Hospital Efficiency Research Articles

Hubungan Aplikasi Metode EUCS (End User Computing Satisfaction) dengan Kepuasan Kerja Perawat dalam Penggunaan SIMRS di RSUD Dr. Doris Sylvanus Palangka Raya

Hospitals, as institutions providing healthcare services, must maximize technological advancements. One such advancement is the use of SIMRS (Hospital Management Information System) in accordance with the Indonesian Minister of Health Regulation No. 82 of 2013. The use of SIMRS is predicted to enhance the hospital's efficiency, effectiveness, professionalism, performance, as well as access and services. Nurses' satisfaction in using SIMRS will impact the improvement of patient care quality. One method to evaluate user satisfaction with SIMRS is EUCS (End User Computing Satisfaction), developed by Doll and Torkzadeh, which consists of five dimensions: content, format, accuracy, ease of use, and timeliness. This study aims to determine the relationship between the application of the EUCS method and nurses' job satisfaction in the use of SIMRS at RSUD dr. Doris Sylvanus Palangka Raya. Methods: This research is correlational, employing a cross-sectional design. Respondents were selected using purposive sampling and the chi-square statistical test was used. The sample consisted of 175 inpatient nurses at RSUD dr. Doris Sylvanus Palangka Raya. Results: The chi-square statistical test showed a p-value = 0.000, or a significance level of p < 0.05, indicating that there is a relationship between the application of the EUCS method and nurses' job satisfaction in the use of SIMRS at RSUD dr. Doris Sylvanus Palangka Raya. Conclusion: Effectiveness and efficiency are the primary goals of SIMRS usage, which creates convenience and transparency, ultimately expected to enhance nurses' job satisfaction.

Revolutionizing Hospital Operations: A Comprehensive Analysis of Hospital Management System Implementation

Hospital Management System (HMS) technology will transform hospital operations. This summary summarizes HMS implementation's main components, goals, advantages, and drawbacks. Patient registration, appointment scheduling, EMR, billing, inventory management, and analytics are integrated into the HMS. Its main goal is to improve hospital efficiency, automate operations, and centralize information management. Implementing an HMS improves patient care by providing rapid access to precise information, operational efficiency, financial procedures, and data accuracy. The challenges include comprehensive staff training, strong data security, and smooth healthcare system connection. Healthcare organizations are developing digitally, making the hospital administration system essential for patient outcomes and hospital administration

MEASURING HOSPITAL EFFICIENCY DURING COVID-19 PANDEMIC IN INDONESIA

By addressing indicators of inefficiency at the initial of the COVID-19 pandemic, hospitals can immediately respond through resources. This research aims at assessing the efficiency of both government and commercial hospitals in Indonesia during the pandemic. Accounting data was collected from documentation of financial statements. The data was analyzed by using the data envelopment analysis technique and Tobit regression. The research showed that over three years of the pandemic, the hospital's efficiency rate decreased successively by 100%, 86%, and 76%. However, the optimization of hospital efficiency can be significantly improved by revenue factors, specifically the quantifiable quantity of income generated. This highlighted the need to increase revenue to ensure the provision of high-quality services to the general population. It is hoped that this research will shed light on the challenges faced by hospitals during these difficult times and provide insights for future improvements.

Leadership styles on job satisfaction and security among healthcare workers during the COVID-19 pandemic.

Hospitals employ many social workers, whose tasks are complicated and arduous. Quality of life assessments by healthcare professionals (HCWs) are essential for better healthcare and skillful health service delivery. HCWs have been under tremendous mental and psychological strain and at high risk of virus acquisition since the COVID-19 pandemic outbreak. This study evaluated public hospital efficiency issues and remedies in the Kingdom of Saudi Arabia. The study examined leadership styles and job satisfaction among Saudi Arabian healthcare workers (HCWs). Between September 1 and December 31, 2021, a descriptive cross-sectional investigation was conducted. During the COVID-19 pandemic, a verified web-based survey reached the appropriate sample and data was collected. A web-based self-administered survey collected demographic data, leadership style using Multifactor Leadership Questionnaire Form 6-S (MLQ-6S), and health care employee satisfaction (SEHC) from HCWs. Leadership styles affected HCW job satisfaction, according to this study. Individual concern, idealized influence, contingent compensation, and inspiring motivation were negatively correlated with work satisfaction ratings. Creating a special training programme for new leaders should cover leadership styles and approaches, the skills needed to foster a productive workplace with high-quality care, and the creation of motivated work opportunities that can boost worker engagement and satisfaction.

