Hospital-level Quality Research Articles

Hospital Quality and Racial Differences in Outcomes After Genitourinary Cancer Surgery.

Prior work has demonstrated racial disparities in surgical outcomes for solid organ cancers. We sought to assess the relationship between hospital quality and racial disparities in achievement of textbook outcomes among patients undergoing surgery for prostate, kidney, and bladder cancer. We used 100% national Medicare Provider Analysis and Review files from 2017 to 2020 to assess textbook outcomes in Patients undergoing bladder (i.e., radical cystectomy), kidney (i.e., radical or partial nephrectomy), and prostate (i.e., radical prostatectomy) surgery for genitourinary malignancies. Our exposure was hospital-level quality, assessed by the predicted to expected ratio of achievement of textbook outcomes, agnostic to social and economic determinants of health. Our main outcome was achievement of textbook outcomes in White and Black patients. We defined the textbook outcome as the absence of in-hospital mortality, mortality within 30 days of surgery, readmission within 30 days of discharge, a postoperative complication, and prolonged length of stay. The secondary outcome was percentage of Black and White patients treated at the highest quality hospitals. As hospital quality increased, disparities in the receipt of textbook outcome for White and Black patients narrowed. For every 0.1 increment increase in the predicted to expected ratio of hospital quality, Black-White disparities in the odds of achieving textbook outcomes decreased by 5.7% (interaction OR: 1.06; 95% CI 1.01-1.11 p = 0.026). Black patients were less likely to be treated at the highest quality hospitals compared to White patients (45.2% vs. 49.5% p = < 0.001%). Compared to White patients, Black patients had lower odds of textbook outcomes after surgery for prostate, kidney, and bladder cancer. The racial differences in achieving textbook outcomes were narrowed as hospital quality increased. Black patients were less likely than White patients to be treated at the highest-quality hospitals. Our findings underscore the importance of improved access to high quality care among Black patients.

Home Heart Hospital Associated With Reduced Hospitalizations and Costs Among High-Cost Patients With Cardiovascular Disease.

There is no widely accepted care model for managing high-need, high-cost (HNHC) patients. We hypothesized that a Home Heart Hospital (H3), which provides longitudinal, hospital-level at-home care, would improve care quality and reduce costs for HNHC patients with cardiovascular disease (CVD). To evaluate associations between enrollment in H3, which provides longitudinal, hospital-level at-home care, care quality, and costs for HNHC patients with CVD. This retrospective within-subject cohort study used insurance claims and electronic health records data to evaluate unadjusted and adjusted annualized hospitalization rates, total costs of care, part A costs, and mortality rates before, during, and following H3. Ninety-four patients were enrolled in H3 between February 2019 and October 2021. Patients' mean age was 75 years and 50% were female. Common comorbidities included congestive heart failure (50%), atrial fibrillation (37%), coronary artery disease (44%). Relative to pre-enrollment, enrollment in H3 was associated with significant reductions in annualized hospitalization rates (absolute reduction (AR): 2.4 hospitalizations/year, 95% confidence interval [95% CI]: -0.8, -4.0; p < 0.001; total costs of care (AR: -$56 990, 95% CI: -$105 170, -$8810; p < 0.05; and part A costs (AR: -$78 210, 95% CI: -$114 770, -$41 640; p < 0.001). Annualized post-H3 total costs and part A costs were significantly lower than pre-enrollment costs (total costs of care: -$113 510, 95% CI: -$151 340, -$65 320; p < 0.001; part A costs: -$84 480, 95% CI: -$121 040, -$47 920; p < 0.001). Longitudinal home-based care models hold promise for improving quality and reducing healthcare spending for HNHC patients with CVD.

Ensuring safe and equitable discharge: a quality improvement initiative for individuals with hypertensive disorders of pregnancy

ObjectiveTo improve timely and equitable access to postpartum blood pressure (BP) monitoring in individuals with hypertensive disorders of pregnancy (HDP).MethodsA quality improvement initiative was implemented at a large academic medical...

Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation

BackgroundRisk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify covariates as main effects, but they often ignore interactions or use stratification to account for effect modification, despite limitations due to rare events and sparse data. Three Agency for Healthcare Research and Quality (AHRQ) hospital-level Quality Indicators currently use stratified models, but their variable performance and limited interpretability motivated the design of better models.MethodsWe analysed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. We used hierarchical group lasso regularisation (HGLR) to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 (Pancreatic Resection Mortality Rate), IQI 11 (Abdominal Aortic Aneurysm Repair Mortality Rate), and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO).ResultsHGLR identified clinically meaningful interactions for all models. Synergistic IQI 11 interactions, such as between hypertension and respiratory failure, suggest patients who merit special attention in perioperative care. Antagonistic IQI 11 interactions, such as between shock and chronic comorbidities, illustrate that naïve main effects models overestimate risk in key subpopulations. Interactions for PSI 14 suggest key subpopulations for whom the risk of wound dehiscence is similar between open and laparoscopic approaches, whereas laparoscopic approach is safer for other groups. Model performance was similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features.ConclusionsIn this application to high-profile, high-stakes risk-adjustment models, HGLR selected interactions that maintained or improved model performance in populations with heterogeneous risk, while identifying clinically important interactions. The HGLR package is scalable to handle a large number of covariates and their interactions and is customisable to use multiple CPU cores to reduce analysis time. The HGLR method will allow scholars to avoid creating stratified models on sparse data, improve model calibration, and reduce bias. Future work involves testing using other combinations of risk factors, such as vital signs and laboratory values. Our study focuses on a real-world problem of considerable importance to hospitals and policy-makers who must use RA models for statutorily mandated public reporting and payment programmes.

Stability of hospital quality indicators over time: A multi-year observational study of German hospital data.

Retrospective hospital quality indicators can only be useful if they are trustworthy signals of current or future quality. Despite extensive longitudinal quality indicator data and many hospital quality public reporting initiatives, research on quality indicator stability over time is scarce and skepticism about their usefulness widespread. Based on aggregated, widely available hospital-level quality indicators, this paper sought to determine whether quality indicators are stable over time. Implications for health policy were drawn and the limited methodological foundation for stability assessments of hospital-level quality indicators enhanced. Two longitudinal datasets (self-reported and routine data), including all hospitals in Germany and covering the period from 2004 to 2017, were analysed. A logistic regression using Generalized Estimating Equations, a time-dependent, graphic quintile representation of risk-adjusted rates and Spearman's rank correlation coefficient were used. For a total of eight German quality indicators significant stability over time was demonstrated. The probability of remaining in the best quality cluster in the future across all hospitals reached from 46.9% (CI: 42.4-51.6%) for hip replacement reoperations to 80.4% (CI: 76.4-83.8%) for decubitus. Furthermore, graphical descriptive analysis showed that the difference in adverse event rates for the 20% top performing compared to the 20% worst performing hospitals in the two following years is on average between 30% for stroke and AMI and 79% for decubitus. Stability over time has been shown to vary strongly between indicators and treatment areas. Quality indicators were found to have sufficient stability over time for public reporting. Potentially, increasing case volumes per hospital, centralisation of medical services and minimum-quantity regulations may lead to more stable and reliable quality of care indicators. Finally, more robust policy interventions such as outcome-based payment, should only be applied to outcome indicators with a higher level of stability over time. This should be subject to future research.

Changes in Older Adult Trauma Quality When Evaluated Using Longer-Term Outcomes vs In-Hospital Mortality

Lack of knowledge about longer-term outcomes remains a critical blind spot for trauma systems. Recent efforts have expanded trauma quality evaluation to include a broader array of postdischarge quality metrics. It remains unknown how such quality metrics should be used. To examine the utility of implementing recommended postdischarge quality metrics as a composite score and ascertain how composite score performance compares with that of in-hospital mortality for evaluating associations with hospital-level factors. This national hospital-level quality assessment evaluated hospital-level care quality using 100% Medicare fee-for-service claims of older adults (aged ≥65 years) hospitalized with primary diagnoses of trauma, hip fracture, and severe traumatic brain injury (TBI) between January 1, 2014, and December 31, 2015. Hospitals with annual volumes encompassing 10 or more of each diagnosis were included. The data analysis was performed between January 1, 2021, and December 31, 2022. Reliability-adjusted quality metrics used to calculate composite scores included hospital-specific performance on mortality, readmission, and patients' average number of healthy days at home (HDAH) within 30, 90, and 365 days among older adults hospitalized with all forms of trauma, hip fracture, and severe TBI. Associations with hospital-level factors were compared using volume-weighted multivariable logistic regression. A total of 573 554 older adults (mean [SD] age, 83.1 [8.3] years; 64.8% female; 35.2% male) from 1234 hospitals were included. All 27 reliability-adjusted postdischarge quality metrics significantly contributed to the composite score. The most important drivers were 30- and 90-day readmission, patients' average number of HDAH within 365 days, and 365-day mortality among all trauma patients. Associations with hospital-level factors revealed predominantly anticipated trends when older adult trauma quality was evaluated using composite scores (eg, worst performance was associated with decreased older adult trauma volume [odds ratio, 0.89; 95% CI, 0.88-0.90]). Results for in-hospital mortality showed inverted associations for each considered hospital-level factor and suggested that compared with nontrauma centers, level 1 trauma centers had a 17 times higher risk-adjusted odds of worst (highest quantile) vs best (lowest quintile) performance (odds ratio, 17.08; 95% CI, 16.17-18.05). The study results challenge historical notions about the adequacy of in-hospital mortality as the single measure of older adult trauma quality and suggest that, when it comes to older adults, decisions about how quality is evaluated can profoundly alter understandings of what constitutes best practices for care. Composite scores appear to offer a promising means by which postdischarge quality metrics could be used.

