Search results

As hospital costs continue to rise, increasing attention is being paid to the way these organizations are and should be managed. This attention typically comes in the form of focus on costs of services, quality (often measured through mortality rates) and length of stay. Hospital management has a broad array of choices at their disposal to address these challenges. As service operations, hospitals present a significant opportunity to apply the many tools and techniques from the field of operations strategy to this important industry. The objective of this paper is to use the operations strategy framework to assess the relationship between a set of operational elements and hospital performance in terms of average length of stay (ALOS), so that hospital managers improve the effectiveness and efficiency of patient care of their hospitals.

Using the structural and infrastructural operations strategy framework, this study examines the relationship between several strategic variables and hospital performance. To analyze these relationships the paper employs data from the population of hospitals in New York State. The performance measure is the ALOS for patients, adjusted for the mix and severity of cases in each hospital.

The paper finds that a direct relationship exists between the dependent variable and location, capacity, and teaching status, and failed to find a direct relationship for capital expenditures, salary, and staffing levels. However, the paper did find significant interaction effects between capital expenses and both salary and staffing levels.

There appear to be trade‐offs between capital expenditures and workforce decisions that have significant implications in light of current and expected hospital staffing shortages. The findings indicate that reductions in staff may not be perfectly replaced by corresponding increases in capital expenditures.

This paper further expands the body of research that addresses the important challenges hospitals face from an operations management perspective.

The purpose of this paper is to investigate the quality of management practices of public hospitals in the Australian healthcare system, specifically those in the state-managed health systems of Queensland and New South Wales (NSW). Further, the authors assess the management practices of Queensland and NSW public hospitals jointly and globally benchmark against those in the health systems of seven other countries, namely, USA, UK, Sweden, France, Germany, Italy and Canada.

In this study, the authors adapt the unique and globally deployed Bloom et al. (2009) survey instrument that uses a “double blind, double scored” methodology and an interview-based scoring grid to measure and internationally benchmark the management practices in Queensland and NSW public hospitals based on 21 management dimensions across four broad areas of management – operations, performance monitoring, targets and people management.

The findings reveal the areas of strength and potential areas of improvement in the Queensland and NSW Health hospital management practices when compared with public hospitals in seven countries, namely, USA, UK, Sweden, France, Germany, Italy and Canada. Together, Queensland and NSW Health hospitals perform best in operations management followed by performance monitoring. While target management presents scope for improvement, people management is the sphere where these Australian hospitals lag the most.

This paper is of interest to both hospital administrators and health care policy-makers aiming to lift management quality at the hospital level as well as at the institutional level, as a vehicle to consistently deliver sustainable high-quality health services.

This study provides the first internationally comparable robust measure of management capability in Australian public hospitals, where hospitals are run independently by the state-run healthcare systems. Additionally, this research study contributes to the empirical evidence base on the quality of management practices in the Australian public healthcare systems of Queensland and NSW.

The purpose of this paper is to investigate the determinants of best management practices in an Australian state-run healthcare system, namely New South Wales (NSW), and studies the impact of a range of hospital factors in driving best management practices as a means of enhancing healthcare delivery.

This study adapts a unique survey instrument globally tested to quantify the multi-dimensional nature of hospital management practices in 42 acute care public hospitals of NSW. The authors then analysed the role of hospital-specific characteristics in driving best management practices, namely hospital size (measured by the number of hospital beds, employees and doctors), level of skill and education, degree of hospital manager autonomy and organisational hierarchy.

The findings of this study show the areas of strength and potential areas of improvement in NSW hospitals. The authors find a positive association between the adoption of better management practices and hospital size (measured by the number of hospital beds and employees), level of skills and education, degree of hospital manager autonomy and organisational hierarchy. However, hospital size as measured by the number of doctors did not have a statistically significant relationship.

This paper is of interest to both hospital administrators, clinical doctors and healthcare policy-makers who want to improve and develop strategies for better management in the healthcare sector.

