Hospital Rankings Research Articles

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

An integrated decision framework for evaluating and recommending health care services

Quality management techniques such as the quality function deployment model can help hospitals assess and improve the quality of their services by integrating the voice of customers. The different quality parameters of this model are usually determined and assessed by experts; nonetheless, obtaining such experts is not always easy or inexpensive. Moreover, in this method, patient opinions are not usually considered directly, although they are the real users of the services and those who can best assess those services. Nevertheless, these opinions are easily accessible today, owing to the development of medical social networks where patients directly convey their opinions about the different services and features of a hospital. Therefore, it is feasible to replace expert knowledge with the information provided by these opinions. Based on this idea, this study proposes a novel fuzzy recommendation model based on the quality function deployment method to rank hospitals depending on patient opinions and preferences regarding hospital services. This model integrates a topic modeling strategy for determining hospital requirements, customer needs, and the relationship between them as well as a sentiment analysis algorithm for assessing customer satisfaction regarding hospital services. To demonstrate the usefulness of the proposed method, several experiments were conducted using patient reviews from real hospitals, and the method was compared against other recommendation models. The results prove that this approach represents a step toward more personalized and effective health care system selection considering patient preferences and opinions.

Hospital-level variation in hospitalization costs for spinal fusion in the United States.

With a growing emphasis on value of care, understanding factors associated with rising healthcare costs is increasingly important. In this national study, we evaluated the degree of center-level variation in the cost of spinal fusion. All adults undergoing elective spinal fusion were identified in the 2016 to 2020 National Inpatient Sample. Multilevel mixed-effect models were used to rank hospitals based on risk-adjusted costs. The interclass coefficient (ICC) was utilized to tabulate the amount of variation attributable to hospital-level characteristics. The association of high cost-hospital (HCH) status with in-hospital mortality, perioperative complications, and overall resource utilization was analyzed. Predictors of increased costs were secondarily explored. An estimated 1,541,740 patients underwent spinal fusion, and HCH performed an average of 9.5% of annual cases. HCH were more likely to be small (36.8 vs 30.5%, p<0.001), rural (10.1 vs 8.8%, p<0.001), and located in the Western geographic region (49.9 vs 16.7%, p<0.001). The ICC demonstrated 32% of variation in cost was attributable to the hospital, independent of patient-level characteristics. Patients who received a spinal fusion at a HCH faced similar odds of mortality (0.74 [0.48-1.15], p = 0.18) and perioperative complications (1.04 [0.93-1.16], p = 0.52), but increased odds of non-home discharge (1.30 [1.17-1.45], p<0.001) and prolonged length of stay (β 0.34 [0.26-0.42] days, p = 0.18). Patient factors such as gender, race, and income quartile significantly impacted costs. The present analysis identified 32% of the observed variation to be attributable to hospital-level characteristics. HCH status was not associated with increased mortality or perioperative complications.

Missed opportunities in hospital quality measurement during the COVID-19 pandemic: a retrospective investigation of US hospitals’ CMS Star Ratings and 30-day mortality during the early pandemic

ObjectivesIn the USA and UK, pandemic-era outcome data have been excluded from hospital rankings and pay-for-performance programmes. We assessed the relationship between US hospitals’ pre-pandemic Centers for Medicare and Medicaid...

An approach for analysis of the effects of COVID-19 on ranking of hospitals based on waste management practices

An approach for analysis of the effects of COVID-19 on ranking of hospitals based on waste management practices

Characteristics of Medical Quality in Tertiary Traditional Chinese Medicine Hospitals by TOPSIS and RSR Methods.