Surgeon-level versus hospital-level quality variance in kidney cancer surgery.

To determine whether variance in kidney cancer surgery quality indicators (QIs) is most impacted by surgeon-level or hospital-level factors in order to inform quality improvement initiatives. The ICES and Veterans Affairs (VA) databases were queried for patients undergoing surgery for localized kidney cancer. Kidney cancer surgery QIs were defined within each cohort. Quality of care was benchmarked at a surgeon- vs. hospital-level to identify statistical outliers, using available clinicopathological data to adjust for differences in case-mix. Variance between surgeons and hospitals was calculated for each QI using a random-effects model. The QI with the greatest amount of variance explained by hospital and surgeon-level factors was proportion of cases performed with minimally invasive surgery (MIS). The majority of this variance was due to surgeon-level factors for both the VA and ICES cohorts. The proportion of cases performed using an MIS approach was also the QI with the greatest number of outlier hospitals and surgeons compared to the average performance. The proportion of partial nephrectomies performed for patients at risk of chronic kidney disease was the QI with the greatest amount of variance due to hospital-level factors for the ICES cohort. The proportion of localized kidney cancer cases performed using an MIS approach is the QI requiring the greatest attention. Quality improvement initiatives should focus on surgeon-level factors to increase the number of MIS cases being performed for patients with localized renal masses.

Influence of Perioperative COVID-19 Infection on Surgical Outcomes and Risk Adjustment for Quality Assessment in the American College of Surgeons NSQIP.

Surgical patients with perioperative COVID-19 infection experience higher rates of adverse events than those without COVID, which may lead to imprecision in hospital-level quality assessment. Our objectives were to quantify differences in COVID-19 associated adverse events in a large national sample and examine distortions in surgical quality benchmarking if COVID status is not considered. Data included 793,280 patient records from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) from April 1, 2020 to March 31, 2021. Models predicting 30-day Mortality, Morbidity, Pneumonia, Ventilator Dependency > 48 hours, and unplanned intubation were constructed. Risk adjustment variables were selected for these models from standard NSQIP predictors and perioperative COVID status. 5,878 (0.66%) had preoperative COVID and 5,215 (0.58%) had postoperative COVID. COVID rates demonstrated some consistency across hospitals (median preoperative 0.84%, IQR=0.14%-0.84%; median postoperative 0.50%, IQR=0.24%-0.78%). Postoperative COVID was always associated with increased adverse events. For postoperative COVID among all cases, there was nearly a six-fold increase in Mortality (1.07% to 6.37%) and15-fold increase in Pneumonia (0.92% to 13.57%), excluding the diagnosis of COVID itself. The effects of preoperative COVID were less consistent. Inclusion of COVID in risk-adjustment models had minimal effects on surgical quality assessments. Perioperative COVID was associated with a dramatic increase in adverse events. However, quality benchmarking minimally affected. This may be the result of low overall COVID rates or balance in rates established across hospitals over the 1-year observational period. There remains limited evidence for restructuring ACS NSQIP risk-adjustment for the time-limited effects of the COVID pandemic.

Performance of ICD-10-AM codes for quality improvement monitoring of hospital-acquired pneumonia in a haematology-oncology casemix in Victoria, Australia.

The Australian hospital-acquired complication (HAC) policy was introduced to facilitate negative funding adjustments in Australian hospitals using ICD-10-AM codes. The aim of this study was to determine the positive predictive value (PPV) of the ICD-10-AM codes in the HAC framework to detect hospital-acquired pneumonia in patients with cancer and to describe any change in PPV before and after implementation of an electronic medical record (EMR) at our centre. A retrospective case review of all coded pneumonia episodes at the Peter MacCallum Cancer Centre in Melbourne, Australia spanning two time periods (01 July 2015 to 30 June 2017 [pre-EMR period] and 01 September 2020 to 28 February 2021 [EMR period]) was performed to determine the proportion of events satisfying standardised surveillance definitions. HAC-coded pneumonia occurred in 3.66% (n = 151) of 41,260 separations during the study period. Of the 151 coded pneumonia separations, 27 satisfied consensus surveillance criteria, corresponding to an overall PPV of 0.18 (95% CI: 0.12, 0.25). The PPV was approximately three times higher following EMR implementation (0.34 [95% CI: 0.19, 0.53] versus 0.13 [95% CI: 0.08, 0.21]; p = .013). The current HAC definition is a poor-to-moderate classifier for hospital-acquired pneumonia in patients with cancer and, therefore, may not accurately reflect hospital-level quality improvement. Implementation of an EMR did enhance case detection, and future refinements to administratively coded data in support of robust monitoring frameworks should focus on EMR systems. Although ICD-10-AM data are readily available in Australian healthcare settings, these data are not sufficient for monitoring and reporting of hospital-acquired pneumonia in haematology-oncology patients.

