Differences In Hospital Quality Research Articles

Hospital-level variation in rates of postpartum hemorrhage in California.

To examine the extent of hospital-level variation in risk-adjusted rates of postpartum hemorrhage (PPH). We performed a cross-sectional study examining live births in 257 California hospitals between 2011 and 2015 using linked birth certificate and maternal discharge data. PPH was measured using International Classification of Diseases Codes version 9. Mixed-effects logistic regression models were used to examine the presence and extent of hospital-level variation in PPH before and after adjustment for patient-level risk factors and select hospital characteristics (teaching status and annual delivery volume). Risk-adjusted rates of PPH were estimated for each hospital. The extent of hospital variation was evaluated using the median odds ratio (MOR) and intraclass correlation coefficient (ICC). Our study cohort comprised 1,904,479 women who had a live birth delivery hospitalization at 247 hospitals. The median, lowest, and highest hospital-specific rates of PPH were 3.48%, 0.54%, and 12.0%, respectively. Similar rates were observed after adjustment for patient and hospital factors (3.44%, 0.60%, and 11.48%). After adjustment, the proportion of the total variation in PPH rates attributable to the hospital was low, with a MOR of 2.02 (95% confidence interval [CI]: 1.89-2.15) and ICC of 14.3% (95% CI: 11.9%-16.3%). Wide variability exists in the rate of PPH across hospitals in California, not attributable to patient factors, hospital teaching status, and hospital annual delivery volume. Determining whether differences in hospital quality of care explain the unaccounted-for variation in hospital-level PPH rates should be a public health priority.

Hospital Factors Associated With Interhospital Transfer Destination for Stroke in the Northeast United States.

BackgroundWe aimed to determine if there is an association between hospital quality and the likelihood of a given hospital being a preferred transfer destination for stroke patients.Methods and ResultsData from Medicare claims identified acute ischemic stroke transferred between 394 northeast US hospitals from 2007 to 2011. Hospitals were categorized as transferring (n=136), retaining (n=241), or receiving (n=17) hospitals based on the proportion of acute ischemic stroke encounters transferred or received. We identified all 6409 potential dyads of sending and receiving hospitals, and categorized dyads as connected if ≥5 patients were transferred between the hospitals annually (n=82). We used logistic regression to identify hospital characteristics associated with establishing a connected dyad, exploring the effect of adjusting for different quality measures and outcomes. We also adjusted for driving distance between hospitals, receiving hospital stroke volume, and the number of hospitals in the receiving hospital referral region. The odds of establishing a transfer connection increased when rate of alteplase administration increased at the receiving hospital or decreased at the sending hospital, however this finding did not hold after applying a potential strategy to adjust for clustering. Receiving hospital performance on 90‐day home time was not associated with likelihood of transfer connection.ConclusionsAmong northeast US hospitals, we found that differences in hospital quality, specifically higher levels of alteplase administration, may be associated with increased likelihood of being a transfer destination. Further research is needed to better understand acute ischemic stroke transfer patterns to optimize stroke transfer systems.

This Month in Anesthesiology.

This Month in Anesthesiology.

Readmissions After Revascularization Procedures for Peripheral Arterial Disease: A Nationwide Cohort Study.

