Hospital-wide Mortality Research Articles

Abstract 16653: Assessment of Price Variation for TAVR and SAVR Procedures Across US Hospitals

Introduction Transcatheter Aortic Valve Replacement (TAVR) has become a mainstay of aortic stenosis treatment, alongside Surgical Aortic Valve Replacement (SAVR). Despite their widespread use, little is known about their commercial cost burden as this data was confidential prior to the 2021 Federal Price Transparency Final Rule. Using newly available pricing data, we aimed to compare hospital variation in TAVR and SAVR prices and describe factors predictive of prices. Methods Prices for DRG 267 (TAVR) and 220 (SAVR) were extracted from the Turquoise Health database and linked to hospital characteristics using Medicare IDs. Price variation within and across hospitals was assessed by calculating the ratio between the 90th and 10th percentile commercial or hospital rates, respectively. The Kruskal-Wallis test was used to test for associations between price and categorical factors. Results 1116 hospitals reported prices for TAVR and 1215 hospitals reported prices for SAVR. Commercial prices for TAVR ($70,482) and SAVR ($66,629) were 1.83 and 1.77x the Medicare price. Within hospitals, the 90th percentile payer-negotiated TAVR price was 1.80x the 10th percentile price, while across hospitals, the 90th percentile hospital by median commercial price was 3.40x the 10th percentile price. This was similar for SAVR. Several factors were associated with higher prices, including major teaching status (TAVR $17,736 and SAVR $19,057 vs non-teaching). For-profit status and investor-ownership were associated with lower prices. While higher hospital-wide readmission rates and higher hospital ratings were associated with higher prices, there was no significant association with hospital-wide mortality. Conclusion This analysis demonstrates substantial markups for TAVR and SAVR commercial prices and significant variation both within and between hospitals associated with teaching status, hospital ratings, and readmissions but not hospital mortality.

What Went Right? A Mixed-Methods Study of Positive Feedback Data in a Hospital-Wide Mortality Review Survey.

Toxic work culture contributes to healthcare worker burnout and attrition, but little is known about how healthcare organizations can systematically create and promote a culture of civility and collegiality. To analyze peer-to-peer positive feedback collected as part of a systematized mortality review survey to identify themes and recognition dynamics that can inform positive organizational culture change. Convergent mixed-methods study design. A total of 388 physicians, 212 registered nurses, 64 advanced practice providers, and 1 respiratory therapist at four non-profit hospitals (2 academic and 2 community). Providing optional positive feedback in the mortality review survey. Key themes and subthemes that emerged from positive feedback data, associations between key themes and positive feedback respondent characteristics, and recognition dynamics between positive feedback respondents and recipients. Approximately 20% of healthcare workers provided positive feedback. Three key themes emerged among responses with free text comments: (1) providing extraordinary patient and family-centered care; (2) demonstrating self-possession and mastery; and (3) exhibiting empathic peer support and effective team collaboration. Compared to other specialties, most positive feedback from medicine (70.2%), neurology (65.2%), hospice and palliative medicine (64.3%), and surgery (58.8%) focused on providing extraordinary patient and family-centered care (p = 0.02), whereas emergency medicine (59.1%) comments predominantly focused on demonstrating self-possession and mastery (p = 0.06). Registered nurses (40.2%) provided multidirectional positive feedback more often than other clinician types in the hospital hierarchy (p < 0.001). Analysis of positive feedback from a mortality review survey provided meaningful insights into a health system's culture of teamwork and values related to civility and collegiality when providing end-of-life care. Systematic collection and sharing of positive feedback is feasible and has the potential to promote positive culture change and improve healthcare worker well-being.

