Hospital Phenotypes in the Management of Patients Admitted for Acute Myocardial Infarction.

OBJECTIVES: To characterize hospital phenotypes by their combined utilization pattern of percutaneous coronary interventions (PCI), coronary artery bypass grafting (CABG) procedures, and intensive care unit (ICU) admissions for patients hospitalized for acute myocardial infarction (AMI). RESEARCH DESIGN: Using the Premier Analytical Database, we identified 129,138 hospitalizations for AMI from 246 hospitals with the capacity for performing open-heart surgery during 2010-2013. We calculated year-specific, risk-standardized estimates of PCI procedure rates, CABG procedure rates, and ICU admission rates for each hospital, adjusting for patient clinical characteristics and within-hospital correlation of patients. We used a mixture modeling approach to identify groups of hospitals (ie, hospital phenotypes) that exhibit distinct longitudinal patterns of risk-standardized PCI, CABG, and ICU admission rates. RESULTS: We identified 3 distinct phenotypes among the 246 hospitals: (1) high PCI-low CABG-high ICU admission (39.2% of the hospitals), (2) high PCI-low CABG-low ICU admission (30.5%), and (3) low PCI-high CABG-moderate ICU admission (30.4%). Hospitals in the high PCI-low CABG-high ICU admission phenotype had significantly higher risk-standardized in-hospital costs and 30-day risk-standardized payment yet similar risk-standardized mortality and readmission rates compared with hospitals in the low PCI-high CABG-moderate ICU admission phenotype. Hospitals in these phenotypes differed by geographic region. CONCLUSIONS: Hospitals differ in how they manage patients hospitalized for AMI. Their distinctive practice patterns suggest that some hospital phenotypes may be more successful in producing good outcomes at lower cost.

Measuring Adverse Events in Hospitalized Patients: An Administrative Method for Measuring Harm.

CONTEXT: Current methods for tracking harm either require costly full manual chart review (FMCR) or rely on proxy methods that have questionable accuracy. We propose an administrative measure of harm detection that uses electronically captured data. OBJECTIVE: Determine the level of agreement on harm event occurrence when harm is detected based on an administrative harm measurement tool (AHMT) compared with FMCR. DESIGN: A retrospective chart review was used to measure the level of agreement in harm detection between an AHMT that uses electronically captured data and a FMCR. SETTING: The inpatient hospital setting was used. PATIENTS: Approximately 771 medical records from 5 hospitals were reviewed. MAIN OUTCOME MEASURES: Measures of positive predictive value, negative predictive value, weighted sensitivity, weighted specificity, and concordance were used to evaluate agreement between the 2 methods. RESULTS: Although there was agreement at the harm-event level, the results were not all as high as desired: adjusted sensitivity 65%, adjusted specificity 85%, positive predictive value (PPV) 59%, negative predictive value (NPV) 88%, and concordance 75%. The patient-level results show greater agreement: adjusted sensitivity 95%, adjusted specificity 86%, PPV 61%, NPV 99%, and concordance 81%. CONCLUSION: The AHMT is sufficiently accurate for use as a within hospital tool to reliably detect and track harm. Nevertheless, it is not recommended as a tool to make comparisons across institutions, which has policy and payment implications. Further research using administrative harm detection, including the use of a broader set of measures and electronic health records, is needed.

Patient Factors Predictive of Hospital Readmissions Within 30 Days.

BACKGROUND: Under the Affordable Care Act, the Congress has mandated that the Centers for Medicare and Medicaid Services reduce payments to hospitals subject to their Inpatient Prospective Payment System that exhibits excess readmissions. Using hospital-coded discharge abstracts, we constructed a readmission measure that accounts for cross-hospital variation that enables hospitals to monitor their entire inpatient populations and evaluate their readmission rates relative to national benchmarks. METHODS: Multivariate logistic regressions are applied to determine which patient factors increase the odds of a readmission within 30 days and by how much. This study uses deidentified discharge abstract data from a database of approximately 15 million inpatient discharges representing 611 acute care hospitals from Premier healthcare alliance over a 2-year period (2008q4-2010q3). The hospitals are geographically diverse and represent large urban academic centers and small rural community hospitals. RESULTS: This study demonstrates that meaningful risk-adjusted readmission rates can be tracked in a dynamic database. The clinical conditions responsible for the index admission were the strongest predictive factor of readmissions, but factors such as age and accompanying comorbid conditions were also important. Socioeconomic factors, such as race, income, and payer status, also showed strong statistical significance in predicting readmissions. CONCLUSIONS: Payment models that are based on stratified comparisons might result in a more equitable payment system while at the same time providing transparency regarding disparities based on these factors. No model, yet available, discriminates potentially modifiable readmissions from those not subject to intervention highlighting the fact that the optimum readmission rate for any given condition is yet to be identified.

