Hospital Rankings Research Articles

The Short and the Long of it: Timing of Mortality for Older Adults in a State Trauma System

BackgroundThe impact of injury extends beyond the hospital stay, but trauma center performance metrics typically focus on in-hospital mortality. We compared risk adjusted rates of in-hospital and long-term mortality among Pennsylvania trauma centers. We hypothesized that centers with low rates of in-hospital mortality would also have low rates of long-term mortality. MethodsWe identified injured patients (age ≥ 65) admitted to Pennsylvania trauma centers in 2013 and 2014 using the Pennsylvania Trauma Outcomes Study, a robust, state-wide trauma registry. We matched trauma registry records to Medicare claims from the y 2013 to 2015. Matching variables included admission date and patient demographics including date of birth, zip, sex, and race and/or ethnicity. Outcomes examined were inpatient, 30-day, and 1-y mortality. Multivariable logistic regression models including presenting physiology, comorbidities, injury characteristics, and demographics were developed to calculate expected mortality rates for each trauma center at each time point. Trauma center performance was assessed using observed-to-expected ratios and ranking for in-hospital, 30-day, and 1-y mortality. ResultsOf the 15,451 patients treated at 28 centers, 8.1% died before discharge or were discharged to hospice. Another 3.4% died within 30 d, and another 14.7% died within 1 y of injury. Of patients who survived hospitalization but died within 30 d, 92.5% were injured due to fall, and 75.0% sustained head injuries. Survival at 1 y was higher in patients discharged home (88.4%), compared to those discharged to a skilled nursing facility or long-term acute care hospital (72.7% and 52.6%, respectively). Three centers were identified as outliers (two low and one high) for in-hospital mortality, none of which were outliers when the horizon was stretched to 30 d from injury. At 30 d, two different low and two different high outliers were found. ConclusionNearly one-in-three injured older adults who die within 30 d of injury dies after hospital discharge. Hospital rankings for in-hospital mortality correlate poorly with long-term outcomes. These findings underscore the importance of looking beyond survival to discharge for quality improvement and benchmarking.

Rating Hospital Performance in China: Review of Publicly Available Measures and Development of a Ranking System.

BackgroundIn China, significant emphasis and investment in health care reform since 2009 has brought with it increasing scrutiny of its public hospitals. Calls for greater accountability in the quality of hospital care have led to increasing attention toward performance measurement and the development of hospital ratings. Despite such interest, there has yet to be a comprehensive analysis of what performance information is publicly available to understand the performance of hospitals in China.ObjectiveThis study aims to review the publicly available performance information about hospitals in China to assess options for ranking hospital performance.MethodsA review was undertaken to identify performance measures based on publicly available data. Following several rounds of expert consultation regarding the utility of these measures, we clustered the available options into three key areas: research and development, academic reputation, and quality and safety. Following the identification and clustering of the available performance measures, we set out to translate these into a practical performance ranking system to assess variation in hospital performance.ResultsA new hospital ranking system termed the China Hospital Development Index (CHDI) is thus presented. Furthermore, we used CHDI for ranking well-known tertiary hospitals in China.ConclusionsDespite notable limitations, our assessment of available measures and the development of a new ranking system break new ground in understanding hospital performance in China. In doing so, CHDI has the potential to contribute to wider discussions and debates about assessing hospital performance across global health care systems.