This study provides an internationally comparable robust measure of management capability in public hospitals, and contributes to the evidence-base of management practices and performance in hospitals.

The purpose of this paper is to focus on the contributions of queueing theory to hospital capacity management to improve organizational performance and deal with increased demand in the healthcare sector.

Models were applied to six months of inpatient records from a university hospital to determine operation measures such as utilization rate, waiting probability, estimated bed capacity, capacity simulations and demand behavior assessment.

Irrespective of the findings of the queueing model, the results showed that there is room for improvement in capacity management. Balancing admissions and the type of patient over the week represent a possible solution to optimize bed and nurse utilization. Patient mixing results in a highly sensitive delay rate due to length of stay (LOS) variability, with variations in both the utilization rate and the number of beds.

The outcomes suggest that operational managers should improve patient admission management, as well as reducing variability in LOS and in admissions during the week.

The queueing theory revealed a quantitative portrait of the day-by-day reality in a fast and flexible manner which is very convenient to the task of management.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

Radio-frequency identification (RFID) systems are transforming hospital operations by improving care and reducing costs. However, hospital managers have some concerns about adopting RFID technologies. These are cost, return on investment, privacy and other technology issues. This study examines RFID adoption in health-care industry based on the Technology-Organization-Environment framework. This paper aims to demonstrate how US hospitals are adopting RFID technologies and make recommendations, using empirical data.

An online survey was sent to Chief Information Officers and the top information technology (IT) executives of US acute care hospitals to determine the extent to which hospitals have implemented RFID technologies including the reasons, concerns and applications of RFID technologies, and the antecedents of RFID adoption in hospitals.

The study found that a high percentage of respondents have adopted or are considering adopting RFID technology as a new management tool. Organizational and technological factors have strong positive influence on adoption, whereas environmental factors do not significantly affect the adoption decisions. The main reasons for implementing RFID technology are improved hospital operations, including inventory management, asset control, workflow and patient flow. Some of the concerns are cost of RFID tags and cost associated with implementation.

This study is limited to a small sample (n = 86) of US hospitals. This study should continue to be replicated in the future, as adoption of RFID technologies matures in the health-care sector.

This study informs health-care managers on the challenges and opportunities of RFID by identifying motivations, barriers and issues related to RFID adoption. The findings of this study will expand the emerging literature and provide direction to academicians for further research in technology adoption.

In the face of growing demand for care and tightening resource constraints, hospitals need to ensure access to care that is affordable and effective. Yet, the multiplicity of objectives is a key challenge in this industry. An understanding of the interrelationships (tradeoffs) between the multiple outcome objectives of care (throughput, experiential and financial performance) and returns to operational inputs (diversification of care) is fundamental to improving access to care that is effective and affordable. This study serves to address this need.

The empirical analysis in the study builds on an output-oriented distance function model and uses a longitudinal panel dataset from 153 hospitals in California.

This study results point to key insights related to output–output tradeoffs along the production frontier. Specifically, the authors find that higher throughput rates may lead to significantly lower levels of experiential quality, and net revenue from operations, accounting for the clinical quality of care. Similarly, the authors’ findings highlight the resource intensity and operational challenges of improving experiential quality of care. In regards to input–output relationships, this study finds diversification of care is associated with increased throughput, improvements in service satisfaction and a corresponding increase in the net revenue from operations.

Highlighting the tradeoffs along the production frontier among the various outcomes of interest (throughput, experiential quality and net revenue from operations), and highlighting the link between diversification of care and care delivery outcomes at the hospital level are key contributions of this study. An understanding of the tradeoffs and returns in healthcare delivery serves to inform policy-making with key managerial implications in the delivery of care.

Overall equipment effectiveness (OEE) is a performance indicator that is been used to measure manufacturing productivity. The purpose of this paper is to propose the operating room effectiveness for hospital operating rooms (ORs), adapted from the OEE, to measure performance and identify losses based on lean health-care principles.