Association of Quality and Technology With Patient Mobility for Colorectal Cancer Surgery

Many health care systems publish hospital-level quality measures as a driver of hospital performance and to support patient choice, but it is not known if patients with cancer respond to them. To investigate hospital quality and patient factors associated with treatment location. This choice modeling study used national administrative hospital data. Patients with colon and rectal cancer treated in all 163 English National Health Service (NHS) hospitals delivering colorectal cancer surgery between April 2016 and March 2019 were included. The extent to which patients chose to bypass their nearest surgery center was investigated, and conditional logistic regression was used to estimate the association of additional travel time, hospital quality measures, and patient characteristics with treatment location. Additional travel time in minutes, hospital characteristics, and patient characteristics: age, sex, cancer T stage, socioeconomic status, comorbidity, and rural or urban residence. Treatment location. Overall, 44 299 patients were included in the final cohort (mean [SD] age, 68.9 [11.6] years; 18 829 [42.5%] female). A total of 8550 of 31 258 patients with colon cancer (27.4%) and 3933 of 13 041 patients with rectal cancer (30.2%) bypassed their nearest surgical center. Travel time was strongly associated with treatment location. The association was less strong for younger, more affluent patients and those from rural areas. For rectal cancer, patients were more likely to travel to a hospital designated as a specialist colorectal cancer surgery center (odds ratio, 1.45; 95% CI, 1.13-1.87; P = .004) and to a hospital performing robotic surgery for rectal cancer (odds ratio, 1.43; 95% CI, 1.11-1.86; P = .007). Patients were less likely to travel to hospitals deemed to have inadequate care by the national quality regulator (odds ratio, 0.70; 95% CI, 0.50-0.97; P = .03). Patients were not more likely to travel to hospitals with better 2-year bowel cancer mortality outcomes. Patients appear responsive to hospital characteristics that reflect overall hospital quality and the availability of robotic surgery but not to specific disease-related outcome measures. Policies allowing patients to choose where they have colorectal cancer surgery may not result in better outcomes but could drive inequities in the health care system.

Combined impact of Medicare’s hospital pay for performance programs on quality and safety outcomes is mixed

BackgroundThree major hospital pay for performance (P4P) programs were introduced by the Affordable Care Act and intended to improve the quality, safety and efficiency of care provided to Medicare beneficiaries. The financial risk to hospitals associated with Medicare’s P4P programs is substantial. Evidence on the positive impact of these programs, however, has been mixed, and no study has assessed their combined impact. In this study, we examined the combined impact of Medicare’s P4P programs on clinical areas and populations targeted by the programs, as well as those outside their focus.MethodsWe used 2007–2016 Healthcare Cost and Utilization Project State Inpatient Databases for 14 states to identify hospital-level inpatient quality indicators (IQIs) and patient safety indicators (PSIs), by quarter and payer (Medicare vs. non-Medicare). IQIs and PSIs are standardized, evidence-based measures that can be used to track hospital quality of care and patient safety over time using hospital administrative data. The study period of 2007–2016 was selected to capture multiple years before and after introduction of program metrics. Interrupted time series was used to analyze the impact of the P4P programs on study outcomes targeted and not targeted by the programs. In sensitivity analyses, we examined the impact of these programs on care for non-Medicare patients.ResultsMedicare P4P programs were not associated with consistent improvements in targeted or non-targeted quality and safety measures. Moreover, mortality rates across targeted and untargeted conditions were generally getting worse after the introduction of Medicare’s P4P programs. Trends in PSIs were extremely mixed, with five outcomes trending in an expected (improving) direction, five trending in an unexpected (deteriorating) direction, and three with insignificant changes over time. Sensitivity analyses did not substantially alter these results.ConclusionsConsistent with previous studies for individual programs, we detect minimal, if any, effect of Medicare’s hospital P4P programs on quality and safety. Given the growing evidence of limited impact, the administrative cost of monitoring and enforcing penalties, and potential increase in mortality, CMS should consider redesigning their P4P programs before continuing to expand them.

