Background: This study aimed to identify key priorities for the development of guidelines for information and communication technology (ICT)-based patient education tailored to the needs of patients with rheumatic diseases (RDs) in the Republic of Korea, based on expert consensus. Methods: A two-round modified Delphi study was conducted with 20 rheumatology, patient education, and digital health literacy experts. A total of 35 items covering 7 domains and 18 subdomains were evaluated. Each item was evaluated for its level of importance, and the responses were rated on a 4-point Likert scale. Consensus levels were defined as “high” (interquartile range [IQR] ≤ 1, agreement ≥ 80%, content validity ratio [CVR] ≥ 0.7), "Moderate" (IQR ≥ 1, agreement 50–79%, CVR 0.5–0.7), and "Low" (IQR > 1, agreement < 50%, CVR < 0.5). Results: Strong consensus was reached for key priorities for developing guidelines in areas such as health literacy, digital health literacy, medical terminology, user interface, and user experience design for mobile apps. Chatbot use and video (e.g., YouTube) also achieved high consensus, whereas AI-powered platforms such as ChatGPT showed moderate-to-high agreement. Telemedicine was excluded because of insufficient consensus. Conclusion The key priorities identified in this study provide a foundation for the development of ICT-based patient education guidelines for RDs in the Republic of Korea.Future efforts should focus on integrating digital tools into clinical practice to enhance patient engagement and improve clinical outcomes.

Background: This study aimed to identify key priorities for the development of guidelines for information and communication technology (ICT)-based patient education tailored to the needs of patients with rheumatic diseases (RDs) in the Republic of Korea, based on expert consensus. Methods: A two-round modified Delphi study was conducted with 20 rheumatology, patient education, and digital health literacy experts. A total of 35 items covering 7 domains and 18 subdomains were evaluated. Each item was evaluated for its level of importance, and the responses were rated on a 4-point Likert scale. Consensus levels were defined as “high” (interquartile range [IQR] ≤ 1, agreement ≥ 80%, content validity ratio [CVR] ≥ 0.7), "Moderate" (IQR ≥ 1, agreement 50–79%, CVR 0.5–0.7), and "Low" (IQR > 1, agreement < 50%, CVR < 0.5). Results: Strong consensus was reached for key priorities for developing guidelines in areas such as health literacy, digital health literacy, medical terminology, user interface, and user experience design for mobile apps. Chatbot use and video (e.g., YouTube) also achieved high consensus, whereas AI-powered platforms such as ChatGPT showed moderate-to-high agreement. Telemedicine was excluded because of insufficient consensus. Conclusion The key priorities identified in this study provide a foundation for the development of ICT-based patient education guidelines for RDs in the Republic of Korea.Future efforts should focus on integrating digital tools into clinical practice to enhance patient engagement and improve clinical outcomes.

Background: This study aimed to identify key priorities for the development of guidelines for information and communication technology (ICT)-based patient education tailored to the needs of patients with rheumatic diseases (RDs) in the Republic of Korea, based on expert consensus. Methods: A two-round modified Delphi study was conducted with 20 rheumatology, patient education, and digital health literacy experts. A total of 35 items covering 7 domains and 18 subdomains were evaluated. Each item was evaluated for its level of importance, and the responses were rated on a 4-point Likert scale. Consensus levels were defined as “high” (interquartile range [IQR] ≤ 1, agreement ≥ 80%, content validity ratio [CVR] ≥ 0.7), "Moderate" (IQR ≥ 1, agreement 50–79%, CVR 0.5–0.7), and "Low" (IQR > 1, agreement < 50%, CVR < 0.5). Results: Strong consensus was reached for key priorities for developing guidelines in areas such as health literacy, digital health literacy, medical terminology, user interface, and user experience design for mobile apps. Chatbot use and video (e.g., YouTube) also achieved high consensus, whereas AI-powered platforms such as ChatGPT showed moderate-to-high agreement. Telemedicine was excluded because of insufficient consensus. Conclusion The key priorities identified in this study provide a foundation for the development of ICT-based patient education guidelines for RDs in the Republic of Korea.Future efforts should focus on integrating digital tools into clinical practice to enhance patient engagement and improve clinical outcomes.

