Hospital Performance Evaluation Research Articles

Using Potentially Preventable Severe Maternal Morbidity to Monitor Hospital Performance.

The Centers for Disease Control and Prevention (CDC) measure of severe maternal morbidity (SMM) quantifies the burden of SMM but is not restricted to potentially preventable SMM. The authors adapted the CDC SMM measure for this purpose and evaluated it for use as a hospital performance measure. Guidelines for defining performance SMM (pSMM) were (1) exclusion of preexisting conditions from outcome; (2) exclusion of inconsistently documented outcomes; and (3) risk adjustment for conditions that preceded hospitalization. California maternal hospital discharge data from 2016 to 2017 were used for model development, and 2018 data were used for model testing and evaluation of hospital performance. Separate models were developed for hospital types (Community, Teaching, Integrated Delivery System [IDS], and IDS Teaching), generating model-based expected pSMM values. Observed-to-expected (O/E) ratios were calculated for hospitals and used to categorize them as overperforming, average performing, or underperforming using 95% confidence intervals. Performance categories were compared for pSMM vs. CDC SMM (excluding blood transfusion). The overall 2016-2018 pSMM rate was 0.44%. All hospital types had over- and underperformers, and the proportions of Community, Teaching, IDS, and IDS Teaching hospitals whose performance differed from their performance on the CDC SMM measure were 12.1%, 25.0%, 38.9%, and 66.7%, respectively. The rate of potentially preventable SMM as defined by pSMM (0.44%) was less than half the previously published rate of CDC SMM (1.03%). pSMM identified differences in performance across hospitals, and pSMM and CDC SMM classified hospitals' performances differently. pSMM may be suitable for hospital comparisons because it identifies potentially preventable, hospital-acquired SMM that should be responsive to quality improvement activities.

Evaluating hospital performance with plant capacity utilization and machine learning

This study extends the measurement of plant capacity utilization by incorporating undesirable outputs. We select indicators through feature selection in machine learning and also introduce an undesirable output for assessment in these models. By defining and applying four plant capacity concepts, we analyze plant capacity utilization in health institutions in 31 provinces in China over the last 11 years (2009 to 2019). This paper has two main contributions. First, we propose a refined by-production hospital technology by introducing the mortality rate into the performance evaluation of public hospitals. Second, we expand the measures of plant capacity utilization with undesirable outputs. The preliminary results show that after the introduction of the death rate, the long-run output-oriented plant capacity utilization of medical institutions is significantly impacted. Furthermore, we find a high level of long-run input-oriented plant capacity utilization tends to increase mortality.

Factors Associated with Patients' Ratings of Hospitals among Japanese Inpatients: A Cross-sectional Study.

The evaluation of hospital performance often receives great attention. Hospitals refer to patient ratings to undertake quality-improvement activities. However, little is known about the factors that contribute the most to these patient ratings. This study aimed to investigate the association of relevant factors, such as doctors' and nurses' performance, with patients' ratings of hospitals, using the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPSⓇ) questionnaire. A cross-sectional study was conducted among patients who were hospitalized in Japan, from January 2020 to September 2021. Patients' hospital rating scale scores between 0 and 10 were collected and dichotomized. A score of 8 or higher was defined as a high rating. A multivariate logistic regression analysis was conducted to investigate the association between patients' ratings of the hospital and other items in the HCAHPSⓇ questionnaire. The frequency of patients' high and poor hospital ratings were 207 (69%) and 93 (31%), respectively, of 300 respondents. A significant association was observed for the patient's age (adjusted odds ratio (AOR): 1.02; 95% confidence interval (CI): 1.00-1.04), doctor's communication (AOR: 10.47; 95% CI: 3.17-34.58), and discharge planning (AOR: 3.53; 95% CI: 1.96-6.36) with a positive patient rating of the hospital. An emphasis on doctor communication and discharge planning is essential in improving patients' ratings of hospitals. Further research is needed to determine the factors that contribute the most to patients' ratings of hospitals.

Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach.

Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. [Formula: see text] programs to implement these newly proposed hospital compare models were thoroughly developed. The [Formula: see text] programs are freely available via a GitHub (https:\\www.GitHub.com) repository. We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability.

An econometric approach to aggregating multiple cardiovascular outcomes in German hospitals

ObjectiveDevelopment of an aggregate quality index to evaluate hospital performance in cardiovascular events treatment.MethodsWe applied a two-stage regression approach using an accelerated failure time model based on variance weights to estimate hospital quality over four cardiovascular interventions: elective coronary bypass graft, elective cardiac resynchronization therapy, and emergency treatment for acute myocardial infarction. Mortality and readmissions were used as outcomes. For the estimation we used data from a statutory health insurer in Germany from 2005 to 2016.ResultsThe precision-based weights calculated in the first stage were higher for mortality than for readmissions. In general, teaching hospitals performed better in our ranking of hospital quality compared to non-teaching hospitals, as did private not-for-profit hospitals compared to hospitals with public or private for-profit ownership.DiscussionThe proposed approach is a new method to aggregate single hospital quality outcomes using objective, precision-based weights. Likelihood-based accelerated failure time models make use of existing data more efficiently compared to widely used models relying on dichotomized data. The main advantage of the variance-based weights approach is that the extent to which an indicator contributes to the aggregate index depends on the amount of its variance.

Dynamic Performance Assessment of Hospitals by Applying Credibility-Based Fuzzy Window Data Envelopment Analysis.

The goal of the current research is to propose the credibility-based fuzzy window data envelopment analysis (CFWDEA) approach as a novel method for the dynamic performance evaluation of hospitals during different periods under data ambiguity and linguistic variables. To reach this goal, a data envelopment analysis (DEA) method, a window analysis technique, a possibilistic programming approach, credibility theory, and chance-constrained programming (CCP) are employed. In addition, the applicability and efficacy of the proposed CFWDEA approach are illustrated utilizing a real data set to evaluate the performance of hospitals in the USA. It should be explained that three inputs including the number of beds, labor-related expenses, patient care supplies, and other expenses as well as three outputs including the number of outpatient department visits, the number of inpatient department admissions, and overall patient satisfaction level, are considered for the dynamic performance appraisal of hospitals. The experimental results show the usefulness of the CFWDEA method for the evaluation and ranking of hospitals in the presence of fuzzy data, linguistic variables, and epistemic uncertainty.

Performance evaluation of general public hospitals in territories of the Former Yugoslavia: An ecological study using the Pabon Lasso model.

Measuring hospital performance is an area of great importance due to limited resources. Additionally, hospital activities are vital during emergencies providing care under pressure of external factors. This study aimed at analysing performances of general public hospitals from territories of the Former Yugoslavia. This ecological study includes 99 hospitals from 6 territories based on annual reports of institutions in charge of territorial health data. The Pabon Lasso method was used, considering hospitals in the third quadrant of the plot as top-performing in the territory. A particular focus was on performance changes during the pandemic caused by SARS-COV-2 coronavirus. There is considerable heterogeneity in the length of stay, bed occupancy and turnover rate between territories. The proportion of top-performing hospitals varied between 20% and 43%, providing between 16% and 69% of inpatients episodes within the territory. However, countries did not differ in how their hospitals were distributed across the four quadrants of the Pabon Lasso plot. Comparison of Croatian and Kosovar data from 2019 to 2020 suggested a significant decrease in turnover and occupancy rates, but most hospitals retained their plot quartiles from the previous year. This supports the finding that there were no significant changes in hospital performance based on the Pabon Lasso method. The analysis showed significant room for performance improvement and highlighted the ability of the Croatian and Kosovar hospitals and their health systems to maintain efficiency during emergencies.