PO-03-182 ASSESSMENT OF PRICE VARIATION IN CARDIOVASCULAR CATHETER ABLATIONS AT US HOSPITALS

Ablation of atrial fibrillation (AF) and ventricular tachycardia (VT) comprise a significant proportion of catheter ablation procedures. However, few studies have explored the commercial cost footprint of these procedures, despite the 2021 Hospital Price Transparency Final Rule mandating US hospitals publish their insurer-negotiated prices. This study sought to characterize and identify factors contributing to variation in AF and VT ablation prices across the US. We queried a commercial database containing hospital-disclosed prices to characterize variation in ablation procedure prices within and across US hospitals. This price data was linked to hospital characteristics using Medicare Facility IDs. Univariate analysis was conducted to compare median prices across multiple factors. In total, 1,030 hospitals reported commercial prices for AF ablation, while 1,006 reported prices for VT ablation. For AF ablation, median prices were higher at major investor-owned facilities (p<0.01), while for VT ablation, investor-owned facilities had lower prices (p<0.01). For both AF and VT ablation, the New England region exhibited the highest median price ($28,141 and $28,145, respectively), while the Middle Atlantic exhibited the lowest median price ($16,829 and $15,666, respectively). Higher prices were associated with better than average all-cause readmission rates (p<0.01 for AF and VT ablation) and higher patient hospital ratings (p<0.01 for both). However, CMS hospital ratings (AF: p=0.42; VT: p=0.08) and hospital-wide mortality were not associated with price (AF: p=0.36; VT: p=0.56). For both AF and VT ablation, commercial rates did not differ widely from the Medicare price. Within hospitals, the 90th percentile payer-negotiated rate was 2.42 (IQR 1.34-4.61) times the 10th percentile rate (within-center ratio) for AF ablation and 2.40 (IQR 1.41-5.16) for VT ablation. Across hospitals within the same hospital referral region (HRR), the median price at the 90th percentile hospital was 1.54 (IQR 1.00-2.63) times that at the 10th percentile hospital (between-center ratio) for AF ablation and 1.47 (IQR 1.00-2.61) for VT ablation. Prices for ablation procedures varied widely within and between hospitals. Commercial markup from the Medicare price was limited. Several factors correlated closely with reimbursement, including hospital-wide readmissions and patient ratings. However, there was no association between price and hospital-wide mortality or CMS rating.Tabled 1Univariate Analysis of Factors Associated with Cardiovascular Catheter Ablations Commercial Price (PO-03-182)Atrial Fibrillation AblationVentricular Tachycardia AblationCategorical Values(% Reporting)# ofHospitals(%)MedianPrice($)IQR($)P-Value †# ofHospitals(%)MedianPrice($)IQR($)P-Value †Region (92.1, 92.4)<0.01<0.01 New England (CT, MA, ME, NH, RI, VT)Middle Atlantic (NJ, NY, PA)South Atlantic (DC, DE, MD, VA, WV, C, SC, GA, FL)East North Central (OH, IN, IL, MI, WI)East South Central (KY, TN, AL, MS)West