Background: This study aimed to identify key priorities for the development of guidelines for information and communication technology (ICT)-based patient education tailored to the needs of patients with rheumatic diseases (RDs) in the Republic of Korea, based on expert consensus. Methods: A two-round modified Delphi study was conducted with 20 rheumatology, patient education, and digital health literacy experts. A total of 35 items covering 7 domains and 18 subdomains were evaluated. Each item was evaluated for its level of importance, and the responses were rated on a 4-point Likert scale. Consensus levels were defined as “high” (interquartile range [IQR] ≤ 1, agreement ≥ 80%, content validity ratio [CVR] ≥ 0.7), "Moderate" (IQR ≥ 1, agreement 50–79%, CVR 0.5–0.7), and "Low" (IQR > 1, agreement < 50%, CVR < 0.5). Results: Strong consensus was reached for key priorities for developing guidelines in areas such as health literacy, digital health literacy, medical terminology, user interface, and user experience design for mobile apps. Chatbot use and video (e.g., YouTube) also achieved high consensus, whereas AI-powered platforms such as ChatGPT showed moderate-to-high agreement. Telemedicine was excluded because of insufficient consensus. Conclusion The key priorities identified in this study provide a foundation for the development of ICT-based patient education guidelines for RDs in the Republic of Korea.Future efforts should focus on integrating digital tools into clinical practice to enhance patient engagement and improve clinical outcomes.

OBJECTIVES: We developed and validated the Health Literacy Index for the Community (HLIC) to assess the health literacy of the Korean population within the framework of the Korean National Health and Nutrition and Examination Survey. METHODS: The HLIC was developed through (1) defining the conceptual framework and generating the item pool and (2) finalizing the items and identifying the cut-off value. Interviews were conducted to examine items’ face validity, and a cross-sectional survey was performed to analyze the item-response theory and Rasch models to investigate the instrument’s psychometric properties. RESULTS: In this study of 1,041 participants, most had no difficulty understanding health information; however, 67.9% struggled to assess the reliability of health information from the Internet or media. A 4-factor structure was identified through factor analysis, leading to the exclusion of some items. This resulted in 10 items across 4 domains: (1) disease prevention, (2) health promotion, (3) health care, and (4) technology and resources. The HLIC demonstrated good internal consistency, with a Cronbach’s α of 0.87. It also showed high test-retest reliability and correlations with other health literacy instruments. A socio-demographic analysis of the HLIC revealed disparities in health literacy across various age groups, education levels, and income brackets. CONCLUSIONS The HLIC was developed to systematically measure health literacy in Korea’s general population. Its simplicity and conciseness ensure reliability and validity and improve its accessibility, making it particularly suitable for the broader Korean population, including those with lower literacy levels.

OBJECTIVES: We developed and validated the Health Literacy Index for the Community (HLIC) to assess the health literacy of the Korean population within the framework of the Korean National Health and Nutrition and Examination Survey. METHODS: The HLIC was developed through (1) defining the conceptual framework and generating the item pool and (2) finalizing the items and identifying the cut-off value. Interviews were conducted to examine items’ face validity, and a cross-sectional survey was performed to analyze the item-response theory and Rasch models to investigate the instrument’s psychometric properties. RESULTS: In this study of 1,041 participants, most had no difficulty understanding health information; however, 67.9% struggled to assess the reliability of health information from the Internet or media. A 4-factor structure was identified through factor analysis, leading to the exclusion of some items. This resulted in 10 items across 4 domains: (1) disease prevention, (2) health promotion, (3) health care, and (4) technology and resources. The HLIC demonstrated good internal consistency, with a Cronbach’s α of 0.87. It also showed high test-retest reliability and correlations with other health literacy instruments. A socio-demographic analysis of the HLIC revealed disparities in health literacy across various age groups, education levels, and income brackets. CONCLUSIONS The HLIC was developed to systematically measure health literacy in Korea’s general population. Its simplicity and conciseness ensure reliability and validity and improve its accessibility, making it particularly suitable for the broader Korean population, including those with lower literacy levels.

OBJECTIVES: We developed and validated the Health Literacy Index for the Community (HLIC) to assess the health literacy of the Korean population within the framework of the Korean National Health and Nutrition and Examination Survey. METHODS: The HLIC was developed through (1) defining the conceptual framework and generating the item pool and (2) finalizing the items and identifying the cut-off value. Interviews were conducted to examine items’ face validity, and a cross-sectional survey was performed to analyze the item-response theory and Rasch models to investigate the instrument’s psychometric properties. RESULTS: In this study of 1,041 participants, most had no difficulty understanding health information; however, 67.9% struggled to assess the reliability of health information from the Internet or media. A 4-factor structure was identified through factor analysis, leading to the exclusion of some items. This resulted in 10 items across 4 domains: (1) disease prevention, (2) health promotion, (3) health care, and (4) technology and resources. The HLIC demonstrated good internal consistency, with a Cronbach’s α of 0.87. It also showed high test-retest reliability and correlations with other health literacy instruments. A socio-demographic analysis of the HLIC revealed disparities in health literacy across various age groups, education levels, and income brackets. CONCLUSIONS The HLIC was developed to systematically measure health literacy in Korea’s general population. Its simplicity and conciseness ensure reliability and validity and improve its accessibility, making it particularly suitable for the broader Korean population, including those with lower literacy levels.