Preexisting frailty and outcomes in older patients with acute myocardial infarction

Little is known about the prevalence and prognostic impact of preexisting frailty on acute care and in-hospital outcomes in older adults in the setting of acute myocardial infarction (AMI). Preexisting frailty was assessed at baseline in consecutive AMI patients ≥65 years of age treated at 778 hospitals participating in the NCDR ACTION Registry between January 1, 2015 to December 31, 2016. Three domains of preexisting frailty (cognition, ambulation, and functional independence) were abstracted from chart review and summed in 2 ways: an ACTION Frailty Scale based on responses to 6 groups adapted from the Canadian Study of Health and Aging Clinical Frailty Scale and an ACTION Frailty Score derived by summing a rank score of 0-2 assigned for each grade (total ranged between 0 to 6). Multivariable logistic regression examined the association between assigned frailty by score or scale and in-hospital mortality. Among 143,722 older AMI patients, 108,059 (75.2%) were fit and/or well and 6,484 (4.5%) were vulnerable to frailty, while 7,527 (5.2%) had mild, 3,913 (2.7%) had moderate, 2,715 had (1.9%) severe, and 632 (0.4%) had very severe frailty according to the ACTION Frailty Scale, while 14,392 (10.0%) could not be categorized due to incomplete ascertainment. Frail patients were older, more frequently female, of non-white race and/or ethnicity, and less likely to be treated with guideline-recommended therapies. Increasing severity of frailty by this scale was associated with a step-wise higher risk for in-hospital mortality (P-trend < .001). Patient categories of the ACTION Frailty Score provided similar results. After adjustment, each 1-unit increase in Frailty Score was associated with a 12% higher mortality risk (OR 1.12, 95% CI 1.10-1.15). Among older patients with acute myocardial infarction, frailty is common and independently associated with in-hospital mortality. These findings show the importance of pragmatic evaluation of frailty in hospital-level quality scores, guideline recommendations, and incorporation into other registry data collection efforts.

Hospital readmission after blunt traumatic rib fractures.

Hospital readmissions are resource intensive, associated with increased morbidity, and often used as hospital-level quality indicators. The factors that determine hospital readmission after blunt thoracic trauma have not been sufficiently defined. We sought to identify predictors of hospital readmission in patients with traumatic rib fractures. We performed an 8-year (2011-2019) retrospective chart review of patients with traumatic rib fractures who required unplanned readmission within 30 days of discharge at a Level 1 trauma center. Patient characteristics, injury severity, and hospital complications were examined using quantitative analysis to identify readmission risk factors. There were 13,046 trauma admissions during the study period. The traumatic rib fracture cohort consisted of 3,720 patients. The cohort included 206 patients who were readmitted within 30 days of discharge. The mean age of the traumatic rib fracture cohort was 57 years, with a 6-day median length of stay. The 30-day mortality rate was 5%. Use of anticoagulation (11.0 vs. 5.4; p = 0.029), diagnosis of a psychiatric disorder (10.2 vs. 5.3; p = 0.01), active smoking (7.3 vs. 5.0; p = 0.008), associated hemothorax (8.3 vs. 5.2; p = 0.010), higher abdominal Abbreviated Injury Scale (33.3 vs. 8.4 vs. 6.5; p = 0.002), rapid response activation (8.9 vs. 5.2; p = 0.005), admission to intensive care unit (7.7 vs. 4.5; p = 0.001), and diagnosis of in-hospital pneumonia (10.1 vs. 5.4; p = 0.022) were predictors of hospital readmission. On multivariate analysis, prescribed anticoagulation (odds ratio [OR], 2.22; p = 0.033), active smoking (OR, 1.58; p = 0.004), higher abdominal Abbreviated Injury Scale (OR, 1.50; p = 0.054), and diagnosis of a psychiatric disorder (OR, 2.00; p = 0.016) predicted hospital readmission. In patients with traumatic rib fractures, those with anticoagulant use, those who actively smoke, those with a psychiatric diagnosis, or those with associated abdominal injuries are at the highest risk of rehospitalization following discharge. Quality improvement should focus on strategies and protocols directed toward these groups to reduce nonelective readmissions. Prognostic and Epidemiological; Level IV.