Performance evaluation is important for improving medical quality and services. But, there is a lack of research for medical quality in traditional Chinese medicine (TCM) hospitals. This study examines the medical quality and various indicators of tertiary public traditional Chinese medicine hospitals in Gansu Province, to establish a foundation for improving the medical and management standards of these hospitals. This study collected performance assessment data from 10 tertiary TCM hospitals in Gansu Province from 2019 to 2022. Thirteen indicators with TCM characteristics were selected and categorized into 3 aspects: control of medical costs, internal operational dimensions, and comprehensive management. The level of medical quality in different hospitals and in different years were determined using the TOPSIS method for ranking and the RSR method for grading. Firstly, in terms of TCM characteristic indicators, hospital H had the highest control of medical costs and comprehensive management among different hospitals, with 45.87% and 24.20% respectively. The highest values for control of medical costs and comprehensive management were observed in 2020, with 40.65% and 18.69% respectively among different years. When evaluating the medical quality of different hospitals using the TOPSIS method, it was found that hospital H had the highest ranking from 2020 to 2022, with Ci values of 0.725, 0.778, and 0.667 respectively. Additionally, the RSR method indicated that hospital H had a high level of grading from 2020 to 2022, with Pi values of 0.687, 0.690, and 0.723 respectively. These findings suggest that the medical quality of hospital H is at a high and stable level of development. Based on the TOPSIS method to evaluate the performance appraisal results and ranking of different hospitals from 2019 to 2022. The results showed that the highest ranking was hospital B(Ci = 0.913) in 2019. The highest ranking was hospital C(Ci = 0.809)in 2020. The highest ranking was hospital D(Ci = 0.689) in 2021. The highest ranking was hospital J(Ci = 0.865) in 2022. The RSR method indicated that high grading level were hospitals B(Pi = 0.899),F(Pi = 0.795) in 2019. The highest grading level was hospital C(Pi = 0.809) in 2020. The highest grading level were hospitals A(Pi = 0.868), D(Pi = 0.813), E(Pi = 0.689), G(Pi = 0.873), J(Pi = 0.813), K(Pi = 0.842) in 2022. Based on the above results indicate that there is a large variation in the medical quality profile of different hospitals from 2019 to 2022. By comparing the results of TOPSIS and RSR method from 2019 to 2022, we found that the hospitals with identical ranking were D and J, and the hospitals with ≤2 difference in ranking was A,B,C,E in 2019, the hospitals with >2 ranking was A, F in 2020, the hospitals with >2 ranking were C, G in 2021, and the hospitals with identical ranking results were B,D,E,G,J in 2022. Comparing the ranking results of TOPSIS and RSR methods, showed that the hospitals with identical rankings were B, F from 2019 to 2022. The difference in ranking results ≤2 were A, C, D, E, G, H, J, K, indicating that high consistency between TOPSIS and RSR methods and credible results. The findings reveal significant fluctuations in medical quality across different years, while the overall level of medical quality remains relatively stable among the various hospitals. It is recommended that TCM hospitals focus on improving management efficiency, optimizing hospital operations, enhancing the utilization of medical resources, and fostering the efficient development of hospitals.

Developing a streamlined risk-adjusted cesarean section rate model for evaluation of obstetrical quality across hospitals by using EHRs: A provincial-scale multicenter retrospective study.

This study aims to explore a streamlined risk-adjusted cesarean section rate (RCSR) model and to compare its practical application effects with the traditional RCSR models. Utilizing obstetric electronic health record (EHR) data from provincial multicenter hospitals, this study establishes a streamlined RCSR model alongside the traditional RCSR model and evaluates the efficacy of both models. Subsequently, the RCSRs of 56 hospitals within the province are calculated and ranked using both models. The consistency of these rankings is then quantified using Kendall's tau coefficient of concordance. Comparison of model effectiveness evaluation of the traditional RCSR model versus the streamlined RCSR model is as follows: AUC (0.840 vs 0.839), accuracy (0.875 vs 0.872), sensitivity (0.690 vs 0.685), specificity (0.898 vs 0.892), positive predictive value (0.908 vs 0.903), negative predictive value (0.664 vs 0.660), and Brier score (0.069 vs 0.067). In the test of the consistency of hospital rankings based on two models, Kendall's tau coefficients were observed to be 0.979 (year 2017), 0.978 (year 2018), and 0.978 (year 2019) over a span of 3 years, with an aggregate coefficient of 0.974. In the realm of model performance evaluation as well as the pragmatic application within hospital settings, the streamlined model exhibits a substantial congruence with the traditional model. Therefore, the streamlined model can effectively serve as a viable surrogate for the traditional model, potentially establishing itself as a refined paradigm for the appraisal of quality in obstetric healthcare services.

