• Journal of Preventive Medicine and Public Health
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• 제20권1호
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• pp.56-66
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• 1987

Information on productivity of hospital personnel is required for optimum staffing and hospital management. This study deals with the quantitative aspects of workload of medical personnel in training hospitals by their specific characteristics. Specifically this study attempted to find relevant determinants of the productivity of medical personnel using multiple stepwise regression analysis based on data obtained from 135 training hospitals. The findings of this study were as follows: 1) Daily average number of outpatients and inpatients treated by a physician were 20.4 and 10.2, respectively. 2) Daily average number of patients cared by a nurse was 8.2. Daily average number of tests performed by pathologic technician and radiologic technician were 83.2 and 21.5, respectively. 3) Productivity of medical personnel were significantly different for the three groups of factors: hospital sire (number of beds, number of medical personnel per 100 beds): institutional characteristics (medical school affiliation, training type, profit status); and environmental factors (location, number of physician and beds per 1,000 population in the region). 4) The factors a(footing the productivity varied according to the types of medical profession: the number if beds, the number of physicians per 100 beds, training type, and profit status for physicians; the number of nurses per 100 beds, the number of beds, medical school affiliation for nurses; the number of physicians per 100 beds, the number of technicians per 100 beds, and ownership for pathologic technicians; the number o( technicians, training type, and the number of physicians per 100 beds for radiologic technician.

• 한국병원경영학회지
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• 제13권4호
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• pp.1-26
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• 2008

This study examines the statistical relationship between medical specialists and managerial performance, using regression analysis with the number of medical specialists per 100 beds as the independent variable and the managerial performance index as the dependent variable. Managerial performance index incorporated the number of out-patients per specialist, the number of in-patients per specialist, the volume of revenue per specialist, the number of beds per specialist, and the average length of stay. To compare different groups of hospitals, dummy variable was applied to five groups of hospitals according to size: 100-299 beds, 300-599 beds, 600-899 beds, 900-1199 beds, and more than 1200 beds. The data consisted of 181 general hospitals with more than 100 beds, which included 28 public hospitals, 73 corporate hospitals, 64 university hospitals and 16 private hospitals. Of those, 87 hospitals were located in big cities and 94 hospitals in medium to small cities. This study used hospitals from the Korean Hospital Association, and data published in 2004. The collected data sample was analyzed using the SPSSWIN 12.0 version, and the study hypothesis was tested using regression analysis. The findings of this study are summarized as follows: Hypothesis 1 predicting a negative effect of the number of medical specialists on the number of out-patients per specialist was supported with statistical significance. The analysis of dummy variable showed causality in all the hospital groups larger than the group of 100-299 beds. Hypothesis 2 predicting a negative effect of the number of medical specialists on the number of in-patients per specialist was supported with statistical significance. The analysis of dummy variable showed causality in the hospital group of 300-599 beds when compared to the group of 100-299 beds. Hypothesis 3 predicting a negative effect of the number of medical specialists on the volume of revenue per specialist was not supported. However, the analysis of dummy variable showed that the volume of revenue per specialist increased in the hospital groups of 600-899 beds, 900-1199 beds, and over 1200 beds, when compared to the group of 100-299 beds. Hypothesis 4 predicting a negative effect of the number of medical specialists on the average length of stay was supported with statistical significance. The analysis of dummy variable showed causality in the hospital group of 300-599 beds, when compared to the group of 100-299 beds. Results of this study show that the number of the medical specialists per 100 beds is an important factor in hospital managerial performance. Most hospitals have tried to retain as many medical specialists as possible to keep the number of patients stable, to ensure adequate revenue, and to maintain efficient managerial performance. Especially, the big hospitals with greater number of beds and medical specialists have shown greater revenue per medical specialist despite the smaller number of patients per medical specialist. Findings of this study explains why hospitals in Korea are getting bigger.