Performance of Management Strategies With Class I Recommendations Among Patients Hospitalized With ST-Segment Elevation Myocardial Infarction in China

Despite advances in the treatment of ST-segment elevation myocardial infarction (STEMI), little is known about how this evolving knowledge is applied in current clinical practice in China. To evaluate hospital performance and temporal trends in the management of STEMI. This study used data from the Improving Care for Cardiovascular Disease in China-Acute Coronary Syndrome Project, a nationwide quality improvement registry, in collaboration with the American Heart Association and the Chinese Society of Cardiology. Participants included patients with STEMI admitted to 143 tertiary hospitals across China from November 2014 to July 2019, and data were analyzed from November 2020 to December 2021. Levels, hospital-level variations, and trends for utilization rates of the 9 management strategies with Class I recommendations in Chinese and US guidelines. A total of 57 560 hospitalizations with STEMI were included. Overall, 20.0% of patients received all the care according to the 9 guideline-recommended strategies. The performance rate of quality measures was low for reperfusion therapy (61.0%, 35 115/57 560 patients), β-blocker at discharge (68.3%, 37 750/55 285 patients), angiotensin-converting enzyme inhibitor or angiotensin receptor blocker at discharge (55.1%, 2524/4578 patients), and smoking cessation counseling (36.5%, 9586/26 265 patients) among those who were eligible. Of 25 563 patients who underwent primary percutaneous coronary intervention (PCI), 66.8% underwent this procedure within 90 minutes of hospital arrival. Of 1128 patients who underwent fibrinolysis therapy, 253 (22.4%) underwent this treatment within 30 minutes of hospital arrival. Measures with high performance rates included receipt of dual antiplatelet therapy within 24 hours (95.5%, 54 263/56 848 patients) and at discharge (91.8%, 51 452/56 019 patients) and receipt of statin at discharge (93.0%, 52 214/56 141 patients) for those eligible. There was significant variation between hospitals in all-or-none score (ranging from 0 to 61.9%) and performance of individual measures. The quality of care improved during the study period, especially for reperfusion therapy, primary PCI within the first 90 minutes of hospital arrival, and smoking cessation counseling. The quality of care for patients hospitalized with STEMI does not meet guideline-recommended strategies in China, with only 1 in 5 patients receiving all the care according to the 9 guideline-recommended strategies. Large disparities in the quality of care exist across hospitals.

Application of Performance Module in Health Service Quality Evaluation: Viewpoints of Beneficiary-A case Study in Al-Salaam General Hospital in Mosul

The issue of health service quality has become a growing global concern in an effort by medical organizations to provide the best health service that achieves maximum satisfaction for the beneficiary. This study represents an attempt to evaluate the performance of Al-Salam Hospital in Mosul from the perspective of the beneficiary, and the subject acquires great importance derived from being one of the contemporary technologies for designing the hospital performance evaluation system, which is one of the strategic management systems. Due to the limited studies that focused on the views of the beneficiary about the level of health service, the study focused on the components of the quality of the health service in order to study the evaluation of the views of the beneficiaries of the dimensions of the quality of the health service provided to them, to know which of the dimensions of health quality achieves the greatest possible satisfaction of the beneficiary for the purpose of activating it in pursuit of achieving The highest possible satisfaction and to improve the reality of the medical service in the organization. اعتماد أنموذج الأداء الفعلی فی تقییم مستوى جودة الخدمة الصحیة من وجهة نظر المستفید – دراسة حالة فی مستشفى السلام العام فی الموصل

The Balanced Score Card in Improving Performance in the Health Care Sector: A Literature Review

This paper is a literature review that discusses the use of the Blanced Score Card (BSC) to improve organizational performance, especially in the field of health services. The purpose of this study is to explore and connect theories from various studies. This paper identifies 21 articles with related topics published in the last decade. The results in this study indicate that BSC has been used in the health sector but is mostly used in the hospital sector, amounting to 87.5% (measuring performance against national standards and guidelines in the essential package of hospital services, assessing the quality of health services to strengthen health services in developing a country, integrated health care in hospitals, measuring service quality with the cost of patient care in the hospital, evaluate hospital performance, evaluation of the main performance of the hospital as an effort to achieve the hospital’s strategic goals efficiently) and in part small bada in the field of pharmaceutical services by 12.5% (measuring and evaluating the performance of pharmacies and stressing patient-centered services). While in other health service sectors there has not been any research that causes this cause based on this research it can be used to develop other research to examine the effectiveness of the use of BSC in the broader health service sector such as national health insurance, basic health services, or at the ministry or agency health.