North Central (MN, IA, MO, ND, SD, NE, KS)West South Central (AR, LA, OK, TX)Mountain (MT, ID, WY, CO, NM, AZ, UT, NV)Pacific (WA, OR, CA, AK, HI)37 (3.9)112 (11.8)226 (23.8)188 (19.8)79 (8.3)97 (10.2)140 (14.8)42 (4.4)28 (3.0)28141168291808220065215512439926517258912460626243-305588362-2330411750-2588211817-2913316087-2799216203-2839416641-3176322631-3315017616-3916037 (4.0)108 (11.6)226 (24.3)176 (18.9)81 (8.7)91 (9.8)142 (15.3)40 (4.3)29 (3.1)28145156661808217831211982081224674258892727725174-309468071-2164711019-2589111817-2823914531-2789011792-2672412432-3169522516-3274423006-42535Hospital Type (88.7, 89.2)0.06<0.01 For-ProfitNot-For-ProfitGovernment194 (21.2)651 (71.2)69 (7.5)21798216562256111338-2817712739-3033818147-31958190 (21.2)640 (71.3)67 (7.5)1734020544233759273-2646612003-3020416843-36485Major Investor Owned (96.6, 96.7)<0.01<0.01 Major Investor OwnedNot Major Investor Owned222 (22.3)773 (77.7)221512002711751-2798113330-30470215 (22.1)758 (77.9)16841211819273-2588912720-30719Teaching Status (92.1, 92.4)0.830.59 Major TeachingMinor TeachingNon-Teaching132 (13.9)624 (65.8)193 (20.3)21940216132165611551-3022112443-2962315899-30347129 (13.9)492 (52.9)309 (33.2)20856198602085510934-3090211763-2852812365-28998Urban/Rural (99.9, 100.0)0.760.35 UrbanRural937 (91.1)92 (8.9)217882167712584-2960114804-30488917 (91.2)89 (8.8)201252161911823-2895314803-29936Disproportionate Share Hospital (82.4, 83.0)0.02<0.01 Disproportionate Share Hospital (DSH)Sole Community Hospital (not DSH-eligible)Not DSH692 (81.5)38 (4.5)119 (14.0)21583264502113412124-2958119779-3331412829-30029684 (81.9)35 (4.2)116 (13.9)19706263661900511782-2885921826-3236511935-28034CMS Hospital-Wide Mortality (80.4, 80.7)0.360.56 Above National AverageSame as National AverageBelow National Average159 (19.2)553 (66.8)116 (14.0)21470216212383713394-30164127226-2968912021-32762158 (19.5)537 (66.1)117 (14.4)19465203562162111985-2811311854-2927311834-33024CMS Hospital-Wide Readmission Rate (82.2, 82.7)<0.01<0.01 Above National AverageSame as National AverageBelow National Average346 (40.9)123 (14.5)378 (44.6)23431237131916215948-3173916936-2945211091-28180340 (40.9)121 (14.5)371 (44.6)22515221981855914028-3145011730-3021710846-27912HCAHPS Patient Overall Hospital Rating (80.8, 81.2)<0.01<0.01 1234540 (4.8)149 (17.9)303 (36.4)311 (37.4)29 (3.5)156711863621198243192950211775-2284713066-2704511718-2878915103-3175120076-3437136 (4.4)142 (17.4)298 (36.5)312 (38.2)29 (3.5)168121684519526239822823610945-2206610870-2393610687-2799114446-3180418905-34272CMS Overall Hospital Rating (82.0, 83.2)0.420.08 1234543 (5.1)160 (18.9)246 (29.1)264 (31.2)132 (15.6)184712122321716223762208312445-2486412816-2831411471-2992111841-3046515648-3180541 (4.9)154 (18.6)244 (29.4)260 (31.3)131 (15.8)154061861620466203732252710378-2254411751-2589311228-2914511782-3060212829-31296† Comparisons were done via the Kruskal-Wallis test. 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PO-03-184 ASSESSMENT OF PRICE VARIATION IN PERCUTANEOUS LEFT ATRIAL APPENDAGE CLOSURE DEVICE PROCEDURES AT US HOSPITALS