A Comparison of statistical methods for hospital performance assessment

During hospital quality improvement activities, statistical approaches are critical to help assess hospital performance for benchmarking. Current statistical approaches are used primarily for research and reimbursement purposes. In this multiinstitutional study, these established statistical methods were evaluated for quality improvement applications. Leveraging a dataset of 42,199 patients who underwent coronary artery bypass grafting surgery from 2014 to 2016 across 90 hospitals, six statistical approaches were applied. The non-shrinkage methods were: (1) indirect standardization without hospital effect; (2) indirect standardization with hospital fixed effect; (3) direct standardization with hospital fixed effect. The shrinkage methods were: (4) indirect standardization with hospital random effect; (5) direct standardization with hospital random effect; (6) Bayesian method. Hospital performance related to operative mortality and major morbidity or mortality was compared across methods based on variation in adjusted rates, rankings, and performance outliers. Method performance was evaluated across procedure volume terciles: small (< 96 cases/year), medium (96-171), and large (> 171). Shrinkage methods reduced inter-hospital variation (min-max) for mortality (observed: 0%-10%; adjusted: 1.5%-2.4%) and major morbidity or mortality (observed: 2.6%-35%; adjusted: 6.9%-17.5%). Shrinkage methods shrunk hospital rates toward the group mean. Direct standardization with hospital random effect, compared to fixed effect, resulted in 16.7%-38.9% of hospitals changing quintile mortality ranking. Indirect standardization with hospital random effect resulted in no performance outliers among small and medium hospitals for mortality, while logistic and fixed effect methods identified one small and three medium outlier hospitals. The choice of statistical method greatly impacts hospital ranking and performance outlier’ status. These findings should be considered when benchmarking hospital performance for hospital quality improvement activities.

Ranking hospitals’ burn care capacity using cluster analysis on open government data

Background and objectiveTo deal with burn mass casualty incidents (BMCIs), various countries have established national or regional BMCI emergency response plans (ERPs). A burn care capacity ranking model for hospitals can play an integral role in ERPs by providing essential information to emergency medical services for distributing and handling mass burn patients. Ranking models vary across countries and contexts. However, Taiwan has had no such model. The study aims to develop a ranking model for classifying hospitals’ burn care capacity in preparation for the development of a national BMCI ERP. MethodsMultiple methods were adopted. An expert panel provided consultations on data selection and clustering validation. Data on 116 variables from 535 hospitals were collected via open data platforms under the Ministry of Health and Welfare. Data selection and streamlining was conducted to determine 42 variables for cluster analysis. SAS 9.4 was used to analyze the data set -via a hierarchical cluster analysis using Ward's method, followed by a tree-based model analysis to identify the criteria for each cluster. Both internal and external cluster validation were performed. ResultsFour clusters of burn care capacity were determined to be a suitable number of clusters. All hospitals were arranged into capacity levels accordingly. Results of the Kruskal-Wallis test showed that the difference between clusters were significant. Tree-based model analysis revealed four determining variables, among which the refined level of emergency care responsibility hospital was found to be most influential on the clustering process. Responses from the questionnaire were used as an external validation tool to corroborate with the cluster analysis results. ConclusionThe use of open government data and cluster analysis was suitable for developing a ranking model to determine hospitals’ burn care capacity levels in Taiwan. The proposed ranking model can be used to develop a BMCI emergency response plan and can also serve as a reference for using cluster analysis with open government data to rank care capacity or quality in other domains.

Editorial Comment.

No AccessJournal of UrologyPediatric Urology1 Aug 2021Editorial CommentThis article comments on the following:Sample Size, Power, and Risk of Misclassification in Pediatric Urology Hospital Rankings Julia B. Finkelstein Julia B. FinkelsteinJulia B. Finkelstein Division of Pediatric Urology, Morgan Stanley Children’s Hospital of New York Presbyterian, Columbia University Irving Medical Center, New York, New York More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001844.02AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Editorial Comment." The Journal of Urology, 206(2), p. 446 © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of UrologyMay 13, 2021, 12:00:00 AMSample Size, Power, and Risk of Misclassification in Pediatric Urology Hospital Rankings Volume 206Issue 2August 2021Page: 446-446 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Julia B. Finkelstein Division of Pediatric Urology, Morgan Stanley Children’s Hospital of New York Presbyterian, Columbia University Irving Medical Center, New York, New York More articles by this author Expand All Advertisement Loading ...

Editorial Comment.