The Magnet Recognition Program® and Its Influence on Hospital Rankings: A Connection With US News and World Report.

In the complex landscape of healthcare, 2 prominent entities have emerged as benchmarks for quality and excellence: the Magnet Recognition Program® and the U.S. News & World Report rankings. Although distinct in their focus, these 2 organizations share a common goal of driving improvements in healthcare standards and patient outcomes. The synergy between the Magnet Recognition Program® and U.S. News & World Report is a testament to their impact on shaping the healthcare industry in the United States.

Chain mediations of perceived social support and emotional regulation efficacy between role stress and compassion fatigue: insights from the COVID-19 pandemic.

Nurses at the frontline faced high risks of the COVID-19 infection, undertook heavy workloads of patient care, and experienced tremendous stress that often led to compassion fatigue. This study was to explore the role of positive psychosocial resources (i.e., perceived social support and emotional regulation efficacy) in the relationship between role stress and compassion fatigue. A cross-sectional design was conducted in Hubei Province, China between May and September 2021. The Role Stress Questionnaire, the Perceived Social Support Scale, the Emotional Regulation Efficacy Scale, and the Professional Quality of Life Scale were used to measure key variables of interest. Nurse socio-demographic data were also collected. Structural equation modeling was used to explore the relationships, including potential mediating effect, among role stress, perceived social support, emotional regulation efficacy, and compassion fatigue. A total of 542 nurses participated in this investigation, and 500 were eventually enrolled in the analysis. The incidence of compassion fatigue among nurses was 94.2%, including 65.8% of nurses reporting at least moderate compassion fatigue. Univariate analysis showed that educational level, marital status, hospital rank, sleep time were the factors affecting compassion fatigue of the nurses. The structural equation modeling revealed that: Role stress had a direct positive effect on compassion fatigue; Perceived social support and emotional regulation efficacy partially mediated the link between role stress and compassion fatigue respectively; And there was a chain mediating role of perceived social support and emotional regulation efficacy between role stress and compassion fatigue. The incidence of compassion fatigue was high during the COVID-19 pandemic among bedside nurses in China. Improving social support and enhancing the efficacy of emotion regulation may help alleviate compassion fatigue directly and/or via buffering the impact of role stress.

An assessment of sustainable transport infrastructure in a national healthcare system

IntroductionHealthcare contributes significantly to carbon dioxide emissions, which can be reduced by promoting sustainable mobility amongst staff commuting. This study aims to investigate the national sustainable transport infrastructure for staff of healthcare facilities and utilise this data to develop a novel scoring and ranking system. MethodsThis was an empirical retrospective observational study. Data was collected on all 47 hospitals sustainable transport infrastructure. A working group calculated the weighted scores for each sustainable transport data point. These scores were used to calculate the Total and Active Sustainability Scores for each hospital, allowing a ranking to be formed. Results7 of 47 (15 %) hospitals had EV charging on campus. 17 of 47 (36 %) hospitals had secure bike parking. 2 of 47 (4 %) hospitals had a “bike hub”. 18 of 47 (38 %) hospitals had a bike lane. 13 of 22 (59 %) city hospitals had bike sharing facilities. 42 of 47 (89 %) hospitals had one public transport route. City hospitals ranked higher in both Total & Active Sustainability Scores. DiscussionThis study explored a new concept of measuring sustainable transport infrastructure. Frameworks examining sustainability are available, however, none allowed for ranking of hospitals. This study highlights the lack of both research in this field and sustainable transport infrastructure in hospitals.