Relationship Between Perioperative Outcomes Used for Profiling Hospital Noncardiac Surgical Quality

BackgroundRisk-adjusted morbidity and mortality are commonly used by national surgical quality improvement (QI) programs to measure hospital-level surgical quality. However, the degree of hospital-level correlation between mortality, morbidity, and other perioperative outcomes (like reoperation) collected by contemporary surgical QI programs has not been well-characterized. Materials and MethodsVeterans Affairs (VA) Surgical Quality Improvement Program (VASQIP) data (2015-2016) were used to evaluate hospital-level correlation in performance between risk-adjusted 30-d mortality, morbidity, major morbidity, reoperation, and 2 composite outcomes (1- mortality, major morbidity, or reoperation; 2- mortality or major morbidity) after noncardiac surgery. Correlation between outcomes rates was evaluated using Pearson's correlation coefficient. Correlation between hospital risk-adjusted performance rankings was evaluated using Spearman's correlation. ResultsBased on a median of 232 [IQR 95-331] quarterly surgical cases abstracted by VASQIP, statistical power for identifying 30-d mortality outlier hospitals was estimated between 3.3% for an observed-to-expected ratio of 1.1 and 45.7% for 3.0. Among 230,247 Veterans who underwent a noncardiac operation at 137 VA hospitals, there were moderate hospital-level correlations between various risk-adjusted outcome rates (highest r = 0.40, mortality and composite 1; lowest r = 0.32, mortality and morbidity). When hospitals were ranked based on performance, there was low-to-moderate correlation between rankings on the various outcomes (highest ρ = 0.47, mortality and composite 1; lowest ρ = 0.37, mortality and major morbidity). ConclusionsModest hospital-level correlations between perioperative outcomes suggests it may be difficult to identify high (or low) performing hospitals using a single measure. Additionally, while composites of currently measured outcomes may be an efficient way to improve analytic sample size (relative to evaluations based on any individual outcome), further work is needed to understand whether they provide a more robust and accurate picture of hospital quality or whether evaluating performance across a portfolio of individual measures is most effective for driving QI.

Relative contributions of hospital versus skilled nursing facility quality on patient outcomes

BackgroundHospitals and health systems worldwide have adopted value-based payment to improve quality and reduce costs. In the USA, skilled nursing facilities (SNFs) are now financially penalised for higher-than-expected readmission rates....

Evaluation of Risk-Adjusted Home Time After Hospitalization for Heart Failure as a Potential Hospital Performance Metric

Thirty-day home time, defined as time spent alive and out of a hospital or facility, is a novel, patient-centered performance metric that incorporates readmission and mortality. To characterize risk-adjusted 30-day home time in patients discharged with heart failure (HF) as a hospital-level quality metric and evaluate its association with the 30-day risk-standardized readmission rate (RSRR), 30-day risk-standardized mortality rate (RSMR), and 1-year RSMR. This hospital-level cohort study retrospectively analyzed 100% of Medicare claims data from 2 968 341 patients from 3134 facilities from January 1, 2012, to November 30, 2017. Home time, defined as time spent alive and out of a short-term hospital, skilled nursing facility, or intermediate/long-term facility 30 days after discharge. For each hospital, a risk-adjusted 30-day home time for HF was calculated similar to the Centers for Medicare & Medicaid Services risk-adjustment models for 30-day RSRR and RSMR. Hospitals were categorized into quartiles (lowest to highest risk-adjusted home time). The correlations between hospital rates of risk-adjusted 30-day home time and 30-day RSRR, 30-day RSMR, and 1-year RSMR were estimated using the Pearson correlation coefficient. Distribution of days lost from a perfect 30-day home time were calculated. Reclassification of hospital performance using 30-day home time vs 30-day RSRR was also evaluated. Overall, 2 968 341 patients (mean [SD] age, 81.0 [8.3] years; 53.6% female) from 3134 hospitals were included in this study. The median hospital risk-adjusted 30-day home time for patients with HF was 21.77 days (range, 8.22-28.41 days). Hospitals in the highest quartile of risk-adjusted 30-day home time (best-performing hospitals) were larger (mean [SD] number of beds, 285 [275]), with a higher volume of patients with HF (median, 797 patients; interquartile range, 395-1484) and were more likely academic hospitals (59.9%) with availability of cardiac surgery (51.1%) and cardiac rehabilitation (68.8%). A total of 72% of home time lost was attributable to stays in an intermediate- or long-term care facility (mean [SD], 2.65 [6.44] days) or skilled nursing facility (mean [SD], 3.96 [9.04] days), 13% was attributable to short-term readmissions (mean [SD], 1.25 [3.25] days), and 15% was attributable to death (mean [SD], 1.37 [6.04] days). Among 30-day outcomes, the 30-day RSRR and 30-day RSMR decreased in a graded fashion across increasing 30-day home time categories (correlation coefficients: 30-day RSRR and 30-day home time, -0.23, P < .001; 30-day RSMR and 30-day home time, -0.31, P < .001). Similar patterns of association were also noted for 1-year RSMR and 30-day home time (correlation coefficient, -0.35, P < .001). Thirty-day home time meaningfully reclassified hospital performance in 30% of the hospitals compared with 30-day RSRR and in 25% of hospitals compared with 30-day RSMR. In this study, 30-day home time among patients discharged after a hospitalization for HF was objectively assessed as a hospital-level quality metric using Medicare claims data and was associated with readmission and mortality outcomes and with reclassification of hospital performance compared with 30-day RSRR and 30-day RSMR.

