Analyzing the Trend of Templestay Keywords Using Text Mining Techniques: Focusing on the Comparison Before and After the Covid-19

PURPOSE OpenNotes is a national movement established in 2010 that gives patients access to their visit notes through online patient portals, and its goal is to improve transparency and communication. To determine whether granting patients access to their medical notes will have a measurable effect on provider behavior, we developed novel methods to quantify changes in the length and frequency of use of n-grams (sets of words used in exact sequence) in the notes. METHODS We analyzed 102,135 notes of 36 hematology/oncology clinicians before and after the OpenNotes debut at Beth Israel Deaconess Medical Center. We applied methods to quantify changes in the length and frequency of use of sequential co-occurrence of words ( n-grams) in the unstructured content of the notes by unsupervised hierarchical clustering and proportional analysis of n-grams. RESULTS The number of significant n-grams averaged over all providers did not change, but for individual providers, there were significant changes. That is, all significant observed changes were provider specific. We identified eight providers who were late note signers. This group significantly reduced its late signing behavior after OpenNotes implementation. CONCLUSION Although the number of significant n-grams averaged over all providers did not change, our text-mining method detected major content changes in specific providers’ documentation at the n-gram level. The method successfully identified a group of providers who decreased their late note signing behavior.

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The impact of COVID-19 on hotel customer satisfaction: evidence from Beijing and Shanghai in China

International Journal of Contemporary Hospitality Management ◽

10.1108/ijchm-03-2021-0356 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Shaolong Sun ◽

Fuxin Jiang ◽

Gengzhong Feng ◽

Shouyang Wang ◽

Chengyuan Zhang

Keyword(s):

Text Mining ◽

Customer Satisfaction ◽

Consumer Satisfaction ◽

Topic Model ◽

Hotel Industry ◽

Online Reviews ◽

Control Measures ◽

Content Type ◽

Before And After ◽

The Impact

Purpose The purpose of this study is to provide better service to hotel customers during the COVID-19 era. Specifically, this study focuses on understanding the changes in hotel customer satisfaction during the epidemic and formulating effective marketing strategies to satisfy and attract guests. Design/methodology/approach As the first victim of the COVID-19 virus, China’s hotel industry has been profoundly affected and customer satisfaction and needs have also changed. Taking 105,635 hotel reviews obtained from Tripadvisor.com in Beijing and Shanghai as samples, this study explores the changes in consumer satisfaction by using text-mining methods. Findings The results suggest that there are significant differences in overall ratings, spatial distribution and ratings of different traveller types before and after the epidemic. Generally, customers have higher “tolerance” and are more inclined to give higher ratings and pay more attention to hotel prevention and control measures to reduce health risks after the COVID-19. Research limitations/implications This paper proves the changes in customer satisfaction before and after the COVID-19 at the theoretical level and reveals the changes in customer attention through the topic model and provides a basis for guiding hotel managers to reduce the impact of the COVID-19 crisis. Practical implications Empirical findings would provide useful insights into tourism management and improve hotel service quality during the COVID-19 epidemic era. Originality/value This research explores the hotel customer satisfaction in the field of hotel management before COVID-19 and after COVID-19, by using text mining to analyse mandarin online reviews. The results of this study will suggest that the hotel industry should continuously adjust its products and services based on the effective information obtained from customer reviews, so as to realize the activation and revitalization of the hotel industry in the epidemic era.

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Analysis of the Impressions before and after Appreciation of Japanese, Chinese and Korean Traditional Dance Based on Text Mining

Psychology ◽

10.4236/psych.2020.116055 ◽

2020 ◽

Vol 11 (06) ◽

pp. 845-864

Author(s):

Hongjie Zheng ◽

Shogo Komatsu ◽

Koichiro Aoki ◽

Chieko Kato

Keyword(s):

Text Mining ◽

Traditional Dance ◽

Before And After

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The text-mining analysis of the role lettering method before and after the first clinical practice for nursing students.

The Proceedings of the Annual Convention of the Japanese Psychological Association ◽

10.4992/pacjpa.84.0_pp-045 ◽

2020 ◽

Vol 84 (0) ◽

pp. PP-045-PP-045

Author(s):

Takenobu Ohishi

Keyword(s):

Clinical Practice ◽

Nursing Students ◽

Text Mining ◽

Before And After

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Comparison of Nurses’ Image in Korean Online Newspaper Articles before and after COVID-19: A Text Mining Analysis

Medico-Legal Update ◽

10.37506/mlu.v22i1.3180 ◽

2022 ◽

Vol 22 (1) ◽

Keyword(s):

Text Mining ◽

Online Newspaper ◽

Before And After ◽

Newspaper Articles

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Outcomes Research in Physical Therapy

Cases on Health Outcomes and Clinical Data Mining ◽

10.4018/978-1-61520-723-7.ch007 ◽

2010 ◽

pp. 145-172

Author(s):

Jennifer Ferrell Pleiman

Keyword(s):

Physical Therapy ◽

Text Mining ◽

Data Fusion ◽

Prediction Model ◽

Functional Independence ◽

Physical Rehabilitation ◽

Other Information ◽

Before And After ◽

Patient Will ◽

Using Data

This research investigates the outcomes of physical therapy by using data fusion methodology to develop a process for sequential episode grouping data in medicine. By using data fusion, data from different sources will be combined to review the use of physical therapy in orthopedic surgical procedures. The data that were used to develop sequential episode grouping consisted of insurance claims data from the Thomson Medstat MarketScan database. The data will be reviewed as a continuous time lapse for surgery date; that is, the utilization of physical therapy for a defined time period both before and after surgery will be used and studied. The methodology of this research will follow a series of preprocessing cleaning and sequential episode grouping, culminating in text mining and clustering the results to review. Through this research, it was found that the use of physical therapy for orthopedic issues is not common and was utilized in under 1% of the data sampled. Text mining was further utilized to examine the outcomes of physical rehabilitation in cardiopulmonary research. The functional independence measures score at discharge can be predicted to identify the potential benefits of physical rehabilitation on a patient by patient basis. By text mining and clustering comorbidity codes, the severity of those clusters were used in a prediction model to determine rehabilitation benefits. Other information such as preliminary functional independence scores and age (in relation to independence scores) were used in the prediction model to provide the prescribing physician a way to determine if a patient will benefit from rehabilitation after a cardiopulmonary event.

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