Severe Maternal Morbidity in California Hospitals: Performance Based on a Validated Multivariable Prediction Model.

Severe maternal morbidity (SMM) is under development as a quality indicator for maternal health care. The aim of this study is to evaluate California hospital performance based on a standardized SMM measure. California maternal hospital delivery discharge data from 2016 to 2017 were used to develop logistic regression models for SMM, adjusted for clinical risk factors at admission. Data from 2018 were used to test the models and evaluate hospital performance. SMM was defined per the Centers for Disease Control and Prevention, including (excluding) blood transfusion. Independent models were developed for each hospital type: community, teaching, integrated delivery system (IDS), and IDS teaching. Within each type, model-based expected SMM values and observed-to-expected (O/E) ratios were calculated for each hospital. For each hospital type, hospitals were ranked by O/E ratio, and over- and underperforming hospitals were identified using 95% confidence intervals. Rates of SMM including (excluding) transfusion by hospital type were 1.7% (0.9%) for community, 2.7% (1.5%) for teaching, 2.3% (1.2%) for IDS, and 3.0% (1.6%) for IDS teaching hospitals. In higher-volume community hospitals (≥ 500 births/year), the proportion of underperformers including (excluding) transfusion was 20.7% (11.0%). Summing over all hospital types, 25.3% (14.9%) of hospitals were identified as underperformers in that they experienced significantly more SMM events than expected including (excluding) transfusion. California hospital discharge data demonstrated significant hospital variation in standardized childbirth SMM. These data suggest that a standardized SMM measure may help guide and monitor statewide quality improvement efforts.

A novel multicriteria decision‐making approach with unknown weight information under q‐ rung orthopair fuzzy environment

The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method has been developed as one of the effective techniques to accomplish the best alternative selection of multicriteria decision-making (MCDM) problems. However, the existing PROMETHEE methods fail to adjust the representation range of uncertain information. Moreover, the weight determination and information aggregation in the PROMETHEE model still suffer from an excessive dependence of decision makers' (DMs') subjective judgments and the lack of considering the interrelationship among criteria. To solve the aforementioned drawbacks and provide DMs with a reliable and robust method of MCDM, a novel PROMETHEE method based on the q-rung orthopair fuzzy sets, that is, q-ROF-PROMETHEE model, is presented herein. First, the PROMETHEE-II method is extended to q-rung orthopair fuzzy (q-ROF) environment, which not only handles the uncertainty of human cognition but also provides DMs with a broader space to represent their decisions information. Second, a new optimization model is constructed based on the proposed cross entropy and q-ROF entropy, which is capable of leveraging the preference of DMs to obtain the optimal objective weights among criteria. In addition, the q-ROF weighted Hamy mean operator is embedded in the preference information aggregation of the PROMETHEE method so as to deal with the interrelationship among criteria. Finally, q-ROF-PROMETHEE is applied to a hospital performance evaluation case to demonstrate the proposed method's superiority, validity, and feasibility.

A new standardized mortality ratio method for hospital quality evaluation

Evaluation of hospital quality is of great significance for the promotion of the development of medical care. Hospital standardized mortality ratio (HSMR) is the ratio of hospital observed mortality to expected mortality (O/E ratio) and is an important indicator for the evaluation of hospital performance. Given the importance of HSMR, accurate estimation of HSMR confidence intervals is essential. All existing methods assume that the distributions of the O/E ratios are close to a normal distribution. However, this assumption is not reasonable. In this article, we propose a new method for calculating the HSMR confidence intervals. We derive the confidence intervals for the O/E ratios by calculating the confidence intervals of log(O/E). Then, we use the coverage probability of the confidence intervals to compare the performance of our method with the performance of existing methods. In the scenarios with different true relative risks, if the mortality rate is less than or equal to 1%, the bias of our method is substantially lower than that of the existing methods. The simulation results show that our method provides a more accurate estimate of the confidence intervals of the O/E ratios in the case of low mortality rates than that provided by the existing methods.