The effectiveness of a multicenter quality improvement collaborative in reducing inpatient mortality.

MOTIVATION AND BACKGROUND: This study examines the evidence that a particular quality improvement collaborative that focused on Quality, Efficiency, Safety and Transparency (QUEST) was able to improve hospital performance. SETTING: The collaborative included a range of improvement vehicles, such as sharing customized comparative reports, conducting online best practices forums, using 90-day rapid-cycle initiatives to test specific interventions, and conducting face-to-face meetings and quarterly one-on-one coaching sessions to elucidate opportunities. METHODS: With these kinds of activities in mind, the objective was to test for the presence of an overall "QUEST effect" via statistical analysis of mortality results that spanned 6 years (2006-2011) for more than 600 acute care hospitals from the Premier alliance. RESULTS: The existence of a QUEST effect was confirmed from complementary approaches that include comparison of matched samples (collaborative participants against controls) and multivariate analysis. CONCLUSION: The study concludes with a discussion of those methods that were plausible reasons for the successes.

"Phenotyping" hospital value of care for patients with heart failure.

OBJECTIVE: To characterize hospitals based on patterns of their combined financial and clinical outcomes for heart failure hospitalizations longitudinally. DATA SOURCE: Detailed cost and administrative data on hospitalizations for heart failure from 424 hospitals in the 2005-2011 Premier database. STUDY DESIGN: Using a mixture modeling approach, we identified groups of hospitals with distinct joint trajectories of risk-standardized cost (RSC) per hospitalization and risk-standardized in-hospital mortality rate (RSMR), and assessed hospital characteristics associated with the distinct patterns using multinomial logistic regression. PRINCIPAL FINDINGS: During 2005-2011, mean hospital RSC decreased from $12,003 to $10,782, while mean hospital RSMR declined from 3.9 to 3.2 percent. We identified five distinct hospital patterns: highest cost and low mortality (3.2 percent of the hospitals), high cost and low mortality (20.4 percent), medium cost and low mortality (34.6 percent), medium cost and high mortality (6.2 percent), and low cost and low mortality (35.6 percent). Longer hospital stay and greater use of intensive care unit and surgical procedures were associated with phenotypes with higher costs or greater mortality. CONCLUSIONS: Hospitals vary substantially in the joint longitudinal patterns of cost and mortality, suggesting marked difference in value of care. Understanding determinants of the variation will inform strategies for improving the value of hospital care.

Governing board, C-suite, and clinical management perceptions of quality and safety structures, processes, and priorities in U.S. hospitals.

To achieve quality improvement in hospitals requires greater attention to systems thinking than is typical at this time, including a shared understanding across different levels of the hospital of the current state of quality improvement efforts. A self-administered survey assessed the perceptions of board members, C-suite executives, and clinical managers regarding quality activities and structures. This instrument, the Hospital Leadership and Quality Assessment Tool (HLQAT), includes 13 domains in six conceptual areas that we believe are major organizational drivers of quality and safety: (1) commitment of senior leaders, (2) a vision of exemplary quality, (3) a supportive culture, (4) accountable leadership, (5) appropriate organizational structures, and (6) adaptive capability. HLQAT survey results from a convenience sample of more than 300 hospitals were linked to performance on the Centers for Medicare & Medicaid Services (CMS) Core Measures. The results show significantly different perceptions between the groups. Higher HLQAT scores for each respondent group were associated with better hospital performance on the CMS Core Measures. There is no magic bullet--no one domain dominates. Leaders in higher-performing hospitals appear to be more effective at conveying their vision of quality care and creating a culture that supports an expectation that staff and leadership will work across traditional boundaries to improve quality.

Measuring cardiac waste: the premier cardiac waste measures.

