A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology.

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.

The role of time in involving patients with cancer in treatment decision making: A scoping review.

BACKGROUND: Time is often perceived as a barrier to shared decision making in cancer care. It remains unclear how time functions as a barrier and how it could be most effectively utilized. OBJECTIVE: This scoping review aimed to describe the role of time in patient involvement, and identify strategies to overcome time-related barriers. METHODS: Seven databases were searched for any publications on patient involvement in cancer treatment decisions, focusing on how time is used to involve patients, the association between time and patient involvement, and/or strategies to overcome time-related barriers. Reviewers worked independently and in duplicate to select publications and extract data. One coder thematically analyzed data, a second coder checked these analyses. RESULTS: The analysis of 26 eligible publications revealed four themes. Time was a resource 1) to process the diagnosis, 2) to obtain/process/consider information, 3) for patients and clinicians to spend together, and 4) for patient involvement in making decisions. DISCUSSION: Time is a resource throughout the treatment decision-making process, and generic strategies have been proposed to overcome time constraints. PRACTICE VALUE: Clinicians could co-create decision-making timelines with patients, spread decisions across several consultations, share written information with patients, and support healthcare redesigns that allocate the necessary time.

Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records.

Objective: To address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS). Patients and Methods: Between 2017 and 2021, we identified 18,000 TUS patients at Mayo Clinic and selected 628 for chart review to create a ground truth dataset based on consensus. We developed a rule-based NLP pipeline to identify TUS as appropriate TUS (aTUS) or inappropriate TUS (iTUS) using patients' clinical notes and additional meta information. In addition, we designed an abbreviated NLP pipeline (aNLP) solely focusing on labels from TUS order requisitions to facilitate deployment at other health care systems. Our dataset was split into a training set of 468 (75%) and a test set of 160 (25%), using the former for rule development and the latter for performance evaluation. Results: There were 449 (95.9%) patients identified as aTUS and 19 (4.06%) as iTUS in the training set; there are 155 (96.88%) patients identified as aTUS and 5 (3.12%) were iTUS in the test set. In the training set, the pipeline achieved a sensitivity of 0.99, specificity of 0.95, and positive predictive value of 1.0 for detecting aTUS. The testing cohort revealed a sensitivity of 0.96, specificity of 0.80, and positive predictive value of 0.99. Similar performance metrics were observed in the aNLP pipeline. Conclusion: The NLP models can accurately identify the appropriateness of a thyroid ultrasound from clinical documentation and order requisition information, a critical initial step toward evaluating the drivers and outcomes of TUS use and subsequent thyroid cancer overdiagnosis.

Developing and validating the Unhurried Conversations Assessment Tool (UCAT).

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.

Physical Examination of the Thyroid: Accuracy in Detecting Thyroid Nodules and Frequency of Additional Findings.

OBJECTIVE: Thyroid palpation is a common clinical practice to detect thyroid abnormalities. However, its accuracy and potential for additional findings remain unclear. This study aimed to assess the diagnostic accuracy of physical exams in detecting thyroid nodules. METHODS: A retrospective observational study was conducted on a random sample of adult patients who underwent their first-time thyroid ultrasound between January 2015 and September 2017, following a documented thyroid physical exam. The study assessed the performance of thyroid palpation in detecting 1 or multiple thyroid nodules, as well as the proportion of additional findings on ultrasounds due to false positive thyroid palpation. RESULTS: We included 327 patients, mostly female (65.1%), white (84.1%), and treated in a primary care setting (54.4%) with a mean age of 50.8 years (SD 16.9). For solitary thyroid nodules, the physical exam had a sensitivity of 20.3%, specificity of 79.1%, an accuracy of 68.5%, negative predictive value of 81.8%, and positive predictive value of 17.6%. For detecting a multinodular goiter, physical exams demonstrated a sensitivity of 10.8%, specificity of 96.5%, accuracy of 55.4%, negative predictive value of 53.9, and positive predictive value of 73.9%. Among 154 cases with palpable nodules, 60% had additional nodules found in subsequent thyroid ultrasound. CONCLUSION: Thyroid physical exam has limited diagnostic performance and leads to additional findings when followed by a thyroid ultrasound. Future efforts should be directed at improving the accuracy of thyroid physical exams or re-evaluating its routine use.