Since their approval in the US in 2015, left atrial appendage closure (LAAC) devices have served as an alternative to oral anticoagulation in atrial fibrillation (AF) patients at increased risk of bleeding. However, few studies have explored the commercial cost footprint of LAAC procedures, despite the 2021 Hospital Price Transparency Final Rule mandating US hospitals publish their insurer-negotiated prices. This study sought to characterize and identify factors contributing to variation in LAAC procedure prices across the US. We queried a commercial database containing hospital-disclosed prices to characterize variation in LAAC procedure prices within and across US hospitals. We then linked this price data to hospital characteristics using Medicare Facility IDs. Univariate analysis was conducted to compare commercial prices across multiple factors. In total, 683 hospitals reported commercial prices for LAAC procedures. Among reporting hospitals, median prices were greater at major investor-owned vs. non-major investor-owned facilities ($10,991 vs. $7,227, p<0.01) and for-profit centers vs. not-for-profit and government centers ($10,991 vs. $7,417 vs. $5,051, p<0.01). Prices were also greater at non-teaching hospitals compared to minor and major teaching hospitals (p<0.01). The Pacific region exhibited the highest median price ($18,652), while the Middle Atlantic exhibited the lowest median price ($5191). There was no association between price and CMS hospital-wide mortality (p=0.21) or all-cause readmissions (p=0.29). Patient hospital ratings were correlated with price (p<0.01) but not CMS overall hospital ratings (p=0.62). Commercial and self-pay prices were 2.30- and 2.61-times higher than the Medicare price. Within hospitals, the 90th percentile payer-negotiated rate was 2.0 (IQR 1.00-4.06) times the 10th percentile rate (within-center ratio). Across hospitals within the same hospital referral region (HRR), the median price at the 90th percentile hospital was 1.54 (IQR 1.00-2.63) times that at the 10th percentile hospital (between-center ratio). Prices for LAAC procedures varied widely, with commercial and self-pay rates over double the Medicare price. Within-center variation in price was higher than between-center variation. Non-teaching, investor-owned, and for-profit hospitals had higher prices; however, there was no association between prices and hospital-wide mortality or readmission rates.Tabled 1Univariate Analysis of Factors Associated with Left Atrial Appendage Closure Device Procedures Commercial PriceCategorical Values (% Reporting)# ofHospitals (%)Median Price ($)Interquartile Range ($)P-ValueRegion (93.6)<0.01 New England (CT, MA, ME, NH, RI, VT)Middle Atlantic (NJ, NY, PA)South Atlantic (DC, DE, MD, VA, WV, C, SC, GA, FL))East North Central (OH, IN, IL, MI, WI)East South Central (KY, TN, AL, MS)West North Central (MN, IA, MO, ND, SD, NE, KS)West South Central (AR, LA, OK, TX)Mountain (MT, ID, WY, CO, NM, AZ, UT, NV)Pacific (WA, OR, CA, AK, HI)26 (4.1)59 (9.2)157 (24.6)119 (18.6)57 (8.9)78 (12.2)107 (16.7)26 (4.1)10 (1.6)90915191104657682113425900719911150186524865-125783839-94483652-145313562-113323005-152592780-124784030-114763094-122562891-26874Hospital Type (87.8)<0.01 For-ProfitNot-For-ProfitGovernment154 (25.7)407 (67.8)39 (6.5)10991741750516853-152603077-124772731-14581Major Investor Owned (96.8)<0.01 Major Investor OwnedNot Major Investor Owned173 (26.2)488 (73.8)1099172276997-152603050-12344Teaching Status (93.6)<0.01 Major TeachingMinor TeachingNon-Teaching84 (13.1)332 (52.0)223 (34.9)56488012106162475-91313340-123443840-15260Urban/Rural (100)0.06 UrbanRural620 (90.8)63 (9.2)823488373428-129183634-13784Disproportionate Share Hospital (82.3)0.63 Disproportionate Share Hospital (DSH)Sole Community Hospital (not DSH-eligible)Not DSH474 (84.3)14 (2.5)74 (13.2)81681073987693510-129885052-128633241-14189CMS Hospital-Wide Readmission Rate (82.6)0.26 Above National AverageSame as National AverageBelow National Average227 (40.2)80 (14.2)257 (45.6)8633863286493340-133264079-142673840-12579CMS Hospital-Wide Mortality (80.7)0.25 Above National AverageSame as National AverageBelow National Average99 (18.0)375 (68.1)77 (14.0)8819823789043486-119703544-132163473-15159HCAHPS Patient Overall Hospital Rating (81.1)<0.01 1234517 (3.1)96 (17.3)211 (38.1)209 (37.7)21 (3.8)10991875869649044136316295-205573635-128202856-112883844-146829078-18372CMS Overall Hospital Rating (83.1)0.62 1234526 (4.6)92 (16.2)160 (28.2)186 (32.7)104 (18.3)922490048620761886934492-120864918-126313632-130232864-132513803-13693Comparisons were done via the Kruskal-Wallis test. Open table in a new tab