Analysis of Density and Patient Wait Times In Terms of System Management In Turkish Hospitals: Setting A Pattern by Days and Hours of The Week

The time spent waiting is an important problem regarding patient satisfaction and hospital efficiency, and increases the need for evidence-based information for management to make decisions towards a solution. This study aims to examine the process flows in hospitals based on actual data with the intent to design a better service delivery system and set a pattern in terms of determining the congestion in the process and planning the necessary improvements. For this purpose, the data sets for 2016 pertaining to secondary and tertiary level hospitals in Turkey, which are stored in the central physician appointment system (CPAS), are investigated. The data is analyzed through pre-processing, cleaning and transformation stages. Congestion patterns are determined by days and hours spent in hospitals. Monday is found to be the day with the highest patient density and the longest wait time in Turkish hospitals. Additionally, when analyzed by working hours, it is determined that the first 2 hours in the morning (9.00 a.m. to 11.00 a.m.) is the period when most patentse are examined. The lunchtime (between 12.00 p.m. - 1.00 p.m.) and the afternoon from 4.00 p.m. to 5.00 p.m. are the times when patient density is the lowest, but average wait time is the longest. Turkish hospitals are found to be particularly congested on some days and during some hours regarding patient wait times. Thus, policy recommendations can be developed specifically to the days and times when congestion patterns are identified rather than suggesting a general policy. This study is the most comprehensive study conducted in Turkey through process data. The working model is reproducible in different countries and regions.

Interventions for increasing energy efficiency in hospitals

Interventions for increasing energy efficiency in hospitals

Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022.

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals.We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

Computer-aided hospital layout optimization based on patient flow analysis: A case study from China

Chinese hospitals face challenges with overwhelming patient flow and efficiency. This study focuses on optimizing the outpatient building layout of a general hospital to reduce patients’ travel distances. Electronic medical records (EMRs) (N = 64,315) were analyzed using process mining and systematic layout planning to understand patient flows. Optimal path planning was then used to simulate travel routes and calculate travel distances. The existing layout was optimized using a genetic algorithm to minimize total travel distance for general patients, high-volume department patients, and those with mobility limitations. The final layout was selected manually considering relocation expense, local building codes and regulations, functional continuity, and design flexibility. The layout effectively met the optimization objectives, reducing total travel distance for general outpatients by 5.84%, high-volume department patients by 4.58%, and limited mobility patients by 1.90%. Additionally, the final layout demonstrates outstanding performance in spatial adjacency, site-building relationship, and flow disturbance reduction. This study proposes a computer-aided design framework to improve outpatient building renovations. Utilizing EMR data with a large sample size, it broadens the research scope, refines travel distance calculation methods, and includes practical architectural indicators for a comprehensive analysis. The novelty of the study lies in integrating the optimal path planning method considering travel costs of a multi-story building into the framework of Hospital Layout Problems (HLPs) based on Patient Flow Analysis (PFA), and incorporating the opinions of stakeholders at various stages of computerized design. The method offers the potential to enhance hospital building performance and efficiency with minimal adjustments to existing structures.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review.

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Scheduling and Vendor Consistency Improves Turnover Time Efficiency in Total Joint Arthroplasty

BackgroundMaximizing operative room (OR) efficiency is important for hospital efficiency, patient care, and positive surgeon and staff morale. Reducing turnover time (TOT) has become a popular focus to improve OR efficiency. The present study evaluated if TOT is influenced by changing case type, implant vendor, and/or laterality. MethodsIn total, 444 turnovers from January to July 2023 were retrospectively analyzed. All turnovers were same-surgeon turnovers between primary arthroplasty cases in dedicated, overlapping rooms. Single linear regression models tested the predictability of TOT based on case type, vendor, and laterality. A multivariate multiple regression and 1-way Analyses of Variance analyzed variables against each other. Independent sample t-tests evaluated TOTs when all variables were the same or different. ResultsChanging versus keeping the same case type increased TOT by 2.4 minutes (95% confidence interval [CI] = 0.7, 4.0; P = .004). Changing vendors increased TOT by 2.9 minutes (95% CI = 1.1, 4.7; P = .002). Laterality did not affect TOT, with a change of 0.9 minutes (95% CI = −0.6, 2.5; P = .229). Vendor (P = .030) independently predicted TOT when analyzed as a covariate with case type (P = .410). The TOT with same case type and vendor (mean 38.2 minutes; range, 22 to 62) was less than that of different case types and vendors (mean 41.4 minutes; range, 26 to 73) (P = .017). Mean TOT differed by 5.5 minutes when keeping all variables the same versus all different (P = .018). ConclusionsMaintaining a consistent case type, vendor, and laterality had a synergistic effect in reducing TOT in arthroplasty ORs with the same primary surgeon running 2 overlapping rooms. Changing vendor representatives was found to independently predict TOT increases, which is likely attributed to a disruption in workflow and collaboration of the multidisciplinary OR team. Level of EvidenceLevel III.

