Efficient Hospital Management Research Articles

Analysis Of The Implementation Of Management Information Systems In Budi Setia Langowan Hospital

The rapid and extensive development and progress of information technology today, so that for an agency / company the information used as a basis for administration and data processing is an application system in various fields that are required to use the information system. Good service quality is the implementation of a reliable, effective and efficient Hospital Management Information System (SIMRS) and upgradeable. The hospital information system has an important role in clinical and administrative services. Hospitals that use a website-based system (SIMRS) are designed to integrate the main functions of the hospital into one integrated system stored in a database center. This study aims to analyze the implementation of a hospital information system using the PIECES method. The type of research used is qualitative.

Prediction of Patient Visit Levels Based on Type of Service at Patar Asih General Hospital Using the Monte Carlo Method

This study aims to predict the level of patient visits at RSU Patar Asih based on the type of service provided using the Monte Carlo method. The Monte Carlo method was chosen because of its ability to handle data variability and uncertainty that often arises in the context of patient hospital visits. The data used in this study includes the number of patient visits based on the type of service at RSU Patar Asih, such as health services for pregnant women, health services for postpartum mothers, health services for newborns, health services for toddlers, health services at primary education age, health services at productive age. , health services for the elderly, health services for people with hypertension, health services for people with diabetes mellitus, health services for people with serious mental disorders (ODGJ), health services for people suspected of having tuberculosis, health services for people at risk of being infected with the human immune system (HIV)during the period 2021, 2022, 2023.The prediction results from this simulation obtained an accuracy of 81.75%, providing a fairly accurate estimate of the level of patient visits in the future. These predictions can be used by hospital management for resource planning, such as medical staff allocation, drug and medical equipment inventory management, and operational scheduling. Thus, the results of this research have the potential to improve operational efficiency and overall quality of hospital services. It is hoped that this research can contribute to more effective and efficient hospital management through data-based predictions, thereby increasing patient satisfaction and optimizing hospital resources.

MED-TECH CONNECT

Med-Tech Connect also termed as Smart Health System is an organized computerized system designed and programmed to deal with day-to-day operations and management of hospital activities. The software system is designed to manage inpatients and outpatients, maintain records, handle treatment databases, track illness status, process pharmacy billings, and manage lab operations, ensuring efficient hospital management. Currently, a significant challenge faced by patients is accessing their medical reports post-consultation. Although many hospitals maintain these reports within their systems, patients often encounter difficulty retrieving them while outside the hospital premises. In addition, the system maintains patient data in a structured format. As part of this project, we aim to enhance functionality by enabling storage of reports in the database, facilitating access from any location worldwide. Keywords: Machine Learning, Database management, Web UI, Modules.

DLSDHMS: Design of a deep learning-based analysis model for secure and distributed hospital management using context-aware sidechains

Designing an efficient hospital management solution requires the integration of multidomain operations that include secure storage, alert system modelling, infrastructure management, staff management, report analysis, and feedback-based learning tasks. Existing hospital management models are either highly complex or do not incorporate comprehensive deep learning analysis, which limits their deployment capabilities. Moreover, most of these models use mutable storage solutions, which restricts their trust levels under multi-patient to multi-doctor mapping scenarios. To overcome these issues, this article proposes the design of a novel deep Learning-based analysis model for secure and distributed hospital management via context-aware sidechains. The model initially collects large-scale information sets from different hospital entities via an IoT-based network and stores the information on context-sensitive sidechains. These context-sensitive sidechains store information sets related to Medicine Management, Doctor Management, Insurance and Billing Management, and Appointment Management operations. These chains are optimized via an Iterative Genetic Algorithm (IGA) that assists in improving storage and retrieval performance via intelligent merging and splitting operations. Information stored on these chains is processed via a combination of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), that assist in identifying patient-level diseases and issues. The information obtained from these classifiers is updated on the central repository and assists in the pre-emption of diseases for other patients. Due to these integrations, the proposed model is capable of reducing computational delay by 3.5 % and reducing storage cost by 8.3 % when compared to other blockchain-based deployments. The model is also able to pre-empt patient issues with 9.3 % higher accuracy and 4.8 % higher precision, which makes it useful for real-time clinical deployments.