OBJECTIVES: We developed and validated the Health Literacy Index for the Community (HLIC) to assess the health literacy of the Korean population within the framework of the Korean National Health and Nutrition and Examination Survey. METHODS: The HLIC was developed through (1) defining the conceptual framework and generating the item pool and (2) finalizing the items and identifying the cut-off value. Interviews were conducted to examine items’ face validity, and a cross-sectional survey was performed to analyze the item-response theory and Rasch models to investigate the instrument’s psychometric properties. RESULTS: In this study of 1,041 participants, most had no difficulty understanding health information; however, 67.9% struggled to assess the reliability of health information from the Internet or media. A 4-factor structure was identified through factor analysis, leading to the exclusion of some items. This resulted in 10 items across 4 domains: (1) disease prevention, (2) health promotion, (3) health care, and (4) technology and resources. The HLIC demonstrated good internal consistency, with a Cronbach’s α of 0.87. It also showed high test-retest reliability and correlations with other health literacy instruments. A socio-demographic analysis of the HLIC revealed disparities in health literacy across various age groups, education levels, and income brackets. CONCLUSIONS The HLIC was developed to systematically measure health literacy in Korea’s general population. Its simplicity and conciseness ensure reliability and validity and improve its accessibility, making it particularly suitable for the broader Korean population, including those with lower literacy levels.

Background: Few studies have evaluated the use of automated artificial intelligence (AI)-based pain recognition in postoperative settings or the correlation with pain intensity. In this study, various machine learning (ML)-based models using facial expressions, the analgesia nociception index (ANI), and vital signs were developed to predict postoperative pain intensity, and their performances for predicting severe postoperative pain were compared. Methods: In total, 155 facial expressions from patients who underwent gastrectomy were recorded postoperatively; one blinded anesthesiologist simultaneously recorded the ANI score, vital signs, and patient self-assessed pain intensity based on the 11-point numerical rating scale (NRS). The ML models’ area under the receiver operating characteristic curves (AUROCs) were calculated and compared using DeLong’s test. Results: ML models were constructed using facial expressions, ANI, vital signs, and different combinations of the three datasets. The ML model constructed using facial expressions best predicted an NRS ≥ 7 (AUROC 0.93) followed by the ML model combining facial expressions and vital signs (AUROC 0.84) in the test-set. ML models constructed using combined physiological signals (vital signs, ANI) performed better than models based on individual parameters for predicting NRS ≥ 7, although the AUROCs were inferior to those of the ML model based on facial expressions (all P < 0.050). Among these parameters, absolute and relative ANI had the worst AUROCs (0.69 and 0.68, respectively) for predicting NRS ≥ 7. Conclusions The ML model constructed using facial expressions best predicted severe postoperative pain (NRS ≥ 7) and outperformed models constructed from physiological signals.

Purpose: The purpose of the study was to validate the Korean version of Patient-Reported Outcomes Measurement Information System 29 Profile v2.1 (K-PROMIS-29 V2.1) among cancer survivors. Materials and Methods: Participants were recruited from outpatient clinics of the Comprehensive Cancer Center at the Samsung Medical Center in Seoul, South Korea, from September to October 2018. Participants completed a survey questionnaire that included the K-PROMIS-29 V2.1 and the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30). Principal component analysis and confirmatory factor analysis (CFA) and Pearson’s correlations were used to evaluate the reliability and validity of the K-PROMIS-29 V2.1. Results: The mean age of the study participants was 54.4 years, the mean time since diagnosis was 1.2 (±2.4) years, and 349 (87.3%) completed the entire questionnaire. The Cronbach’s alpha coefficients of the seven domains in the K-PROMIS-29 V2.1 ranged from 0.81 to 0.96, indicating satisfactory internal consistency. In the CFA, the goodness-of-fit indices for the K-PROMIS-29 V2.1 were high (comparative fit index, 0.91 and standardized root-mean-squared residual, 0.06). High to moderate correlations were found between comparable subscales of the K-PROMIS-29 V2.1 and subscales of the EORTC QLQ-C30 (r=0.52-0.73). Conclusion The K-PROMIS-29 V2.1 is a reliable and valid measure for assessing the health-related quality of life domains in a cancer population, thus supporting their use in studies and oncology trials.