No AccessJournal of UrologyPediatric Urology1 Aug 2021Editorial CommentThis article comments on the following:Sample Size, Power, and Risk of Misclassification in Pediatric Urology Hospital Rankings Hillary L. Copp Hillary L. CoppHillary L. Copp Pediatric Urology, University of California, San Francisco, San Francisco, California More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001844.01AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Editorial Comment." The Journal of Urology, 206(2), p. 446 © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of UrologyMay 13, 2021, 12:00:00 AMSample Size, Power, and Risk of Misclassification in Pediatric Urology Hospital Rankings Volume 206Issue 2August 2021Page: 446-446 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hillary L. Copp Pediatric Urology, University of California, San Francisco, San Francisco, California More articles by this author Expand All Advertisement Loading ...

Sample Size, Power, and Risk of Misclassification in Pediatric Urology Hospital Rankings.

We investigated 2019 and 2020 U.S. News & World Report methodologies of assessing pediatric urology surgical revision rates for distal hypospadias, pyeloplasty, and ureteral reimplantation to evaluate statistical power and misclassification risks. Median annual volumes of distal hypospadias, pyeloplasty, and ureteral reimplantation procedures by hospital from 2016 to 2018 were calculated using the Pediatric Health Information System® database. U.S. News & World Report 2019 and 2020 methodologies were assessed to calculate power required to detect differences between hospitals and risk of hospital misclassifications. Median (IQR) annual hospital procedure volume was 72 (43-97) for distal hypospadias procedures, 19 (9-34) for pyeloplasties, and 35 (19-50) for ureteral reimplantations. Based on 2019 methodology, in order to achieve 80% power 764 cases/hospital are required to distinguish between a 1% vs 3% surgical revision rate, 1,500 cases/hospital are required to distinguish between a 3% vs 5% revision rate, and 282 cases/hospital are required to distinguish between a 1% vs 5% revision rate. Based on 2020 methodology, 98.0% of hospitals do not have adequate ureteral reimplantation volume to achieve full points even when reporting no revisions; similarly, 66.0% do not have adequate pyeloplasty volume, and 10.9% do not have adequate distal hypospadias volume. Risks of misclassification exceed 50% in several instances among hospitals reporting distal hypospadias and pyeloplasty revisions using both 2019 and 2020 methodology. Based on median-volume hospitals, current U.S. News & World Report methods for classifying revision rates for distal hypospadias, pyeloplasty, and ureteral reimplantation have insufficient power and are at high risk for misclassification.

Developing Hospital Emergency and Disaster Management Index Using TOPSIS Method

Indonesia is a country prone to experiencing natural hazards and disasters, which have frequently damaged public infrastructure, including hospitals. The role of hospitals is crucial to alleviate the impact of disasters. However, there is still a lack of study that analyzes the factors that influence the readiness of hospitals in emergency situations. Filling in this gap, the aim of this paper is to analyze and rank hospitals across West Java and Yogyakarta, Indonesia by the resilience of their emergency management approaches. This research seeks to measure hospital resiliency during emergencies and disasters. Results indicate that the emergency and disaster management coordination, response and disaster recovery planning, communication and information management, logistics and evacuation, human resources, finance, patient care and support services, decontamination and security are key attributes for the decision-making matrix. Based on the Hospital Safety Index tool, this research proposes the Hospital Emergency and Disaster Management (HEDM) index by combining the key attributes and sub-attributes using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as a multi-attribute decision-making technique. The paper concludes that the anticipated benefits of analyzing the resilience of hospitals by using HEDM is the identification of the most susceptible hospitals based on their levels of readiness and resiliency in areas which are prone to experiencing disasters. This prioritization is important for resource allocation and budget planning.

Total shoulder arthroplasty (TSA) at United States News and World Report top-ranked hospitals in orthopedics – do rankings correlate with complications and cost?