• 한국산학기술학회논문지
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• 제16권1호
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• pp.453-461
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• 2015

본 연구는 병원에 근무하는 간호사의 이직률과 병원의 구조적 특성 간 관련성을 확인하기 위하여 시행하였다. 자료는 한국보건산업진흥원에서 매년 조사하는 병원경영분석자료를 이용하였으며, 2008년에 전국에서 자기기입식으로 입력한 병원은 247개였다. 2008년 우리나라 간호사의 평균 이직률은 32.0%(공공병원 15.5%; 사립병원 35.3%)였으며, 민간병원, 300병상 이하 병원, 비도시 지역병원, 간호사평균급여가 2,000만원 미만인 병원, 100병상 당 간호사 수가 20명 미만 인 병원에서 높았다. 실제 이직률에 영향을 미치는 요인은 경영주체(공공 또는 사립), 병원 규모, 병원 소재지, 간호사의 평균 임금, 100병상당 간호사 수였다. 각 병원의 이직률과 총 병상 수(r=-0.322), 간호사 평균급여(r=-0.186), 100병상 당 간호사 수(r=-0.390)는 상관관계가 있었으나, 이직률에 설명력이 있는 변수는 경영주체의 공공성 여부, 총 병상 수, 100병상 당 간호사 수였다($R^2=.257$). 결론적으로 병원간호사의 이직률은 민간병원 근무자가, 총 병상수가 적을수록, 100병상 당 간호사 수가 적을수록 높았다. 간호이직 관련요인을 명확히 하기 위하여 개인요인, 사회요인과 병원요인을 동시에 분석하는 추가연구가 필요하다.

• 한국병원경영학회지
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• 제4권1호
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• pp.41-65
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• 1999

This study was attempted to identify the factors affecting profitability of general hospital in Kyung-In Region. Operating profit to gross revenues and net profit to gross revenues were used as a proxy indicator for profitability of hospitals. The unit of analysis was hospital, and the data were collected 5 years data from 20 hospitals. The major findings are as follows; (1) The average operating profit rate was 1.03% and the net profit rate was -5.00% in twenty hospitals in the Kyung-In Region for the last five years. In terms of maximum surplus, the operating profit rate was 14% and net profit rate was 3.40%. In terms of maximum loss revenue, the operating profit rate was -16.56% and the net profit rate was -22.83%. (2) Since the year 1993, which was the starting year of this study, the operating profits and the net profits consistently decreased. (3) Analyzing the difference in profits among various hospital groups, the tertiary hospital group and the 501-1000 beds group exhibited the highest in operating profit rate. Also, among the higher grade number of beds in hospital group, per 100 beds group, the 41-50 beds group exhibited the highest in operating profit rate. There is a statistically significant difference in those groups(p<0.05, p<0.01). (4) In the health care delivery system, the profit gain in the secondary hospital was 51.5% and in the tertiary hospital was 72.4%. Based on the number of beds in each hospital group, the highest profit gain was 75.0% in the over 1001 beds group, and 71.4% in the 501-1000 beds group. Also, among the higher grade number of beds in hospital group, per 100 beds group, the 41-50 beds group exhibited 88.6% surplus. (5) According to the surplus difference based on the analysis of health care utilization, a group with over 31 patients in bed turnover rate, a group with over 96% in bed occupancy rate and group with over 9% in emergency cases to outpatient visits exhibited the highest profit gains. In addition, a group with over 301 patients in daily outpatient visits per 100 beds and group with 11-12 days average length of stay exhibited the highest profit gains. These results are statistically significant(p<0.05, p<0.01). (6) According to a stepwise regression analysis, the variables measuring the bed turnover rate, number of licensed beds, and number of outpatient visits per specialist explain 34.1% of the variation in operating profits. In terms of net profits, the new outpatient visits, the bed turnover rates and the number of general bed variables explain 30.6%. These results are statistically significant(p<0.01).

