• Journal of Korean Clinical Nursing Research
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• v.24 no.1
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• pp.113-126
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• 2018

Purpose: The purpose of this study was to revise the KPCS-1 and to standardize the three patient classification systems for general ward, ICU and NICU. The actual utilization of the KPCS-1 score and each nursing activity was evaluated and the relationships between KPCS-1 score and nursing related variables were reviewed. Methods: The 47,711 KPCS-1 scores of 6,931 patients who discharged from $1^{st}$ to $30^{th}$ April 2017 were analyzed and the statistical significance between KPCS-1 score and nursing related variables was reviewed by Generalized Estimating Equation. The revision of the KPCS-1 was carried out by Partial Least Square model. The 3 patient classification systems (KPCS-1,KPCSC and KPCSN) were standardized by professional reviews. Results: KPCS-1 was a valid instrument to express nursing condition adequately and was revised as a new version which has 34 nursing activity items. The names and terminologies of pre-existing 3 patient classification systems developed by KHNA were standardized as KPCS-GW, KPCS-ICU, KPCS-NICU. Conclusion: KPCS-1 was a valid instrument to represent diverse nursing conditions precisely and was revised as a 34-item KPCS-GW. The terminologies of the other patient classification systems by KHNA were standardized as KPCS-ICU and KPCS-NICU.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.1
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• pp.15-23
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• 2001

This study was a methodological research to develop computerized patient classification system. The subjects of this investigation were 435 inpatients except redundant data and outliers in P University Hospital from January 18, 2000 to January 24, 2000. The data was analyzed by discrimination analysis and adopted discriminant variables were 1) sum of frequency for the nursing activities, 2) the number of nursing activities that do not need to consider intensity of the activities, and 3) total hours of nursing activities that need to consider their intensities. Discriminant function developed by this study classified the patients into 4 groups; class I, 251 ; class II, 125 ; class III, 39 ; class IV, 20. The Hit ratio was 89.23. Based on this study, following suggestions can be made for the future research 1. Inclusive patient classification system, which includes more expanded direct nursing care factors, need to be developed and examined. 2. This developed classification system can be utilized to evaluate patient distribution and to estimate adequate numbers of nursing staffs in each nursing unit.

• Asian Oncology Nursing
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• v.5 no.1
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• pp.3-10
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• 2005

Back ground: Nurses now occupy one third of all hospital human resources. Therefore, efficient management of nursing manpower is getting more important. While it is very clear that nursing workload requirement analysis and patient severity classification should be done first for the efficient allocation of nursing workforce, these processes have been conducted manually with ad hoc rule. Purposes: This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower. Methods: The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1-2003.12.31 were assessed by trained nurses. This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed. Results: The data set was created using 165,073 records of 2,228 patients classification database. Main explaining variables were as follows in 3 different data mining techniques. 1) Logistic regression : age, month and section. 2) Decision tree : section, month, age and tumor. 3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month. Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%. Conclusion: The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.

• Korea Journal of Hospital Management
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• v.6 no.3
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• pp.130-147
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• 2001

This study is about major symptoms of elderly and medical services for elderly in long-tenn care facilities. The subject of this study was 298 patients over 00 years old staying in two geriatric hospitals and two nursing homes. The symptoms and medical services were level of patient classification from RUG(Resource Utilization Group)-III which is applied for both Medicare and Medicaid for skilled nursing facilities reimbursement system in US and designed for measuring patient characteristics and medical staff time. This classification is explained by each patient resource(staff time) utilization level which is called CMI(Case-Mix Index). In this study, the symptoms and services were compared by facility type and they were categorized by level and compared by CMI. Major findings are as follows; 1. There were more elderly who have cognitive function problems in nursing homes than patients in geriatric hospitals. There were more patients with behavioral problems in geriatric hospitals than residents in nursing homes. These results were both statistically significant. 2. The patients in geriatric hospitals received significantly more nursing rehabilitation services, rehabilitation services and extensive services than residents in nursing homes. Other hands, special care services were provided significantly more to residents in nursing homes than elderly in geriatric hospitals. 3. ADL and depression variables had higher CMI when the symptoms were heavier condition. The CMI were not matched with levels of cognitive function problems and behavioral problems. 4. The CMI matched well significantly with levels of nursing rehabilitation services, special care services, and clinically complex services provided for the patient in geriatric hospitals and only nursing rehabilitation services in nursing homes. The CMI for rehabilitation services level and extensive services had regular trends. From the result of this study, the resource utilization level and services provided for elderly in each long-term care facilities were figured out. For the further study, it needs to have more concern about RUG-ill which classification variables were just analyzed.