IntroductionThe U.S. News & World Report (USNWR) hospital rankings remain the most publicly referenced system for identifying top hospitals for each specialty. The current study aims to evaluate whether there are any differences in outcomes and costs for patients undergoing anatomic TSA (ATSA) or reverse TSA (RTSA) for degenerative pathologies at ranked vs. non–ranked hospitals. MethodsThe 2011-2014 USNWR hospital rankings were used to identify ranked hospitals in “Orthopedics.” The 2011-2014 100% Medicare Standard Analytical Files (SAF100) were used to identify patients undergoing ATSA or RTSA for degenerative arthritis. The study cohort was divided into two groups, based on whether patients received surgery at a ranked hospital vs. a non–ranked hospital. Multi-variate logistic and linear regression analyses were used to assess for differences in 90-day outcomes and costs between ranked and non–ranked hospitals, while controlling for potential co-variates/confounders. ResultsA total of 82,417 TSA were included in the study – out of which 8,868 (10.8%) were performed in one of the 68 hospitals that were ranked by the USNWR between 2011 and 2014. Following adjustment for baseline demographics, and clinical characteristics, there were no statistically significant differences between ranked vs. non–ranked hospitals, with regards to rates of infectious complications (0.8% vs. 1.0%, OR 0.91 [95% CI 0.82-1.01]; P = .079), dislocations (0.8% vs. 0.9%, OR 1.07 [95% CI 0.79-1.43]; P = .658), peri-prosthetic fractures (0.3% vs. 0.3%, OR 0.77 [95% CI 0.47-1.21]; P = .269), pulmonary embolism (0.7% vs. 0.8%, OR 0.88 [95% CI 0.64-1.20]; P = .435), deep venous thrombosis (1.1% vs. 0.9%, OR 1.21 [95% CI 0.93-1.56]; P = .153), myocardial infarction (0.4% vs. 0.5%, OR 1.00 [95% CI 0.67-1.46]; P = .995), sepsis (6.0% vs. 7.3%, OR 0.95 [95% CI 0.85-1.05]; P = .314), urinary tract infections (4.8% vs. 5.6%, OR 0.99 [95% CI 0.87-1.11]; P = .831) and readmissions (7.1% vs. 8.3%, OR 0.91 [95% CI 0.82-1.01]; P = .079). Ranked hospitals, however, did have higher risk-adjusted charges (+ $6,167; P < .001), and costs (+ $1.831; P < .001) across the entire episode of care. ConclusionBased on our findings, patients undergoing TSA at ranked hospitals vs. non–ranked hospitals have similar outcomes, despite the former facilities having higher costs and charges across the episode of care. Level of EvidenceLevel III

The body image disturbance questionnaire-scoliosis better correlates to quality of life measurements than the spinal assessment questionnaire in pediatric idiopathic scoliosis.

Idiopathic scoliosis (IS) patients can have body dissatisfaction which can affect their perception of health. Two body image measures, the Spinal Appearance Questionnaire (SAQ) and the Body Image Disturbance Questionnaire-Scoliosis (BIDQ-S), have been used in pediatric IS with variable correlation to the SRS-22r and radiographs, but have not been compared to each other. As patient reported outcomes (PROs) continue to be highlighted in large database studies and national hospital ranking system scoring, we should narrow use to the best and most efficient. We aim to determine which of two better correlates to pediatric IS patients' radiographs and quality of life (QoL) scores. Consecutive IS patients aged 10-19years old without surgery prospectively completed BIDQ-S, SAQ, SRS-22r, and PedsQL self-reported outcome measures. BIDQ-S and SAQ were compared in correlation to the two QoL surveys, as well as to radiographic major curve, shoulder asymmetry, lateral upright ribcage offset at apex, and coronal/sagittal balance. Spearman's r was used for correlations. 104 surveys with mean age 14.4years and mean major curve 42° (14°-74°) were included. BIDQ-S and SAQ scores strongly correlated to each other (r = 0.76), but BIDQ-S had a stronger correlation to total SRS-22r (-0.75 vs -0.61 SAQ), PedsQL total (-0.76 vs -0.55) and better or no difference in each SRS-22r and PedsQL domain. Both poorly correlated to radiographs (main curve: r = 0.32 BIDQ-S, 0.31 SAQ). The BIDQ-S correlates better to SRS-22r and PedsQL for pediatric IS patients than the SAQ. Neither correlate well to radiographs. III.

