A Question Answering Chatbot for Gastric Cancer Patients After Curative Gastrectomy: Development and Evaluation of User Experience and Performance.

Postoperative gastric cancer patients have many questions about managing their daily lives with various symptoms and discomfort. This study aimed to develop a knowledge-based question answering (QA) chatbot for their self-management and to evaluate the user experience and performance of the chatbot. To support the chatbot's natural language processing, we analyzed QA texts from an online self-help group, clinical guidelines, and refined frequently asked questions related to gastric cancer. We developed a named entity classification with seven superconcepts, 4544 subconcepts, and 1415 synonyms. We also developed a knowledge base by linking the users' classified question intents with the experts' answers and knowledge resources, including 677 question intents and scripts with standard QA pairs and similar question phrases. A chatbot called "GastricFAQ" was built, reflecting the question topics of the named entity classification and QA pairs of the knowledge base. User experience evaluation (N = 56) revealed the highest mean score for usefulness (4.41/5.00), with all other items rated 4.00 or higher, except desirability (3.85/5.00). The chatbot's accuracy, precision, recall, and F score ratings were 85.2%, 87.6%, 96.8%, and 92.0%, respectively, with immediate answers. GastricFAQ could be provided as one option to obtain immediate information with relatively high accuracy for postoperative gastric cancer patients.

Effects of communication team training on clinical competence in Korean Advanced Life Support: A randomized controlled trial.

We conducted a randomized controlled trial to study the effects of interprofessional communication team training on clinical competence in the Korean Advanced Life Support provider course using a team communication framework. Our study involved 73 residents and 42 nurses from a tertiary hospital in Seoul. The participants were randomly assigned to the intervention or control group, forming 10 teams per group. The intervention group underwent interprofessional communication team training with a cardiac arrest simulation and standardized communication tools. The control group completed the Korean Advanced Life Support provider course. All participants completed a communication clarity self-reporting questionnaire. Clinical competence was assessed using a clinical competency scale comprising technical and nontechnical tools. Blinding was not possible due to the educational intervention. Data were analyzed using a Mann-Whitney U test and a multivariate Kruskal-Wallis H test. While no significant differences were observed in communication clarity between the two groups, there were significant differences in clinical competence. Therefore, the study confirmed that the intervention can enhance the clinical competence of patient care teams in cardiopulmonary resuscitation.

A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology.

BACKGROUND: South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends. OBJECTIVE: The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues. METHODS: An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea's Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals. RESULTS: A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future. CONCLUSIONS: The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.

Correction: A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology.

[This corrects the article DOI: 10.2196/25028.].

Factors of quality of care and their association with smartphone based PHR adoption in South Korean hospitals.

BACKGROUND: Healthcare organizations have begun to adopt personal health records (PHR) systems to engage patients, but little is known about factors associated with the adoption of PHR systems at an organizational level. The objective of this study is to investigate factors associated with healthcare organizations' adoption of PHR systems in South Korea. METHODS: The units of analysis were hospitals with more than 100 beds. Study data of 313 hospitals were collected from May 1 to June 30, 2020. The PHR adoption status for each hospital was collected from PHR vendors and online searches. Adoption was then confirmed by downloading the hospital's PHR app and the PHR app was examined to ascertain its available functions. One major outcome variable was PHR adoption status at hospital level. Data were analysed by logistic regressions using SAS 9.4 version. RESULTS: Out of 313 hospitals, 103 (32.9%) hospitals adopted PHR systems. The nurse-patient ratio was significantly associated with PHR adoption (OR 0.758; 0.624 to 0.920, p = 0.005). The number of health information management staff was associated with PHR adoption (OR 1.622; 1.228 to 2.141, p = 0.001). The number of CTs was positively associated with PHR adoption (OR 5.346; 1.962 to 14.568, p = 0.001). Among the hospital characteristics, the number of beds was significantly related with PHR adoption in the model of standard of nursing care (OR 1.003; 1.001 to 1.005, p < 0.001), HIM staff (OR 1.004; 1.002 to 1.006, p < 0.001), and technological infrastructure (OR 1.050; 1.003 to 1.006, p < 0.001). CONCLUSIONS: One-third of study hospitals had adopted PHR systems. Standard of nursing care as well as information technology infrastructure in terms of human resources for health information management and advanced technologies were significantly associated with adoption of PHR systems. A favourable environment for adopting new technologies in general may be associated with the adoption and use of PHR systems.