A Quality Improvement Initiative to Improve Patient Safety Event Reporting by Residents.

Residents play a key role in patient care at academic medical centers and have unique insights into safety improvement opportunities. At our institution, <1% of safety events were reported by resident trainees. The primary objective of this quality improvement (QI) initiative was to increase the monthly incidence of event reporting by pediatric residents by 20% from baseline within 12 months. A QI team used the model for improvement to identify barriers to submitting safety event reports. The team used multiple intervention cycles to increase knowledge and promote engagement in event reporting. Interventions included educational tip sheets, a hospital-wide Morbidity and Mortality (M&M) conference, peer recognition and acknowledgment by senior leadership for report submission, and an interactive reporting activity. The outcome measure was monthly number of reports filed by residents. The process measure was the number of unique residents submitting a report each month. Time to complete a report was a balancing measure. The number of reports placed by residents increased significantly, with a centerline shift from 15 to 29 reports per month (statistical process control chart-Fig. 3). The number of unique residents submitting reports increased from 10 to 22 per month. The time to complete a report was unchanged. Engaging residents in patient safety initiatives through education, experiential learning, and recognition can increase safety event reporting by residents. Future planned interventions include enhancing safety event reporting technology, developing patient safety faculty and resident champions, and increasing transparency regarding outcomes of safety event reports.

Beneficial and harmful outcomes of tocilizumab in severe COVID-19: A systematic review and meta-analysis.

IntroductionThe results of studies of tocilizumab (TCZ) in COVID‐19 are contradictory. Our study aims to update medical evidence from controlled observational studies and randomized clinical trials (RCTs) on the use of TCZ in hospitalized patients with COVID‐19.MethodsWe searched the following databases from January 1, 2020 to April 13, 2021 (date of the last search): MEDLINE database through the PubMed search engine and Scopus, using the terms (“COVID‐19" [Supplementary Concept]) AND "tocilizumab" [Supplementary Concept]).ResultsSixty four studies were included in the present study: 54 were controlled observational studies (50 retrospective and 4 prospective) and 10 were RCTs. The overall results provided data from 20,616 hospitalized patients with COVID‐19: 7668 patients received TCZ in addition to standard of care (SOC) (including 1915 patients admitted to intensive care units (ICU) with reported mortality) and 12,948 patients only receiving SOC (including 4410 patients admitted to the ICU with reported mortality). After applying the random‐effects model, the hospital‐wide (including ICU) pooled mortality odds ratio (OR) of patients with COVID‐19 treated with TCZ was 0.73 (95% confidence interval (CI) = 0.56–0.93). The pooled hospital‐wide mortality OR was 1.25 (95% CI = 0.74–2.18) in patients admitted at conventional wards versus 0.66 (95% CI = 0.59–0.76) in patients admitted to the ICU. The pooled OR of hospital‐wide mortality (including ICU) of COVID‐19 patients treated with TCZ plus corticosteroids (CS) was 0.67 (95% CI = 0.54–0.84). The pooled in‐hospital mortality OR was 0.71 (95% CI = 0.35–1.42) when TCZ was early administered (≤10 days from symptom onset) versus 0.83 (95% CI 0.48–1.45) for late administration (>10 days from symptom onset). The meta‐analysis did not find significantly higher risk for secondary infections in COVID‐19 patients treated with TCZ.ConclusionsTCZ prevented mortality in patients hospitalized for COVID‐19. This benefit was seen to a greater extent in patients receiving concomitant CS and when TCZ administration occurred within the first 10 days after symptom onset.

Using structured pathology data to predict hospital-wide mortality at admission.

Early prediction of in-hospital mortality can improve patient outcome. Current prediction models for in-hospital mortality focus mainly on specific pathologies. Structured pathology data is hospital-wide readily available and is primarily used for e.g. financing purposes. We aim to build a predictive model at admission using the International Classification of Diseases (ICD) codes as predictors and investigate the effect of the self-evident DNR ("Do Not Resuscitate") diagnosis codes and palliative care codes. We compare the models using ICD-10-CM codes with Risk of Mortality (RoM) and Charlson Comorbidity Index (CCI) as predictors using the Random Forests modeling approach. We use the Present on Admission flag to distinguish which diagnoses are present on admission. The study is performed in a single center (Ghent University Hospital) with the inclusion of 36 368 patients, all discharged in 2017. Our model at admission using ICD-10-CM codes (AUCROC = 0.9477) outperforms the model using RoM (AUCROC = 0.8797 and CCI (AUCROC = 0.7435). We confirmed that DNR and palliative care codes have a strong impact on the model resulting in a decrease of 7% for the ICD model (AUCROC = 0.8791) at admission. We therefore conclude that a model with a sufficient predictive performance can be derived from structured pathology data, and if real-time available, can serve as a prerequisite to develop a practical clinical decision support system for physicians.

