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.
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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.
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Purpose To examine the characteristics, core variables, and their correlations in articles published in the Journal of Korean Academy of Nursing Administration (JKANA) from 2012-2021 and suggest future directions for nursing management research. Methods A total of 506 articles were analyzed according to study design, participants and setting, statistical methods, keywords, and core concepts and variables. Results Quantitative research accounted for 73.5%, and most participants were staff nurses (66.8%) and nursing students (9.1%). Furthermore, 318 studies (62.8%) conducted surveys, and settings were mainly acute hospitals(81.5%) and nursing schools (9.7%). Statistical methods for data analysis included independent t-test (81.2%), one-way ANOVA (77.2%), Pearson correlation coefficients (77.2%), post-hoc testing (74.3%), and linear regression(65.9%). Among 2,058 keywords, the most frequent were “nurses” (49.2%), “job satisfaction” (10.7%), and “personnel turnover” (9.1%). The most frequently core concepts were job satisfaction (10.5%), turnover intention(9.5%), organizational commitment (8.5%), and job stress (7.5%). The most frequently variables with significant correlations were turnover intention, work environment, job satisfaction, job stress, burnout, and emotional labor. Conclusion Most JKANA studies examined nursing-related outcomes and performance. Future research should examine the effects of nursing practice and policy on patient outcomes.
Purpose This study aims to propose revised inpatient nursing fee schedules that address three discrepancies between actual nurse staffing levels in general wards and the corresponding patient payment structures. Methods A total of 45 tertiary hospitals, 329 general hospitals, and 1,379 hospitals from publicly released data for 2021~2022 were analyzed. This analysis focused on three primary discrepancies between (1) the staffing grades under which patients were hospitalized and the corresponding grades for which they were charged; (2) the staffing grades determined by bed-to-nurse and patient-to-nurse criteria; and (3) the current differentiation rates of nursing fees and the expected differentiation rates based on the number of nurses required for each grade. Results The first discrepancy occurred in 8.9% of tertiary hospitals, 21.0% of general hospitals, and 26.0% of hospitals. The bed-to-nurse and patient-to-nurse grades differed by 2.23 and 2.29 grades on average in general hospitals and hospitals, respectively. The current differentiation rates were higher than the expected differentiation rates. New nursing fee schedules were suggested to resolve those discrepancies. Conclusion Nursing fees should be charged to reflect the staffing levels under which patients were cared for and proportionate to the number of nurses required to provide the corresponding staffing levels.
Purpose This study examined patients’ call bell use and the relationship between call bell use and nursing care needs.
Methods: Nursing staff was asked to report patients’ call bell use during 15 shifts over five days in integrated nursing care wards. Nursing care needs were measured using summary scores of nursing activities and activities of daily living (ADLs). The relationship between call bell use and nursing care needs was analyzed using a zero-inflated negative binomial regression model.
Results: A total of 251 patients used call bells 235 times, with an average of 0.94 times per day. Only 72 patients (28.7%) used call bells once or more per day (range, 1~14 times), whereas the rest did not use call bells. Male gender, a high risk for falling, and a higher score on nursing activities were associated with a greater likelihood of using call bells. Pain and higher dependency on ADLs were associated with an increase in the frequency of call bell use.
Conclusion: Patients' call bell use needs to be minimized by meeting their nursing care needs to improve patient safety and nursing performance.
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Purpose To develop a web-based solution for patient need-driven staffing (PNDS) that automatically determines nurses’ staffing requirements.
Methods: Activities provided by nurses in four integrated nursing care wards (INCWs) and non-INCWs each in a tertiary hospital were observed over three days. Nursing hours per patient hour (NHPPH) were calculated by dividing nursing hours by patient stay hours per day. Patient needs were evaluated using 19 items.
Results: The nurse-patient ratios in INCWs and non-INCWs were 1:4.5 and 1:8.1 (including overtime), respectively. Admitted and transferred-in patients had higher NHPPHs than those with continuing stays. The patients were classified into five groups: Group A for admissions and transfers-in, and Groups 1~4 for the remainder. In INCWs, the nurse-patient ratios ranged from 1:5.3 (Group 1) to 1:2.4 (Group 4), and Group A required 1:3.0, the secondhighest level. In non-INCWs, ratios ranged from 1:9.4 (Group 1) to 1:5.2 (Group 4 and Group A). The PNDS solution was developed to determine staffing requirements by classifying patients into five groups using the entered data on patient needs, assigning the group’s NHPPH to each patient, and calculating the staffing ratio required in the unit.
Conclusion: The PNDS is expected to support staffing decisions to meet patient needs.