Using Social Media Data to Understand Consumers' Information Needs and Emotions Regarding Cancer: Ontology-Based Data Analysis Study.

BACKGROUND: Analysis of posts on social media is effective in investigating health information needs for disease management and identifying people's emotional status related to disease. An ontology is needed for semantic analysis of social media data. OBJECTIVE: This study was performed to develop a cancer ontology with terminology containing consumer terms and to analyze social media data to identify health information needs and emotions related to cancer. METHODS: A cancer ontology was developed using social media data, collected with a crawler, from online communities and blogs between January 1, 2014 and June 30, 2017 in South Korea. The relative frequencies of posts containing ontology concepts were counted and compared by cancer type. RESULTS: The ontology had 9 superclasses, 213 class concepts, and 4061 synonyms. Ontology-driven natural language processing was performed on the text from 754,744 cancer-related posts. Colon, breast, stomach, cervical, lung, liver, pancreatic, and prostate cancer; brain tumors; and leukemia appeared most in these posts. At the superclass level, risk factor was the most frequent, followed by emotions, symptoms, treatments, and dealing with cancer. CONCLUSIONS: Information needs and emotions differed according to cancer type. The observations of this study could be used to provide tailored information to consumers according to cancer type and care process. Attention should be paid to provision of cancer-related information to not only patients but also their families and the general public seeking information on cancer.

Comparison of the predictive validity of three fall risk assessment tools and analysis of fall-risk factors at a tertiary teaching hospital.

AIMS AND OBJECTIVES: The main purpose of this study was to identify the best fall-risk assessment tool, among the Morse Fall Scale, the Johns Hopkins fall-risk Assessment Tool and the Hendrich II fall-risk Model, for a tertiary teaching hospital. The study also analysed fall-risk factors in the hospital, focusing on the items of each fall assessment tool. METHODS: Data on falls were obtained from the patient safety reports and electronic nursing records of a tertiary teaching hospital. A retrospective study was conducted to compare the sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, Youden index and accuracy of the Morse Fall Scale, the Johns Hopkins fall-risk Assessment Tool and the Hendrich II fall-risk Model. This study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology guideline for reporting case-control studies. RESULTS: By analysing the association between falls and the items included in the three tools, we identified significant fall-risk factors such as gait, dizziness or vertigo, changes in mental status, impulsivity, history of falling, elimination disorder, drugs affecting falls, and depression. CONCLUSIONS: The Hendrich II fall-risk Model had the best predictive performance for falls of the three tools, considering the highest in the area under the curve and the Youden index that comprehensively analysed sensitivity and specificity, while the Johns Hopkins fall-risk Assessment Tool had the highest accuracy. The most significant fall-risk predictors are gait, dizziness or vertigo, change in mental state, and history of falling. RELEVANCE TO CLINICAL PRACTICE: To improve the fall assessment performance of the Morse Fall Scale at the study hospital, we propose that it be supplemented with four most significant fall-risk predictors identified in this study.

Improving Prediction of Fall Risk Using Electronic Health Record Data With Various Types and Sources at Multiple Times.

Inpatient falls are among the most common adverse events threatening patient safety. Although many studies have developed predictive models for fall risk, there are some drawbacks. First, most previous studies have relied on an incident-reporting system alone to identify fall events. Thus, it has been found that falls are more likely to be underreported. Second, there has been a controversy on how to select accurate representative values for patient status data across multiple times and various data sources in electronic health records. Given this background, this study used nurses' progress notes as a complementary data source to detect fall events. In addition, we developed criteria including coverage, currency, and granularity in order to integrate electronic health records data documented at multiple times in various data types and sources. Based on this methodology, we developed three models, logistic regression, Cox proportional hazard regression, and decision tree, to predict risk of patient falls and evaluate the predictive performance of these models by comparing the results to results from the Hendrich II Fall Risk Model. The findings of this study will be used in a clinical decision support system to predict risk of falling and provide evidence-based tailored recommendations in the future.

