Hospital quality leaders and clinicians in urology receive conflicting signals from hospital rankings, such as the US News & World Report (USNWR), which reports prostatectomy patients to target for quality improvement. Our objective was to develop a clinically coherent cohort definition for radical prostatectomy spanning inpatient and outpatient settings and to assess overlap with USNWR prostate cancer surgery cohorts. This is a cross-sectional, multisite study of all consecutive radical prostatectomies performed within a large, integrated US health system in 2024. We created an institutional definition of prostate cancer surgery inclusive of inpatient and outpatient procedures across all payers, requiring an ICD (International Classification of Diseases)-10 diagnosis code for prostate cancer (C61) plus a procedural code for radical prostatectomy (Common Procedural Terminology 55810, 55812, 55815, 55840, 55842, 55845, or 55866, or ICD-10 procedure codes with clinical confirmation). We then reconstructed the 3 USNWR prostate cancer surgery cohorts: (1) outpatient volume, (2) outpatient potentially preventable complications, and (3) inpatient measures. Our main outcome was percent agreement between the institutional prostatectomy cohort and each USNWR comparator cohort. Only 716/1591 (45%) radical prostatectomies performed at our institution were captured in at least 1 USNWR definition, 613/1591 (39%) were captured by the USNWR potentially preventable outpatient complications definition, and 97 (6.1%) were captured by the inpatient outcomes definition. USNWR prostatectomy cohorts capture less than half of clinically relevant cases. More inclusive, clinically coherent definitions-such as our institutional cohort-are needed to reliably benchmark hospital performance in radical prostatectomy.

The research examines the mediating role (through digital technologies) of the relationship between social media and hospitality performance in Jordan. As the scope of using online platforms to engage customers, their services, and communication with brand has intensified, knowledge of their direct and indirect effects on performance has become more relevant particularly as the digital transformation redefines operations within the hospitality sector. The study was done with a quantitative, cross-sectional research design where a structured questionnaire was given to 167 professionals working in hospitality related organizations in Jordan. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the received data. It has been established that social media has strong direct influence on the performance of the hospitality industry, and its influence seems to be better mediated by means of the use of digital technologies. It is worth mentioning that the digital integration proves to be a strong mediator that has contributed to the impact of social media on customer satisfaction, service quality, and operational efficiency. These findings highlight the strategic value of integrating digital technologies in the social media models in a bid to initiate sustainable performance gains. The study adds value to the literature in that it empirically proves the concept of digital integration as a dynamic capability, as well as introduces a model that can be applied to the development policy of the hospitality sector in emerging economies. It also provides valuable professional knowledge to managers and policymakers in the hospitality industry by indicating that they should invest in the infrastructural development of technology, digital solutions, and workforce skills to take full advantage of the potential of social media. Through digital innovations coupled with social media strategies, the hospitality organizations in the emerging economies such as Jordan will be in a position to leverage sustainable growth and competitive edge in the digital age.

Diagnostic uncertainty in sepsis contributes to both undertreatment (delayed antibiotics) and overtreatment (unnecessary early antibiotics in non-sepsis patients). We hypothesized hospitals with high relative performance on sepsis undertreatment and overtreatment measures have improved patient outcomes. We conducted a retrospective cohort study of hospitalized adults at 20 hospitals (Advocate Health, Michigan Medicine) between 2022-2024. Patients met ≥2 systemic inflammatory response syndrome criteria within 6 hours of presentation. Hospital-level diagnostic performance was characterized using risk-standardized rates (RSR) for undertreatment and overtreatment. "Diagnostic Excellence" (DxEx) was prespecified as top-tertile performance in one measure without bottom-tertile performance in the other. The primary outcome was hospital mortality or hospice discharge. Secondary outcomes were hospital-free days alive at 28 days (HFD), length of stay (LOS), and total antibiotic days. Among 152,779 patients, 49,109 (32.1%) received antibiotics within 3 hours and 16,361 (10.7%) ultimately met sepsis criteria. Undertreatment RSRs ranged 29.3%-46.8%. Overtreatment RSRs ranged 21.5%-37.2%. There was negative correlation between performance ranks (τ=-0.51, p=0.01). Four hospitals met DxEx criteria. Receiving treatment at DxEx hospitals was associated with lower odds of hospital mortality or hospice discharge (adjusted OR=0.61, 95% Confidence Interval [95%CI]=0.41-0.90; p=0.01), greater HFD (adjusted difference=0.59, 95%CI=0.04-1.14; p=0.04), reduced LOS (adjusted difference=-1.20, 95%CI=-2.23- -0.17; p=0.02), and fewer antibiotic days (adjusted difference=-0.62, 95%CI=-1.28-0.05; p=0.07), versus non-DxEx hospitals. Hospital performance that jointly minimizes sepsis undertreatment and overtreatment was associated with improved patient outcomes, after adjustment for patient and hospital factors. These findings highlight the importance of reducing diagnostic error in efforts to improve sepsis outcomes.