Risk-Standardized Home Time as a Novel Hospital Performance Metric for Pneumonia Hospitalization Among Medicare Beneficiaries: a Retrospective Cohort Study.

The Centers for Medicare & Medicaid Services (CMS) use hospital readmissions as a performance metric to incentivize hospital care for acute conditions including pneumonia. However, there are limitations to using readmission alone as a hospital performance metric. To characterize 30-day risk-standardized home time (RSHT), a novel patient-centered post-discharge performance metric for acute pneumonia hospitalizations in Medicare patients, and compare hospital rankings based on this metric with mortality and readmissions. Retrospective, cohort study. A cohort of Medicare fee-for-service beneficiaries admitted between January 01, 2015 and November 30, 2017. None. Risk-standardized hospital-level home time within 30 days of discharge was evaluated as a novel performance metric. Multilevel regression models were used to calculate hospital-level estimates and rank hospitals based on RSHT, readmission rate (RSRR), and mortality rate (RSMR). A total of 1.7 million pneumonia admissions admitted to one of the 3116 hospitals were eligible for inclusion. The median 30-day RSHT was 20.5 days (interquartile range: 18.9-21.9 days; range: 5-29 days). Hospital-level characteristics such as case volume, bed size, for-profit ownership, rural location of the hospital, teaching status, and participation in the bundled payment program were significantly associated with home time. We found a modest, inverse correlation of RSHT with RSRR (rho: -0.233, p< 0.0001) and RSMR (rho: -0.223, p< 0.0001) for pneumonia. About 1/3rd of hospitals were reclassified as high performers based on their RSHT metric compared with the rank on their RSRR and RSMR metrics. Home time is a novel, patient-centered, hospital-level metric that can be easily calculated using claims data and accounts for mortality, readmission to an acute care facility, and admission to a skilled nursing facility or long-term care facility after discharge. Utilization of this patient-centered metric could have policy implications in assessing hospital performance on delivery of healthcare to pneumonia patients.

Outcomes of Complex Gastrointestinal Cancer Resection at US News & World Report Top-Ranked vs Non-Ranked Hospitals

The US News & World Report (USNWR) annual ranking of the best hospitals for gastroenterology and gastrointestinal surgery offers direction to patients and healthcare providers, especially for recommendations on complex medical and surgical gastrointestinal (GI) conditions. The objective of this study was to examine the outcomes of complex GI cancer resections performed at USNWR top-ranked, compared to non-ranked, hospitals. Using the Vizient database, data for patients who underwent esophagectomy, gastrectomy, and pancreatectomy for malignancy between January and December 2018 were reviewed. Perioperative outcomes were analyzed according to USNWR rank status. Primary outcome was in-hospital mortality. Secondary outcomes include length of stay, mortality index (observed-to-expected mortality ratio), rate of serious complication, and cost. Secondary analysis was performed for outcomes of patients who developed serious complications. There were 3,054 complex GI cancer resections performed at 42 top-ranked hospitals vs 3,608 resections performed at 198 non-ranked hospitals. The mean annual case volume was 73 cases at top-ranked hospitals compared to 18 cases at non-ranked hospitals. Compared with non-ranked hospitals, top-ranked hospitals had lower in-hospital mortality (0.96% vs 2.26%, respectively, p< 0.001) and lower mortality index (0.71 vs 1.53, respectively). There were no significant differences in length of stay, rate of serious complications, or direct cost between groups. In patients who developed serious morbidity, top-ranked hospitals had a lower mortality compared with non-ranked hospitals (8.2% vs 16.8%, respectively, p < 0.01). Within the context of complex GI cancer resection, USNWR top-ranked hospitals performed a 4-fold higher case volume and were associated with improved outcomes. Patients with complex GI-related malignancies may benefit from seeking surgical care at high-volume regional USNWR top-ranked hospitals.