Perceived cultural differences in healthcare for foreign patients visiting South Korea: tool development and measurement.

BACKGROUND: We developed a 41-item tool measuring cultural differences in healthcare as perceived by foreign patients visiting South Korea. METHODS: The tool was tested on 256 foreign patients who visited three tertiary hospitals in Seoul, South Korea. Content validity was explored by two physicians and eight nurses working in an international healthcare department. Structural validity was tested via exploratory factor analysis and by testing two hypotheses: (1) there are perceived cultural differences between the South Korean healthcare and those of foreign patients' home countries (one-sample t-test); and, (2) Perceived cultural differences vary among language groups (analysis of variance). We also calculated Cronbach's alpha. RESULTS: The content validity index of the tool was 0.97. Exploratory factor analysis identified seven significant factors: hospital care and services, food, the healthcare system, communication, the healthcare facility, religion, and cultural values. The overall Cronbach's alpha for the tool was 0.96, indicating very high internal consistency. We found that foreign patients visiting South Korean hospitals perceived that the healthcare culture differed significantly from that of their home country. The perceived cultural differences varied significantly by language group. CONCLUSIONS: Nurses can use our new tool to understand the cultural differences of foreign patients and provide them with culturally competent nursing care.

Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology.

BACKGROUND: Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates. OBJECTIVE: The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data. METHODS: The domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules. RESULTS: Our childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent's health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents' health belief, vaccination availability, and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments. CONCLUSIONS: The childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data.

Development and Comparison of Predictive Models for Pressure Injuries in Surgical Patients: A Retrospective Case-Control Study.

PURPOSE: The purpose of this study was to develop and compare 3 predictive models for pressure injury (PI) occurrence in surgical patients. DESIGN: Retrospective case-control study. SUBJECTS AND SETTING: Data on PI risk assessment and preanesthesia evaluation records from 400 patients (80 patients who developed PIs after surgery and 320 patients who did not) in a South Korean acute care setting who underwent surgery between January 2015 and May 2016 were extracted from the electronic health record. METHODS: Three models were developed with items from the Braden Scale (model 1), the Scott Triggers tool (model 2), and the Scott Triggers tool in addition to type of anesthesia, laboratory test results, and comorbid conditions (model 3) using logistic regression to analyze items (factors) in each model. Predictive performance indices, which included sensitivity, specificity, positive predictive value, negative predictive value, area under the receiver operating characteristics curve, and Akaike information criterion, were compared among the 3 models. RESULTS: Findings showed there were no significant factors in model 1, the estimated surgery time and serum albumin level were significant in model 2, and the estimated surgery time, serum albumin level, and brain disease were significant in model 3. The model performance evaluation revealed that model 2 was the best fitting model, demonstrating the highest sensitivity (84.4%), highest negative predictive value (94.6%), and lowest Akaike information criterion (302.03). CONCLUSIONS: The Scott Triggers tool in model 2, which consists of simple items that are easy to extract from preanesthesia evaluation records, was the best fitting model. We recommend the Scott Triggers tool be used to predict the development of PIs in surgical patients in acute care settings.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

BACKGROUND: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. OBJECTIVE: The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. METHODS: The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. RESULTS: We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, "academic stresses" and "suicide" contributed negatively to the sentiment of adolescent depression. CONCLUSIONS: The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology.

Relationships between evidence-based practice, quality improvement and clinical error experience of nurses in Korean hospitals.