Inpatient antimicrobial stewardship programs (ASPs) promote avoiding unnecessary initiation, excessively long duration, and overly broad-spectrum selection of antimicrobials, but currently available metrics do not adequately reflect or distinguish these three components. We aimed to develop a novel framework that captures them while providing information specific to each, based on Days of Antimicrobial Spectrum Coverage (DASC) and length of therapy (LOT). We developed a three-level conditional modeling framework to extract hospital-level variability, risk-adjusted for three components (initiation, duration, and spectrum). This was applied to data from 118 Veterans Health Administration (VHA) hospitals, with models built on 2022-2023 data and validated with 2024 data. Patient demographics, intensive care status, admitting specialty, 86 comorbidities, and 224 procedure categories were considered candidate variables for risk adjustment. Overall hospital performance was evaluated using a composite metric that integrated three components ("S3 [start, stop, select] Metric") and was visualized in a radar chart for each hospital. The cohort included 727,958 unique patients with 9,363,922 days present. Hospital-level usage ranged widely (DASC per 1,000 DP: 1,311-5,275 [interquartile range (IQR): 2,738-3,563]; LOT per 1,000 DP: 132.2-517.6 [IQR: 301.0-367.5]). Risk-adjustment models included 116 variables for initiation, 77 for duration, and 91 for spectrum components. S3 metrics ranged from 0.703 to 1.572 (IQR: 0.906-1.086). A three-component evaluation could provide specific information on each hospital's usage patterns. We propose a DASC-LOT framework and S3 metric to assess ASP practices in initiation, duration, and spectrum separately while providing overall composite benchmarking.

This study investigates the role of complementary clusters of human resource management (HRM) practices in explaining the persistent productivity variations across English NHS hospitals. While prior research has highlighted the importance of individual HRM practices, their impact is often indeterminate. A critical gap remains in the systematic analysis of complementarity, non-linearity, and the interdependencies between these practices. To address this gap, we draw on complementarity theory to propose and test a novel framework. This framework posits that hospital performance is shaped by three distinct effects: indeterminacy, where isolated practices have an inconsistent impact; non-linearity, where practices are effective only above a certain intensity threshold; and complementarity, where synergies between practices amplify their collective impact. We use this framework to explain the significant productivity dispersion observed across hospitals. Using panel data from English NHS hospitals, we employ machine learning techniques to identify distinct HRM clusters and test their significance using correlated random effects models. Our findings confirm that individual HRM practices often have no significant effect until their intensity surpasses a critical threshold. Crucially, we identify several distinct complementary clusters involving incentives, workplace flexibility, training, team quality, and job design. High-performing hospitals are distinguished by their adoption of these synergistic bundles-such as combining job design, team quality, and incentives-whereas persistent low performance is linked to the adoption of incomplete clusters. For instance, workplace flexibility or training only boosts productivity when supported by an effective incentive system, underscoring the powerful non-linear and interdependent effects at play. This study advances the HRM and healthcare management literature by systematically demonstrating the non-linear and complementary nature of HRM practices and by introducing data-driven methods for their analysis. The key implication for policymakers and hospital administrators is the need to prioritise bundles of complementary practices and holistic organisational change over isolated, piecemeal initiatives.

