Diagnosis clarification by generalization to patient-friendly terms and definitions: Validation study.

BACKGROUND

Now that patients increasingly get access to their healthcare records, its contents require clarification. The use of patient-friendly terms and definitions can help patients and their significant others understand their medical data. However, it is costly to make patient-friendly descriptions for the myriad of terms used in the medical domain. Furthermore, a description in more general terms, leaving out some of the details, might already be sufficient for a layperson. We developed an algorithm that employs the SNOMED CT hierarchy to generalize diagnoses to a limited set of concepts with patient-friendly terms for this purpose. However, generalization essentially implies loss of detail and might result in errors, hence these generalizations remain to be validated by clinicians. We aim to assess the medical validity of diagnosis clarification by generalization to concepts with patient-friendly terms and definitions in SNOMED CT. Furthermore, we aim to identify the characteristics that render clarifications invalid.

RESULTS

Two raters identified errors in 12.7% (95% confidence interval - CI: 10.7-14.6%) of a random sample of 1,131 clarifications and they considered 14.3% (CI: 12.3-16.4%) of clarifications to be unacceptable to show to a patient. The intraclass correlation coefficient of the interrater reliability was 0.34 for correctness and 0.43 for acceptability. Errors were mostly related to the patient-friendly terms and definitions used in the clarifications themselves, but also to terminology mappings, terminology modelling, and the clarification algorithm. Clarifications considered to be most unacceptable were those that provide wrong information and might cause unnecessary worry.

CONCLUSIONS

We have identified problems in generalizing diagnoses to concepts with patient-friendly terms. Diagnosis generalization can be used to create a large amount of correct and acceptable clarifications, reusing patient-friendly terms and definitions across many medical concepts. However, the correctness and acceptability have a strong dependency on terminology mappings and modelling quality, as well as the quality of the terms and definitions themselves. Therefore, validation and quality improvement are required to prevent incorrect and unacceptable clarifications, before using the generalizations in practice.