AIJul 21, 2016

Exploring Differences in Interpretation of Words Essential in Medical Expert-Patient Communication

arXiv:1607.06187v110 citations
Originality Synthesis-oriented
AI Analysis

This addresses a critical problem in medical communication for cancer patients and professionals, but it is incremental as it applies existing methods to a specific domain.

The study investigated differences in how patients and medical professionals interpret words in a quality-of-life questionnaire for cancer treatment, using fuzzy sets and Jaccard similarity to show significant discrepancies that risk miscommunication.

In the context of cancer treatment and surgery, quality of life assessment is a crucial part of determining treatment success and viability. In order to assess it, patients completed questionnaires which employ words to capture aspects of patients well-being are the norm. As the results of these questionnaires are often used to assess patient progress and to determine future treatment options, it is important to establish that the words used are interpreted in the same way by both patients and medical professionals. In this paper, we capture and model patients perceptions and associated uncertainty about the words used to describe the level of their physical function used in the highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS) questionnaire. The paper provides detail about the interval-valued data capture as well as the subsequent modelling of the data using fuzzy sets. Based on an initial sample of participants, we use Jaccard similarity on the resulting words models to show that there may be considerable differences in the interpretation of commonly used questionnaire terms, thus presenting a very real risk of miscommunication between patients and medical professionals as well as within the group of medical professionals.

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