Hospital productivity: The role of efficiency drivers

Performance measurement of Brazilian federal university hospitals: an overview of the public health care services through principal component analysis

PURPOSE

This paper aimed to contextualize the process of public hospital providing services, based on the measurement of the performance of Federal University Hospitals (HUFs) of Brazil, using the technique of multivariate statistics of principal component analysis.

DESIGN/METHODOLOGY/APPROACH

This research presented a descriptive and quantitative character, as well as exploratory purpose and followed the inductive logic, being empirically structured in two stages, that is, the application of principal component analysis (PCA) in four healthcare performance dimensions; subsequently, the full reapplication of principal component analysis in the most highly correlated variables, in module, with the first three main components (PC1, PC2 and PC3).

FINDINGS

From the principal component analysis, considering mainly component I, with twice the explanatory power of the second (PC2) and third components (PC3), it was possible to evidence the efficient or inefficient behavior of the HUFs evaluated through the production of medical residency, by specialty area. Finally, it was observed that the formation of two groups composed of seven and eight hospitals, that is, Groups II and IV shows that these groups reflect similarities with respect to the scores and importance of the variables for both hospitals' groups.

RESEARCH LIMITATIONS/IMPLICATIONS

Among the main limitations it was observed that there was incomplete data for some HUFs, which made it impossible to search for information to explain and better contextualize certain aspects. More specifically, a limited number of hospitals with complete information were dealt with for 60% of SIMEC/REHUF performance indicators.

PRACTICAL IMPLICATIONS

The use of PCA multivariate technique was of great contribution to the contextualization of the performance and productivity of homogeneous and autonomous units represented by the hospitals. It was possible to generate a large quantity of information in order to contribute with assumptions to complement the decision-making processes in these organizations.

SOCIAL IMPLICATIONS

Development of public policies with emphasis on hospitals linked to teaching centers represented by university hospitals. This also involved the projection of improvements in the reach of the efficiency of the services of assistance to the public health, from the qualified formation of professionals, both to academy, as to clinical practice.

ORIGINALITY/VALUE

The originality of this paper for the scenarios of the Brazilian public health sector and academic area involved the application of a consolidated performance analysis technique, that is, PCA, obtaining a rich work in relation to the extensive exploitation of techniques to support decision-making processes. In addition, the sequence and the way in which the content, formed by object of study and techniques, has been organized, generates a particular scenario for the measurement of performance in hospital organizations.

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.

Association of Hospital Bed Turnover With Patient Outcomes in Digestive Surgery

To determine the influence of hospital bed turnover rate (BTR) on the occurrence of complications following minor or major digestive surgery.

Background

Performance improvement in surgery aims at increasing productivity while preventing complications. It is unknown whether this relationship can be influenced by the complexity of surgery.

Methods

A nationwide retrospective cohort study was conducted, based on generalized estimating equation modeling to determine the effect of hospital BTR on surgical outcomes, adjusting for patient mix and clustering within 631 public and private French hospitals. All patients who underwent minor or major digestive surgery between January 1, 2013 and December 31, 2018 were included. Hospital BTR was defined as the annual number of stays per bed for digestive surgery and categorized into tertiles. The primary endpoint was a composite measurement of events occurring within 30 days after surgery: inpatient death, extended intensive care unit (ICU) admission, and reoperation.

Results

Rate of adverse events was 2.51% in low BTR hospitals versus 2.25% in high BTR hospitals for minor surgery, and 16.79% versus 16.83% for major surgery. Patients who underwent minor surgery in high BTR hospitals experienced lower complications (odds ratio [OR], 0.89; 95% confidence interval [CI], 0.81-0.97; P = 0.009), mortality (OR, 0.87; 95% CI, 0.78-0.98, P = 0.02), ICU admission (OR, 0.83; 95% CI, 0.70-0.99; P = 0.03), and reoperation (OR, 0.91; 95% CI, 0.85-0.97; P = 0.002) compared to those in low BTR hospitals. Such differences were not consistently observed among patients admitted for major surgery.

Conclusions

High turnover of patients in beds is beneficial for minor procedures, but questionable for major surgeries.

The application of machine learning algorithms in predicting the length of stay following femoral neck fracture

PURPOSE

Femoral neck fracture is a frequent cause of hospitalization, and length of stay is an important marker of hospital cost and quality of care provided. As an extension of traditional statistical methods, machine learning provides the possibility of accurately predicting the length of hospital stay. The aim of this paper is to retrospectively identify predictive factors of the length of hospital stay (LOS) and predict the postoperative LOS by using machine learning algorithms.