Limited data suggest high rates of unplanned rehospitalization after endovascular and surgical revascularization for peripheral arterial disease. However, the overall burden of readmissions has not been comprehensively explored. To evaluate nationwide readmissions after peripheral arterial revascularization for peripheral arterial disease and to assess whether readmission risk varies among hospitals. Retrospective cohort study. 1085 U.S. acute care hospitals participating in the Nationwide Readmissions Database. 61969 unweighted hospitalizations of patients with peripheral arterial disease who had peripheral arterial revascularization and were discharged alive between 1 January and 30 November 2014. 30-day readmission rates, causes, and costs of unplanned rehospitalizations after peripheral arterial revascularization; 30-day risk-standardized readmission rates (RSRRs), calculated using hierarchical logistic regression, to assess for heterogeneity of readmission risk between hospitals. Among 61969 hospitalizations of patients with peripheral arterial disease who were discharged alive after peripheral arterial revascularization, the 30-day nonelective readmission rate was 17.6%. The most common cause of readmission was procedural complications (28.0%), followed by sepsis (8.3%) and complications due to diabetes mellitus (7.5%). Among rehospitalized patients, 21.0% underwent a subsequent peripheral arterial revascularization or lower extremity amputation, 4.6% died, and the median cost of a readmission was $11013. Thirty-day RSRRs varied from 10.0% to 27.3% (interquartile range, 16.6% to 18.8%). Inability to distinguish out-of-hospital deaths after discharge and potential misclassification bias due to use of billing codes to ascertain diagnoses and interventions. More than 1 in 6 patients with peripheral arterial disease who undergo peripheral arterial revascularization have unplanned readmission within 30 days, with high associated mortality risks and costs. Procedure- and patient-related factors were the primary reasons for readmission. Readmission rates varied moderately between institutions after hospital case mix was accounted for, suggesting that differences in hospital quality may only partially account for readmission. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center.

Age-Related Differences in Hospital Quality of Care for Acute Myocardial Infarction: Getting to the Heart of the Matter.

The editorialist discusses why hospital quality might vary by age, as well as why a single metric calculated by a complex formula that weighs performance in different domains is not what patients n...

Impact of Risk Adjustment for Socioeconomic Status on Risk-adjusted Surgical Readmission Rates.

To assess whether differences in readmission rates between safety-net hospitals (SNH) and non-SNHs are due to differences in hospital quality, and to compare the results of hospital profiling with and without SES adjustment. In response to concerns that quality measures unfairly penalizes SNH, NQF recently recommended that performance measures adjust for socioeconomic status (SES) when SES is a risk factor for poor patient outcomes. Multivariate regression was used to examine the association between SNH status and 30-day readmission after major surgery. The results of hospital profiling with and without SES adjustment were compared using the CMS Hospital Compare and the Hospital Readmissions Reduction Program (HRRP) methodologies. Adjusting for patient risk and SES, patients admitted to SNHs were not more likely to be readmitted compared with patients in in non-SNHs (AOR 1.08; 95% CI:0.95-1.23; P = 0.23). The results of hospital profiling based on Hospital Compare were nearly identical with and without SES adjustment (ICC 0.99, κ 0.96). Using the HRRP threshold approach, 61% of SNHs were assigned to the penalty group versus 50% of non-SNHs. After adjusting for SES, 51% of SNHs were assigned to the penalty group. Differences in surgery readmissions between SNHs and non-SNHs are due to differences in the patient case mix of low-SES patients, and not due to differences in quality. Adjusting readmission measures for SES leads to changes in hospital ranking using the HRRP threshold approach, but not using the CMS Hospital Compare methodology. CMS should consider either adjusting for the effects of SES when calculating readmission thresholds for HRRP, or replace it with the approach used in Hospital Compare.

Association of Hospital Prices for Coronary Artery Bypass Grafting With Hospital Quality and Reimbursement

Although prices for medical services are known to vary markedly between hospitals, it remains unknown whether variation in hospital prices is explained by differences in hospital quality or reimbursement from major insurers. We obtained "out-of-pocket" price estimates for coronary artery bypass grafting (CABG) from a random sample of US hospitals for a hypothetical patient without medical insurance. We compared hospital CABG price to (1) "fair price" estimate from Healthcare Bluebook data using each hospital's zip code and (2) Society of Thoracic Surgeons composite CABG quality score and risk-adjusted mortality rate. Of 101 study hospitals, 53 (52.5%) were able to provide a complete price estimate for CABG. The mean price for CABG was $151,271 and ranged from $44,824 to $448,038. Except for geographic census region, which was weakly associated with price, hospital CABG price was not associated with other structural characteristics or CABG volume (p >0.10 for all). Likewise, there was no association between a hospital's price for CABG with average reimbursement from major insurers within the same zip code (ρ = 0.07, p value = 0.6), Society of Thoracic Surgeoncomposite quality score (ρ = 0.08, p value = 0.71), or risk-adjusted CABG mortality (ρ = -0.03 p value = 0.89). In conclusion, the price of CABG varied more than 10-fold across US hospitals. There was no correlation between price information obtained from hospitals and the average reimbursement from major insurers in the same market. We also found no evidence to suggest that hospitals that charge higher prices provide better quality of care.