New ICU Design and Patient Outcome

Hospital length of stay (LOS) is a quality metric health systems use as a proxy of efficient hospital management (1). Many strategies to reduce ICU length of stay (LOS) have been implemented, but few studies have evaluated the role of ICU design in reduction of LOS and outcome. This study was informed by the need to compare ICU performance in terms of length of stay and outcome before the upgrade of the ICU facilities and organizational structure (January 2018 – December 2019) and after upgrade of the ICU facilities (January 2020 – December 2021). Finally we demonstrate the impact of ICU design in reducing costs of ICU patients.

Complication Severity and Its Association with the Treatment Management of COVID-19 Patients in Bangladesh: A Cross-Sectional Study

In an unprecedented way, COVID-19 patients in Bangladesh have suffered greatly due to the severity of the complications. Additionally, treatment management was also a grave concerning issue. This study explored the extent of COVID-19 complications associated with the pattern of COVID-19 Prevention and Treatment Management in the context of Bangladesh. This cross-sectional study included 659 COVID-19-positive patients aged 18 and up who were treated at home or in hospitals and lived in Dhaka city From April to September 2021. The results showed that 20.5% had a mild COVID-19 infection. Family income, the number of co-morbidities, and the location of therapy were found to have a substantial impact on recovery time. In addition, there were high correlations between age, the number of co-morbidities, level of education, and the place of treatment with the severity of Covid-19 infection. The government should place a greater emphasis on training and resources to ensure efficient hospital management and widespread public knowledge as a precaution for further phenomena like COVID-19.

Optimizing the Management of Medical Information Technology: Best Practices and Innovations

Medical Information Technology (MedIT) has become indispensable in contemporary healthcare, presenting transformative possibilities ranging from improved patient care to efficient hospital management. However, optimizing its potential necessitates a clear understanding and effective management of its multifaceted components. This article elucidates key strategies and innovative practices pivotal for the proficient management of MedIT. Notably, it underscores the significance of streamlined Electronic Health Records (EHRs), emphasizing their customization, accessibility, and mobile compatibility. Furthermore, the article accentuates the growing role of telehealth, especially its versatility in offering both regular and emergency services. The potential of Artificial Intelligence (AI) and data analytics in predicting patient needs, customizing treatments, and real-time reporting is highlighted. Importantly, amidst digitalization, the article prioritizes cybersecurity, emphasizing regular audits, comprehensive staff training, and the need for swift incident response plans. The narrative promotes system interoperability, endorsing universally recognized data protocols and ensuring hassle-free patient data mobility. It also advocates for the continuous professional development of healthcare personnel through MedIT workshops and e-learning platforms. Lastly, it champions patient-centricity by enhancing patient portals with interactive features and real-time communication tools, and by integrating adaptable cloud solutions for better data management.

Abstract 241: Length Of Hospital Stay Is Lessened In Patients That Present With Chest Pain When Serial Troponins Are Measured Every Three Hours Compared To Every Six Hours

Troponins have close to 100% sensitivity when drawn 6 to 12 hours after the start of chest pain in acute coronary syndrome (ACS). Length of hospital stay (LOS) is a marker of efficient hospital management. Since troponin measurements at 3 (Q3H) and 6-hour (Q6H) intervals have comparable efficacy for ACS, we suggest considering Q3H troponins as opposed to Q6H in chest pain admissions to shorten LOS. A retrospective review of 389 patients who presented to Stony Brook Southampton Hospital in 2020 and 2021 with chest pain was conducted. Two patient care strategies were examined, one involved collecting troponins Q6H and the other Q3H. 319 patients with the Q6H strategy were analyzed and 61.1% were ruled in for ACS. For the Q3H strategy, 70 patients were analyzed in which 42.9% were ruled in for ACS. LOS was defined as hours between triage and submission of discharge instructions. A two-sample t-test was performed to find correlation between frequency of troponins and LOS. A two-sample t-test assuming unequal variance was conducted to compare LOS in hours between Q6H and Q3H strategies. There was a significant difference in LOS for Q6H (M = 123, SD = 201) and Q3H (M = 66, SD = 135) troponins; t = 2.91, p = 0.002. These results suggest that LOS is lessened when troponin intervals are shorter. The variance was elevated (40249,18343), possibly due to patients who stayed longer for other needs, but mean LOS and standard deviation remained decreased both when excluding patients that stayed longer than one week, and when including all patients in the analysis. Optimizing LOS has a variety of benefits. In patients with chest pain, shortening the interval of troponin collection can lessen LOS. Studies of comparable efficacy of shorter troponin intervals in evaluation of ACS have led to the practice of trending at Q3H intervals in Europe. Our study demonstrates that trending troponins Q3H is viable in a community hospital in the United States and should be further investigated and considered in the future.

