? patients. Results:When compared by specialty, paediatriciansâ?? models for decision 1 were more likely to include as a cue level of alertness (54% v. 16%); for decision 2 presence of crepitations (49% v. 16%), and less likely to include inhaled CS (8% v. 40%), respiratory rate (0% v. 24%), and air entry (70% v. 100%). When compared to other grades, the models derived for decision 3 by consultants/general practitioners were more likely to include wheeze severity as a cue (39% v. 6%). Conclusions: Clinicians differed in their use of individual cues and the number included in their models. Patient safety and quality of care will benefit from clarification of decision making strategies as general learning points during medical training, in the development of guidelines and care pathways, and by clinicians developing self-awareness of their own preferences.