Abstract 208: Association of Hospital Prices for Coronary Artery Bypass Graft Surgery with Hospital Quality and Reimbursement

Background: Although prices for medical services are known to vary markedly between hospitals especially for patients without medical insurance, it remains unknown whether this large variation in price is explained by differences in hospital quality or reimbursement from major insurers. Methods: We obtained an “out-of-pocket” hospital and physician price estimate for a coronary artery bypass graft (CABG) surgery for a hypothetical patient without medical insurance via telephone call from a random sample of U.S. hospitals during January 2014. We examined whether the price provided by a hospital was associated with its 1) structural characteristics obtained from the American Hospital Association data, 2010 2) risk-standardized CABG mortality obtained from the Society of Thoracic Surgeons data, 2013 and 3) “fair price” estimate for CABG base don reimbursement data from major insurers in the zip code of the hospital location (Healthcare Bluebook, www.healthcarebluebook.com). Results: We contacted a total of 111 U.S. hospitals, of which 56 (50.5%) were able to provide a price estimate for CABG. The mean price for CABG obtained from the hospitals was $151,784.64, and ranged from $44,824 - $448,039. Except geographic location (p=0.03), which was weakly associated, hospital prices for a CABG surgery were not associated with teaching status, for-profit/not-for-profit/government classification, urban/rural location, or CABG volume (p >0.10 for all). Likewise, we found no association between a hospital’s quoted price for CABG and its risk-standardized CABG mortality (r= -0.03 p=0.89), or its average reimbursement for CABG surgery from major insurers within the same zip code (r= 0.07, p=0.6). Conclusion: We found a 10-fold variation in the prices obtained from U.S. hospitals for CABG surgery for an uninsured patient. Although there was slight variation in CABG surgery prices based on geographic region, we found no evidence to suggest that hospitals with higher prices have lower mortality or receive higher reimbursement from insured patients.

Explaining Racial Disparities in Outcomes After Cardiac Surgery

Racial disparities in mortality rates after coronary artery bypass graft (CABG) surgery are well established. We have yet to fully understand how care at high-mortality, low-quality hospitals contributes to racial disparities in surgical outcomes. To determine the effects of hospital quality on racial disparities in mortality rates after CABG surgery. The national Medicare database (2007-2008) was used to identify 173,925 patients undergoing CABG surgery in US hospitals. Our primary measure of quality was the risk-adjusted mortality rate for each hospital. Logistic regression was used to determine the relationship between race and mortality rates, accounting for patient characteristics, socioeconomic status, and hospital quality. Nonwhite patients had 33% higher risk-adjusted mortality rates after CABG surgery than white patients (odds ratio [OR], 1.33; 95% CI, 1.23-1.45). In hospitals treating the highest proportion of nonwhite patients (>17.7%), the mortality was 4.8% in nonwhite and 3.8% in white patients. When assessed independently, differences in hospital quality explained 35% of the observed disparity in mortality rates (OR, 1.22; 95% CI, 1.12-1.34). We were able to explain 53% of the observed disparity after adjusting for differences in socioeconomic status and hospital quality. However, even after these factors were taken into account, nonwhite patients had a 16% higher mortality (OR, 1.16; 95% CI, 1.05-1.27). Hospital quality contributes significantly to racial disparities in outcomes after CABG surgery. However, a significant fraction of this racial disparity remains unexplained. Efforts to decrease racial disparities in health care should focus on underperforming centers of care treating disproportionately high numbers of nonwhite patients.

Do patient sharing networks between hospitals afford access to better care?

Contemporary health care management research has given considerable attention to interhospital patient sharing as an important form of coordination and integration between hospitals. Even though extant research provides empirical evidence on the different antecedents of patient sharing relations between hospitals, relatively little is still known about how effect of differences in hospital quality on patient mobility. More specifically, in this paper we ask: Do patients flow from lower to higher quality hospitals? This question is relevant because a positive answer may be taken as evidence that inter-hospital collaboration involves the patient as the ultimate beneficiary. We address this question by examining the dynamics of patient sharing relations within an Italian regional community of 35 hospitals serving a target population of approximately 1,300,000. We reconstruct the complete temporal sequence of the 3461 consecutive interhospital patient sharing events observed between each pair of hospitals in ...

