Purpose: To explore the relationship between nursing care needs and acuity based on the Korean Patient Classification System for Critical Care Nurses (KPCSC) and APACHE II, and to identify their prognostic value in predicting mortality. Methods: A total of 617 patients admitted to a surgical intensive care unit in a tertiary hospital from January 1 to June 30, 2021 were included. The correlation between KPCSC and APACHE II scores, and their predictive power regarding mortality were examined. Results: KPCSC and APACHE II scores showed a significant, positive correlation (r=.32, p<.001). The KPCSC score was significantly correlated with 10 out of 11 KPCSC categories and 2 out of 3 APACHE II domains, whereas the APACHE II score had a significant correlation with all APACHE II domains and only 4 out of 11 KPCSC categories. Both KPCSC and APACHE II demonstrated moderate discriminatory performance in predicting ICU and in-hospital death, and their AUC values were not significantly different. Conclusion KPCSC, reflecting the severity of illness, predicted mortality as well as APACHE II. However, KPCSC was found to consider factors other than severity, such as patient dependency, which substantiates its value as an assessment tool for nursing care needs.

Purpose: To explore the relationship between nursing care needs and acuity based on the Korean Patient Classification System for Critical Care Nurses (KPCSC) and APACHE II, and to identify their prognostic value in predicting mortality. Methods: A total of 617 patients admitted to a surgical intensive care unit in a tertiary hospital from January 1 to June 30, 2021 were included. The correlation between KPCSC and APACHE II scores, and their predictive power regarding mortality were examined. Results: KPCSC and APACHE II scores showed a significant, positive correlation (r=.32, p<.001). The KPCSC score was significantly correlated with 10 out of 11 KPCSC categories and 2 out of 3 APACHE II domains, whereas the APACHE II score had a significant correlation with all APACHE II domains and only 4 out of 11 KPCSC categories. Both KPCSC and APACHE II demonstrated moderate discriminatory performance in predicting ICU and in-hospital death, and their AUC values were not significantly different. Conclusion KPCSC, reflecting the severity of illness, predicted mortality as well as APACHE II. However, KPCSC was found to consider factors other than severity, such as patient dependency, which substantiates its value as an assessment tool for nursing care needs.

Purpose: To explore the relationship between nursing care needs and acuity based on the Korean Patient Classification System for Critical Care Nurses (KPCSC) and APACHE II, and to identify their prognostic value in predicting mortality. Methods: A total of 617 patients admitted to a surgical intensive care unit in a tertiary hospital from January 1 to June 30, 2021 were included. The correlation between KPCSC and APACHE II scores, and their predictive power regarding mortality were examined. Results: KPCSC and APACHE II scores showed a significant, positive correlation (r=.32, p<.001). The KPCSC score was significantly correlated with 10 out of 11 KPCSC categories and 2 out of 3 APACHE II domains, whereas the APACHE II score had a significant correlation with all APACHE II domains and only 4 out of 11 KPCSC categories. Both KPCSC and APACHE II demonstrated moderate discriminatory performance in predicting ICU and in-hospital death, and their AUC values were not significantly different. Conclusion KPCSC, reflecting the severity of illness, predicted mortality as well as APACHE II. However, KPCSC was found to consider factors other than severity, such as patient dependency, which substantiates its value as an assessment tool for nursing care needs.

Purpose: To explore the relationship between nursing care needs and acuity based on the Korean Patient Classification System for Critical Care Nurses (KPCSC) and APACHE II, and to identify their prognostic value in predicting mortality. Methods: A total of 617 patients admitted to a surgical intensive care unit in a tertiary hospital from January 1 to June 30, 2021 were included. The correlation between KPCSC and APACHE II scores, and their predictive power regarding mortality were examined. Results: KPCSC and APACHE II scores showed a significant, positive correlation (r=.32, p<.001). The KPCSC score was significantly correlated with 10 out of 11 KPCSC categories and 2 out of 3 APACHE II domains, whereas the APACHE II score had a significant correlation with all APACHE II domains and only 4 out of 11 KPCSC categories. Both KPCSC and APACHE II demonstrated moderate discriminatory performance in predicting ICU and in-hospital death, and their AUC values were not significantly different. Conclusion KPCSC, reflecting the severity of illness, predicted mortality as well as APACHE II. However, KPCSC was found to consider factors other than severity, such as patient dependency, which substantiates its value as an assessment tool for nursing care needs.