AIM: This study investigated individual and work-related factors associated with nurses' perceptions of evidence-based practice (EBP) and quality improvement (QI), and the relationships between evidence-based practice, quality improvement and clinical errors. BACKGROUND: Understanding the factors affecting evidence-based practice and quality improvement activities and their relationships with clinical errors is important for designing strategies to promote evidence-based practice, quality improvement and patient safety. METHOD: A cross-sectional survey was conducted with 594 nurses in two Korean teaching hospitals using the evidence-based practice Questionnaire and quality improvement scale developed in this study. RESULT: Four hundred and forty-three nurses (74.6%) returned the completed survey. Nurses' ages and educational levels were significantly associated with evidence-based practice scores whereas age and job position were associated with quality improvement scores. There were positive, moderate correlations between evidence-based practice and quality improvement scores. Nurses who had not made any clinical errors during the past 12 months had significantly higher quality improvement skills scores than those who had. CONCLUSION: The findings indicated the necessity of educational support regarding evidence-based practice and quality improvement for younger staff nurses who have no master degrees. Enhancing quality improvement skills may reduce clinical errors. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers should consider the characteristics of their staff when implementing educational and clinical strategies for evidence-based practice and quality improvement.

Nurses' perception of ethical climate, medical error experience and intent-to-leave.

We examined nurses' perceptions of the ethical climate of their workplace and the relationships among the perceptions, medical error experience and intent to leave through a cross-sectional survey of 1826 nurses in 33 Korean public hospitals. Ethical climate was measured using the Hospital Ethical Climate Survey. Although the sampled nurses perceived their workplace ethical climate positively, 19% reported making at least one medical error during the previous year, and 25% intended to leave their jobs in the near future. Controlling for individual and organizational characteristics, we found that nurses with a more positive perception of the 'patient' dimension of ethical climate were less likely to have made medical errors. Nurses with a more positive perception of the 'patient', 'manager', 'hospital' and 'physician' dimensions of ethical climate were less likely to leave their current job. Enhancing workplace ethical climate could reduce medical errors and improve nurses' retention in public hospitals.

Current Status of SNOMED CT National Extensions and Terminology Managements.

We reviewed and surveyed 15 SNOMEDCT national member countries for SNOMED CT national extensions and terminology managements. We found that national extensions were used for adding new contents, developing reference sets, translating, and mapping with other classification system; and terminology management varies in composition and content due to healthcare environment of each member country, eHealth strategy, and infrastructure of national release centers.

Patient safety incident-reporting items in Korean hospitals.

OBJECTIVE: To examine incident-reporting items in tertiary hospitals using the framework of the World Health Organization's International Classification for Patient Safety (ICPS). DESIGN: Cross-sectional survey. SETTING AND PARTICIPANTS: Forty acute-care tertiary hospitals in Korea (response rate = 90.9%). METHODS: Data were collected using a semistructured questionnaire during on-site interviews or via e-mail. Items were extracted from incident-reporting forms that required a reporter's input, and were analyzed using the ICPS framework. After removing redundant items, unique reporting items were mapped onto ICPS elements. The data are summarized using descriptive statistics. RESULTS: On average, hospitals used 2.4 incident-reporting forms (range = 1-9) and 136.7 reporting items (range = 31-310). All of the hospitals had incident-reporting items that described 'incident type' and 'incident characteristics'; however, only 7 hospitals (17.5%) had reporting items on incident 'detection', and 18 hospitals (45.0%) collected information on the 'organizational outcomes'. Of the 1145 unique reporting items, 297 (25.9%) were completely mapped onto ICPS elements at different levels of granularity, and 12.7% (n = 145) were mapped onto ICPS elements that had more granular subcategories. CONCLUSIONS: The ICPS framework is a useful reference model for the classification of incident-reporting items. However, further refinements to both the ICPS framework and incident-reporting items are needed in order to better represent data on patient safety. Furthermore, the use of a common reporting form at the national level is recommended for reducing variations in reporting items and facilitating the efficient collection and analysis of patient safety data.

Development of an obesity management ontology based on the nursing process for the mobile-device domain.