Will My Patient Survive an Anastomotic Leak? Predicting Failure to Rescue Using the Modified Frailty Index.

Failure to rescue (FTR), or death after major complications, has emerged as a marker of hospital-level quality of care. To evaluate the predictive performance of the ACS-NSQIP modified frailty index (mFI) in determining FTR following an anastomotic leak (AL) after a colectomy for colorectal cancer. Retrospective cohort study. Multicenter interrogation of the 2012-2016 American College of Surgeons (ACS) colectomy procedure targeted National Surgical Quality Improvement Program (NSQIP) database. A total of 50,944 patients who underwent colectomy for colorectal cancer. Frailty as measured by: (1) Age, ASA, and emergency status (model 1), (2) Age, ASA, emergency status, and mFI (model 2), (3) ACS-NSQIP mortality prediction (model 3). Primary outcome was FTR after AL. A total of 1755 patients experienced an AL (3.46%) with a FTR rate of 6.44%. The mean age was 65.6years (95% CI 65.28-65.58years), median ASA was 3 (IQR 2-3), 51 patients (2.92%) were partially or totally dependent, 366 (20.86%) were diabetic, 105 (5.98%) had a history of chronic obstructive pulmonary disease (COPD), 32 (1.82%) had a history of congestive heart disease (CHD), and 966 (55.04%) were on hypertensive treatment. The performance of model 1 (AUROC 0.77; 95% CI 0.72-0.81), model 2 (AUROC 0.77; 95% CI 0.73-0.82), and model 3 (AUROC 0.79; 95% CI 0.75-0.83) to predict FTR was not different (p = 0.44). Age and ASA remain the most reliable predictors of failure to rescue anastomotic leak after colectomy for colorectal cancer. Addition of the modified frailty index, or all variables collected by NSQIP, did not significantly improve predictive performance.

Hospital-level Effects Contribute to Variations in Prostate Cancer Quality of Care

A paucity of real-world data exists highlighting whether variations in prostate cancer quality of care occur at a hospital level, independent of differences in case mix. To overcome this knowledge gap, we benchmarked hospital-level quality (n = 1245 hospitals) across a broad multidisciplinary panel of previously reported disease-specific, expert-defined quality indicators (QIs), adjusting for differences in patient case mix by indirect standardization. A composite measure of prostate cancer quality—the prostate cancer quality score (PC-QS)—was derived, and associations between PC-QS and hospital volume, academic status, and location as well as patient all-cause mortality were determined. After adjusting for the case mix, of the total of 1245 hospitals evaluated, 2–37% were identified as those performing significantly below the national average for a given QI. Hospitals with a higher PC-QS displayed larger patient volumes, were more commonly academic affiliated, and had lower overall mortality. Collectively, our data-driven benchmarking analysis reveals that widespread hospital-level variations exist in prostate cancer quality of care after adjusting for differences in case mix, with the PC-QS serving as a novel, validated, quality benchmarking tool. Patient summaryOur statistical benchmarking method shows that the quality of prostate cancer care varies between hospitals, after accounting for differences in patient characteristics. The prostate cancer quality score is a novel, validated, quality benchmarking tool.

The Association Between Hospital End-of-Life Care Quality and the Care Received Among Patients With Heart Failure

ContextImproving end-of-life care (EOLC) quality among heart failure patients is imperative. Data are limited as to the hospital processes of care that facilitate this goal. ObjectivesTo determine associations between hospital-level EOLC quality ratings and the EOLC delivered to heart failure patients. MethodsRetrospective analysis of the Veterans Health Administration (VA) and the Bereaved Family Survey data of heart failure patients from 2013 to 2015 who died in 107 VA hospitals. We calculated hospital-level observed-to-expected casemix-adjusted ratios of family reported excellent EOLC, dividing hospitals into quintiles. Using logistic regression, we examined associations between quintiles and palliative care consultation, receipt of chaplain and bereavement services, inpatient hospice, and intensive care unit death. ResultsOf 6256 patients, mean age was 77.4 (SD = 11.1), 98.3% were male, 75.7% were white, and 18.2% were black. Median hospital scores of “excellent” EOLC ranged from 41.3% (interquartile range 37.0%–44.8%) in the lowest quintile to 76.4% (interquartile range 72.9%–80.3%) in the highest quintile. Patients who died in hospitals in the highest quintile, relative to the lowest, were slightly although not significantly more likely to receive a palliative care consultation (adjusted proportions 57.6% vs. 51.2%; P = 0.32) but were more likely to receive chaplaincy (92.6% vs. 81.2%), bereavement (86.0% vs. 72.2%), and hospice (59.7% vs. 35.9%) and were less likely to die in the intensive care unit (15.9% vs. 31.0%; P < 0.05 for all). ConclusionPatients with heart failure who die in VA hospitals with higher overall EOLC quality receive more supportive EOLC. Research is needed that integrates care processes and develops scalable best practices in EOLC across health care systems.