The present study is an exploratory, descriptive and applied research work. Literature review, documents of the hospital, observation and interviews with employees of a large university hospital in southern Brazil were analyzed to organize the proposed effectiveness indicator. After that, historical data of the ORs was collected and the usefulness of the indicator was analyzed. The indicator was applied for 10 months and validated with an expert committee from the hospital.

The present study describes an adaptation of a performance indicator to ORs of hospitals, allowing to classify its types of operational losses in a lean health-care context. The application of this indicator and the development of improvement actions to a university hospital, resulted in operational efficiency gains of 12 per cent and estimated annual savings of US$400,000.

ORs are a critical service for hospitals. This paper presents a new way to measure the performance of ORs and identify their main types of wastes. It also shows how to implement it and the potential gains of its application. The main research limitations are related to technical analysis of care data from doctors and nurses involved.

This paper fulfills the need to study how ORs performance can be measured and its operational wastes can be identified. In addition, this paper classifies the planning, performance and quality related losses, which can be used by researchers and practitioners to improve the performance of operation rooms.

The purpose of this study was to investigate how Lean Six Sigma (LSS) may help mitigate the impact of COVID-19 within health care environments. The goals of this study were to understand the current knowledge of LSS and COVID-19 through a systematic review of the current literature, identify the gap in the current knowledge of LSS in COVID-19 mitigation within health care environments and define the principles of LSS, within organizational resilience that support a health care organization’s ability to mitigate the impact of COVID-19.

A narrative literature review was conducted to identify relevant research. A total of 21 subject matter experts (SMEs) meeting the inclusion criteria were approached through a guided interview process. Content analysis was conducted to describe how LSS principles contribute to supporting health care organizations operating in the era of COVID-19.

Study results report that personal safety is the primary subject, followed by supporting dimensions of process redesign, and telemedicine. LSS topics that directly relate to COVID-19 are in four thematic areas: tools, applications, benefits and challenges. Particular areas of application, techniques, challenges and benefits are identified and discussed that could be applied proactively and reactively, to organizational and supply chain resilience to recover from COVID-19.

There were a number of limitations to the generalizability of this work. The sample size was small and purposeful, thus, external validity of the study results are not determined. The SMEs in this study have not implemented the practices noted in the results at the time of the study, and knowledge of results is limited to the study aims.

This study of LSS principles and COVID-19 has implications for practitioners and offers specific guidance for areas of health care adoption of LSS techniques and tools that benefit patient safety, challenges for the user to be mindful of and potential benefits in resilience of operations in the era of COVID-19.

This research aims to explore the fine-grained relationships between nurse staffing and hospital operational performance with respect to care quality and operating costs. The authors also investigate the moderation effect of competition in local hospital markets on these relationships.

A six-year panel data is assembled from five separate sources to obtain information of 2,524 USA hospitals. Fixed-effect (FE) models are used to test the proposed hypotheses.

First, nurse staffing is initially associated with improved care quality until nurse staffing reaches a turning point, beyond which nurse staffing is associated with worse care quality. Second, a similar pattern applies to the relationship between nurse staffing and operating costs, although the turning point is at a much lower nurse staffing level. Third, market competition moderates the relationship between nurse staffing and care quality so that the turning point of nurse staffing will be higher when the degree of competition is higher. This shift of turning point is also observed in the relationship between nurse staffing and operating costs.

The study identifies three ranges of nurse staffing in which hospitals will likely experience simultaneous improvements, a tradeoff or simultaneous decline of care quality and operating costs when investing in more nursing capacity. Hospitals should adjust nurse staffing levels to the right directions to achieve better care or reduce operating costs.

Nurses constitute the largest provider group in hospitals and profoundly impact care quality and operating costs among all health care professionals. Optimizing the level of nurse staffing, therefore, can significantly impact the care quality and operating costs of hospitals.