Monetize and upgrade hospital rankings through engineering and productivity systems (Case Study: Endowment Hospital of Mofarah (Yak chi Abad))

In Iran, the health sector has been undergoing reforms since its inception, each of which has had implications for the overall purpose of responding to the needs of society and improving the health system. Implementation of these reforms, considering the factors that impede the implementation of the program, has led to significant achievements for the health of the country. The purpose of this study was to collect data from statistical population and analyze it through SPSS software in a target population of 100 people who participated in this study. They were distributed and asked to complete it. The questionnaire contains topics related to the satisfaction of the recently refurbished main hospital ward. Satisfaction with the performance of hospital spaces that has created proper access and workflow has also had a positive effect on increasing overall satisfaction with 92.9% satisfaction. Keep it as high as possible.

Palliative Care Specialist Access is Associated With Rankings of Hospital Quality

ContextIncreasing evidence has shown that access to specialty palliative care, particularly outpatient palliative care clinics, can yield improved health outcomes and be a marker of hospital quality. ObjectiveTo determine whether an association exists between access to specialty palliative care programs and hospital rankings found in the 2020-2021 U.S. News & World Report Best Hospitals. MethodsThis study used publicly available data from the Center to Advance Palliative Care (CAPC) Provider Directory to determine access to in-patient and out-patient palliative care in the 2020-2021 U.S. News & World Report Best Hospitals Rankings. Descriptive statistics and chi-squares were performed. Data were also analyzed across the four U.S. Census Bureau regions (Northeast, South, Midwest, West). ResultsAround 100% of the Top 20 hospitals include hospital-based palliative care consultation teams, and 95% offered outpatient palliative care. Of the second cohort of 83 hospitals, 99% offered inpatient palliative care, and 65% offered outpatient palliative care. Of the third cohort of 75 hospitals ranked, 96% had inpatient palliative care services, while only 41.3% offered outpatient palliative care. This represents a significant association between rank position and access to outpatient palliative care (P < 0.01). Ranked hospitals also have significantly higher access to hospital-based palliative care teams compared to the national prevalence rate (P < 0.01). ConclusionThese findings reflect the association of access to specialty palliative care with USNWR rankings for hospital quality. Further study is necessary to determine the specific influence of access to palliative care and USNWR rank position.

Novel method of calculating adjusted antibiotic use by microbiological burden.

To determine the usefulness of adjusting antibiotic use (AU) by prevalence of bacterial isolates as an alternative method for risk adjustment beyond hospital characteristics. Retrospective, observational, cross-sectional study. Hospitals in the southeastern United States. AU in days of therapy per 1,000 patient days and microbiologic data from 2015 and 2016 were collected from 26 hospitals. The prevalences of Pseudomonas aeruginosa, extended-spectrum β-lactamase (ESBL)-producing bacteria, methicillin-resistant Staphylococcus aureus (MRSA), and vancomycin-resistant enterococci (VRE) were calculated and compared to the average prevalence of all hospitals in the network. This proportion was used to calculate the adjusted AU (a-AU) for various categories of antimicrobials. For example, a-AU of antipseudomonal β-lactams (APBL) was the AU of APBL divided by (prevalence of P. aeruginosa at that hospital divided by the average prevalence of P. aeruginosa). Hospitals were categorized by bed size and ranked by AU and a-AU, and the rankings were compared. Most hospitals in 2015 and 2016, respectively, moved ≥2 positions in the ranking using a-AU of APBL (15 of 24, 63%; 22 of 26, 85%), carbapenems (14 of 23, 61%; 22 of 25; 88%), anti-MRSA agents (13 of 23, 57%; 18 of 26, 69%), and anti-VRE agents (18 of 24, 75%; 15 of 26, 58%). Use of a-AU resulted in a shift in quartile of hospital ranking for 50% of APBL agents, 57% of carbapenems, 35% of anti-MRSA agents, and 75% of anti-VRE agents in 2015 and 50% of APBL agents, 28% of carbapenems, 50% of anti-MRSA agents, and 58% of anti-VRE agents in 2016. The a-AU considerably changes how hospitals compare among each other within a network. Adjusting AU by microbiological burden allows for a more balanced comparison among hospitals with variable baseline rates of resistant bacteria.