• 보건행정학회지
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• 제7권2호
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• pp.1-18
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• 1997

This study was conducted to analyze patient days and medical care benefits of finger-amputated patients due to industrial accident. The 161 personal data on medical care for finger-amputated patients due to industrial accident(88 in 1994, 73 in 1995) of Regional Labor Office and hospital characteristics were analyzed. The major results of this study were as follows : According to stepwise multiple regression analysis of patient days, number of amputated finger, location of hospital, bed capacity of hospital, presence of plastic surgery in hospital, number of orthopedic specialist per 100 beds, sick leave benefits per day were the major significant variables in order. In stepwise multiple regression analysis with medical care benefits as a dependent variable, presence of plastic surgery in hospital, number of orthopedic specialist per 100 beds, number of amputated finger, sick leave benefits per day, age, bed capacity of hospital were the major significant variables in order. The minimum optimal size with the lowest medical care benefits was a hospital with 300 beds. This shows that the economy of scale is also applicable for hospital industry. In summary, presence of plastic surgery in hospital, number of orthopedic specialist per 100 beds, number of amputated finger, sick leave benefits per day, bed capacity of hospital were the major significant variables in both patient days and medical care benefits.

• 한국의료질향상학회지
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• 제12권1호
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• pp.62-76
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• 2006

Background : There were so many patients who are waiting for admission in Emergency room in spite of more than one hundred empty beds everyday. This study was conducted to evaluate admission-discharge module system by OCS which reduce empty beds. Methods : The data of bed utilization in general beds from 2004 were reviewed. For evaluation of performance at admission-discharge module system by OCS, the change of Occupancy of bed were calculated. Results : The percentage of Average Bed Emptiness was changed from 13.8% to 9.2%. The residents in surgery(100%) and in internal medicine(75.5%) approved this system. Conclusion : The personnel in hospital recognized that it was very important to manage bed. The management of beds by OCS was helpful to reduce empty beds and was important.

• 지역사회간호학회지
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• 제31권1호
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• pp.1-12
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• 2020

Purpose: The purpose of this study was to identify the factors influencing organizational commitment of staffs according to the size of long-term care facility. Methods: A cross-sectional descriptive study was designed. Data collection was conducted for a total of 315 employees in long-term care facilities located in Seoul, Gyeonggi, Gangwon, Gyeongbuk, and Chungnam. Data were collected from July 2018 to October 2018 using questionnaires which included emotional labor, job satisfaction, organizational commitment, and general characteristics. In order to confirm the differences in the size of the facility, the facilities with less than 30 beds, those with 30-99 beds, and those with more than 100 beds were analyzed. Data were analyzed using descriptive statistics, t-test, ANOVA, Mann-Whitney U test, Kruskal-Wallis H test, Pearson's correlation analysis, and multiple regression. Results: The job satisfaction and organizational commitment were significantly different according to the size of long-term care facility. Organizational commitment was influenced by 'external job satisfaction' in less than 30 beds, was influenced by 'external job satisfaction, and attentiveness to required display rules of emotional labor' in 30~99 beds, and then was influenced by 'type of job, and internal job satisfaction' in more than 100 beds. The predict variables accounted for 23.0%, 41.0%, and 34.0% of organizational commitment respectively. Conclusion: These findings show that tailored interventions should be provided depending on the size of facility in order to increase organizational commitment. In addition, organizational commitment programs should be developed by considering strategies to reduce the emotional labor and to increase job satisfaction.

• 보건행정학회지
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• 제33권1호
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• pp.55-64
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• 2023

Background: Despite the various activities of the regional public hospitals, discussions are being made as to whether or not to continue due to the issue of financial deficit. Therefore, the main factors affecting the fiscal deficit were analyzed with 10-year data. Methods: This study is a panel analysis that analyzed the characteristics of 34 regional public hospitals and influencing factors on medical benefits for 10 years from 2010 to 2019. First, we analyze the determinants of medically vulnerable areas set by the government, analyze the trend of medical profit per 100 beds and medical profit rate from 2010 to 2019, and identify the factors that affect them. Results: Differences in medical profit per 100 beds and medical profit-to-medical profit rate were caused by market share representing regional characteristics, and both indicators improved as the number of outpatients increased. The important influencing variables are the number of doctors and nurses, and both indicators improve when there are specialists, but medical benefits decrease as the number of doctors increases when judged by the number of people per 100 beds. In addition, the number of nurses per 100 beds does not contribute to medical profit and has a negative effect on the medical profit ratio. Conclusion: As only regional characteristics were taken into account for medically vulnerable areas, operational characteristics need to be considered. The greatest impact on the finances of local medical centers is the proper staffing of doctors and nurses, and their efficient arrangement is the most important factor in financial stability.

