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Aim: This study aimed to develop a valid and reliable new intensive care unit nursing classification tool, including direct and indirect nursing activities, by measuring the nursing intensity provided to patients. Background: Prior tools primarily examine patients' medical records or disease severity/interactions, systematically failing to reflect comorbidity risk factors. Design: The Delphi technique was used to test the content validity of the Korean Patient Classification System on Nursing Intensity for Critical Care Nurses (KPCSNIC). Methods: Data were collected from four hospitals in two provinces from 26 December 2017 to 30 January 2018. To verify construct validity, staff nurses classified 365 patients, comparing differences by medical department and type of stay. To verify interrater reliability, data collectors and the head nurses of three intensive care units classified 87 patients. Results: The KPCSNIC had 8 categories, 44 nursing activities and 105 criteria. Reliability was high (r =.84). Construct validity was verified by revealing differences according to medical department and type of patient. Using total scores, four KPCSNIC groups were identified. Conclusion: The KPCSNIC developed in this study can support staffing for nursing intensity by providing more specific evaluation criteria. Moreover, it reflects nursing intensity, including direct and indirect nursing activities. Summary statement: What is already known about this topic? In order to secure the quality of nursing care, it is important to allocate an appropriate number of nurse staffing based on nursing demand and nursing time reflecting to the number and severity of patients, and shift of work.The patient acuity tools used in Korea are limited as they primarily examine patients' physiological characteristics based on medical records or quantify disease severity and interactions, systematically failing to reflect risk factors for comorbidities.The intensive care unit patient classification tool that is currently used underestimates clinical nursing intensity, as it does not realistically evaluate patient‐related disease factors and indirect nursing activities. What this paper adds? This study developed a new patient classification tool that reflects patients' clinical characteristics and classified them according to nursing intensity.In addition to direct nursing activities, indirect nursing activities that were invisible and difficult to measure can be scored using this tool. The implications of this paper: Since this tool enables quantitative measurement of nursing workload, data using this tool can be used as evidence for invoicing for nursing care fees, previously calculated as a percentage of inpatient fees per day.This study was conducted in a first‐grade hospital with a high level of nurse staffing. To secure the validity and reliability of the tool, additional research is needed, using the tool in a greater number and wider range of hospitals. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to calculate the total daily nursing workload and the optimal number of nurses per general unit based on the nursing intensity. Design: This study was conducted using a cross‐sectional study. Methods: Three units at one general hospital were investigated. Patient classification according to nursing needs was performed for over 10 days in each unit in September 2018. The direct and non‐direct nursing time and nursing intensity scores were analysed using descriptive statistics. Results: For the internal medicine unit, the average direct nursing time per patient was 1.0, 1.5, 2.2 and 2.9 h for Groups 1, 2, 3 and 4, respectively. For the surgical unit, the average direct nursing time per patient was 0.9, 1.4, 2.1 and 2.6 h for Groups 1, 2, 3 and 4, respectively. 5 and 9 additional nurses were needed in the internal medicine and surgical nursing units. Patient Contribution This study confirmed that the optimal number of nurses was not achieved and that the nursing intensity was very high. Long‐term efforts, such as improving the nursing environment, should be made to ensure an optimal number of nurses in various nursing units. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to review the literature on patient classification tools (PCTs) for assessing nursing strength and to compare South Korean and international tools. Design: Integrative literature review. Methods: We searched the relevant literature published from 2000–2017 in PubMed and two electronic databases in Korea in March 2017. Twenty‐five published studies were reviewed. Results: In most Korean studies, patients were classified by PCT, which were tested to ensure validity and reliability, followed by measurement of nursing time and development of the conversion index. In international studies, PCTs used were simpler than those in Korean studies; the results of estimation for optimum nurse staffing were reported. Most studies insufficiently reflected indirect nursing needs and patients' non‐clinical factors. The current PCTs used in Korean studies inadequately reflect the reality of nursing field. Therefore, development of new PCTs reflecting nursing intensity based on direct and indirect nursing activities is necessary. [ABSTRACT FROM AUTHOR]

Aim: To develop a new general wards patient classification tool based on the nursing intensity level that reflects patients' clinical characteristics and indirect nursing activities. Design: A cross‐sectional design was adopted. This methodological study developed a patient classification system to sort general ward patients based on the intensity of their nursing needs and verified the validity and reliability of this classification system. Methods: Thirteen experts verified the tools' content validity. Data collectors and head nurses classified 150 patients from two hospitals with four general wards and various nurse staffing levels. Inter‐rater reliability was analysed. Staff nurses classified 846 patients following the Korean patient classification system on nursing intensity scores that reflected patients' clinical status. Content validity was verified based on the classification results. Using K‐group cluster analysis, score ranges for four groups were identified. Results: The developed tool includes 8 domains, (symptom management, infection control, nutrition and medication, personal hygiene and secretion, activity, sleep and rest, guidance in nursing/emotional support, nursing activity planning and coordination, indirect activity), 24 subdomains, 66 nursing activities and 124 criteria. Inter‐rater reliability showed high agreement. [ABSTRACT FROM AUTHOR]