Are Mashhad hospitals ready for medical tourists?

Introduction: Journey to treat certain diseases or performing surgery in medical centers in another country is called medical tourism. This new industry to earn millions of dollars annually participating countries. Due to differences in hospital quality and cost effective services across the world they are attracting medical tourists. This study aimed to assess the level of preparedness of hospitals in Mashhad was to attract medical tourists in 2013. Materials and Methods: This cross-sectional study in four hospitals in Mashhad which attracts medical tourists (Imam Reza, Ghaem, Doctor Shaikh and Taleghani) was conducted in 2013. Data were collected using a researcher-made Checklist (22 questions), the field observations and interviews with hospital staff. Data using descriptive statistics (mean, standard deviation, and percent) and the Pearson correlation coefficient was also tested. Results: Most preparedness in attracting medical tourists among hospitals was 63.5% and minimum level was 46.75%. Most preparedness related to medical equipment and geographic conditions (87%) and the least amount of preparation on the website (20%). Pearson correlation between the level of preparedness of hospitals and numbers of foreign patients was not significant. Conclusion: In accordance with the regulations of the Ministry of Health hospitals were been in relative preparedness in attracting medical tourists. Hospitals should invest more on their strengths and unique abilities as they introduce their services; their role has become more prominent in this market. It can also be modified on the basis of the comprehensive plan, staff training and improvement of processes to produce the desired result.

Patient hospital choice for hip replacement: empirical evidence from the Netherlands

In the Dutch health care system, hospitals are expected to compete. A necessary condition for competition among hospitals is that patients do not automatically choose the nearest hospital, but are-at least to some extent-sensitive to differences in hospital quality. In this study, an analysis is performed on the underlying features of patient hospital choice in a setting where prices do not matter for patients as a result of health insurance coverage. Using claims data from all Dutch hospitals over the years 2008-2010, a conditional logit model examines the relationship between patient characteristics (age, gender and reoperations) and hospital attributes (hospital quality information, waiting times on treatments and travel time for patients to the hospitals) in the market for general non-emergency hip replacement treatments. The results show that travel time is the most important determinant in patient hospital choice. From our analysis, however, it follows that publicly available hospital quality ratings and waiting times also have a significant impact on patient hospital choice. The panel data used for this study (2008-2010) is rather short, which may explain why no coherent and persistent changes in patient hospital choice behaviour over time are found.

Abstract 12: Impact of Community Factors on Readmission Rates

Objective Readmissions in the Medicare population are common and expensive, and have thus become a major target for intervention by federal and state policymakers. Starting in 2012, the Centers for Medicare and Medicaid Services will penalize hospitals with high readmission rates, in hopes of reducing readmissions nationally. However, many worry that variations in readmission rates are driven less by the quality of the hospital’s care and more by community-level factors such as socioeconomic status and availability of primary care. Empirical data here would be immensely helpful. Therefore, we sought to understand to what extent variations in readmission rates across communities are driven by the quality of hospital care in those communities versus socioeconomic factors and supply-side factors. Methods We used national Medicare data and Dartmouth Atlas data to calculate hospital referral region (HRR)-level readmission rates for congestive heart failure (CHF). We examined the impact of community poverty levels, racial makeup, and supply of physicians and hospital beds on readmission rates using linear regression models. We also examined the impact of patient case-mix and hospital quality performance on readmission rates. Finally, using partial r-square values from multivariable models, we examined the degree to which community-level variations in readmission rates are driven by variation in the sickness of the population, the quality of the hospital care, the supply of physicians and hospital beds, and the socioeconomic makeup of the population. Results Readmission rates across communities (as defined by HRRs) ranged from 10% to 32% for CHF. In univariate analysis, HRRs with higher poverty rates, a higher proportion of black patients, more physicians and hospital beds in the community, and a sicker population had higher readmission rates. In multivariate models that examined the degree to which variation in readmission rates for CHF between HRRs was explained by each of these different community-level factors, we found that supply-side variables (physician and bed supply in the community) were most important (explaining 17% of the variation) followed by socioeconomic characteristics of the community (poverty rate and racial makeup) at 9%. Differences in hospital quality explained 5% of the variation in readmission rates and differences in case mix explained 4%. Conclusions and Implications Community-level socioeconomic variables and supply-side variables play a much larger role in explaining variations in readmissions than the quality of hospital care or underlying sickness of the population. These findings suggest that the current federal initiatives to penalize hospitals may not be optimally targeted. Focusing on community-level factors such as the supply and mix of physicians and targeting efforts towards poor and minority communities may be more fruitful approaches to reducing readmissions. Otherwise, we risk targeting the providers that care for disenfranchised patients in ways that are likely to worsen existing disparities in care.

