In a primary care setting the diagnostic process typically starts with a symptom or sign reported by the patient. Primary care physicians face the challenge to consider a broad spectrum of possible aetiologies or differential diagnoses when choosing appropriate diagnostic tests. The classical diagnostic cross-sectional study investigates the accuracy of a diagnostic test or a combination of several tests in regard to just one target disease. The complexity facing the clinician remains unconsidered or is being split and presented in several parts which the clinician has to combine. In this paper we suggest a design for diagnostic studies that considers the requirements of diagnosis in primary care more comprehensively: the comprehensive diagnostic study. The essential characteristic of the design is the simultaneous consideration of the whole spectrum of relevant aetiologies when evaluating several diagnostic tests. We present single characteristics and specific features of this design in regard to research question, study sampling, index test, reference standard and analysis, and illustrate them using the example of a study investigating chest pain in primary care.
Keywords: Anamnese und Befund; Diagnose; Diagnosis; Informationstheorie; Maschinelles Lernen; Primärversorgung; Studiendesign; Symptomevaluation; information theory; machine learning; medical history taking; primary healthcare; research design; symptom assessment.
Copyright © 2013. Published by Elsevier GmbH.