Purpose: : This study aimed to investigate changes in critical nursing activities from 2009 to 2020 and explore the relationship between nurse staffing and such activities in intensive care units. Methods: : A total of 446,445 adult patients admitted to intensive care units in tertiary and general hospitals from 2009 to 2020 were identified using the National Health Insurance claims database. The Critical Nursing Activities Index was calculated based on the following critical nursing activities: ventilator, extracorporeal membrane oxygenation (ECMO), and continuous renal replacement therapy (CRRT). Trend analysis was performed to analyze changes in critical nursing activities over 12 years and to assess linear trends across different staffing levels. Results: : The annual utilization days for ventilators, ECMO, and CRRT, as well as the Critical Nursing Activities Index significantly increased over the study period (p-for-trend＜.001) in tertiary and general hospitals, except for ventilator use in general hospitals. Ventilator, ECMO, and CRRT utilization exhibited a significant upward trend with higher nurse staffing levels (Bonferroni adjusted p-for-trend＜.001). The Critical Nursing Activities Index was significantly higher in hospitals with higher staffing levels compared to those with lower staffing levels (Bonferroni adjusted p ＜.05). Conclusion : The findings underscore the need for improved nurse staffing levels in intensive care units. Government policies should ensure that staffing levels align with critical nursing activities among critically ill patients to uphold the quality of care.

Purpose: This study aimed to examine changes in nurse staffing grades and nursing fee revenues in intensive care units (ICUs) following the third amendment of the resource-based relative value scale, which was implemented in January 2024. Methods: Changes in staffing grades from the fourth quarter of 2023 to the first quarter of 2024 were analyzed among 588 general ICUs, 94 neonatal ICUs, and 13 pediatric ICUs. Annual nursing fee revenues per nurse were estimated based on the new nursing fee structure for each grade. Results: In general ICUs, the highest grade (grade S) and the second-highest grade (grade A) accounted for 7.3% and 41.5%, respectively, in tertiary hospitals, whereas 3.8% were grade S and 11.5% were grade A in general hospitals. In neonatal ICUs, the proportion of higher grades (S, A, and 1) was greater in general hospitals (54.3%) than in tertiary hospitals (38.6%). In pediatric ICUs, 30.8% were grade S and 61.5% were grade A. When applying the same grading criteria (i.e., beds per nurse) across both quarters, staffing levels remained unchanged in most ICUs. Nursing fees and their revenues did not increase proportionally to staffing requirements (i.e., the number of nurses required per patient). Conclusion Revisions to staffing grade and nursing fee systems are necessary to induce medical institutions to improve their ICU staffing levels.

Purpose: This study aimed to examine changes in nurse staffing grades and nursing fee revenues in intensive care units (ICUs) following the third amendment of the resource-based relative value scale, which was implemented in January 2024. Methods: Changes in staffing grades from the fourth quarter of 2023 to the first quarter of 2024 were analyzed among 588 general ICUs, 94 neonatal ICUs, and 13 pediatric ICUs. Annual nursing fee revenues per nurse were estimated based on the new nursing fee structure for each grade. Results: In general ICUs, the highest grade (grade S) and the second-highest grade (grade A) accounted for 7.3% and 41.5%, respectively, in tertiary hospitals, whereas 3.8% were grade S and 11.5% were grade A in general hospitals. In neonatal ICUs, the proportion of higher grades (S, A, and 1) was greater in general hospitals (54.3%) than in tertiary hospitals (38.6%). In pediatric ICUs, 30.8% were grade S and 61.5% were grade A. When applying the same grading criteria (i.e., beds per nurse) across both quarters, staffing levels remained unchanged in most ICUs. Nursing fees and their revenues did not increase proportionally to staffing requirements (i.e., the number of nurses required per patient). Conclusion Revisions to staffing grade and nursing fee systems are necessary to induce medical institutions to improve their ICU staffing levels.