Using Publicly Reported Global Hospital Rankings to Improve Dissemination of Patient-Reported Outcome Measures (PROMs)

Summary Patient-reported outcome measures (PROMs) represent an essential element of value-based care in health care sectors worldwide by transferring the quality definition from process- to outcome-based indicators that focus on the patients’ needs. However, the adoption rate of PROMs in hospitals is still low. To address this challenge and to account for the growing importance of value-based health care, Newsweek and Statista developed a PROMs implementation survey along with a global board of medical experts to determine the current state of PROMs implementation in hospitals. The results of this survey were incorporated into the 2023 editions of Newsweek’s World’s Best Specialized Hospitals and World’s Best Hospitals rankings. The inclusion of PROMs adds a patient outcome–focused dimension that overcomes methodologic limitations and improves the comprehensiveness of these rankings. Furthermore, the public reporting of clinical outcomes such as PROMs serves as a catalyst to improve the quality of care. In this article, the authors describe what they believe is the increasing relevance of value-based health care and PROMs, the process of developing and launching the PROMs implementation survey, and its incorporation into hospital rankings to improve the value of care for patients on a global level.

Improving the Value of Health Care and Accelerating Care Delivery Transformation

Summary This issue of NEJM Catalyst Innovations in Care Delivery includes articles, case studies, and research reports on U.S. Centers for Medicare & Medicaid payment models, mental health navigation, patient transfers, machine learning, HIV preexposure prophylaxis, patient-reported outcome measures and hospital rankings, and post-acute care.

Evaluating the Correlation Between Various Orthopaedic Foot and Ankle Fellowship Characteristics and Total Industry Payments Through the Open Payments Database.

Since the Physician Payments Sunshine Act in 2010, a substantial body of work has explored the supplemental income received by physicians to understand trends in industry payments and investigate sources of bias. To date, no study has examined how various fellowship characteristics impact industry earning levels at foot and ankle orthopaedic surgery fellowships. The purpose of this study is to examine the various fellowship and faculty-specific variables in correlation with industry earnings in foot and ankle orthopaedic surgery fellowships. This study is a retrospective analysis of foot and ankle orthopaedic surgery fellowships and respective faculty along with various fellowship characteristics in correlation to industry lifetime earning levels as of March 2023. Industry total lifetime earnings represent income directly paid to physicians, is not part of the physician's salary, and does not include any research grants or funding. Lifetime earnings represent all years recorded on the Open Payments Database website (2015-2021). There are 165 faculty physicians and 48 programs with complete data out of all foot and ankle orthopaedic surgery fellowship programs in the United States. The mean fellowship H-Index per fellowship was 48.94 ± 38.92, and the mean fellowship lifetime earning was $1 551 791.66 ± $4 136 091.64. There was no significant association between fellowship lifetime earnings and Newsweek ranking of fellowship-affiliated hospitals (P = .906), Doximity ranking of fellowship-affiliated residencies (P = .703), and region of the United States (P = .126). There was a statistically significant increase in total lifetime earnings in programs with 4 fellows as compared to 1 fellow (P = .035). There was no statistically significant correlation between a variety of foot and ankle fellowship-specific factors and lifetime industry earnings, aside from increased earnings in programs having 4 fellows. Prestige factors, such as Doximity and Ranked Hospital Newsweek List rank, as well as region of the United States is not associated with industry earnings. Level III, retrospective cohort study.

Decision Making for the Best Hospital Evaluation Using the AHP-COPRAS Method

The assessment of the Hospital is very important in providing a rating as a measure of accreditation. Hospitals should provide the best service to visiting patients. The purpose of this study is to provide a ranking of hospitals that should be a measure of the appropriateness of a hospital as seen from a number of criteria as a true assessment. The criteria used as a barometer for the assessment consist of ten criteria, namely Administrative Services, Doctor Services, Pharmaceutical Installation Services, Cleanliness, Convenience, Security, Number of Administrative Personnel, Number of Pharmaceutical Personnel, Administrative Complaints, and Pharmacy Installation Complaints. The scoring system that will be applied uses the Analytical Hierarchy Process (AHP) method as a determinant of the weight of each criterion and the Fuzzy assessment uses the Complex Proportional Assessment (COPRAS) method. The two methods will be collaborated as a determinant in the process of giving a rating system to the ten hospitals that are included in the priority assessment. The results obtained from the ranking process of the two AHP and COPRAS methods are seen from the acquisition of the utility value produced, the largest value obtained from the utility gives the best rating. The utility value is obtained from the total assessment of a number of criteria for each hospital and the largest utility rating does not exceed one. The highest value of the utility generated by the RS10 alternative with a utility scale of 0.098 as the best value.