METHOD

Based on the admission and perioperative data of the patients, linear regression was used to analyze the predictive factors of the LOS. Multiple machine learning models were developed, and the performance of different models was compared.

RESULT

Stepwise linear regression showed that preoperative calcium level (P = 0.017) and preoperative lymphocyte percentage (P = 0.007), in addition to intraoperative bleeding (p = 0.041), glucose and sodium chloride infusion after surgery (P = 0.019), Charlson Comorbidity Index (p = 0.007) and BMI (P = 0.031), were significant predictors of LOS. The best performing model was the principal component regression (PCR) with an optimal MAE (1.525) and a proportion of prediction error within 3 days of 90.91%.

CONCLUSION

Excessive intravenous glucose and sodium chloride infusion after surgery, preoperative hypocalcemia, preoperative high percentages of lymphocytes, excessive intraoperative bleeding, lower BMI and higher CCI scores were related to prolonged LOS by using linear regression. Machine learning could accurately predict the postoperative LOS. This information allows hospital administrators to plan reasonable resource allocation to fulfill demand, leading to direct care quality improvement and more reasonable use of scarce resources.

Investigating the link between medical urgency and hospital efficiency - Insights from the German hospital market

With hospital budgets remaining tight and healthcare expenditure rising due to demographic change and advances in technology, hospitals continue to face calls to contain costs and allocate their resources more efficiently. In this context, efficiency has emerged as an increasingly important way for hospitals to withstand competitive pressures in the hospital market. Doing so, however, can be challenging given unpredictable fluctuations in demand, a prime example of which are emergencies, i.e. urgent medical cases. The link between medical urgency and hospitals' efficiency, however, has been neglected in the literature to date. This study therefore aims to investigate the relationship between hospitals' urgency characteristics and their efficiency. Our analyses are based on 4094 observations from 1428 hospitals throughout Germany for the years 2015, 2016, and 2017. We calculate an average urgency score for each hospital based on all cases treated in that hospital per year and also investigate the within-hospital dispersion of medical urgency. To analyze the association of these urgency measures with hospitals' efficiency we use a two-stage double bootstrap data envelopment analysis approach with truncated regression. We find a negative relationship between the urgency score and hospital efficiency. When testing for non-linear effects, the results reveal a u-shaped association, indicating that having either a high or low overall urgency score is beneficial in terms of efficiency. Finally, our results reveal that higher within-hospital urgency dispersion is negatively related to efficiency.

An integrated MUSA to measure health care service quality from a patient's perspective in a resource-constrained setting

PURPOSE

Although assessing the quality of health services offered in a least developed country such as Yemen is very important, it is not yet given attention. As a result, Yemeni patients started to look for higher quality of health services abroad. Thus, Yemeni health private providers need to know how to link their patients' satisfaction to the quality of the services offered to end up with more satisfied patients and higher health service quality offered.

METHODOLOGY

Data were collected form 5310 patients in 249 private clinics. The patients evaluated their satisfaction on the quality of service on the basis of nine criteria that comprised 31 subcriteria. We used multicriteria satisfaction analysis (MUSA) to analyze the data.

FINDINGS AND CONCLUSION

The data analysis results showed low level of satisfaction on the health care quality services offered by the private clinics in Yemen. The majority of the criteria and subcriteria showed low level of satisfaction, high demand, and high mandate for improvement.

Combining regression trees and panel regression for exploring and testing the impact of complementary management practices on short-notice elective operation cancellation rates

Variation in the performance of providers across healthcare systems is pervasive. It is recognised as both a major concern and an opportunity for learning and improvement. Variation between providers is broadly considered to be due to management practices and contextual factors such as catchment-area demographics. However, there is little understanding of the ways in which these impact on performance and how they can be measured. We use recent developments in both regression trees and panel regression techniques to explore and then statistically test complementary alignments of management practices whilst taking into account contextual factors. We apply this to 5 years of NHS hospital trust data, examining performance on short-notice cancellation rates. We find that different alignments of management practices give rise to quite different short-notice cancellation rates between trusts, with some being substantially lower. Our research offers a data-driven approach for identifying optimal clusters of management practices.