This study examines the quality management system of a large hospital in Northern India, utilising the Malcolm Baldrige National Quality Award (MBNQA) framework. It aims to identify both strengths and gaps in the hospital's practices to support continuous improvement and organisational excellence. A structured 76-item questionnaire based on the MBNQA criteria was given to 125 staff members from medical, dental, nursing and paramedical departments. The survey responses were combined with interviews, document reviews and on-site observations. Data were analysed using the scoring system prescribed in the MBNQA model. The hospital achieved a score of 64.3%, placing it in the "moderate" category. Strong performance was observed in operations and leadership, but weaker results were found in workforce focus and strategic planning. The primary issues include limited employee involvement, inadequate adaptability to change, unsatisfactory customer feedback, and a lack of robust financial performance metrics. The findings are based on staff self-reports and do not fully capture patient perspectives, which may limit the depth of insights into service quality. Future research could include multiple hospitals, patient viewpoints and longitudinal studies to better assess changes over time. The results offer hospital leaders a clear roadmap for improvement. Priorities include engaging and recognising staff, strengthening long-term strategic planning, improving financial accountability and enhancing patient satisfaction. Addressing these issues can help build resilience, improve efficiency and enhance the quality of care. This is one of the few empirical studies applying the MBNQA model in Indian healthcare. By combining quantitative scoring with qualitative insights, it provides a comprehensive view of hospital performance rather than focusing on isolated aspects.

The quality of medical services in public hospitals is significantly influenced by managerial styles, which shape organizational efficiency, patient satisfaction, and hospital accreditation outcomes. This study examines the impact of managerial leadership styles and organizational characteristics on accreditation performance in Romanian public hospitals. Using survey data from hospital managers, the study employs a structured analytical approach combining Chi-square tests and cross-tabulations, Kendall's Tau correlations, the Kruskal-Wallis H test, ordinal regression modeling, and exploratory K-means cluster analysis to examine how hospital size, managerial education, employee consultation, team motivation, and access to information are associated with accreditation outcomes. Results indicate that hospital administration type, field of study, and team motivation are significant predictors of accreditation success, while hospital size negatively correlates with accreditation scores, suggesting larger hospitals face greater challenges in meeting accreditation standards. Conversely, employee consultation and information transparency show no statistically significant impact on accreditation performance. These findings highlight the critical role of managerial education and team engagement in achieving higher accreditation outcomes. Strengthening leadership training and team-based quality initiatives could enhance accreditation performance in public hospitals. Policy makers should prioritize managerial development programs and strategic leadership approaches to improve healthcare quality and compliance.

ABSTRACT OBJECTIVES Prehospital routing decisions for patients with suspected stroke are complex, and consideration of actual hospital performance data for door-to-needle and door-to-puncture times for thrombolysis and endovascular thrombectomy (EVT) may impact on-scene decision making. We sought to determine whether optimal routing destinations for patients with suspected stroke in the prehospital setting differ between three strategies: 1) American Heart Association (AHA) consensus recommendations, 2) personalized decision modeling without hospital-specific performance data (base model) and 3) with inclusion of actual hospital data for door-to-needle and door-to-puncture times for thrombolysis and EVT (enhanced model). METHODS Our previously published decision-analytic model (base model) incorporates geographic, patient, and hospital data to determine optimal transport destination, using a standard distribution of thrombolysis and EVT times by hospital type (primary vs comprehensive stroke center). We enhanced the model by incorporating real hospital performance data on thrombolysis and EVT treatment times from all Northeast United States Get with the Guidelines-Stroke hospitals (enhanced model). We generated 500 probable pick-up locations with 800 patient profiles of varying age, stroke severity, and last known well time, giving 400,000 patient-location scenarios. For each scenario we determined the optimal destination based on AHA consensus recommendations and from 1000 simulations each using base and enhanced models to determine the optimal destination for each strategy. We compared destination recommendations and examined conditions under which results changed. RESULTS Of the 400,000 patient-location scenarios, 56.1% and 63.1% of routing destinations were different from the consensus recommendations in the base and enhanced models, respectively, as patients were more often directed to hospitals with faster times to reperfusion. Higher stroke severity and greater odds of large vessel occlusion were associated with concordance of recommendations. CONCLUSIONS Prehospital routing models for patients with suspected stroke may benefit from inclusion of actual hospital performance characteristics.