Trends in Response Rate to USNews & World Report Best Hospitals Reputation Survey, 2015 to 2023.

Reputation score has been shown to be the strongest predictor of ranking in the USNews & World Reports (USNWR) Best Hospitals report. However, the extent to which physicians participate in the underlying USNWR reputation survey is not well-characterized. We conducted a retrospective cross-sectional study of USNWR public methodology reports from 2015 to 2023 to characterize trends in physician response rates by specialty, region, and Doximity membership. Overall response rates declined between 2015 (24.0%) and 2023 (8.9%). In 2023, rates ranged from 4.7% (psychiatry) to 13.9% (otolaryngology). Otolaryngology had the highest response rate among all specialties between 2017 and 2023. Within otolaryngology, both response rates (25.0% to 13.9%) and count (2106 to 1724 physicians) declined between 2015 and 2023. Among Doximity members, response rates were consistently higher for otolaryngologists in the Northeast and Midwest compared to other regions. Though hospital rankings often influence where patients seek care, our findings suggest USNWR reputation scores may not be reliable or representative.

Association of persistent poverty and U.S. News and World Report hospital rankings among patients undergoing major surgery

BackgroundWe sought to determine the association of persistent poverty on patient outcomes relative to US News World Report (USNWR) rankings among individuals undergoing common major surgical procedures. MethodsMedicare beneficiaries who underwent AAA repair, CABG, colectomy, or lung resection were identified. Multivariable logistic regression was used to evaluate the relationship between care at USNWR hospitals, county-level duration of poverty (never-high poverty (NHP); intermittent high poverty (IHP): persistent-poverty (PP)) and 30-day mortality. ResultsAmong 916,164 beneficiaries, individuals residing in PP neighborhoods who received surgical care at ranked hospitals had lower risk-adjusted 30-day mortality (5.89% vs 8.89%; p ​< ​0.001). On multivariable analysis, 30-day mortality was lower at ranked hospitals across all poverty categories with greatest decrease among patients from PP regions (NHP: OR-0.91, 95%CI0.87–0.95; IHP: OR-0.78, 95%CI0.69–0.88; PP: OR-0.69, 95%CI0.57–0.83; p ​< ​0.001). ConclusionReceipt of surgical care at top-ranked hospitals was associated with improvement in postoperative mortality, especially among patients residing in persistent poverty..

Evaluation of Chinese healthcare organizations' innovative performance in the digital health era.

Healthcare workers' relationship with industry is not merely an agent mediating between consumer and vendor, but they are also inventors of the interventions they exist to deliver. Driven by the background of the digital health era, scientific research and technological (Sci-tech) innovation in the medical field are becoming more and more closely integrated. However, scholars shed little light on Sci-tech relevance to evaluate the innovation performance of healthcare organizations, a distinctive feature of healthcare organizations' innovation in the digital health era. Academic publications and patents are the manifestations of scientific research outputs and technological innovation outcomes, respectively. The study extracted data from publications and patents of 159 hospitals in China to evaluate their innovation performance. A total of 18 indicators were constructed, four of which were based on text similarity match and represented the Sci-tech relevance. We then applied factor analyses, analytical hierarchy process, and logistic regression to construct an evaluation model. We also examined the relationship between hospitals' innovation performance and their geographical locations. Finally, we implemented a mediation analysis to show the influence of digital health on hospital innovation performance. A total of 16 indicators were involved, four of which represented the Sci-tech including the number of articles matched per patent (NAMP), the number of patents matched per article (NPMA), the proportion of highly matched patents (HMP), and the proportion of highly matched articles (HMA). Indicators of HMP (r = 0.52, P = 2.40 × 10-12), NAMP (r = 0.52, P = 2.54 × 10-12), and NPMA (r = 0.51, P = 5.53 × 10-12) showed a strong positive correlation with hospital innovation performance score. The evaluation model in this study was different from other Chinese existing hospital ranking systems. The regional innovation performance index (RIP) of healthcare organizations is highly correlated with per capita disposable income (r = 0.58) and regional GDP (r = 0.60). There was a positive correlation between digital health innovation performance scores and overall hospital innovation performance scores (r = 0.20). In addition, the hospitals' digital health innovation performance affected the hospital's overall innovation score with the mediation of Sci-tech relevance indicators (NPMA and HMA). The hospitals' digital health innovation performance score showed a significant correlation with the number of healthcare workers (r = 0.44). This study constructed an assessment model with four invented indicators focusing on Sci-tech relevance to provide a novel tool for researchers to evaluate the innovation performance of healthcare organizations in the digital health era. The regions with high RIP were concentrated on the eastern coastal areas with a higher level of economic development. Therefore, the promotion of scientific and technological innovation policies could be carried out in advance in areas with better economic development. The innovations in the digital health field by healthcare workers enhance the Sci-tech relevance in hospitals and boost their innovation performance. The development of digital health in hospitals depends on the input of medical personnel.