• Health Policy and Management
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• v.26 no.4
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• pp.289-304
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• 2016

Background: Rehabilitations in subacute phase are different from acute treatments regarding the characteristics and required resource consumption of the treatments. Lack of accuracy and validity of the Korean Diagnosis Related Group and Korean Out-Patient Group for the acute patients as the case-mix and payment tool for rehabilitation inpatients have been problematic issues. The objective of the study was to develop the Korean Rehabilitation Patient Group (KRPG) reflecting the characteristics of rehabilitation inpatients. Methods: As a retrospective medical record survey regarding rehabilitation inpatients, 4,207 episodes were collected through 42 hospitals. Considering the opinions of clinical experts and the decision-tree analysis, the variables for the KRPG system demonstrating the characteristics of rehabilitation inpatients were derived, and the splitting standards of the relevant variables were also set. Using the derived variables, we have drawn the rehabilitation inpatient classification model reflecting the clinical situation of Korea. The performance evaluation was conducted on the KRPG system. Results: The KRPG was targeted at the inpatients with brain or spinal cord injury. The etiologic disease, functional status (cognitive function, activity of daily living, muscle strength, spasticity, level and grade of spinal cord injury), and the patient's age were the variables in the rehabilitation patients. The algorithm of KRPG system after applying the derived variables and total 204 rehabilitation patient groups were developed. The KRPG explained 11.8% of variance in charge for rehabilitation inpatients. It also explained 13.8% of variance in length of stay for them. Conclusion: The KRPG version 1.0 reflecting the clinical characteristics of rehabilitation inpatients was classified as 204 groups.

• Journal of Korean Academy of Nursing
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• v.24 no.4
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• pp.584-596
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• 1994

The objective of the study is to explore the relationships between the variables of nursing productivity on the framework of system del in the tertiary university based care hospital in Korea. Productivity is basically defined as the relation-ship between inputs and outputs. Under the proposition that the nursing unit is a system that produces nursing care output using personal and material resources through the nursing intervention and nursing care management. And this major conception of nursing productivity system comproises input, process and output and feed-back. These categorized variables are essential parts to produce desirable and meaningful out-put. While nursing personnel from head nurse to staff nurses cooperate with each other, the head nurse directs her subordinates to achieve the goal of nursing care unit. In this procedure, the head nurse uses the leadership of authority and benevolence. Meantime nursing productivity will be greatly influenced by environment and surrounding organizational structures, and by also the operational objectives, the policy and standards of procedures. For the study of nursing productivity one sample hospital with 15 general nursing care units was selected. Research data were collected for 3 weeks from May 31 to June 20 in 1993. Input variables were measured in terms of both the served and the server. And patient classification scores were measured drily by degree of nursing care needs that indicated patent case-mix. And also nurses' educational period for profession and clinical experience and the score of nurses' personality were measured as producer input variables by the questionnaires. The process varialbes act necessarily on leading input resources and result in desirable nursing outputs. Thus the head nurse's leadership perceived by her followers is defined as process variable. The output variables were defined as length of stay, average nursing care hours per patient a day the score of quality of nursing care, the score of patient satisfaction, the score of nurse's job satis-faction. The nursing unit was the basis of analysis, and various statistical analyses were used : Reliability analysis(Cronbach's alpha) for 5 measurement tools and Pearson-correlation analysis, multiple regression analysis, and canonical correlation analysis for the test of the relationship among the variables. The results were as follows : 1. Significant positive relationship between the score of patient classification and length of stay was found(r=.6095, p.008). 2. Regression coefficient between the score of patient classification and length of stay was significant (β=.6245, p=.0128), and variance explained was 39%. 3. Significant positive relationship between nurses’ educational period and length of stay was found(r=-.4546, p=.044). 5. Regression coefficient between nurses' educational period and the score of quality of nursing care was significant (β=.5600, p=.029), and variance explained was 31.4%. 6. Significant positive relationship between the score of head nurse's leadership of authoritic characteristics and the length of stay was found (r=.5869, p=.011). 7. Significant negative relationship between the score of head nurse's leadership of benevolent characteristics and average nursing care hours was found(r=-.4578, p=.043). 8. Regression coefficient between the score of head nurse's leadership of benevolent characteristics and average nursing care hours was significant(β=-.6912, p=.0043), variance explained was 47.8%. 9. Significant positive relationship between the score of the head nurse's leadership of benevolent characteristics and the score of nurses' job satis-faction was found(r=.4499, p=050). 10. A significant canonical correlation was found between the group of the independent variables consisted of the score of the nurses' personality, the score of the head nurse's leadership of authoritic characteristics and the group of the dependent variables consisted of the length of stay, average nursing care hours(Rc²=.4771, p=.041). Through these results, the assumed relationships between input variables, process variable, output variables were partly supported. In addition it is also considered necessary that-further study on the relationships between nurses' personality and nurses' educational period, between nurses' clinical experience including skill level and output variables in many research samples should be made.

