Capturing Clinical Knowledge: The Digital Modeling of Expert Knowledge Concerning the Care for Patients with Chronic Obstructive Pulmonary Disease

Background Encoding medical knowledge in a digital clinical knowledge model (CKM) enables its usage for automation and decision support. Formalized sources of knowledge are usually not sufficient to construct a complete model. Clinical experts may also hold relevant, implicit knowledge that they have gained through experience or from other unrecorded sources. Our aim was to study whether early modeling of medical knowledge in a CKM and the fictional application of the resulting model together with clinical experts might help elicit this semi-hidden, but highly relevant domain knowledge. Methods We created a CKM to support patients suffering from chronic obstructive pulmonary disease in self-managing their condition by generating recommendations based on measurements and questionnaires they perform at home. We subsequently interviewed 8 pulmonary experts about their recommendations for synthetic patient cases versus those generated by the CKM. At the same time, we collected feedback from the professionals to study their attitude towards the CKM and its generated recommendations. Results The interviews enabled us to elicit further domain knowledge on various themes: retaking measurements, asking the patient additional questions, contacting the care professional, medication, continuation of monitoring, and lifestyle advice. Secondly, the elicited knowledge revealed interprofessional differences between different types of care professionals and within groups of the same type. Additionally, our results show a trend that the experienced professionals accepted the model’s advice more readily than other groups. Conclusions The themes we identified indicate that case-based interviewing is a suitable technique for knowledge elicitation regarding clinical knowledge. The interprofessional differences in recommendations form a hurdle in expanding the accepted knowledge encoded in the CKM. The experienced professionals being more accepting of the model’s advice contrasts with existing literature. This highlights the need for further research to understand the correlation between a care professional’s experience and the adoption of automatically generated recommendations. Patients were intentionally excluded from this preliminary evaluation of the CKM to first determine if the model aligned with current medical practice. Future studies should include both patients and care professionals to assess the tool’s usability.