Energy Efficiency in Hospitals –towards Sustainable Healthcare

Energy efficiency plays a crucial role in the healthcare sector, particularly in hospitals, where the demand for energy-intensive operations is high. This essay explores the significance of energy efficiency in hospitals and its impact on sustainable healthcare. It examines the challenges faced by hospitals in achieving energy efficiency, the potential benefits, and the strategies and technologies that can be employed to improve energy efficiency. Furthermore, the essay highlights the role of stakeholders, policymakers, and healthcare professionals in promoting energy efficiency practices to ensure a greener and more sustainable future for hospitals.

Effects of private health insurance on waiting time in public hospitals.

The Australian government pays $6.7 billion per year in rebates to encourage Australians to purchase private health insurance (PHI) and an additional $6.1 billion to cover services provided in private hospitals. What is the justification for large government subsidies to a private industry when all Australians already have free coverage under Medicare? The government argues that more people buying PHI will relieve the burden on the public system and may reduce waiting times. However, the evidence supporting this is sparse. We use an instrumental variable approach to study the causal effects of higher PHI coverage in the area on waiting times in public hospitals in the same area. The instrument used is area-level average house prices, which correlate with average income and wealth, thus influencing the purchase of PHI due to tax incentives, but not directly affecting waiting times in public hospitals. We use 2014-2018 hospital admission and elective surgery waiting list data linked at the patient level from the Victorian Center for Data Linkage. These data cover all inpatient admissions in all hospitals in Victoria (both public and private hospitals) and those registered on the waiting list for elective surgeries in public hospitals in Victoria. We find that one percentage point increase in PHI coverage leads to about 0.34days (or 0.5%) reduction in waiting times in public hospitals on average. The effects vary by surgical specialities and age groups. However, the practical significance of this effect is limited, if not negligible, despite its statistical significance. The small effect suggests that raising PHI coverage with the aim to taking the pressure off the public system is not an effective strategy in reducing waiting times in public hospitals. Alternative policies aiming at improving the efficiency of public hospitals and advancing equitable access to care should be a priority for policymakers.

A Study on Decision-Making for Improving Service Efficiency in Hospitals

The provision of efficient healthcare services is essential, driven by the increasing demand for healthcare resources and the need to optimize hospital operations. In this context, the motivation to innovate and improve services while addressing urgent concerns is critical. Hospitals face challenges in managing internal dispatch services efficiently. Outsourcing such services can alleviate the burden on hospital staff, reduce costs, and introduce professional expertise. However, the pressing motivation lies in enhancing service quality, minimizing costs, and exploring innovative approaches. With the rising demand for healthcare services, there is an immediate need to streamline hospital operations. Delays in internal transportation services can have far-reaching implications for patient care, necessitating a prompt and effective solution. Drawing upon dispatch data from a healthcare center in Taiwan, this study constructed a decision-making model to optimize the allocation of hospital service resources. Employing simulation techniques, we closely examine how hospital services are currently organized and how they work. In our research, we utilized dispatch data gathered from a healthcare center in Taichung, Taiwan, spanning from January 2020 to December 2020. Our findings underscore the potential of an intelligent dispatch strategy combined with deployment restricted to the nearest available workers. Our study demonstrates that for cases requiring urgent attention, delay rates that previously ranged from 5% to 34% can be notably reduced to a much-improved 3% to 18%. However, it is important to recognize that the realm of worker dispatch remains subject to a multifaceted array of influencing factors. It becomes evident that a comprehensive dispatching mechanism must be established as part of a broader drive to enhance the efficiency of hospital service operations.