Identifying key performance indicators for healthcare facilities maintenance

Hospitals and health centers use a lot of energy due to 24/7 al l-round year working hours and using different equipment. It has highlighted the importance of Facilities Management (FM) in hospitals, because the unexpected and dynamic nature of maintenance management not only increases energy usage but also decreases hospital functionality. Considering the significance of energy consumption in FM, particularly maintenance management and hospital functionality, the aim of this paper is to identify key performance indicators that lead to energy savings while improving functionality and reducing maintenance costs. In this regard, the relevant data are qualitatively examined through expert interviews and a literature review, accompanied by a questionnaire survey to produce quantitative performance data, which are analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). The importance of individual indicators and their relationship to three categories of “energy savings,” “improving functionality,” and “reducing maintenance costs” are then investigated. The findings demonstrated the direct link between energy savings and improving functionality and reducing costs. It develops an appropriate framework for evaluating hospital performance based on FM in hospitals focused on maintenance and functionality and its impact on energy consumption. Moreover, a case study is conducted to investigate the impact of the identified indicators on saving energy in public hospitals in Iran and comparing electricity and gas bills in two consecutive years. The results show “gradual replacement of old electrical appliances and equipment concerning the age of the appliances”, “condition-based preventive maintenance”, “utilizing maintenance instructions and standards”, and “considering the impact of experienced and qualified managers” are the most important key performance indicators.

Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory.

Large-scale group decision-making (LSGDM) deals with complex decision- making problems which involve a large number of decision makers (DMs). Such a complex scenario leads to uncertain contexts in which DMs elicit their knowledge using linguistic information that can be modelled using different representations. However, current processes for solving LSGDM problems commonly neglect a key concept in many real-world decision-making problems, such as DMs’ regret aversion psychological behavior. Therefore, this paper introduces a novel consensus based linguistic distribution LSGDM (CLDLSGDM) approach based on a statistical inference principle that considers DMs’ regret aversion psychological characteristics using regret theory and which aims at obtaining agreed solutions. Specifically, the CLDLSGDM approach applies the statistical inference principle to the consensual information obtained in the consensus process, in order to derive the weights of DMs and attributes using the consensus matrix and adjusted decision-making matrices to solve the decision-making problem. Afterwards, by using regret theory, the comprehensive perceived utility values of alternatives are derived and their ranking determined. Finally, a performance evaluation of public hospitals in China is given as an example in order to illustrate the implementation of the designed method. The stability and advantages of the designed method are analyzed by a sensitivity and a comparative analysis.

Predictors of 30-day hospital readmission after mechanical thrombectomy for acute ischemic stroke.

The 30-day readmission rate is of increasing interest to hospital administrators and physicians, as it is used to evaluate hospital performance and is associated with increased healthcare expenditures. The estimated yearly cost to Medicare of readmissions is $17.4 billion. The Centers for Medicare and Medicaid Services therefore track unplanned 30-day readmissions and institute penalties against hospitals whose readmission rates exceed disease-specific national standards. One of the most important conditions with potential for improvement in cost-effective care is ischemic stroke, which affects 795,000 people in the United States and is a leading cause of death and disability. Recent widespread adoption of mechanical thrombectomy has revolutionized stroke care, requiring reassessment of readmission causes and costs in this population. The authors retrospectively analyzed a prospectively maintained database of stroke patients and identified 561 patients who underwent mechanical thrombectomy between 2010 and 2019 at the authors' institution. Univariate and multivariate analyses were conducted to identify clinical variables and comorbidities related to 30-day readmissions in this patient population. Of the 561 patients, 85.6% (n = 480) survived their admission and were discharged from the hospital to home or rehabilitation, and 8.8% (n = 42/480) were readmitted within 30 days. The median time to readmission was 10.5 days (IQR 6.0-14.3). The most common reasons for readmission were infection (33.3%) and acute cardiac or cerebrovascular events (19% and 20%, respectively). Multivariate analysis showed that hypertension (p = 0.030; OR 2.72) and length of initial hospital stay (p = 0.040; OR 1.032) were significantly correlated with readmission within 30 days, while hemorrhagic conversion (grades 3 and 4) approached significance (p = 0.053; OR 2.23). Other factors, such as unfavorable outcome at discharge, history of coronary artery disease, and discharge destination, did not predict readmission. The study data demonstrate that hypertension, length of hospital stay, and hemorrhagic conversion were predictors of 30-day hospital readmission in stroke patients after mechanical thrombectomy. Infection was the most common cause of 30-day readmission, followed by cardiac and cerebrovascular diagnoses. These results therefore may serve to identify patients within the stroke population who require increased surveillance following discharge to reduce complications and unplanned readmissions.