• 한국병원경영학회지
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• 제24권2호
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• pp.67-83
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• 2019

The aim of this study is to analyze the differences in the publicness indices depending on the environmental factors of regional public hospitals to derive the policy implications for improving management for regional public hospitals. The data of the 34 regional public hospitals from 2016 was used for the analysis. Major results of this study are as follows. First, the analysis of the differences in the scores of the medical safety net function showed significantly higher scores for regional public hospitals with a larger location, a larger number of hospitals in a unit area, a larger number of nurses per 100 beds, and the lower management fee ratio. Second, the analysis of the differences in the scores of the unmet healthcare needs showed significantly higher scores for regional public hospitals with a larger number of hospitals in a unit area, and a larger number of beds. Third, the analysis of the differences in the scores of the hospital-specialized services showed significantly higher scores for regional public hospitals with a larger location, a higher financial independence of the local government, a larger number of hospitals in a unit area, a larger number of beds, and a larger number of nurses per 100 beds. Major conclusions of this study are as follows. Consideration should be given to the appropriate number of nurses for each regional public hospital to maximize publicness by providing the appropriate amount of medical services, but not to incur unnecessary labor costs. In addition, efforts should be made to enhance profitability, which can be a means of strengthening publicness, by identifying the minimum administrative expenses required for efficient operation and reducing unnecessary administrative expenses. Finally, it is necessary to identify the appropriate number of beds to meet the needs of the customers and to create maximum profits.

• 보건행정학회지
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• 제25권1호
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• pp.40-52
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• 2015

Background: The purpose of this study was to analyze the increase in Grade of Nursing Management Fee of medical institutions and establish a reasonable government policy by examining which factors affect the increase of nurse staffing. Methods: Analyzing data collected from the Health Insurance Review & Assessment Service resource management department with targets of 1,104 medical institutions. The study period was 5 years from June 30, 2008 to June 30, 2013. SAS ver. 9.2 (SAS Institute Inc., Cary, NC, USA) was used for statistical analysis. The data was analyzed by a chi-square test and also conducted muiltivariate logistic regression analyses for variables of basic characteristics, human resource characteristics, and material resources. Results: Adjusted odds ratio (AOR) of the rise in Grade of Nursing Management Fee among other hospitals compared to hospitals owned by government or universities was 0.264. The AOR in hospitals established after November 2006 compared to those before June 1995 was 2.383. The AOR in Gangwon, Chungcheng South, and Jeolla South Provinces compared to Seoul was 0.084, 0.036, and 0.194, respectively. The AOR in hospitals with more than 6.75 specialists per 100 beds compared to those with less than 6.75 specialists per 100 beds was 7.514. The AOR in hospitals with more than 17.48 nurse per 100 beds compared to those with less than 17.48 nurse per 100 beds was 3.300. The AOR in hospitals with 50% to 75% bed utilization, 75% to 90% bed utilization and more than 90% bed utilization compared to those with less than 50% bed utilization was 5.428, 9.884, and 10.699, respectively. The AOR in hospitals with one magnetic resonance imaging (MRI) and more than two MRI compared to those with no MRI was 2.018 and 2.942, respectively. Conclusion: This result has showed policies to induce the rise in Grade of Nursing Management Fee among old hospitals and the incentive system for local medical institutions are needed. Also we need to develop a governmental policy for medium-small hospitals with low operation rate of beds and insufficient medical personnel and number of equipment in hospitals.