Analysis of the Operational Efficiency of Public Hospitals in Tianjin City based on a Three-Stage Data Envelopment Analysis Model

The purpose is to analyze the operational efficiency of 132 public hospitals in Tianjin in 2021, providing reference for them to fully play the role of "leading institutions" and optimize resource allocation. We constructed an evaluation index system with multiple inputs, outputs, and environmental variables and utilized a three-stage DEA model to assess efficiency by removing the influence of random errors and environmental factors. The average comprehensive technical efficiency, pure technical efficiency, and scale efficiency of public hospitals were 0.865, 0.989, and 0.875, respectively. Among them, seven districts, including Heping, Hedong, and Hexi, had a comprehensive technical efficiency of 1. Among the remaining nine districts, except for Baodi, the rest showed an increasing trend in scale efficiency. Public hospitals in Tianjin City exhibit high operational efficiency, but significant variations exist among different districts, leading to uneven development. The main reason for this disparity is the presence of scale inefficiency. It is recommended to prioritize resource optimization and address the shortcomings that hinder efficiency improvement in a targeted manner. Emphasizing the "triple integration" of service capacity, efficiency, and quality in public hospitals is essential to maximize both social and economic benefits."

Do VIP medical services damage efficiency? New evidence of medical institutions' total factor productivity using Chinese panel data.

This study examines the causal impact of very important person (VIP) medical services on hospital total factor productivity in Deyang, a prefectural-level city in western China, spanning the years 2015-2020. This aims to offer empirical evidence and policy recommendations for the implementation of VIP practices in the medical field. A secondary unbalanced panel dataset of 416 observations was collected from the annual reports of the Health Commission and 92 eligible medical institutions were included. This study utilized a two-stage strategy. First, the Global Malmquist index was used to calculate the total factor productivity and its decomposition terms for hospitals from 2015 to 2020. In the second stage, two-way fixed effects models and Tobit models were used to identify the relationship between VIP medical services and hospital efficiency; instrumental variables were used to solve potential endogeneity problems in the model. The results showed that VIP medical services had a significantly negative impact on medical institutions' efficiency. The technological advances and pure technical efficiency related to VIP medical care may help explain these negative impacts, which were heterogeneous across groups divided by the nature of the hospital and the outside environment. It is imperative to prioritize the standardized provision of VIP medical services for medical institutions, optimize management and service process, enhance the training of clinical and scientific research capabilities of medical personnel, and scientifically allocate resources for both VIP and general medical services. This will help mitigate health inequality while improving the overall quality of medical services.

Evaluation and ranking of fuzzy sets under equivalence fuzzy relations as [formula omitted]certainty and [formula omitted]possibility

In this paper, we deal with a finite collection of fuzzy sets that are used to represent a given linguistic variable in real-world problems. These fuzzy sets are usually represented as fuzzy singletons, fuzzy numbers, or fuzzy G-numbers. In many cases, there are significant interactions between elements of a given set, so it is useful to evaluate and rank elements while taking the degree of interactions into consideration. To find the degree of interaction, we develop a fuzzy equivalence relation based on a similarity function and a triangular norm operator. The compositions of the (max -(T-norm)) and (min -(S-norm)) cause the membership function to change from the minimum values (certainty version) to the maximum values (possibility version). These concepts are also useful for improving fuzzy data mining, rough fuzzy set theory, etc. Furthermore, two indices, α and β, are introduced to determine the degree of certainty (α-certainty) and degree of possibility (β-possibility). Examples are provided to compare the proposed method with the existing method. After comparing the experimental results, it can be concluded that the proposed approach is satisfactory for many situations. Finally, we apply the proposed methodology to evaluate the results of a hospital efficiency study conducted for the Office of Inspector General(OIG)-Eastern Virginia Department of Health and Human Services.