Factors Affecting Hospital Network Management in the Social Security Organization of Iran

Background: Hospitals are considered the most important consumer units in the health system. Based on the effect of the hospital network in optimal and efficient hospital management, this study aimed to identify and explain the role of factors influencing hospital network management in the Iranian Social Security Organization (ISSO). Methods: This study was conducted by a mixed method in 2018. First, variables and factors affecting the management of hospital networks were extracted from the literature review and through interviews with 15 experts, and a questionnaire with 40 questions was developed. Then, it was distributed among 448 experts in the healthcare section of the ISSO all over the country, and 405 questionnaires were completed and collected. Data were analyzed via exploratory and confirmatory factor analysis and structural equations using SPSS23 software and AMOS24 software. The validity of the questionnaire was determined via expert judgment (CVRs = 0.40) and reliability was estimated by Cronbach's alpha coefficient (α = 0.87). Results: Based on the confirmatory factor analysis, financing, monitoring and evaluation, accountability, strategic planning, and decision rights were identified as key factors affecting hospital network management. Financing with standard coefficients of 0.83 had the highest and decision rights with standard coefficients of 0.44 had the lowest effect on hospital network management of the ISSO. Conclusion: To efficient hospital network management of the ISSO, while emphasizing all identified factors, special attention should be paid to financing and modifying the method of budgeting and using suitable monitoring and evaluation mechanisms.

Intelligent Recognition of Hospital Image Based on Deep Learning: The Relationship between Adaptive Behavior and Family Function in Children with ADHD.

Chronic diseases are gradually becoming the main threat to human health. By designing an efficient hospital management platform to quickly identify the corresponding chronic diseases, it can effectively reduce the labor cost, improve the accuracy of disease identification, and improve treatment efficiency. ADHD is a common behavioral disorder in school-age children, and it is also one of the most common chronic health problems in this period. The internationally recognized prevalence of ADHD is 3%–9%. ADHD often brings adverse effects on children's life and studying and at the same time increases difficulties for their families. Therefore, this paper designs an intelligent management platform for public hospitals based on a deep learning algorithm, evaluates the current situation and influencing factors of ADHD children through the child adaptive behavior scale and the family function assessment scale, and designs its intelligent platform by using a new technology of fNIRS. According to the nonlinearity and unsteadiness of the fNIRS signal, this paper proposes a motion noise removal method based on EMD algorithm methods: to automatically identify children with ADHD and improve the cognitive function of children with ADHD by intervention technology. The data are from the outpatients of the Department of Child Psychology of the First People's Hospital of Tianshui City in Gansu Province in 2018. The results showed that there were significant differences in the adaptive behavior scale (CABS) and fad scores between the two groups. In the seven dimensions of family function, there were significant differences between the two groups (P < 0.01). fNIRS management platform can effectively identify ADHD patients with high recognition accuracy. The intelligent management platform can significantly reduce the number of physical examination personnel, prolong the diagnosis and treatment time, reduce a lot of repetitive work, and improve the efficiency of diagnosis and treatment. At the same time, this technology also provides great help for better research and improvement of ADHD patients and provides a reference for the information intelligent construction of modern hospitals.

Probabilities of ICU admission and hospital discharge according to patient characteristics in the designated COVID-19 hospital of Kuwait

