Subjective illness knowledge of patients with diabetes mellitus

Background In this clinical pilot study a way was outlined with which it is possible by means of a computer-linguistic method for knowledge assessment to examine larger samples of smaller texts with regard to their subjective illness theories, especially the subjective illness knowledge and their care expectations derived from it and to visualize and classify the ideas. This allows the design of an objective typification of the, subjectively considered true, patient knowledge. It is shown why the transfer of such classified knowledge about disease knowledge and subjective disease theories in the doctor-patient interaction could improve communication and ultimately adherence. Using the methodology, the presence of many individual factors is not a limitation, as is often considered for commonly implemented methodical approaches. Hence, it is rather used as a resource within the data to make patient ideas objectively accessible and comparable within our methodological concept. Method 74 subjects (18 diabetic patients, 56 healthy persons) wrote texts on two tasks on their conception of illness and their expectation for care. The texts were computer-linguistically clustered using T-MITOCAR (Text Model Inspection Trace of Concepts and Relations) and subsequently analyzed. Results The study identified and described several content clusters both for the disease models and for the expectations. There were different models of perception within and between the different sub samples. A higher level of expertise of patients as commonly expected by the care-system was also elicitated by the analysis. Conclusions The results transfer and contribute to a documented toolkit to be used by physicians for a better assessment, classification and understanding of patients' conceptions of diabetes mellitus. The toolkit is designed to simplify communication, to facilitate the implementation of patient orientation, shared decision making and adherence to pragmatic care situations.