BACKGROUND: Lifestyle modification is the most important factor in the management of obesity. It is therefore essential to enhance client participation in voluntary and continuous weight control. OBJECTIVE: The aim of this study was to develop an obesity management ontology for application in the mobile-device domain. We considered the concepts of client participation in behavioral modification for obesity management and focused on minimizing the amount of information exchange between the application and the database when providing tailored interventions. METHODS: An obesity management ontology was developed in seven phases: (1) defining the scope of obesity management, (2) selecting a foundational ontology, (3) extracting the concepts, (4) assigning relationships between these concepts, (5) evaluating representative layers of ontology content, (6) representing the ontology formally with Protégé, and (7) developing a prototype application for obesity management. RESULTS: Behavioral interventions, dietary advice, and physical activity were proposed as obesity management strategies. The nursing process was selected as a foundation of ontology, representing the obesity management process. We extracted 127 concepts, which included assessment data (eg, sex, body mass index, and waist circumference), inferred data to represent nursing diagnoses and evaluations (eg, degree of and reason for obesity, and success or failure of lifestyle modifications), and implementation (eg, education and advice). The relationship linking concepts were "part of", "instance of", "derives of", "derives into", "has plan", "followed by", and "has intention". The concepts and relationships were formally represented using Protégé. The evaluation score of the obesity management ontology was 4.5 out of 5. An Android-based obesity management application comprising both agent and client parts was developed. CONCLUSIONS: We have developed an ontology for representing obesity management with the nursing process as a foundation of ontology.

Adverse-drug-event surveillance using narrative nursing records in electronic nursing records.

The purpose of this study was to determine whether the frequency of adverse drug events can be extracted by analyzing narrative nursing statements documented in standardized terminology-based electronic nursing records. For this study, we reviewed the narrative nursing documentations of 487 admissions of 355 cancer patients who were treated with cisplatin at a tertiary-care hospital in Korea. Narrative nursing statements with the terms "adverse drug reaction," "allergy," "hypersensitivity," and other adverse drug events listed in the safety information were analyzed. In addition, nausea, one of the most frequent adverse drug events, was further examined. Narrative statements documenting the presence or absence of an "adverse drug reaction," "allergy," and "hypersensitivity" were found in 162 admissions (33.3%). The presence or absence of adverse drug events due to cisplatin was documented in 476 admissions (97.7%). At least one adverse drug event was noted in 258 admissions (53.0%). The presence of nausea was documented in 214 admissions (43.9%), and the mean duration of nausea was 5.2 days. The results of this study suggest that adverse drug events can be monitored using narrative nursing statements documented in standardized terminology-based electronic nursing records.

Use of the clinical care classification in South Korean nursing practice: Challenges and opportunities.

BACKGROUND AND OBJECTIVES: A government-driven standardization of nursing terminology including the Clinical Care Classification (CCC) was endorsed in South Korea in 2015, but the number of hospitals who have adopted this standard terminology remains unknown. This study aimed to determine the CCC awareness, adoption, and utilization statuses and its association with patient experience in South Korea. DESIGN, SETTING, AND PARTICIPANTS: A nationwide telephone survey was conducted from January 13 to February 12, 2022 among 217 tertiary and secondary hospitals participating in the health information exchange network. The survey questionnaire included 22 items in 3 categories: current status of electronic nursing records, awareness and adoption of standard terminology, and open-ended questions regarding standard usage and dissemination. General characteristics and experience scores of the patients of the surveyed hospitals were collected from the publicly available data sources. Data analysis was performed using descriptive statistics, t-test, and generalized linear regression. MAIN OUTCOMES AND MEASURES: The rates of awareness and adoption in hospitals to the nursing terminology standard of the CCC were calculated, and the current status of electronic nursing records used in practice was examined. The relationships between CCC awareness and the characteristics of hospitals in their patient experiences of health services were also identified. RESULTS: The survey response rate was 24.9 % (54/217). Two out of three hospitals (68.5 %) were aware of the CCC. These hospitals had 800 beds or more, and higher scores for patient experience. CCC awareness was significantly related to increases in the overall scores for patient experiences (t = 2.70, p =.0103), but no significance with sub-score for nursing service (t = 1.23, p =.1594). CONCLUSIONS: With a high adoption rate of electronic medical record systems, two-third hospitals acknowledged their CCC awareness, but were still lagged in adoption and usage of it in practice with operational challenges. The CCC awareness has potential relationships with positive patient experience.