• Journal of The Korea Institute of Healthcare Architecture
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• v.28 no.2
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• pp.7-16
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• 2022

Purpose: This study aimed to present an analysis model evaluating evacuation performance considering patient types and procedural evacuation in the medical facility. The user group of the medical facility, including users challenged in evacuation behavior, entails the risk of many casualties. Therefore, it is necessary to plan an evacuation procedure that considers the evacuation characteristics of users. Methods: Through the review of precedent studies, the evacuation procedure of the medical facility, the classification of patient types, and the evacuation procedure was set as conditions and variables for the analysis. The result caused by a variety of conditions and variables were explored. Results: 1) The total evacuation completion time and congestion time were shortened at the procedural evacuation. Moreover, it derived many users from evacuating at the initial phase. 2) The proposed model can provide a basis for proposing a space planning direction that considers the possibility of not carrying out the evacuation plan. 3) It supports safe evacuation by identifying variables that reduce overcrowding by comparing the congestion time of overcrowded spaces. 4) The analysis model can identify the overcrowded space through the evacuation route and suggest the basis for architectural improvements that reduce overcrowding. Implications: The study results can be used to analyze the performance of evacuation procedures and support the establishment of evacuation procedures and building plans for safe evacuation for medical facilities.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• Journal of Korean Academy of Nursing
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• v.16 no.3
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• pp.108-122
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• 1986

Changing concepts of health care, are stimulating the demand for health care, thereby orienting society to health care rights to such an extent that they are deemed as fundamental ones inalienable to man. Concomitantly, qualitative as well as quantative improvement is being sought in the nursing service field. Today, efforts are being made in various areas, especially to qualitatively improve nursing services. A second issue concerns proper staffing. It is important to study staffing, in as much as it continues to be the most persistent and critical problem facing hospital nursing administrators today. It involves quantity, quality, and utilization of nursing personnel. A great deal of attention has been focused on this problem since mid 1930's when nursing services began to be felt as an important segment of hospital operation representing the largest single item of hospital budgets. Traditionally, the determination and allocation of nursing personnel resources has relied heavily on gloval approaches which make use of fixed staff-to-patient ratios. It has long been recognized that these ratios are insensitive to variations between institutions and among individual patients. Therefore, the aim of this thesis is to point to the urgent need for the development of methodology and criteria suited to the reality of Korea. The present research selected one place, the W Christian Hospital, and was conducted over a period 10 days from January, and nurses who were them on duty in their unit. The total num-her of patients surveyed was 1,426 and that of 354. The research represents many variables affecting the direct patient care time using the result from the direct observation method, then using a calculation method to estimate the relationship between the patients care time and selected variables in the hospital setting. The amount of direct patient care time varies with many factors, such 89 the patients age. diagnosis and time in hospital. Differences are also found from hospital, clinic to clinic, ward to ward, and even shift to shift. In this research, the calculation method of estimating the required member of nursing staff is obtained by dividing the time of productive patient care activity(with the time of patient care observed), by the sum of the productive time that each the staff can supply, i.e., 360 minutes, which is obtained by deducting the time for personal activities. The results indicate a substantial difference between the time of productive patient care observed directing and the time of the productive patient care estimated using calculating method. If we know accurately the time of the direct patient care on a shift, there required number of staff members calculated if the proper method can be determinded should be able the time of the direct patient care be estimated by the patient classification system, but this research has shown this system to be in accurate in Korea. There are differences in the recommended time of productive patient care and the required number of nursing staff depending upon which method is used. The calculated result is not very accurate, so more research is needed on the patient classification system.

• Journal of Korean Academy of Nursing
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• v.39 no.3
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• pp.409-421
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• 2009

Purpose: In this research multi-level analysis was done to identify factors related to quality of services. Patient characteristics and organizational factors were considered. Methods: The data were collected from the Health Insurance Review and Assessment Service(HIRA) data base. The sample was selected from 17,234 patients who had been admitted between January 2007 and May 2008 to one of 253 long-term care hospitals located in Seoul, six other metropolitan cities or nine provinces The data were analyzed with SAS 9.1 using multi-level analysis. Results: The results indicated that individual level variables related to quality of service were age, cognitive ability, patient classification, and initial quality scores. The organizational level variables related to quality of service were ownership, number of beds, and turnover rate. The explanatory power of variables related to organizational level variances in quality of service was 23.72%. Conclusion: The results of this study indicate that differences in the quality of services were related to organizational factors. It is necessary to consider not only individual factors but also higher-level organizational factors such as nurse' welfare and facility standards if quality of service in long term care hospitals is to be improved.