• Journal of Korean Academy of Nursing Administration
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• v.20 no.5
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• pp.545-557
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• 2014

Purpose: The purpose of this study was to develop a patient classification system based on nursing care intensity for patients with acute stroke-related symptoms and verify its validity and reliability. Methods: Data were collected between November, 2013 and February, 2014. The verification for content validity of the patient classification system was conducted by a group of seven professionals. Both interrater reliability and concurrent validity were verified at stroke units in tertiary hospitals. Results: The intensive nursing care for acute stroke patients consisted of 14 classified domains and 56 classified contents by adding 'neurological assessment and observation' and 'respiratory care': 'hygiene', 'nutrition', 'elimination', 'mobility and exercise', 'education or counselling', 'emotional support', 'communication', 'treatment and examination', 'medication', 'assessment and observation', 'neurological assessment and observation', 'respiratory care', 'coordination between departments', and 'discharge or transfer care'. Each domain was classified into four levels such as Class I, Class II, Class III, and Class IV. Conclusion: The results show that this patient classification system has satisfactory validity for content and concurrent and verified reliability and can be used to accurately estimate the demand for nursing care for patients in stroke units.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.150-156
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• 2023

For a doctor, diagnosing a patient's heart disease is not easy. It takes the ability and experience with high flying hours to be able to accurately diagnose the type of patient's heart disease based on the existing factors in the patient. Several studies have been carried out to develop tools to identify types of heart disease in patients. However, most only focus on the results of patient answers and lab results, the rest use only echocardiography data or electrocardiogram results. This research was conducted to test how accurate the results of the classification of heart disease by using two medical data, namely echocardiography and electrocardiogram. Three treatments were applied to the two medical data and analyzed using the decision tree approach. The first treatment was to build a classification model for types of heart disease based on echocardiography and electrocardiogram data, the second treatment only used echocardiography data and the third treatment only used electrocardiogram data. The results showed that the classification of types of heart disease in the first treatment had a higher level of accuracy than the second and third treatments. The accuracy level for the first, second and third treatment were 78.95%, 73.69% and 50%, respectively. This shows that in order to diagnose the type of patient's heart disease, it is advisable to look at the records of both the patient's medical data (echocardiography and electrocardiogram) to get an accurate level of diagnosis results that can be accounted for.

• The Korean Nurse
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• v.19 no.5 s.108
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• pp.50-53
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• 1980

A new patient classification system, SLC System was introduced. It has been implemented in the Friendship Village Extended Care Facility in St. Louis, Missouri, USA, and it has shown that the system not only simplified the job but also increased the job e

• Journal of Korean Academy of Nursing Administration
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• v.4 no.1
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• pp.229-246
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• 1998

This dissertation classifies sample patients by a measure of K-DRG to identify the most frequent group. and investigates the differences in the dependency of nursing by patient classification system in the SICU of Seoul National University Hospital in Korea. It also calculates the mean nursing care hours and costs per craniotomy patient, who is shown to be the most frequent patient group. The results of the research can be used as basic data for the development of relevant nursing cost system in the future. The results of the research are as follows: 1. Using data from 97 sample patients, as many as 26 groups are identified when the patients are classified by K-DRG. KDRG-001 (craniotomy) is found to be the most frequent group(43.30%). 2. The result from patient classification system grouping in craniotomy patients shows homogeneity in terms of dependency of nursing with 35 patients in the 4th group, 145 patients(74.36%) are in the 5th group. and 15 patients are in the 6th group among the total 195 sample patients. 3. The direct nursing care hours for the 4th, 5th, and 6th patient classification system groups are found to be 381 minuites. 483 minuites, and 519 minuites, respectively, which shows that the nursing care hours increases as the dependency of nursing is intensified. The indirect nursing care hours are found to be 454 minuites(7.57 hours). The total mean nursing care hours, which is the sum of the direct nursing care hours(467 min.: 7.78 hours) and the indirect nursing care hours (454 min.: 7.57 hours), is 921 minuites(15.35 hours) per patient a day. 4. The nursing care cost is calculated to be 123,297 won per patient a day. Considering the average duration in the ICU, we can find the total nursing care cost is 610,318 won.

• Journal of Physiology & Pathology in Korean Medicine
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• v.27 no.4
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• pp.460-464
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• 2013

The purpose of this case was to report the effect of Korean medical treatment for patient with pleural effusion due to congestive heart failure. The patient was treated with herbal medicine(Cheongsingeonbi-tang) and acupuncture. The effect of treatment was evaluated by chest X-ray, New York Heart Association(NYHA) functional classification, and Hugh-Jones classification. After 3 weeks of treatment, the amount of pleural effusion was decreased and NYHA class, Hugh-Jones grade were improved. NYHA functional classification improved class III to II and Hugh-Jones classification changed grade IV to II. This result suggests that herbal medicine(Cheongsingeonbi-tang) and acupuncture treatment might have an effect on patient with pleural effusion due to congestive heart failure.