This study aims to investigate the impact of environmental, social, and governance (ESG) controversies on the performance of private hospitals in China during 2012 to 2023. ESG controversies are measured by the number of ESG controversial issues, private hospital performance is measured by return on assets and return on equity, and fixed effects models are employed. The empirical results reveal that ESG controversies have a negative impact on private hospital performance. This finding is still robust after a series of robustness checks. In addition, heterogeneous analysis shows that this impact is more prominent in hospitals that carry out digital transformation, before and after COVID-19, and in China’s eastern regions. This study will help hospital managers to improve ESG performance and hospital performance through avoiding controversial issues in today’s competitive environment.

Background: Hospital management remains a major challenge for ensuring quality care in a context marked by multiple health issues. This study aimed to identify the components of hospital management and assess the practices and approaches implemented at the District Referral Hospital Center (DRHC) of Moramanga. Methods: An evaluative, descriptive, cross-sectional study with a mixed-methods approach was conducted using retrospective data from 2022 to 2024. All staff members with at least six months of seniority were included. Data were collected through standards-based assessment grids and staff interviews, then analyzed according to the Donabedian model, focusing on structure, process, and outcomes of care. Results: Results showed a very high budget execution rate of approximately 99.5% over the study period, reflecting effective resource management despite a slight decline. Equipment coverage was satisfactory overall, reaching 80.94%. Reception and patient intake services were adequately organized, with six of eight required standards available, including triage systems, dedicated staff, and structured reception areas. Hospital activity trends revealed a marked increase in outpatient consultations in 2023, followed by a slight decline in 2024. Efficiency indicators showed a stable average length of stay of around 2.4 days, while the bed occupancy rate slightly decreased from 20.6 in 2022 to 19 in 2023. The hospital utilization rate also progressively declined from 0.8% to 0.52%. Conclusion: The need to strengthen medical staffing and increase revenue through investments, particularly by creating improved inpatient accommodation conditions, is evident in order to improve the quality of care and hospital performance, while taking into account the specific needs of patients and staff.

Hospitals generate a massive volume of data every day in the form of patient records, doctor information, appointments, treatments, prescriptions, and staff details. Managing and analyzing this large amount of healthcare data efficiently is essential for improving patient care, hospital operations, and administrative decision-making. This paper presents a Hospital Management System developed to automate and streamline various hospital activities through a centralized and integrated platform. The proposed system manages patient registration, doctor and staff information, appointment scheduling, medical records, and treatment details to improve operational efficiency and data accessibility. The system provides secure data storage, efficient information retrieval, and real-time management of hospital resources. Interactive interfaces and reporting features are used to monitor patient activities, doctor schedules, and overall hospital performance. The developed system helps healthcare professionals, administrators, and patients by reducing manual work, minimizing errors, and improving service quality. Furthermore, this work demonstrates how modern web technologies and database management systems can be effectively utilized for efficient healthcare administration and hospital information management.

Germany's 2024 Hospital Transparency Act: A step towards enhanced accountability and quality in healthcare?

ABSTRACT The increasing complexity of public health organizations’ environments make strategic planning more important for these organizations. Therefore , understanding how strategic planning influences performance is essential. This study examines the relationship between strategic planning and performance, with strategy implementation, stakeholder participation, and accountability as mediating variables in the context of Turkish public hospitals. The findings indicate that strategic planning has a positive and significant effect on both financial and non-financial performance through the mediating variables of stakeholder participation and accountability. The results show that strategic planning has no significant influence on financial performance, either directly or indirectly through strategy implementation.