Hospital Preparedness Assessment against COVID-19 Pandemic: A Case Study in Turkish Tertiary Healthcare Services

Hospitals play a critical role in providing essential health services to people in the healthcare system. Healthcare systems around the world have faced some issues in responding to patients with various disease severity levels. Nowadays, the world as a whole is combating a pandemic called COVID-19. This pandemic causes an increase in the disease spread with fluctuated patient demand that may affect the hospitals’ capacity and overall functioning and risks rising based on hospital site, medical staff, patient, and healthcare process. To deal with the challenges of the COVID-19 pandemic, hospitals must have completed their preparations before these events occur. Therefore, this study proposes an integrated approach based on the decision-making concept with interval-valued spherical fuzzy sets (IVSFSs) to the hospital preparedness assessment problem. A technique for order preference by similarity to ideal solution (TOPSIS) extended with IVSFSs is used to rank hospitals from Turkish tertiary healthcare services. A checklist reported by the World Health Organization (WHO) is adapted to conform to Turkey’s COVID-19 pandemic management. Ninety-nine subcomponents of the adapted checklist under ten components are weighted by interval-valued spherical weighted arithmetic mean (IVSWAM) operator. The hospitals in the problem are then ranked concerning these weighted subcomponents. With the proposed approach, a COVID-19 pandemic preparedness index is determined for the observed hospitals. In addition, a separate index based on each main component (component-based ranking) is determined. These indexes are vital indicators in determining in which aspects hospitals are ready and in what aspects hospitals are not prepared for pandemics. The proposed approach can be adaptable and applied by national policymakers in assessing all hospitals of the country against the COVID-19 pandemic.

Evaluating the robustness of the CMS Hospital Value-Based Purchasing measurement system.

The Hospital Value-Based Purchasing Program (HVBP) is a pay for performance system that impacts traditional Medicare fee-for-service payments to hospitals through rewards and penalties. To explore variation in overall and individual-hospital total performance score (TPS) and embedded domains for hospitals during 2014-2018. Hospital data were retrieved from the publicly available HOSArchive dataset. Distribution of annual TPS and HVBP domain scores for 2014-2018 was evaluated using descriptive statistics. Transitional probabilities were analyzed to evaluate annual movement in the TPS ranking for outlier hospitals in the Top and Bottom 5%. TPS scores are positively skewed while the distribution of domain scores vary with patient experience, (clinical) outcome, and efficiency domains having a large number of (positive) outliers. Mean TPS score decreased from 40.54 in 2014 to 38.04 by 2018. Improvement was shown in mean domain scores for clinical process of care and clinical outcome using 95% confidence intervals, with hospitals gaining 10 points over the study period in clinical outcome. Changes in the mean scores for other domains did not show consistent increases or decreases. Chi-square analyses of hospital ranking categories showed some evidence that, as a group, hospitals initially ranked in the Bottom 5% are making consistent annual movements to higher categories. In contrast, over half of the hospitals ranking in the initial Top 5% remained in the top category across all study years. It may be time for CMS to redesign the HVBP incentive program to assure the measures accurately demonstrate sustained improvement, the domain weights appropriately reflect the level of importance, and the TPS comparative ranking methodology does not discourage lower-performing hospitals from actively improving the care they deliver and achieving top ranks.

Correlation between an Independent Electronic Health Record &amp;amp; External Ranking of Children’s Hospitals