BackgroundSubsequent epidemic waves have already emerged in many countries and in the absence of highly effective preventive and curative options, the role of patient characteristics on the development of outcomes needs to be thoroughly examined, especially in middle-east countries where such epidemiological studies are lacking. There is a huge pressure on the hospital services and in particular, on the Intensive Care Units (ICU). Describing the need for critical care as well as the chance of being discharged from hospital according to patient characteristics, is essential for a more efficient hospital management. The objective of this study is to describe the probabilities of admission to the ICU and the probabilities of hospital discharge among positive COVID-19 patients according to demographics and comorbidities recorded at hospital admission.MethodsA prospective cohort study of all patients with COVID-19 found in the Electronic Medical Records of Jaber Al-Ahmad Al-Sabah Hospital in Kuwait was conducted. The study included 3995 individuals (symptomatic and asymptomatic) of all ages who tested positive from February 24th to May 27th, 2020, out of which 315 were treated in the ICU and 3619 were discharged including those who were transferred to a different healthcare unit without having previously entered the ICU. A competing risk analysis considering two events, namely, ICU admission and hospital discharge using flexible hazard models was performed to describe the association between event-specific probabilities and patient characteristics.ResultsResults showed that being male, increasing age and comorbidities such as chronic kidney disease (CKD), asthma or chronic obstructive pulmonary disease and weakened immune system increased the risk of ICU admission within 10 days of entering the hospital. CKD and weakened immune system decreased the probabilities of discharge in both females and males however, the age-related pattern differed by gender. Diabetes, which was the most prevalent comorbid condition, had only a moderate impact on both probabilities (18% overall) in contrast to CKD which had the largest effect, but presented only in 7% of those admitted to ICU and in 1% of those who got discharged. For instance, within 5 days a 50-year-old male had 19% (95% C.I.: [15,23]) probability of entering the ICU if he had none of these comorbidities, yet this risk jumped to 31% (95% C.I.: [20,46]) if he had also CKD, and to 27% in the presence of asthma/COPD (95% C.I.: [19,36]) or of weakened immune system (95% C.I.: [16,42]).ConclusionsThis study provides useful insight in describing the probabilities of ICU admission and hospital discharge according to age, gender, and comorbidities among confirmed COVID-19 cases in Kuwait. A web-tool is also provided to allow the user to estimate these probabilities for any combination of these covariates. These probabilities enable deeper understanding of the hospital demand according to patient characteristics which is essential to hospital management and useful for developing a vaccination strategy.

Modeling the length-of-stay of patients with geriatric diseases or alcohol use disorder using phase-type distributions with covariates

The hospital length-of-stay (LOS), as an important measure of the effectiveness of healthcare, represents the level of medical requirement and is highly related to the treatment costs. As the human life expectancy has being increased rapidly in the past few decades, there is a pressing need to improve health systems for geriatric patients. Similarly, the alcohol use disorder (AUD), as a chronic relapsing brain disease related to severe problem drinking, has caused negative impacts to society and put patients’ health and safety at risk. In both cases, more efficient hospital management is in demand due to increasing requirements for long-term hospital treatment and the continuously rising medical cost. In order to improve the healthcare efficiency, an accurate modeling of the LOS data and the further analysis of potential influencing factors are necessary. In this paper, we utilize the Coxian Phase-Type (PH) distribution and apply Maximum Likelihood Estimation (MLE) to fit the patient flow information of both geriatric patients and AUD patients collected in a hospital. The influences of the covariates of age, gender, admission type, admit source, and financial class on LOS are assessed and compared through Expectation-Maximization (EM) algorithms. The results show that the LOS data of both types of patients can be modeled well, and the differences with respect to covariates can be accurately identified by the proposed methods. Using the fitted Coxian PH distribution and the estimated coefficients of covariates will provide a guide for better decision-making in healthcare service and resource allocation.

Organizational management to balance appropriate provision of health care and efficient hospital management: Case analysis of a committee on medical supply purchase at hospital X

Organizational management to balance appropriate provision of health care and efficient hospital management: Case analysis of a committee on medical supply purchase at hospital X

Development and evaluation of NRMIS (Nursing Resources Management Information System) for managing healthcare resources.

Efficient resource management should consider the improvement of internal factors first as the unique task of medical institutions that can perform the medical services for efficient hospital management under the optimum management condition. This study aims to suggest the efficient model for nurse resource management that can estimate optimum nurse resources according to the nursing intensity of the hospitalized patients. The study was performed with four steps including collection and analysis of requirements, system design, system realization, and evaluation, which took 2 years and 10 months. The measurement tool used in the step of system evaluation was a modified version of Questionnaire for User Interaction Satisfaction (QUIS) 5.0. The system was implemented using Oracle database with Power Builder by Sybase. NRS, PCS, and ONMES were realized with developed NRMIS, and the survey was conducted on the usefulness as the system evaluation. The system evaluation results of User Interaction Satisfaction, means scores of ONMES, PCS, and NRS were 7.15 ± 0.52, 6.21 ± 1.11, and 6.03 ± 1.14 out of 9 points, respectively. Demonstrating the positive change in the subjective usefulness of the respondents after using the system compared to the period before using the system (F= 16.551, p= 0.000). It was confirmed that this system contributed to an enhancement of the working process speed, efficiency, and accuracy by simplifying the works which were the purposes of the nursing information system, which changes dynamically, to support decision making on the management of effective and flexible nurse resources.