The authors developed 8 measures of waste associated with cardiac procedures to assist hospitals in comparing their performance with peer facilities. Measure selection was based on review of the research literature, clinical guidelines, and consultation with key stakeholders. Development and validation used the data from 261 hospitals in a split-sample design. Measures were risk adjusted using Premier's CareScience methodologies or mean peer value based on Medicare Severity Diagnosis-Related Group assignment. High variability was found in resource utilization across facilities. Validation of the measures using item-to-total correlations (range = 0.27-0.78), Cronbach α (.88), and Spearman rank correlation (0.92) showed high reliability and discriminatory power. Because of the level of variability observed among hospitals, this study suggests that there is opportunity for facilities to design successful waste reduction programs targeting cardiac-device procedures.

Identifying hospital-wide harm: a set of ICD-9-CM-coded conditions associated with increased cost, length of stay, and risk of mortality.

This study identifies an expanded set of hospital-acquired conditions (HACs), using the Present-On-Admission (POA) indicator and secondary diagnoses present on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-coded discharge abstracts and evaluates their association with mortality, length of stay (LOS), and cost. A sample of 500 000 de-identified ICD-9-CM-coded discharge abstracts was randomly drawn from a data set of 11 million. A total of 138 secondary condition clusters were identified as potential inpatient complications (PICs). Regression modeling was used to determine marginal association of each PIC with mortality, LOS, and cost. In all, 16% of hospitalized patients developed 1 or more of these conditions while in the hospital compared with less than 1% of inpatients experiencing HACs defined by the Centers for Medicare and Medicaid Services. Also, 74 PICs were associated with seriously higher mortality rates (5 excess deaths per 1000), significantly LOS (0.4 extra days per discharge), and significantly higher costs (an extra $1000 per discharge).

Development of a method to measure and compare hospital waste: the Premier hospital waste index.

The authors developed 15 measures and a comparative index to assist acute care facilities in identifying and monitoring clinical and administrative functions for health care waste reduction. Primary clinical and administrative data were collected from 261 acute care facilities contained within a database maintained by Premier Inc, spanning October 1, 2010, to September 30, 2011. The measures and 4 index models were tested using the Cronbach α coefficient and item-to-total and Spearman rank correlations. The final index model was validated using 52 facilities that had complete data. Analysis of the waste measures showed good internal reliability (α = .85) with some overlap. Index modeling found that data transformation using the standard deviation and adjusting for the proportional contribution of each measure normalized the distribution and produced a Spearman rank correlation of 0.95. The waste measures and index methodology provide a simple and reliable means to identify and reduce waste and compare and monitor facility performance.

The effect of bivalirudin on costs and outcomes of treatment of ST-segment elevation myocardial infarction.

BACKGROUND: Bivalirudin is commonly used during percutaneous coronary intervention (PCI) rather than unfractionated heparin. The higher cost of bivalirudin may be offset if it reduces costly bleeding complications and/or length of stay. We sought to assess the effect of using bivalirudin on the costs of care among patients with ST-segment elevation myocardial infarction (STEMI) undergoing PCI. METHODS: We analyzed data from 64,872 patients treated in 1 of 278 hospitals. The effect of overall hospital use of bivalirudin on clinical and economic outcomes was assessed using multivariable regression, based on average hospital use of treatments. RESULTS: The use of bivalirudin among patients with STEMI treated with PCI varied widely across hospitals, with a median of 6.9% (interquartile range 2.3%-18.6%). After controlling for patient and hospital characteristics, use of bivalirudin rather than heparin and a glycoprotein IIb/IIIa inhibitor reduced bleeding (odds ratio 0.47, P < .001), length of stay (-0.47 days, P < .03), and hospital costs (-14%, P < .04). CONCLUSIONS: Use of bivalirudin among patients with STEMI treated with PCI appears to reduce bleeding and overall costs.

Making hospital mortality measurement more meaningful: incorporating advance directives and palliative care designations.

Accounting for patients admitted to hospitals at the end of a terminal disease process is key to signaling care quality and identifying opportunities for improvement. This study evaluates the benefits and caveats of incorporating care-limiting orders, such as do not resuscitate (DNR) and palliative care (PC) information, in a general multivariate model of mortality risk, wherein the unit of observation is the patient hospital encounter. In a model of the mortality gap (observed - expected from the baseline model), DNR explains 8% to 24% of the gap variation. PC provides additional explanatory power to some disease groupings, especially heart and digestive diseases. One caveat is that DNR information, especially if associated with the later stages of hospital care, may mask opportunities to improve care for certain types of patients. But that is not a danger for PC, which is unequivocally valuable in accounting for patient risk, especially for certain subpopulations and disease groupings.