Do patients choose hospitals with high quality ratings? Empirical evidence from the market for angioplasty in the Netherlands

A necessary condition for competition to promote quality in hospital markets is that patients are sensitive to differences in hospital quality. In this paper we examine the relationship between hospital quality, as measured by publicly available quality ratings, and patient hospital choice for angioplasty using individual claims data from a large health insurer. We find that Dutch patients have a high propensity to choose hospitals with a good reputation, both overall and for cardiology, and a low readmission rate after treatment for heart failure. Relative to a mean readmission rate of 8.5% we find that a 1%-point lower readmission rate is associated with a 12% increase in hospital demand. Since readmission rates are not adjusted for case-mix they may not provide a correct signal of hospital quality. Insofar patients base their hospital choice on such imperfect quality information, this may result in suboptimal choices and risk selection by hospitals.

Variation in Hospital Complication Rates and Failure-to-Rescue for Trauma Patients

To examine whether failure-to-rescue is an important mechanism driving outcome differences across low- and high-mortality hospitals caring for trauma patients. Reducing medical errors and complications has become the focus of quality improvement efforts. Patients undergoing general and vascular surgery in high-mortality hospitals have similar rates of complications but higher failure-to-rescue rates compared to patients in low-mortality hospitals. Retrospective cohort study based on 54,713 patient records in the National Trauma Databank in 2007. Hospitals were classified as low-mortality, average, and high-mortality hospitals using the Trauma Mortality Probability Model. Regression modeling was used to explore the impact of hospital quality ranking on the incidence of major complications and on the incidence of failure-to-rescue (death after a major complication), adjusting for injury severity, mechanism of trauma, and patient physiology. Trauma patients in low-mortality hospitals had similar unadjusted rates of major complications compared to patients in high-mortality hospitals (5.9% vs. 5.5%). However, patients in low-mortality hospital had a lower failure-to-rescue rate compared to patients in high-mortality hospitals (adj OR 0.26: 95% CI 0.20, 0.39) Our findings suggest that the primary driver of differences in hospital quality for trauma patients is failure-to-rescue as opposed to differences in complication rates. Achieving lower mortality rates in trauma patients may require reducing both the incidence of major complications and the incidence of death after major complications.

Assessing Geographical Variations in Hospital Processes of Care Using Multilevel Item Response Models.

With health care reform passing in the United States, much effort is directed toward developing and disseminating comparative information on standardized processes of care for health care providers. We propose the use of Bayesian multilevel item response theory models to estimate hospital quality from multiple process measures and to assess geographical variation in hospital quality. Our approach fully incorporates the nesting structure of measures, patients, hospitals, and various levels of geographical units to provide a summary of hospital quality. A national dataset of patients treated for a heart attack, heart failure, or pneumonia illustrates our methods. We find considerable geographical differences in hospital quality for these conditions with variations across census regions and states accounting for slightly more than 10% of the total variation. Some states performed well for all three conditions (e.g., the respective posterior probabilities of having better than the national average performance was close to 1 in Iowa, New Jersey, South Dakota, and Wisconsin). In contrast, quality of other states varied across conditions (e.g., the corresponding posterior probability was close to 1 in Massachusetts for heart attack and heart failure quality, but less than 0.5 for pneumonia care). Our framework provides a comprehensive approach to assessment of hospital performance at both regional and national levels, and might be informative for policy development.