Prediction modelling of inpatient neonatal mortality in high-mortality settings

ObjectivePrognostic models aid clinical decision making and evaluation of hospital performance. Existing neonatal prognostic models typically use physiological measures that are often not available, such as pulse oximetry values, in...

Evolução e características da avaliação do desempenho hospitalar: uma revisão de literatura

O objetivo deste estudo é analisar as características das publicações internacionais na temática avaliação do desempenho hospitalar (ADH), por meio de um fragmento da literatura, na busca de geração de conhecimento, identificação de lacunas e possibilidade de contribuições. Esta pesquisa utiliza uma abordagem qualitativa e o instrumento selecionado para identificar e selecionar o fragmento de literatura para análise e reflexão foi o Knowledge Development Process – Constructivist (ProKnow-C). O portfólio bibliográfico com 43 artigos identificou os autores e os núcleos de pesquisas de destaque e os periódicos com maior número de publicações. Quanto aos métodos mais utilizados na ADH, destacam-se os que se baseiam na teoria das fronteiras Eficientes, classificados como: Data Envelopment Analysis (DEA - 53,85%), Stochastic Frontier Analysis (SFA - 12,82%), comparação entre DEA e SFA (7,69%) e Partinal Frontier Analysis (2,56%). Quanto ao emprego ou dimensões analisadas, 38,46% dos artigos destinam-se aos gestores. A partir das análises geradas foi possível identificar lacunas, referentes a mecanismos de avaliação do desempenho voltados para as dimensões que extrapolem as métricas. Para geração e aplicação de conhecimento nesta área, sugere-se a adequação da ADH as especificidades destas organizações, considerando o ambiente e a finalidade do sistema ao qual se insere.

Cross-classified Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to evaluate hospital performance: the case of hospital differences in patient survival after acute myocardial infarction

ObjectiveTo describe a novel strategy, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to evaluate hospital performance, by analysing differences in 30-day mortality after a first-ever acute myocardial infarction...

Gri ilişkisel analiz yöntemiyle Türkiye’deki devlet üniversitesi hastanelerinin performans değerlendirmesi

Bu çalışma, Türkiye sağlık sisteminde önemli bir yere sahip olan devlet üniversitesi hastanelerinin finansal performanslarını Gri İlişkisel Analiz yöntemi (GİA) ile değerlendirmeyi amaçlamaktadır. Sağlık hizmetlerinin sürdürülebilirliği sağlamak için finansal kaynakların etkin şekilde yönetilmesi gerekmektedir. Bu bağlamda, likidite, finansal yapı, devir ve kârlılık oranlarıyla analiz gerçekleştirilmiştir. Otuz iki devlet üniversitesi hastanesi için on finansal oran 2015 yılı itibariyle hesaplanarak analizde kullanılmıştır. Hastanelerin likidite oranları açısından ideal değerlerin altında olduğu, hastanelerin finansal yapısının borçlanma ağırlıklı, özsermayesinin zayıf olduğu ve karlılıkların düşük olduğu tespit edilmiştir. GİA analizi sonucunda, likidite ve karlılık oranları yüksek olan hastanelerin finansal performansının da yüksek olabileceği sonucuna varılmıştır. Ayrıca çalışma sonuçlarına göre finansal per-formansı orta ve düşük seviyede olan hastanelerin karlılık problemi ile karşı karşıya oldukları görülmektedir.