Introduction: To evaluate the correlation between the presence of an independent EHR (compared to a shared EHR system within an adult hospital system) and an externally-derived third party ranking of children’s hospitals. Methods: Children’s hospitals that ranked in the top fifty of the 2019-2020 US News and World Report (USNWR) were included in the analysis. The mean and median ranking of children’s hospitals with independent versus a shared EHR was evaluated. The 2019-2020 USNWR rankings of the top twenty adult hospitals in the United States were then evaluated. For each children’s hospital with an associated adult hospital that was both ranked, it was noted as to whether the EHR for the children’s hospital was independent or shared and statistical differences in rankings compared. Results: Among the top 50 children’s hospitals included, the median USNWR ranking for hospitals was statistically different with an independent EHR than with a shared EHR (13 vs. 30.0) (p = 0.002). The 21 top ranked adult hospitals were associated with 17 children’s hospitals ranked in the top 50. The median ranking for those with an independent EHR was statistically different for those with independent EHR versus shared EHR (7 vs. 28) (p = 0.002). Conclusion: Children’s hospitals with an independent EHR are associated with higher scores on an independent external ranking of hospital quality compared to those which share an EHR with a partner adult hospital.

882. Evaluating the Risk Factors in Postoperative Infections Following Hysterectomy Procedures: Is Antibiotic Prophylaxis the Issue?

BackgroundInfectious complications after hysterectomy procedures are associated with an additional financial burden and length of stay for patients. In addition, post-hysterectomy surgical site infection is a metric tied to hospital ranking and financial penalties. The objective of this study was to evaluate the appropriateness of surgical prophylaxis for hysterectomies in patients with postoperative infections.MethodsThis is a 1:1 case-control study, matched based on the year of surgery and surgeon performing the procedure, of women ≥ 18 years who underwent hysterectomies between 2013 and 2019. Cases were diagnosed with infection(s) attributable to the procedure within 90 days post-hysterectomy. Patients who did not receive prophylaxis were excluded. The primary outcome of this study was to determine if postoperative infections following hysterectomies were due to inappropriate antibiotic prophylaxis. Secondary outcomes included hospital readmission and mortality. The primary statistical tests utilized included descriptive statistics and chi-square test.ResultsThe study included 86 patients, 43 in each group. Thirty percent of cases and 7% of controls received inappropriate prophylaxis. For cases, reason for inappropriateness was due to underdosing of cefazolin (38%) and gentamicin (54%), and overdosing of cefotetan (8%). For controls, cefazolin (33%) and gentamicin (67%) were underdosed. Twenty-three percent of the cases and 14% of the controls received inappropriate intraoperative redosing. The most common reason for inappropriateness was timing (40% of cases vs 50% of controls) followed by missed intraoperative redosing (50% of cases vs 33% of controls). No difference was observed in the proportion of cases and proportion of controls who received inappropriate treatment (p=0.21). Eighty-eight percent of cases and 5% of controls were readmitted within 3 months (p< 0.0001). Mortality was not noted in either group.ConclusionIn this study, there was no statistically significant association between inappropriate antibiotic prophylaxis and infection. There was still a high incidence of inappropriate antibiotic prophylaxis in the cases. Therefore, education of providers on antibiotic dosing and criteria for intraoperative redosing is warranted.Disclosures All Authors: No reported disclosures

"Finding a General Surgeon: Self-Referral in the Digital Era".

As technology becomes more prominent in today's society, more patients turn to the Internet to self-refer for a range of surgical problems. Frequently, patients search a nearby hospital's website in order to find a physician. We hypothesized that the variability in hospital websites would make it difficult for patients to find a general surgeon for their care. We used the US News and World Report's Hospital Rankings 2018-2019 for this study. The "Find A Doctor" page within each hospital's website was searched for the following conditions: "hernia" and "gallbladder." Information on all suggested providers was collected, including medical specialty and gender. Descriptive statistics were used to analyze the data. The median number of providers listed in each search was 18 (range: 1-204). For "hernia," general surgeons were not the majority of providers suggested at 12/16 institutions. For "gallbladder," general surgeons were not the majority of providers suggested at 14/16 institutions, and 3/16 institutions did not suggest any. All 16 institutions suggested a strong majority of male providers (range: 62-100% male; median: 83% male). Considerable variation exists in the suggestion of medical providers for common general surgical problems among the top academic hospitals. Most notably, general surgeons are not listed as the primary providers for these conditions which they commonly manage. Health systems need to examine how their website suggest providers and ensure that patients can easily find the physician most suitable for their care.