Hospital Quality Rankings in Vascular Surgery: The Impact of Adjusting for Reliability

Introduction: Because of random variation, differences in risk-adjusted hospital mortality may not accurately reflect differences in hospital quality. We sought to determine the effect of empirical Bayes methods (reliability adjustment) on hospital mortality rankings for vascular surgery. Methods: Patients undergoing 5 common vascular surgery procedures (open and endovascular abdominal aortic aneurysm repair, carotid endarterectomy, lower extremity bypass and aortofemoral bypass). in the National Surgical Quality Improvement Project (NSQIP) in 2007 were identified (n=14,559).

Hospital Performance in Caring for Injured Patients

To determine whether quality measures based on injury-specific models provide a different perspective about relative hospital rankings compared with a single outcome measure based on all trauma patients. We customized the Trauma Mortality Probability Model to create separate injury-specific models for patients who sustained blunt trauma, gunshot wounds, pedestrian trauma, or motor vehicle accident trauma. This analysis was conducted using the National Trauma Data Bank. We limited the study to hospitals with 250 or more trauma admissions per year, which coded more than 90% of patients. The final data set included 54 859 patients admitted to 44 hospitals. We performed hospital-level analyses to examine the correlation between hospital risk-adjusted mortality measures based on all trauma patients vs quality measures based on injury-specific measures. The analysis of the intraclass correlation coefficients suggests fair-to-substantial agreement (0.39-0.68) between the hospital-adjusted odds ratios based on all patients vs odds ratios based on specific injuries. kappa Analysis demonstrated poor-to-fair agreement between hospital categorical quality measures (high, intermediate, and low quality) when hospital quality was based on outcomes for all trauma patients vs specific subgroups of patients (0.0-0.38). However, none of the hospitals classified as high quality, based on data from all trauma patients, was found to be low quality for any specific injury populations. A single composite measure based on all injured patients may not capture all the differences in hospital quality across different populations of injured patients.

Explaining racial disparities in mortality after abdominal aortic aneurysm repair

Black patients have a higher mortality rate than nonblacks after abdominal aortic aneurysm repair. We sought to understand the factors responsible for this racial disparity in the mortality rate after aneurysm repair. The Medicare database (2001-2006) was used to identify 160,785 patients undergoing open and endovascular abdominal aortic aneurysm repairs. We used risk-adjusted mortality as our primary measure of quality and logistic regression to determine the relationship between race and mortality, sequentially adding contributing factors including patient characteristics, the type of repair (endovascular vs open repair), socioeconomic status, and hospital quality. From these sequential regression models, we estimated the proportion of the disparity that can be explained by each factor. Black patients had a 36% higher risk-adjusted mortality after aneurysm repair than nonblack patients (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.20-1.53). Even after accounting for the type of repair, a significant difference in mortality remained (OR, 1.33; 95% CI, 1.18-1.50). Mortality rates were higher in hospitals treating a higher proportion of black patients. Adjusting for these differences in hospital quality, this disparity was no longer significant (OR, 1.07; 95% CI, 0.93-1.25). We estimate that 29% of the observed disparity in mortality is caused by patient comorbidities, 6% from the use of endovascular repairs, 26% due to socioeconomic factors, and 25% because black patients receive care in lower-quality hospitals. Although many factors contribute, a large proportion of observed disparities in outcomes are attributable to black patients receiving care in lower-quality hospitals. Efforts aimed at improving disparities must focus on improved access to high-quality hospitals and improved resources at the hospitals that treat higher proportions of black patients.

The impact of physician characteristics in conditional choice models for hospital care

Recent research has investigated the determinants of the specific hospitals to which patients are admitted. Data limitations have led researchers to examine the effects of patient and hospital characteristics while ignoring the role of physician characteristics. In this study we analyze the effects of all three sets of factors on hospital choice in the greater Phoenix area during 1989. Our results suggest that physician characteristics are strong determinants of hospital choice, accounting for much of the explained variation. Differences in hospital quality and cost, on the other hand, exert significant effects on hospital choice but explain relatively little variation.