Unrecognized physiologic deterioration is a persistent challenge across health care systems, resulting in missed opportunities for early intervention and reduced inpatient mortality. Early detection and structured response protocols may improve clinical outcomes. In this retrospective propensity-matched cohort study, the authors evaluate the effectiveness of the Discover, Evaluate, Triage, Evaluate, and Respond (DETER) program on clinical outcomes among adult inpatients across a large, multihospital health system. Of 99,558 eligible admissions, 39,703 intervention cases were matched 1:1 to control cases. In-hospital mortality was 1.7% in the intervention group and 2.9% in the control group, representing a 41% relative reduction. The average hospital length of stay was 6.7 days in the intervention group and 7.1 days in the control group. The rates of hospice discharge, intensive care unit transfers, and upgrades to higher levels of care were decreased in the intervention group. Rapid response team activations increased from 6.7% in the control group to 10.2% in the intervention group (P<0.001), reflecting earlier clinical recognition and intervention. These findings suggest that the DETER program may represent a scalable model for improving patient safety and overall hospital performance.

Patient selection remains a major challenge in evaluating hospital performance. We exploit the quasi-random assignment of patients to hospitals, based on a rotation schedule between hospitals in the Upper Austrian capital of Linz. In an instrumental variable (IV) framework, we use high-quality administrative data and estimate hospital performance with respect to in-hospital mortality, 30-day mortality, and 30-day readmission. We contrast these results with those of traditional risk adjustment models based on patient observables. We find that the assessment of hospital performance is sensitive to the inclusion of patient observables and that increasing the number of socio-economic covariates to better control for patient risk profiles does not always help bring risk-adjusted estimates closer to IV estimates. The divergence between methods is most pronounced for readmissions, where risk-adjustment models imply large and statistically significant differences between hospitals, whereas IV estimates are substantially smaller and not statistically significant. Our results suggest that common risk adjustment does not adequately control for patient differences between hospitals and that hospital quality indicators based on common administrative data should be interpreted with caution. The trend toward personalized medicine may support the process of collecting more clinical information at the individual level, thus allowing for better quality comparisons between hospitals.

We study nonparametric inference for the causal dose–response curve when the treatment variable is continuous rather than discrete. We develop doubly robust confidence intervals for the continuous treatment effect curve (at a fixed point) under the assumption that it is monotonic, based on inverting a likelihood ratio-type test. Monotonicity of the treatment effect curve is often a very natural assumption, and this assumption removes the need to choose a smoothing or tuning parameter for the nonparametrically estimated curve. The likelihood ratio procedure is effective because it allows us to avoid estimating the curve’s unknown bias, which is challenging to do. Furthermore, we propose a version of our test or confidence interval that is adaptive to a range of the unknown curve’s flatness level. The test statistic is “doubly robust” in that a remainder term is the product of errors for the two so-called nuisance functions that naturally arise (outcome regression and generalized propensity score), which allows one nuisance to be estimated poorly if the other is estimated well. We present versions with and without cross fitting. We illustrate the new methods via simulations and a study of a dataset relating the effect of nurse staffing hours to hospital performance. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Medicare and Medicaid Hospital Quality Ratings and Hospital Community Commitment: A Cross-Sectional Study.