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Comparison of Nurse Work Hours and Nursing Activities Between High‐ and Low‐Staffed General Wards: A Cross‐Sectional Study Jiyeong Seong, Sung‐Hyun Cho, Hyo‐Jeong Yoon, Won‐Hee Sim, Moon‐Sook Kim Nursing Open.2024;[Epub] CrossRef
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Recommending Revised Inpatient Nursing Fee Schedules Based on Actual Nurse Staffing Levels in General Wards Sung-Hyun Cho, Jinhyun Kim, Jiyeong Seong, Ji-Yun Lee Journal of Korean Academy of Nursing Administration.2023; 29(5): 540. CrossRef
Purpose To analyze the changes in nurse staffing grades and to estimate the revenue growth generated by applying government guidelines for improving nurses’ working conditions.
Methods: Staffing grades, ranging from grade 1 (highest) to 7 (lowest), for 2018 and 2020 were analyzed for 326 general hospitals (GHs) and 1,419 non-general hospitals (NGHs). The annual revenue growth per nurse generated by changing inpatient nursing care fee schedules and newly introducing night shift nursing fees were estimated.
Results: Grade 1 GHs increased from 6.9% in 2018 to 39.6% in 2020, whereas grades 6-7 decreased from 31.8% to 17.6%. NGHs with grades 6-7 decreased from 81.8% to 61.6%. GHs and NGHs with no reported staffing grades decreased from 10.6% to 0% and from 63.2% to 14.8%, respectively. The estimated annual revenue growth per nurse from inpatient nursing care fees resulting from 1-grade improvements in staffing was 1.44~7.26 million Korean won (KRW) and 1.25~9.75 million KRW for GHs and NGHs, respectively, while the results from night shift nursing fees were 2.37~5.54 million KRW and 2.20~5.14 million KRW for GHs and NGHs, respectively.
Conclusion: The increased revenues should be utilized to augment nurses’ wages and staffing levels as the guidelines recommend.
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Changes in Nursing Grades and Nurse Staffing Levels following the 2024 Revision of Nursing Management Fee Standards: A Focus on Tertiary Hospitals and Medical Institutions in Seoul Hyeyoung Choi, Kiyoung Kim, Su-Jin Cho, Suyong Jeong Health Insurance Review & Assessment Service Research.2025; 5(1): 58. CrossRef
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Purpose To analyze the effects of average length of stay (ALOS) on RN staffing.
Methods: Public data of patient surveys collected 8 times between 1996 and 2016 were analyzed. The sample included 2,408,669 discharged patients from 2,266 general hospitals. The ALOS for each hospital was computed by dividing the sum of inpatient days by the number of discharges. RN staffing was defined as the number of RNs per 100 inpatients. ALOS was transformed into base-2 logarithmic values for regression analysis.
Results: ALOS decreased from 13.3 to 9.6 days.
Large hospitals in the capital region had the greatest reduction, from 15.7 to 7.4 days. RN staffing increased from 32.7 to 54.8 RNs per 100 patients. ALOS had an inverse relationship with RN staffing. Controlling for other factors, a 50% reduction in ALOS was associated with increases in RN staffing by 12.18 and 13.72 RNs per 100 inpatients in large hospitals in the capital region and elsewhere, respectively.
Conclusion: Hospitals may have to increase staffing to respond to the increased workload resulting from the shortened ALOS. It remains uncertain whether such increases in staffing were sufficient for the increased workload. Changes in ALOS should be taken into account when determining appropriate staffing.
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Purpose To compare actual versus expected nursing hours based on patients’ nursing care needs. Methods The nursing care needs of 898 inpatients in 20 wards at 11 hospitals were measured using the 14 items developed by the National Health Insurance Service (NHIS). Nursing activities from 474 nursing personnel were observed every 10 minutes for 24 hours. Actual hours indicated direct care hours per patient day provided by registered nurses according to 3 types: (1) standard hours based on staffing standards approved by the NHIS, (2) scheduled hours excluding overtime hours, and (3) observed hours including overtime. Expected hours were estimated from the linear mixed effect model including hospital type, nursing care need items and their interaction terms. Results Standard hours ranged from 0.92 to 2.15; scheduled hours from 0.88 to 1.95; observed hours from 1.00 to 2.40; expected hours from 0.88 to 2.33. Eight hospitals had standard hours not meeting the expected hours and 2 hospitals did observed hours not meeting the expected hours due to nurses’ overtime. In 3 hospitals, all types of actual hours exceeded the expected hours. Conclusion Staffing needs to be determined based on patients’ care needs and to be improved to minimize nurses’ overtime work.
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