• 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.

• Journal of Korean Academy of Nursing Administration
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• v.8 no.2
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• pp.309-321
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• 2002

Purpose: A cost analysis for nursing services in operative nursing unit, emergency nursing unit, and ambulatory nursing unit was performed using patient classification system by nursing intensity in order to determine an appropriate nursing fee schedule. Method: The data were collected from 4 secondary hospitals and 5 tertiary hospitals from November 14th 2000 to January 15th 2001. The study was conducted through four phases as follows: 1) Nursing hours of each nursing service in special nursing units were measured using three kinds of patient classification systems by nursing intensity. 2) The nursing cost of nursing services in operative nursing unit, emergency nursing unit, and ambulatory nursing units was estimated based on patient classification system by nursing intensity. Results: As a result, nursing hours by nursing intensity of each special nursing unit were measured, and every nursing cost by nursing intensity in operation room and emergency room was estimated, meanwhile, the cost of nursing services in ambulatory care units was estimated only per visit as shown in chapter 4. Conclusion: Future research on nursing cost should be extended to other special nursing units such as various intensive nursing care units, delivery room, and so on. In addition, the patient classification system should be refined for its appropriateness to apply all levels of medical institutions.

• Journal of Korean Academy of Nursing Administration
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• v.14 no.3
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• pp.229-240
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• 2008

Purpose: To develop the patient classification system based on the resource utilization for reimbursement of long-term care hospitals in Korea. Method: Health Insurance Review & Assessment Service (HIRA) conducted a survey in July 2006 that included 2,899 patients from 35 long-term care hospitals. To calculate resource utilization, we measured care time of direct care staff (physicians, nursing personnel, physical and occupational therapists, social workers). The survey of patient characteristics included ADL, cognitive and behavioral status, diseases and treatments. Major category criteria was developed by modified delphi method from 9 experts. Each category was divided into 2-3 groups by ADL using tree regression. Relative resource use was expressed as a case mix index (CMI) calculated as a proportion of mean resource use. Result: This patient classification system composed of 6 major categories (ultra high medical care, high medical care, medium medical care, behavioral problem, impaired cognition and reduced physical function) and 11 subgroups by ADL score. The differences of CMI between groups were statistically significant (p<.0001). Homogeneity of groups was examined by total coefficient of variation (CV) of CMI. The range of CV was 29.68-40.77%. Conclusions: This patient classification system is feasible for reimbursement of long-term care hospitals.

• Journal of Pharmacopuncture
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• v.12 no.2
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• pp.77-84
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• 2009

Objective : Patients with spinal cord injury are increasing in numbers. However, there is no reliable treatment guide in both conventional & complementary medicine. Also, there are not much clinical case of patients with spina cord injury in oriental medical field. We investigated effect of sweet BV on subacute stage patient with spinal cord injury. Method : 31-year old female patient with spinal cord injury was treated with herb medicine(TID), electro arcupunture (BID), sweet BV injection(QOD), Physical treatment(QD), and conventional-medicine. Result : We had a satisfactory result with using sweet BV injection. The patient's ASIA grade improved from 34 to 52. And Frankle classification of the patient shifted from A to B. Conclusion : We reach a conclusion Using Sweet BV improve the sensation of patient with spinal cord injury. And more study about this disease is needed.

• Progress in Medical Physics
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• v.27 no.3
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• pp.131-138
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• 2016

We analyzed the terminology and classification related to the risk management of radiation treatment overseas to establish the terminology and classification system for Korea. This study investigated the terminology and classification for radiotherapy risk management through overseas research materials from related organizations and associations, including the IAEA, WHO, British group, EC, and AAPM. Overseas risk management commonly uses the terms "near miss", "incident", and "adverse event", classified according to the degree of severity. However, several organizations have ambiguous terminologies. They use the term "near miss" for events such as a near event, close call, and good catch; the term "incident" for an event; and the term "adverse event" for the likes of an accident and an event. In addition, different organizations use different classifications: a "near miss" is generally classified as "incident" in most cases but not classified as such in BIR et al. Confusion might also be caused by the disunity of the terminology and classification, and by the ambiguity of definitions. Patient safety management of medical institutions in Korea uses the terms "near miss", "adverse event", and "sentinel event", which it classifies into eight levels according to the severity of risk to the patient. Therefore, the terminology and classification for radiotherapy risk management based on the patient safety management of medical institutions in Korea will help in improving the safety and quality of radiotherapy.