The Mayo Clinic Hospital Mortality Reduction Project

Quality improvement, regulatory, and payer organizations use various definitions of hospital mortality as clinical outcome measures. In this prospective study, the authors evaluated a multicomponent intervention aimed at reducing inpatient mortality in a multistate healthcare delivery system. The project was initiated because of a statistically nonsignificant upward trend in mortality suggested by a six-quarter rise in the observed/expected mortality ratio generated by the Vizient Clinical Data Base and Resource Manager. The design of the mortality reduction plan was influenced by the known limitations of using hospital-wide mortality as a quality improvement measure. The primary objective was to reduce mortality through focused care redesign. The project leadership team attempted to implement standardized system-wide improvements while allowing individual hospitals to simultaneously pursue site-specific practice redesign opportunities. Between Q3, 2015, and Q4, 2017, system-wide mortality reduced from 1.78 to 1.53 (per 100 admissions; p = .01). The actual plan implemented in Mayo Clinic's hospitals is included as Appendix A to this article, published online as Supplemental Digital Content. The authors included it to allow comparison with similar efforts at other healthcare systems, as well as to stimulate criticism and discussion by readers.

Reflections on implementing a hospital-wide provider-based electronic inpatient mortality review system: lessons learnt

ImportanceDeath due to preventable medical error is a leading cause of death, with varying estimates of preventable death rates (14%–56% of total deaths based on national extrapolated estimates, 3%–11% based...

No more pediatric code blues on the floor: evolution of pediatric rapid response teams and situational awareness plans.

Reducing or eliminating code blues that occur on the inpatient, noncritical care units of children's hospitals is a challenging yet achievable goal. The mechanism to accomplish this involves several levels of effort. The implementation of effective pediatric rapid response teams is a well identified part of the process. Rapid response teams can allow for appropriate clinical interventions for deteriorating patients and may ultimately result in a reduction in hospital-wide mortality as well as efficient transfer to the pediatric intensive care unit (PICU) when necessary. The timely deployment of rapid response teams is dependent upon the appropriate recognition of patients at risk for deterioration. This recognition can be optimized by relying on assessments as simple as utilization of parental intuition to those as complex as big data models which utilize multiple predictor variables extracted from the electronic medical record. Ultimately, the goal to proactively identify patients at risk of deterioration may allow for prevention of clinical decline via appropriate and timely interventions, and if unsuccessful at that level, may allow for improved outcomes via optimized resuscitation care in the PICU.

Comparison of Hospital-Wide Code Rates and Mortality Before and After the Implementation of a Rapid Response Team.

ObjectiveTo compare hospital-wide code rates and mortality before and after the implementation of a rapid response team (RRT).Study designA prospective cohort design with historical controls.Place of studyThis study was conducted at Shifa International Hospital, Islamabad, from January 21, 2016, to January 20, 2017.Materials and methodsThe triggers for the rapid response team (RRT) were displayed on each floor. The in-house staff was trained on when and how to activate the rapid response team (RRT). Data were collected on a specified data collection form. Mortality and hospital-wide code blue rates were calculated and compared with those from one year before the implementation of the rapid response team (RRT) (i.e., from January 21, 2015, to January 20, 2016).ResultsThe total number of admissions during the study period was 40,177. In total, 796 RRTs were activated with a rate of activation of 19.81 per 1000 admissions. The most common activator for RRTs was an altered level of consciousness (24.12%), followed by tachycardia (19.22%), and tachypnea (14.45%). The total number of admissions one year before the implementation of the RRT was 39,460. The total number of mortality events before the implementation of the RRT was 1470 (3.725%) and after the implementation of the RRT was 1529 (3.805%), which was not significantly different (P = .576). The total number of code blues before the implementation of the RRT was 146 (0.369%) and after the implementation of RRT was 148 (0.368%), which was not significantly different (P = .929).ConclusionIn this large single-institution study, rapid response team implementation was not associated with significant reductions in either hospital-wide code blue or mortality.