Satisfaction survey among primary health care outpatients in the backward region: an empirical study from rural Western China.

BackgroundWith a growing health demand, patient satisfaction analysis is essential for evaluating the accessibility and performance of medical services. Previous studies had explored the Chinese outpatient satisfaction and influencing factors in developed areas and tertiary hospitals. Considering the lower education level, less income, and heavier economic burden, it was necessary to conduct a region-specific questionnaire survey for the outpatient’s satisfaction in rural Western China.ObjectiveTo analyze the satisfaction of primary outpatient service in rural Western China, and explore the factors affecting outpatients’ satisfaction.MethodsQuestionnaire composed of nine 5-Likert items was applied to survey outpatient satisfaction among randomly selected samples in 11 provinces of Western China. Exploratory factor analysis (EFA) was conducted to study the factor structure of questionnaire. Stepwise multiple linear regression analysis was performed to study the influencing factors.ResultsA total of 2,754 outpatients completed the questionnaire, the response rate was 88.7%. Respondents were most satisfied with medical staff service attitude (3.71±0.83) and least satisfied with medical cost (2.97±0.83). A 3-factor solution was adopted in EFA to explain the overall satisfaction. Factors identified were “Service attitude”, “Facility and professional skills”, and “Patients’ cost”. And, the questionnaire was proved to have good reliability and acceptable internal consistency. The stepwise multiple linear regression analysis results presented that factors, including sample hospital type (P<0.05), age (P<0.001), education level (P<0.05), occupation (P<0.01), monthly income (P<0.05), and chronic disease conditions (P<0.01) were significantly associated with the dimensional or overall satisfaction.ConclusionThe primary health care outpatient satisfaction in rural Western China is lower than developed areas and tertiary hospitals. Care providers in backward regions should pay more attention to patients’ demographic characteristics and health status, to meet outpatients’ actual demand. Efficient hospital management methods, modern technology, and staff training are needed to improve the service quality and care efficiency.

Analysis of the NASH Efficiency Indicators in Brazilian Federal University Hospitals via Business Intelligence

The hospital area is constantly in need of care and attention. Because of its urgencies, it has vulnerabilities and risks that hinder the quality of hospital management. Technological management tools are effective in improving processes and strategic administration because they provide information reliability. The aim of this work is to propose to the Brazilian’s Federal University Hospitals (BFUHs) and to the Brazilian Hospital Services Company (BHSC) the use of an open source Business Intelligence (BI) tool, Pentaho, to analyze the efficiency of Hospital Indicators (HIs) and to support the clinical decision-making. This paper describes BI and Pentaho, as well as the main points of the efficiency documentations established by the National Agency of Supplementary Health (NASH), with emphasis on the HIs: Hospital Occupancy Rates (HORs) and Averages Length of Stay (ALSs). The Pentaho does the data extraction and the transformation, as well as loading them into the Data Warehouse (DW), all based on a relational database from BHSC. After that, dimensional cubes are created for the generation of graphic panels in dashboards. Most BHSC hospitals are not yet adequately using their operational beds and neither managing correctly the average length of hospitals patients stay, being outside the goals established by NASH. Several steps are taken to achieve efficient hospital administration. However, the joint working of Information and Communication Technologies (ICTs) solutions with the health area and its standards provides a major step towards efficient hospital management.

Professor Changqing Pan: build Shanghai Chest Hospital as an international brand

In 2016, Professor Changqing Pan ( Figure 1 ) shouldered the crossbeam of Shanghai Chest Hospital. Serving in the hospital for more than a year, he used his double decades of experience as a doctor to carry through a simple doctor’s vision, and to exert a comprehensive management concept to make an efficient hospital management come true.

Dengue infection severity score - improvised disease management.