PCR97 Comparison of Patient-Reported Outcomes and Conventional Clinical Measures in Hospital Quality Assessment for Knee Replacement Surgery

Patient-Reported Outcome (PRO) measures have been incorporated in hospital quality of care assessment for bundled-payment initiative in Total Knee Replacement (TKR). This study investigates agreement in hospital ranking between two measurement systems using PROs and conventional clinical outcomes: 1), and 2) a composite conventional measure based on adverse events and revision surgery.

Cost Variation in Bariatric Surgery Across the United States.

High costs have been cited as a barrier to utilization of bariatric surgery despite the increasing prevalence of obesity in the United States. The present work characterizes the center-level variation and risk factors for increased hospitalization costs following bariatric operations. The 2016-2019 Nationwide Readmissions Database was queried to identify all adults undergoing elective laparoscopic sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB). Random effects were estimated using Bayesian methodology and used to rank hospitals by increasing risk-adjusted center-level costs. Of an estimated 687,866 patients at an annual 2435 hospitals, 69.9% underwent SG and 30.1% RYGB, with median costs of $10,900 (interquartile range: 8600-14,000) and $13,600 (10,300-18,000), respectively. Hospitals in the highest tertile of annual SG and RYGB volume were associated with a $1500 (95% CI - 2,100, -800) and $3400 reduction in costs (95% CI -4,200, -2600). Approximately 37.2% (95% CI 35.8-38.6) of variation in hospitalization costs was attributable to the hospital. Hospitals in the top decile of center-level costs were associated with increased odds of developing complications (AOR 1.22, 95% CI 1.05-1.40) but not mortality. The present work identified significant interhospital variation in the costs of bariatric operations. Further efforts to standardize costs may enhance the value of bariatric surgical care in the US.

The impact of surgical volume on hospital ranking using the standardized infection ratio

The Centers for Medicare and Medicaid Services require hospitals to report on quality metrics which are used to financially penalize those that perform in the lowest quartile. Surgical site infections (SSIs) are a critical component of the quality metrics that target healthcare-associated infections. However, the accuracy of such hospital profiling is highly affected by small surgical volumes which lead to a large amount of uncertainty in estimating standardized hospital-specific infection rates. Currently, hospitals with less than one expected SSI are excluded from rankings, but the effectiveness of this exclusion criterion is unknown. Tools that can quantify the classification accuracy and can determine the minimal surgical volume required for a desired level of accuracy are lacking. We investigate the effect of surgical volume on the accuracy of identifying poorly performing hospitals based on the standardized infection ratio and develop simulation-based algorithms for quantifying the classification accuracy. We apply our proposed method to data from HCA Healthcare (2014–2016) on SSIs in colon surgery patients. We estimate that for a procedure like colon surgery with an overall SSI rate of 3%, to rank hospitals in the HCA colon SSI dataset, hospitals that perform less than 200 procedures have a greater than 10% chance of being incorrectly assigned to the worst performing quartile. Minimum surgical volumes and predicted events criteria are required to make evaluating hospitals reliable, and these criteria vary by overall prevalence and between-hospital variability.