Implementation of a structured hospital-wide morbidity and mortality rounds model

ImportanceThere is a paucity of literature on the quality and effectiveness of institutional morbidity & mortality (M&M) rounds processes.ObjectiveWe sought to implement and evaluate the effectiveness of a hospital-wide structured...

Associations between hospital-wide readmission rates and mortality measures at the hospital level: Are hospital-wide readmissions a measure of quality?

Associations between hospital-wide readmission rates and mortality measures at the hospital level: Are hospital-wide readmissions a measure of quality?

Avoidability of hospital deaths and association with hospital-wide mortality ratios: retrospective case record review and regression analysis

Objectives To determine the proportion of avoidable deaths (due to acts of omission and commission) in acute hospital trusts in England and to determine the association with the trust’s hospital-wide...

Measuring Hospital-Wide Mortality-Pitfalls and Potential.

Risk-adjusted hospital-wide mortality has been proposed as a key indicator of system-level quality. Several risk-adjusted measures are available, and one-the hospital standardized mortality ratio (HSMR) - is publicly reported in a number of countries, but not in the United States. This paper reviews potential uses of such measures. We conclude that available methods are not suitable for interhospital comparisons or rankings and should not be used for pay-for-performance or value-based purchasing/payment. Hospital-wide mortality is a relatively imprecise, crude measure of quality, but disaggregation into condition- and service-line-specific mortality can facilitate targeted improvement efforts. If tracked over time, both observed and expected mortality rates should be monitored to ensure that apparent improvement is not due to increasing expected mortality, which could reflect changes in case mix or coding. Risk-adjusted mortality can be used as an initial signal that a hospital's mortality rate is significantly higher than statistically expected, prompting further inquiry.

Patient and hospital factors associated with induction mortality in acute lymphoblastic leukemia

Deaths during induction chemotherapy for pediatric acute lymphoblastic leukemia (ALL) account for one-tenth of ALL-associated mortality and half of ALL treatment-related mortality. We sought to ascertain patient- and hospital-level factors associated with induction mortality. We performed a retrospective cohort analysis of 8,516 children ages 0 to <19 years with newly diagnosed ALL admitted to freestanding US children's hospitals from 1999 to 2009 using the Pediatric Health Information System database. Induction mortality risk was modeled accounting for demographics, intensive care unit-level interventions, and socioeconomic status (SES) using Cox regression. The association of ALL induction mortality with hospital-level factors including volume, hospital-wide mortality and payer mix was analyzed with multiple linear regression. ALL induction mortality was 1.12%. Race and patient-level SES factors were not associated with induction mortality. Patients receiving both mechanical ventilation and vasoactive infusions experienced nearly 50% mortality (hazard ratio 122.30, 95% CI 66.56-224.80). Institutions in the highest induction mortality quartile contributed 27% of all patients but nearly half of all deaths (47 of 95). Hospital payer mix was associated with ALL induction mortality after adjustment for other hospital-level factors (P = 0.046). The overall risk of induction death is low but substantially increased in patients with cardio-respiratory and other organ failures. Induction mortality varies up to three-fold across hospitals and is correlated with hospital payer mix. Further work is needed to improve induction outcomes in hospitals with higher mortality. These data suggest an induction mortality rate of less than 1% may be an attainable national benchmark.