Purpose: This study aimed to examine changes in nurse staffing grades and nursing fee revenues in intensive care units (ICUs) following the third amendment of the resource-based relative value scale, which was implemented in January 2024. Methods: Changes in staffing grades from the fourth quarter of 2023 to the first quarter of 2024 were analyzed among 588 general ICUs, 94 neonatal ICUs, and 13 pediatric ICUs. Annual nursing fee revenues per nurse were estimated based on the new nursing fee structure for each grade. Results: In general ICUs, the highest grade (grade S) and the second-highest grade (grade A) accounted for 7.3% and 41.5%, respectively, in tertiary hospitals, whereas 3.8% were grade S and 11.5% were grade A in general hospitals. In neonatal ICUs, the proportion of higher grades (S, A, and 1) was greater in general hospitals (54.3%) than in tertiary hospitals (38.6%). In pediatric ICUs, 30.8% were grade S and 61.5% were grade A. When applying the same grading criteria (i.e., beds per nurse) across both quarters, staffing levels remained unchanged in most ICUs. Nursing fees and their revenues did not increase proportionally to staffing requirements (i.e., the number of nurses required per patient). Conclusion Revisions to staffing grade and nursing fee systems are necessary to induce medical institutions to improve their ICU staffing levels.

Purpose: This study aimed to examine changes in nurse staffing grades and nursing fee revenues in intensive care units (ICUs) following the third amendment of the resource-based relative value scale, which was implemented in January 2024. Methods: Changes in staffing grades from the fourth quarter of 2023 to the first quarter of 2024 were analyzed among 588 general ICUs, 94 neonatal ICUs, and 13 pediatric ICUs. Annual nursing fee revenues per nurse were estimated based on the new nursing fee structure for each grade. Results: In general ICUs, the highest grade (grade S) and the second-highest grade (grade A) accounted for 7.3% and 41.5%, respectively, in tertiary hospitals, whereas 3.8% were grade S and 11.5% were grade A in general hospitals. In neonatal ICUs, the proportion of higher grades (S, A, and 1) was greater in general hospitals (54.3%) than in tertiary hospitals (38.6%). In pediatric ICUs, 30.8% were grade S and 61.5% were grade A. When applying the same grading criteria (i.e., beds per nurse) across both quarters, staffing levels remained unchanged in most ICUs. Nursing fees and their revenues did not increase proportionally to staffing requirements (i.e., the number of nurses required per patient). Conclusion Revisions to staffing grade and nursing fee systems are necessary to induce medical institutions to improve their ICU staffing levels.

Purpose: To estimate the number of practicing nurses required to resolve staffing differences between capital and non-capital regions and analyze the relationship between regional differences in staffing and salary. Methods: Using public data on population, patients, newly licensed nurses, practicing nurses, and annual salaries, regional differences were analyzed in newly licensed nurses per population, practicing nurses per population, practicing nurses per patient (i.e., staffing level), and salary. The number of additionally required practicing nurses was estimated by multiplying staffing differences by the number of patients in the lower-staffed region. Results: During 2002~2022, 71,107 and 243,611 newly licensed nurses were supplied, while the number of practicing nurses increased by 91,886 and 88,070 in the capital and non-capital regions, respectively. The non-capital region had more practicing nurses per population, whereas the capital region had more practicing nurses per patient. In 2020, 31,330 practicing nurses were additionally required in the non-capital region. Salaries were higher in the capital region, and regional salary differences increased during 2011~2020. Regional salary differences were associated with regional staffing differences and the number of additionally required practicing nurses. Conclusion Government and health insurance policies are required to encourage hospitals in the non-capital region to improve staffing and salaries.