To examine the associations among transformational leadership, nurse self-efficacy and job performance in tertiary hospitals in Shanghai, China. A cross-sectional quantitative study guided by social cognitive theory and the Job Demands-Resources (JD-R) model. Using convenience sampling, registered nurses were recruited from three tertiary Grade-A public hospitals in Shanghai between September and November 2024. Eligibility criteria included holding a valid nurse licence, having at least 1 year of independent clinical experience, and being currently employed full-time. Of 675 invitations distributed, 460 valid responses were included in the final analysis. Validated instruments were used to measure transformational leadership, nurse self-efficacy and job performance. Data were analysed using SPSS 25.0 and AMOS 25.0. Confirmatory factor analysis and covariance-based structural equation modelling were conducted using maximum likelihood estimation, and indirect effects were tested via bootstrapping with 5000 resamples. Transformational leadership was positively associated with nurse self-efficacy (β = 0.33, p < 0.001) and job performance (β = 0.18, p < 0.001). Nurse self-efficacy was positively associated with job performance (β = 0.47, p < 0.001) and partially mediated the association between transformational leadership and job performance. The indirect effect was significant (β = 0.15, 95% CI [0.09, 0.21]) and accounted for 46.3% of the total effect. The final model demonstrated acceptable fit (CFI = 0.97, RMSEA = 0.05). Among nurses in three Shanghai tertiary public hospitals, transformational leadership was associated with higher self-efficacy and better self-reported job performance and self-efficacy showed a statistically significant mediating role. Because the data were cross-sectional and based on single-source self-report, the findings should be interpreted as associational rather than causal. No patient or public contribution.

Evaluating the economic performance of hospitals is crucial for improving efficiency and aligning healthcare services with organizational missions. This study presents an operational model for assessing the economic performance of hospitals in Iran, based on a multi-criteria decision-making (MCDM) approach. An expert panel was employed to evaluate and prioritize key economic performance indicators using the Analytic Hierarchy Process (AHP). Nine primary indicators and four criteria, including validity, alignment with hospital missions, adherence to national accreditation standards, and data availability, were used for prioritization. Pairwise comparison matrices were developed, and consistency ratios (CR) were calculated to ensure accuracy. The final model incorporates normalized scores of indicators to derive the overall economic performance of each hospital. The expert panel identified nine key indicators for assessing hospital economic performance. Among these, bed occupancy rate (weight = 0.219), bed turnover rate (0.167), and insurance claim rejection rate (0.141) received the highest priorities. Validity in measuring efficiency and economic performance was the most influential criterion (weight = 0.505), followed by alignment with hospital missions and objectives (0.331). The final weighted model highlights that improvements in bed utilization, patient flow, and insurance claim management have the greatest potential impact on hospitals' economic performance. Sensitivity analysis confirmed the robustness of the results, as changes in criterion weights did not substantially alter the ranking of the top indicators. The proposed model provides a reliable framework for assessing hospital economic performance in Iran. It highlights key performance indicators that align with organizational goals and economic efficiency. This model can aid hospital administrators in decision-making to improve financial sustainability.

Hand hygiene (HH) is central to infection prevention, but evidence from oncology hospitals - where patients are immunosuppressed and strict adherence is critical - remains limited. We aimed to describe the implementation and development of a HH group and its associated activities in an oncology hospital, as well as to evaluate their impact on HH compliance, alcohol-based hand rub (ABHR) consumption, and Hand Hygiene Self-Assessment Framework (HHSAF) scores. This analysis examined long-term trends in HH performance in a tertiary oncology hospital in Brazil (2010-2024), using adherence, ABHR consumption, and HHSAF scores to assess the impact of a multi-modal programme. Trend analyses of HH adherence, ABHR consumption, and HHSAF scores over time were performed using linear regression. After the HH group was established in 2013, the programme rapidly progressed to the advanced HHSAF level, accompanied by a significant upward trend in HH adherence (P < 0.01), increasing from a baseline of 26%-88% in 2022. In contrast, no significant trend was observed for ABHR consumption (P = 0.29), although levels remained consistently above the World Health Organization (WHO)-recommended threshold. System change was the strongest multi-modal component, whereas institutional safety climate remained the weakest. Unlike most published studies focused on short-term campaigns or isolated interventions, our work evaluated the real-world sustainability of a WHO-based multi-modal programme implemented in routine hospital practice. Sustained improvement was driven by a permanent, structured, multi-disciplinary HH group, resulting in long-term advanced HHSAF performance with increasing trends in HH compliance.