MP66-10 HOSPITAL-LEVEL QUALITY INDICATORS FOR KIDNEY CANCER SURGERY: A VETERAN’S AFFAIR NATIONAL HEALTH SYSTEM VALIDATION OF CONCEPT

You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Quality Improvement & Patient Safety III (MP66)1 Apr 2020MP66-10 HOSPITAL-LEVEL QUALITY INDICATORS FOR KIDNEY CANCER SURGERY: A VETERAN’S AFFAIR NATIONAL HEALTH SYSTEM VALIDATION OF CONCEPT Diego Aguilar Palacios*, Brigid Wilson, Olli Saarela, Mustafa Ascha, Sunah Song, Molly E. DeWitt-Foy, Keith Lawson, Jill Barnholtz-Sloan, Antonio Finelli, Steven C. Campbell, and Robert Abouassaly Diego Aguilar Palacios*Diego Aguilar Palacios* More articles by this author , Brigid WilsonBrigid Wilson More articles by this author , Olli SaarelaOlli Saarela More articles by this author , Mustafa AschaMustafa Ascha More articles by this author , Sunah SongSunah Song More articles by this author , Molly E. DeWitt-FoyMolly E. DeWitt-Foy More articles by this author , Keith LawsonKeith Lawson More articles by this author , Jill Barnholtz-SloanJill Barnholtz-Sloan More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Steven C. CampbellSteven C. Campbell More articles by this author , and Robert AbouassalyRobert Abouassaly More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000941.010AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Validation and implementation of quality indicators (QIs) for oncological surgical care is imperative in national health care systems. However, QIs must be adjusted for significant case-mix variations among hospitals and to capture disparate patient outcomes. Here, we explore and validate a compound quality score (CQS) as a metric for hospital-level quality of care in kidney cancer patients. METHODS: Kidney cancer patients (n = 8233) treated at the VA (2005-2015) were identified. Two previously described and validated process QIs were explored: the proportion of patients with a) T1a tumors undergoing partial nephrectomy; and b) T1-T2 tumors undergoing minimally invasive radical nephrectomy. Demographics, comorbidity, tumor characteristics and treatment year were used for case-mix adjustment using indirect standardization / multivariable regression models. The predicted vs observed ratio of cases was calculated to generate each QI score. CQS represents the sum of both QIs scores. Ninety-six hospitals were benchmarked by CQS and patient-level outcomes were regressed on CQS levels to assess for length of stay (LOS), 30 days complications/readmission, 90 days overall mortality and total cost of surgical admission. RESULTS: CQS identified 25, 33 and 38 hospitals with higher, lower and average performance, respectively. Total CQS score was independently associated with LOS [β = -0.04, p< 0.01, predicted LOS 0.84 days shorter for CQS = 2 vs. CQS = -2], 30 days surgical complications [OR = 0.88, p < 0.01] or 30 days medical complications [OR = 0.93, p < 0.01] and total cost of surgical admission [β = -0.014, p< 0.01, predicted 12% lower cost for CQS = 2 vs. CQS = -2]. No association was found between CQS and 30 day readmissions or 90 days mortality (all p>0.05), although low event rates were observed (8.9% and 1.7%, respectively). CONCLUSIONS: Variability in quality of surgical care at a hospital-level can be captured with the CQS among kidney cancer patients. CQS is associated with length of stay, post-operative complications and total cost of surgical admission. Quality indicators should be used to identify, audit and implement quality improvement strategies across health systems. Source of Funding: Merit Pilot Award #PPO 17-216, VA Grant © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e986-e987 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Diego Aguilar Palacios* More articles by this author Brigid Wilson More articles by this author Olli Saarela More articles by this author Mustafa Ascha More articles by this author Sunah Song More articles by this author Molly E. DeWitt-Foy More articles by this author Keith Lawson More articles by this author Jill Barnholtz-Sloan More articles by this author Antonio Finelli More articles by this author Steven C. Campbell More articles by this author Robert Abouassaly More articles by this author Expand All Advertisement PDF downloadLoading ...