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Background <p>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 modeling medical knowledge in a CKM and piloting the resulting model using fictional cases together with clinical experts might enable us to elicit this semi-hidden, but highly relevant domain knowledge.</p> Methods <p>We developed a CKM to support patients suffering from chronic obstructive pulmonary disease in self-managing exacerbations by generating recommendations based on questionnaires and measurements 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.</p> Results <p>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 non-pharmacological recommendations. 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.</p> Conclusions <p>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.</p>

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Piloting a clinical knowledge model: digital modeling to support the self-management of patients with chronic obstructive pulmonary disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Eric Edelman,
  • Esmee Bellemakers,
  • Fabian Tijssen,
  • Popke Rein Munniksma,
  • Wim Bast,
  • Harold ten Bohmer,
  • Sami O. Simons,
  • Marieke Spreeuwenberg,
  • Frits van Merode

摘要

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 modeling medical knowledge in a CKM and piloting the resulting model using fictional cases together with clinical experts might enable us to elicit this semi-hidden, but highly relevant domain knowledge.

Methods

We developed a CKM to support patients suffering from chronic obstructive pulmonary disease in self-managing exacerbations by generating recommendations based on questionnaires and measurements 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 non-pharmacological recommendations. 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.