Reducing Clostridium difficile Incidence, Colectomies, and Mortality in the Hospital Setting: A Successful Multidisciplinary Approach

Health care associated Clostridium difficile infections are a major cause of morbidity and mortality in hospitals. In the United States, from 2000 through 2009, discharge diagnoses from hospitals in the United States that included C. difficile increased from 139,000 to 336,600, and the yearly national excess hospital cost associated with hospital-onset C. difficile is estimated to be upwards of $1.3 billion. A hospitalwide, multidisciplinary approach was undertaken at Rhode Island Hospital (Providence), a tertiary care hospital. The hospital plan entailed six interventions: (1) develop a C. difficile hospital infection control plan based on a risk assessment; (2) monitor hospitalwide morbidity and mortality associated with C. difficile infection; (3) improve sensitivity of C. difficile toxin detection in stool specimens using a polymerase chain reaction-based nucleic acid amplification assay; (4) enhance environmental cleaning of patient rooms and equipment; (5) develop a C. difficile infection treatment plan; and (6) conduct other interventions. The incidence of health care-associated C. difficile infection was assessed from January 2006 through the third quarter of 2012; the number of colectomies and mortality associated with C. difficile infection were determined from January 2005 through the third quarter of 2012. The incidence of health care-associated C. difficile infection decreased from a peak of 12.2/1,000 discharges during the second quarter of 2006 to 3.6/1,000 discharges during the third quarter of 2012. The yearly mortality in patients with health care-associated C. difficile infection was reduced from a peak of 52 in 2006 to 19 in 2011, with 13 such cases in the first three quarters of 2012. A hospitalwide multidisciplinary approach can reduce health care-associated C. difficile infection morbidity and mortality.

Is the maturity of hospitals' quality improvement systems associated with measures of quality and patient safety?

BackgroundPrevious research addressed the development of a classification scheme for quality improvement systems in European hospitals. In this study we explore associations between the 'maturity' of the hospitals' quality improvement system and clinical outcomes.MethodsThe maturity classification scheme was developed based on survey results from 389 hospitals in eight European countries. We matched the hospitals from the Spanish sample (113 hospitals) with those hospitals participating in a nation-wide, voluntary hospital performance initiative. We then compared sample distributions and explored associations between the 'maturity' of the hospitals' quality improvement system and a range of composite outcomes measures, such as adjusted hospital-wide mortality, -readmission, -complication and -length of stay indices. Statistical analysis includes bivariate correlations for parametrically and non-parametrically distributed data, multiple robust regression models and bootstrapping techniques to obtain confidence-intervals for the correlation and regression estimates.ResultsOverall, 43 hospitals were included. Compared to the original sample of 113, this sample was characterized by a higher representation of university hospitals. Maturity of the quality improvement system was similar, although the matched sample showed less variability. Analysis of associations between the quality improvement system and hospital-wide outcomes suggests significant correlations for the indicator adjusted hospital complications, borderline significance for adjusted hospital readmissions and non-significance for the adjusted hospital mortality and length of stay indicators. These results are confirmed by the bootstrap estimates of the robust regression model after adjusting for hospital characteristics.ConclusionsWe assessed associations between hospitals' quality improvement systems and clinical outcomes. From this data it seems that having a more developed quality improvement system is associated with lower rates of adjusted hospital complications. A number of methodological and logistic hurdles remain to link hospital quality improvement systems to outcomes. Further research should aim at identifying the latent dimensions of quality improvement systems that predict quality and safety outcomes. Such research would add pertinent knowledge regarding the implementation of organizational strategies related with quality of care outcomes.

Hospital-wide Mortality as a Quality Metric

Hospital-wide mortality rates are used as a measure of overall hospital quality. However, their parsimony and apparent simplicity belie significant conceptual and methodological concerns. For many diagnoses included in hospital-wide mortality, the association between short-term mortality and quality of care is not well established. Furthermore, compared with condition-specific or procedure-specific mortality, hospital-wide mortality rates pose greater methodological challenges (ie, eligibility and exclusion criteria, risk adjustment, statistical techniques for aggregating across diagnoses, usability). Many of these result from substantial interprovider heterogeneity in diagnosis frequency, sample sizes, and patient severity. Hospital-wide mortality is problematic as a quality metric for public reporting, although hospitals may elect to use such measures for other purposes. Potential alternative approaches include multidimensional composite metrics or mortality measurement limited to selected conditions and procedures for which the link between hospital mortality and quality is clear, legitimate exclusions are uncommon, and sample sizes, end points, and risk adjustment are adequate.

Anesthesia Literature Review

Anesthesia Literature Review