Dear editor We would like to add our views regarding the paper “Validation of Dengue infection severity score” by Pongpan et al.1 As the paper outlines, the purpose of the Dengue Severity Score is to classify individuals with dengue infection into three levels of severity with clinically acceptable underestimation or overestimation. Dengue virus is transmitted by Aedes mosquitoes, which are sensitive to various environmental conditions including temperature, rainfall, and humidity. These factors are critical to mosquito reproduction and survival, for example, higher temperatures decrease the time required for virus replication and dissemination in the Aedes mosquito.2 The incidence of dengue doubles every decade, it increased from 8.3 million apparent cases in 1990 to 58.4 million apparent cases in 2013.3 Although it is a preventable disease, dengue still has an estimated mortality rate of 20,000 people per year.2 The clinical presentation of dengue infection is characterized by fever, headache, vomiting, muscle and joint pain, as well as rash on the skin. Complicated cases may result in hemorrhagic fever, thrombocytopenia, shock, and death. The World Health Organization classification of dengue fever (2009) includes two separate entities, namely, dengue and severe dengue; however, this has yet to be proven as clinically significant. Very few studies are available, and of them, most are at a secondary or tertiary level of care and not at the level of primary care. Most studies are not carried out in resource-poor settings.4 This World Health Organization gross classification is not practical since dengue epidemic affects a wide range of age groups and manifests as different clinical presentations globally, thus adding diversity to disease spectrum.5 Dengue infection severity score is a handy assessment tool; prerequisite information required for this include patients’ clinical data consisting of their medical history, recorded vitals, complete blood count, history of bleeding episodes with results of tourniquet tests, and recent incidents of plasma leakage determined by assessing hemoconcentration, which shows a 20% increase in hematocrit. In our opinion, utilizing the clinical data in the scoring system would allow for more efficient and cost-effective patient care and may minimize unnecessary hospitalization as patients with a low score are suspected to have dengue fever and could be examined and treated as outpatients, while those with a greater score are suspected of dengue hemorrhagic fever and could be admitted in the hospital. Finally, those with the greatest score, likely to have dengue shock syndrome, could be admitted for immediate monitoring in an intensive care unit. We suggest that the scoring system should be made widely applicable to effectively manage the ongoing changes being currently observed in the virulence factors of dengue virus, for example, a fifth serotype DENV-5 recently discovered in 2015, has been shown to have caused frequent epidemics and life-threatening mortalities.5 We recommend this resourceful and impactful scoring system for efficient hospital care and management of dengue patients.

The way the quality of health services is perceived and treated by the managerial personnel of public hospitals

Managerial knowledge and skills as well as profound understanding of goals and objectives of management determine the effectiveness and efficiency in all areas of managerial activities. In particular, this applies to the quality of healthcare services, perceived as a compliance between the effects (of medical treatment) and the assumed relevant objectives (defined/desired health condition). The aim of the research was to distinguish and define the way the health service quality is perceived by the hospital managerial personnel. The questionnaire was mailed to 836 members of the managerial personnel of public hospitals in the Łódź province. Of this number 122 questionnaires were returned. Only 22 (18.49%) of respondents presented the definition of quality. Attempts to meet patients' expectations and to satisfy them were found to be the prevailing perception of the healthcare quality and 96.64% of respondents considered competences of medical staff contributory. Almost 64% of respondents disagree with the opinion that the number of medical staff does not affect the service quality. According to the respondents, a 46% increase in financial resources on average could significantly improve the quality of healthcare services. More than half (66.76%) of respondents claim that healthcare services that are available cover 82% of patients' needs. Almost 57% (56.52%) of respondents consider that the subordinate- superior relationship influences their work involvement. According to 42.61% of respondents, the offered incentives encourage actions for the quality improvement. The results of the research indicate the need to develop a clear cut definition of the health service quality by the managerial personnel of public hospitals and to change their understanding, perception and treatment of the discussed issue, which provides a basis for the effective and efficient hospital management. Med Pr 2016;67(3):365-373.

Efficiency Analysis of Financial Management Administration of ABC Hospital using Financial Ratio Analysis Method

This paper evaluated the financial performance of ABC hospital within the period of 2012 to 2013. To overcome the problems faced by the hospital related to how to measure and presented its financial performance in which financial ratio analysis was undertaken. These financial ratios were employed to measure the liquidity, assets utilization, long-term solvency and profitability of the hospital. This analysis was conducted in order to prove whether the hospital has been managed efficiently or not in accordance to Indonesian Hospital Quality Accreditation as stated in its clause on Administration Standard No. 5 Parameter No. 3 that the hospital financial management shall be conducted in appropriate way in order to guarantee its operation efficiently. The result showed that overall financial performance of ABC hospital increased considerably in those two years of the analysis. A significant change was occurred on its solvency ratio which was decreased from -2% to -8%, indicating its loose dependency due to its founder’s strong financial support. Therefore, based on this favorable result, the hospital was regarded to have efficient hospital management and thus, together with other standard fulfillment, it was accredited by Indonesian Health Ministry.