An administrative claims model for profiling hospital 30-day mortality rates for pneumonia patients

Procedure intensity and the cost of care

BACKGROUND

The intensive practice style of hospitals with high procedure rates may result in higher costs of care for medically managed patients. We sought to determine how costs for patients with heart failure (HF) not receiving procedures compare between hospital groups defined by their overall use of procedures.

METHODS AND RESULTS

We identified all 2009 to 2010 adult HF hospitalizations in hospitals capable of performing invasive procedures that had at least 25 HF hospitalizations in the Perspective database from Premier, Inc. We divided hospitals into 2 groups by the proportion of patients with HF receiving invasive percutaneous or surgical procedures: low (>0%-10%) and high (≥ 10%). The standard costs of hospitalizations at each hospital were risk adjusted using patient demographics and comorbidities. We used the Wilcoxon rank sum test to assess cost, length of stay, and mortality outcome differences between the 2 groups. Median risk-standardized costs among low-procedural HF hospitalizations were $5259 (interquartile range, $4683-$6814) versus $6965 (interquartile range, $5981-$8235) for hospitals with high procedure use (P<0.001). Median length of stay was 4 days for both groups. Risk-standardized mortality rates were 5.4% (low procedure) and 5.0% (high procedure) (P=0.009). We did not identify any single service area that explained the difference in costs between hospital groups, but these hospitals had higher costs for most service areas.

CONCLUSION

Among patients who do not receive invasive procedures, the cost of HF hospitalization is higher in more procedure-intense hospitals compared with hospitals that perform fewer procedures.

A comparison of administrative and physiologic predictive models in determining risk adjusted mortality rates in critically ill patients

Background

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.

Methods

We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.

Results

We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r² for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.

Conclusions

In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting “report cards” or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models.

Survival after acute myocardial infarction (SAMI) study: the design and implementation of a positive deviance study

Positive deviance studies combining qualitative and quantitative designs-a mixed-methods approach-can discover strategies to produce exemplary performance. We present the SAMI study, a national positive deviance study to discover hospital strategies associated with lower 30-day hospital risk-standardized mortality rates (RSMRs). There is marked variation across hospitals in 30-day hospital RSMRs for patients with acute myocardial infarction and little information about what accounts for differences in performance. We first conducted a qualitative study of hospitals in the United States (n = 11; 158 key staff) that ranked in the top 5% of RSMRs for each of the 2 most recent years of data (2005-2006 and 2006-2007) from the Centers for Medicare & Medicaid Services at the time of sample selection and in the bottom 5% for contrast, with diversity among hospitals in key characteristics. Using hypotheses generated in this qualitative stage, we constructed a quantitative survey that was administered in a cross-sectional study of acute care hospitals in the United States operating from July 1, 2005, through June 30, 2008, that publicly reported Centers for Medicare & Medicaid Services data for RSMRs during this time. We included hospitals with at least 75 acute myocardial infarction discharges during the 3-year period. Of the 600 hospitals we attempted to contact, 10 had closed, leaving a final sample of 590, of which 537 responded (91%). This type of study, using a positive deviance approach and mixed-methods design, can generate and test hypotheses about factors most strongly associated with exemplary performance based on practices currently in use.

Quality of care in the US territories

BACKGROUND

Health care quality in the US territories is poorly characterized. We used process measures to compare the performance of hospitals in the US territories and in the US states.

METHODS

Our sample included nonfederal hospitals located in the United States and its territories discharging Medicare fee-for-service (FFS) patients with a principal discharge diagnosis of acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PNE) (July 2005-June 2008). We compared risk-standardized 30-day mortality and readmission rates between territorial and stateside hospitals, adjusting for performance on core process measures and hospital characteristics.

RESULTS

In 57 territorial hospitals and 4799 stateside hospitals, hospital mean 30-day risk-standardized mortality rates were significantly higher in the US territories (P<.001) for AMI (18.8% vs 16.0%), HF (12.3% vs 10.8%), and PNE (14.9% vs 11.4%). Hospital mean 30-day risk-standardized readmission rates (RSRRs) were also significantly higher in the US territories for AMI (20.6% vs 19.8%; P=.04), and PNE (19.4% vs 18.4%; P=.01) but was not significant for HF (25.5% vs 24.5%; P=.07). The higher risk-standardized mortality rates in the US territories remained statistically significant after adjusting for hospital characteristics and core process measure performance. Hospitals in the US territories had lower performance on all core process measures (P<.05).

CONCLUSIONS

Compared with hospitals in the US states, hospitals in the US territories have significantly higher 30-day mortality rates and lower performance on every core process measure for patients discharged after AMI, HF, and PNE. Eliminating the substantial quality gap in the US territories should be a national priority.