Journal of Korean Academy of Nursing

Research Papers

Structural Topic Modeling Analysis of Patient Safety Interest among Health Consumers in Social Media: Kim, Nari , Lee, Nam-Ju; J Korean Acad Nurs 2024;54(2):266-278. Published online May 31, 2024; DOI: https://doi.org/10.4040/jkan.23156

Abstract PDF: Purpose
This study aimed to investigate healthcare consumers’ interest in patient safety on social media using structural topic modeling (STM) and to identify changes in interest over time.
Methods
Analyzing 105,727 posts from Naver news comments, blogs, internet cafés, and Twitter between 2010 and 2022, this study deployed a Python script for data collection and preprocessing. STM analysis was conducted using R, with the documents’ publication years serving as metadata to trace the evolution of discussions on patient safety.
Results
The analysis identified a total of 13 distinct topics, organized into three primary communities: (1) “Demand for systemic improvement of medical accidents,” underscoring the need for legal and regulatory reform to enhance accountability; (2) “Efforts of the government and organizations for safety management,” highlighting proactive risk mitigation strategies; and (3) “Medical accidents exposed in the media,” reflecting widespread concerns over medical negligence and its repercussions. These findings indicate pervasive concerns regarding medical accountability and transparency among healthcare consumers.
Conclusion
The findings emphasize the importance of transparent healthcare policies and practices that openly address patient safety incidents. There is clear advocacy for policy reforms aimed at increasing the accountability and transparency of healthcare providers. Moreover, this study highlights the significance of educational and engagement initiatives involving healthcare consumers in fostering a culture of patient safety. Integrating consumer perspectives into patient safety strategies is crucial for developing a robust safety culture in healthcare.

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National Petition Analysis Related to Nursing: Text Network Analysis and Topic Modeling: Ko, HyunJung , Jeong, Seok Hee , Lee, Eun Jee , Kim, Hee Sun; J Korean Acad Nurs 2023;53(6):635-651. Published online December 31, 2023; DOI: https://doi.org/10.4040/jkan.23052

Purpose
This study aimed to identify the main keyword, network structure, and main topics of the national petition related to “nursing” in South Korea.
Methods
Data were gathered from petitions related to the national petition in Korea Blue House related to the topic “nursing” or “nurse” from August 17, 2017, to May 9, 2022. A total of 5,154 petitions were searched, and 995 were selected for the final analysis. Text network analysis and topic modeling were analyzed using the Netminer 4.5.0 program.
Results
Regarding network characteristics, a density of 0.03, an average degree of 144.483, and an average distance of 1.943 were found. Compared to results of degree centrality and betweenness centrality, keywords such as “work environment,” “nursing university,” “license,” and “education” appeared typically in the eigenvector centrality analysis. Topic modeling derived four topics: (1) “Improving the working environment and dealing with nursing professionals,” (2) “requesting investigation and punishment related to medical accidents,” (3) “requiring clear role regulation and legislation of medical and nonmedical professions,” and (4) “demanding improvement of healthcare-related systems and services.” Conclusion: This is the first study to analyze Korea's national petitions in the field of nursing. This study's results confirmed both the internal needs and external demands for nurses in South Korea. Policies and laws that reflect these results should be developed.

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Voice of Customer Analysis of Nursing Care in a Tertiary Hospital: Text Network Analysis and Topic Modeling
Hyunjung Ko, Nara Han, Seulki Jeong, Jeong A Jeong, Hye Ryoung Yun, Eun Sil Kim, Young Jun Jang, Eun Ju Choi, Chun Hoe Lim, Min Hee Jung, Jung Hee Kim, Dong Hyu Cho, Seok Hee Jeong
Journal of Korean Academy of Nursing Administration.2024; 30(5): 529. CrossRef
A Study on Internet News for Patient Safety Campaigns: Focusing on Text Network Analysis and Topic Modeling
Sun-Hwa Shin, On-Jeon Baek
Healthcare.2024; 12(19): 1914. CrossRef

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Review Paper

Knowledge Structure of Chronic Obstructive Pulmonary Disease Health Information on HealthRelated Websites and Patients’ Needs in the Literature Using Text Network Analysis: Choi, Ja Yun , Lim, Su Yeon , Yun, So Young; J Korean Acad Nurs 2021;51(6):720-731. Published online December 31, 2021; DOI: https://doi.org/10.4040/jkan.21086

Abstract PDF

Purpose
The purpose of this study was to identify the knowledge structure of health information (HI) for chronic obstructive pulmonary disease (COPD).
Methods
Keywords or meaningful morphemes from HI presented on five health-related websites (HRWs) of one national HI institute and four hospitals, as well as HI needs among patients presented in nine literature, were reviewed, refined, and analyzed using text network analysis and their co-occurrence matrix was generated. Two networks of 61 and 35 keywords, respectively, were analyzed for degree, closeness, and betweenness centrality, as well as betweenness community analysis.
Results
The most common keywords pertaining to HI on HRWs were lung, inhaler, smoking, dyspnea, and infection, focusing COPD treatment. In contrast, HI needs among patients were lung, medication, support, symptom, and smoking cessation, expanding to disease management. Two common sub-topic groups in HI on HRWs were COPD overview and medication administration, whereas three common sub-topic groups in HI needs among patients in the literature were COPD overview, self-management, and emotional management.
Conclusion
The knowledge structure of HI on HRWs is medically oriented, while patients need supportive information. Thus, the support system for self-management and emotional management on HRWs must be informed according to the structure of patients’ needs for HI. Healthcare providers should consider presenting COPD patient-centered information on HRWs.

