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Background: Online physician reviews increase transparency in health care, helping patients make informed decisions about their provider. Language processing techniques can quantify this data and allow providers to better understand patients' experiences, perspectives, and priorities. The objective of this study was to assess patient satisfaction and understand the aspects of care that are valued by patients seeking refractive care using sentiment and word frequency analysis. Methods: Written reviews and Star ratings for members of the Refractive Surgery Alliance Society practicing in the United States were collected from Healthgrades, a popular physician rating website. Surgeons with at least one written review were included in the study. Reviews were scored from -1 (most negative) to +1 (most positive) using Valence Aware Dictionary sEntiment Reasoner (VADER). Reviews were stratified by demographic characteristics, namely gender, region, and years in practice. Word frequency analysis was applied to find the most common words and phrases. Results: A total of 254 specialists and 3104 reviews were analyzed, with an average of 4.4/5 stars and mean 48 ratings each. Most physicians had positive reviews (96%, average VADER ​= ​0.69). Younger physicians (20 years) (P ​

BACKGROUND: Patient sentiment analysis aids in identifying issue areas, timely remediation, and improved patient care by the healthcare professional. The relationship between pain management and patient sentiment analysis is crucial to providing patients with high-quality medical care. Therefore, a self-reported pain level assessment is required for the smart healthcare framework to determine the best course of treatment. OBJECTIVE: An efficient method for a pain sentiment recognition system has been proposed based on the analysis of human facial emotion patterns of patients in the smart healthcare framework. METHODS: The proposed system has been implemented in four phases: (i) in the first phase, the facial regions of the observation patient have been detected using the computer vision-based face detection technique; (ii) in the second phase, the extracted facial regions are analyzed using deep learning based feature representation techniques to extract discriminant and crucial facial features to analyze the level of pain emotion of patient; (iii) the level of pain emotions belongs from macro to micro facial expressions, so, some advanced feature tunning and representation techniques are built along with deep learning based features such as to distinguish low to high pain emotions among the patients in the third phase of the implementation, (iv) finally, the performance of the proposed system is enhanced using the score fusion techniques applied on the obtained deep pain recognition models for the smart healthcare framework. RESULTS: The performance of the proposed system has been tested using two standard facial pain benchmark databases, the UNBC-McMaster shoulder pain expression archive dataset and the BioVid Heat Pain Dataset, and the results are compared with some existing state-of-the-art methods employed in this research area. CONCLUSIONS: From extensive experiments and comparative studies, it has been concluded that the proposed pain sentiment recognition system performs remarkably well compared to the other pain recognition systems for the smart healthcare framework. [ABSTRACT FROM AUTHOR]

IntroductionThe collection and evaluation of patient-reported outcomes is essential to the development of patient and family centered care. Current patient surveying techniques are limited by delayed response times and restriction to specific health systems. The use of random-domain intercept technology (RDIT), by Real-Time Interactive World-Wide Intelligence (RIWI, Toronto, ON, Canada) mitigates current barriers by creating a dynamic real-time feedback environment and providing a mass sampling technique.MethodsRDIT was employed to survey a wide sample of respondents across the United States (US). Respondents who self-identified as having had a surgical procedure or cared for someone having a surgical procedure were included in the analysis.Results1,004 participants completed the survey and answered questions regarding demographics, perioperative details, sentiments on postoperative recovery, postoperative clinical endpoints, sentiments on healthcare professionals, and opinions on future surgical care.DiscussionThe results of this cross-sectional study identified areas with potential for improvement in the patient perioperative experience that could improve the patient experience. This novel use of RDIT provided a valuable tool for real-time feedback and mass sampling allowing the creation of a dynamic healthcare environment that fosters timely and targeted improvements to patient experiences and outcomes.