The residency levels' effect on pediatric dental rehabilitation operation time

BackgroundPostgraduate pediatric dental residents' competency, to perform dental rehabilitation procedures under General anesthesia (GA), at different levels of training is challenging for operation time control. An adequate operation time (OT) for children decreases morbidity risk and improves hospital time utilization efficiency. The aim of the study is to assess the effect of pediatric dental resident training level on OT for pediatric dental rehabilitation procedures under GA at King Abdulaziz Medical City (KAMC).MethodsA cross-sectional study included pediatric dental rehabilitation performed under GA by pediatric dental residents at (KAMC) -Jeddah from October/2015 to September/2022. The primary outcome was OT, and the predictive variable was resident training levels. A linear regression analysis was used to compare OT between procedures performed by junior (years 1–2) or senior (years 3–4) trainees, adjusting for patient and operative factors.ResultsOne thousand seven pediatric dental rehabilitation cases were performed under GA by junior (13) and senior (31) residents. The univariant analysis indicated that OT for senior residents was significantly longer (13 min) than for junior residents. However, the linear regression analysis showed that senior residents had a significantly shorter OT when considering the more dental procedures performed per case under GA than junior residents. Senior residents took significantly more radiographs and performed more primary pulp therapies and multi-surface anterior colored restorations under GA than junior residents.ConclusionsThe OT for pediatric dental rehabilitation procedures under GA is associated with resident training level. The total OT was significantly longer based on procedure number, type, and resident level. The study indicated that senior residents could manage more complex cases in a shorter time. The finding emphasizes the importance of assigning GA cases to residents based on their level and the case's complexity. Additionally, it helps standardize the resident privileges under GA and understand the impact of residency training on hospital efficiency.

Is austerity good for efficiency, at least? A counterfactual assessment for the Italian NHS

In recent decades, austerity measures have been widely adopted in public healthcare systems, so as to cope with financial constraints. This paper assesses the impact of a specific austerity policy implemented in Italy since 2007, with the purpose of reducing the budget deficit of regional governments originated by an excessive healthcare expenditure, the so called Recovery Plans (Piani di rientro). We exploit this context for a counterfactual analysis of the effects of Recovery Plans on the technical efficiency of hospitals operating in the regions where the Plans have been implemented, using as a control group similar hospitals in the regions where the policies were not enacted. The empirical analysis is based on a unique sample of administrative data relative to a large panel of hospitals in the period 2003–2010, and it employs, as identification strategy, the exogenous introduction of the austerity policy in some regions. We find that the policy had a detrimental effect on the efficiency of the hospitals operating in the regions subjected to the policy. The results show that the efficiency loss grows over time, suggesting the existence of negative cumulative effects of the austerity policy.

New Public Management and hospital efficiency: the case of Norwegian public hospital trusts

New Public Management-inspired reforms in the Norwegian hospital sector have introduced several features from the private sector into a predominantly public healthcare system. Since the late 1990s, several reforms have been carried out with the intention of improving the utilization of resources. There is, however, limited knowledge about the long-term, and sector-wide effects of these reforms. In this study, using a panel data set of all public hospital trusts spanning nine years, we provide an analysis of the efficiency of hospital trusts using data envelopment analysis (DEA), as well as a Malmquist productivity index. Thereafter we use the efficiency scores as the dependent variable in a second-stage panel data regression analysis. We show that during the period between 2011 and 2019, on average, efficiency has increased over time. Further, in the second-stage analysis, we show that New Public Management features related to incentivization are associated with the level of hospital efficiency. We find no association between degree of competition and efficiency.

A Study on the Effectiveness and Efficiency of Public or Private Hospitals within Pakistan

Hospitals play a very important role in human lives. People are in search of quality and timely healthcare facilities. This paper talks about the efficiency and effectiveness of healthcare services. One needs to identify the factors and mechanisms to enhance the performance of government and private hospitals. This study aims to find the important variables that were required to find the best solution for public and private hospitals in Pakistan. The major contributions of the paper are as follows. The paper first deliberates on the need for technical excellence as compared to interpersonal excellence for hospital management. It also performs a qualitative review of the literature on the important variables for determining the effectiveness and efficiencies of the hospital. Then, the paper presents a public survey about the effectiveness and efficiency of public and private hospitals using different questionnaires. These questionnaires were filled by the relatives of patients or patients themselves when they visited hospitals. On this dataset, the paper applies a machine learning algorithm i.e., random forest, to predict which hospital type is suitable for them while considering the variables. These variables include; the services of the hospital, admission process, treatment, doctor's behavior, timely treatment, and knowledge of the staff about SOPs. The data was split into 75 % training and 25 % testing dataset. Python’s Library SK-learn was used for implementation. The accuracy of the classifier on the test dataset is 96.91 %. The paper then determines the variables that are contributing the most to the measure of effectiveness and efficiencies of hospitals. The algorithm also ranks these features that can be used to improve a hospital's performance. It also provides a benchmark to the patients in the selection of hospitals for healthcare facilities.