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Original Articles

A Topic Modeling Analysis for Online News Article Comments on Nurses' Workplace Bullying: Jiyeon Kang, Soogyeong Kim, Seungkook Roh; J Korean Acad Nurs 2019;49(6):736-747. Published online December 30, 2019; DOI: https://doi.org/10.4040/jkan.2019.49.6.736

Abstract PDF

Purpose

This study aimed to explore public opinion on workplace bullying in the nursing field, by analyzing the keywords and topics of online news comments.

Methods

This was a text-mining study that collected, processed, and analyzed text data. A total of 89,951 comments on 650 online news articles, reported between January 1, 2013 and July 31, 2018, were collected via web crawling. The collected unstructured text data were preprocessed and keyword analysis and topic modeling were performed using R programming.

Results

The 10 most important keywords were “work” (37121.7), “hospital” (25286.0), “patients” (24600.8), “woman” (24015.6), “physician” (20840.6), “trouble” (18539.4), “time” (17896.3), “money” (16379.9), “new nurses” (14056.8), and “salary” (13084.1). The 22,572 preprocessed key words were categorized into four topics: “poor working environment”, “culture among women”, “unfair oppression”, and “society-level solutions”.

Conclusion

Public interest in workplace bullying among nurses has continued to increase. The public agreed that negative work environment and nursing shortage could cause workplace bullying. They also considered nurse bullying as a problem that should be resolved at a societal level. It is necessary to conduct further research through gender discrimination perspectives on nurse workplace bullying and the social value of nursing work.

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Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers: Seul Ki Park, Hyeoun-Ae Park, Hee Hwang; J Korean Acad Nurs 2019;49(5):575-585. Published online January 15, 2019; DOI: https://doi.org/10.4040/jkan.2019.49.5.575

Abstract PDF

Abstract Purpose

The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital.

Methods

A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale.

Results

The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model.

Conclusion

Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

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Development of a Pressure Injury Machine Learning Prediction Model and Integration into Clinical Practice: A Prediction Model Development and Validation Study
Ju Hee Lee, Jae Yong Yu, So Yun Shim, Kyung Mi Yeom, Hyun A Ha, Se Yong Jekal, Ki Tae Moon, Joo Hee Park, Sook Hyun Park, Jeong Hee Hong, Mi Ra Song, Won Chul Cha
Korean Journal of Adult Nursing.2024; 36(3): 191. CrossRef
Could we prove the nursing outcomes utilising clinical data warehouse? Effectiveness of pressure ulcer intervention in Korean tertiary hospital
Moonsook Kim, Se Yeon Park, Meihua Piao, Earom Lim, Soon Hwa Yoo, Minju Ryu, Hyo Yeon Lee, Hyejin Won
International Wound Journal.2023; 20(1): 201. CrossRef
Data‐driven approach to predicting the risk of pressure injury: A retrospective analysis based on changes in patient conditions
Yinji Jin, Ji‐Sun Back, Sun Ho Im, Jong Hyo Oh, Sun‐Mi Lee
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Knowledge Discovery in Nursing Minimum Data Set Using Data Mining: Myonghwa Park, Jeong Sook Park, Chong Nam Kim, Kyung Min Park, Young Sook Kwon; Journal of Korean Academy of Nursing 2006;36(4):652-661. Published online March 28, 2017; DOI: https://doi.org/10.4040/jkan.2006.36.4.652

Abstract PDF

Purpose

The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making.

Methods

Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules.

Results

Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality.

Conclusions

This study demonstrated the utilization of data mining method through a large data set with stan-dardized language format to identify the contribution of nursing care to patient's health.

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Standardized Nursing Diagnoses in a Surgical Hospital Setting: A Retrospective Study Based on Electronic Health Data
Manuele Cesare, Fabio D’agostino, Massimo Maurici, Maurizio Zega, Valentina Zeffiro, Antonello Cocchieri
SAGE Open Nursing.2023;[Epub] CrossRef
Predictors for Successful Smoking Cessation in Korean Adults
Young-Ju Kim
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Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis: Myonghwa Park, Sora Choi, A Mi Shin, Chul Hoi Koo; J Korean Acad Nurs 2013;43(1):1-10. Published online February 28, 2013; DOI: https://doi.org/10.4040/jkan.2013.43.1.1

Abstract PDF

Purpose

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method.

Methods

A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs.

Results

The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease.

Conclusion

The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

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Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers: In Sook Cho, Eunja Chung; J Korean Acad Nurs 2011;41(3):423-431. Published online June 13, 2011; DOI: https://doi.org/10.4040/jkan.2011.41.3.423

Abstract PDF

Purpose

The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers.

Methods

Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and -II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method.

Results

Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR.

Conclusion

Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.

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Korean Journal of Adult Nursing.2024; 36(3): 191. CrossRef
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Yuxia Ma, Xiang He, Tingting Yang, Yifang Yang, Ziyan Yang, Tian Gao, Fanghong Yan, Boling Yan, Juan Wang, Lin Han
Journal of Clinical Nursing.2024;[Epub] CrossRef
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Mireia Ladios-Martin, José Fernández-de-Maya, Francisco-Javier Ballesta-López, Adrián Belso-Garzas, Manuel Mas-Asencio, María José Cabañero-Martínez
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