ABSTRACT
This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.
ABSTRACT
OBJECTIVE: Given the importance of unhurried conversations for providing careful and kind care, we sought to create, test, and validate the Unhurried Conversations Assessment Tool (UCAT) for assessing the unhurriedness of patient-clinician consultations. METHODS: In the first two phases, the unhurried conversation dimensions were identified and transformed into an assessment tool. In the third phase, two independent raters used UCAT to evaluate the unhurriedness of 100 randomly selected consultations from 184 videos recorded for a large research trial. UCAT's psychometric properties were evaluated using this data. RESULTS: UCAT demonstrates content validity based on the literature and expert review. EFA and reliability analyses confirm its construct validity and internal consistency. The seven formative dimensions account for 89.93% of the variance in unhurriedness, each displaying excellent internal consistency (α > 0.90). Inter-rater agreement for the overall assessment item was fair (ICC = 0.59), with individual dimension ICCs ranging from 0.26 (poor) to 0.95 (excellent). CONCLUSION: UCAT components comprehensively assess the unhurriedness of consultations. The tool exhibits content and construct validity and can be used reliably. PRACTICE IMPLICATIONS: UCAT's design and psychometric properties make it a practical and efficient tool. Clinicians can use it for self-evaluations and training to foster unhurried conversations.
