By 2004, senior leaders at Kaiser Permanente, the largest not-for-profit health plan in the United States, recognizing variations across service areas in quality, safety, service, and efficiency, ...began developing a performance improvement (PI) system to realizing best-in-class quality performance across all 35 medical centers. MEASURING SYSTEMWIDE PERFORMANCE: In 2005, a Web-based data dashboard, "Big Q," which tracks the performance of each medical center and service area against external benchmarks and internal goals, was created. PLANNING FOR PI AND BENCHMARKING PERFORMANCE: In 2006, Kaiser Permanente national and regional continued planning the PI system, and in 2007, quality, medical group, operations, and information technology leaders benchmarked five high-performing organizations to identify capabilities required to achieve consistent best-in-class organizational performance. THE PI SYSTEM: The PI system addresses the six capabilities: leadership priority setting, a systems approach to improvement, measurement capability, a learning organization, improvement capacity, and a culture of improvement. PI "deep experts" (mentors) consult with national, regional, and local leaders, and more than 500 improvement advisors are trained to manage portfolios of 90-120 day improvement initiatives at medical centers.
Between the second quarter of 2008 and the first quarter of 2009, performance across all Kaiser Permanente medical centers improved on the Big Q metrics.
The lessons learned in implementing and sustaining PI as it becomes fully integrated into all levels of Kaiser Permanente can be generalized to other health care systems, hospitals, and other health care organizations.
Little is known about the use of the single self-rated health (SRH) status item measuring health-related quality of life among people with coronary artery disease (CAD). The objective of this study ...was to assess relationships between SRH and recurrent coronary events, mortality, health care utilization, and intermediate clinical outcomes and to assess predictors of fair/poor SRH. A total of 5573 patients enrolled in a comprehensive cardiac risk reduction service managed by clinical pharmacy specialists were evaluated over a 2-year period. Regression modeling explored relationships among variables, modeling SRH separately as an independent and a dependent variable. The 1374 (24.7%) respondents reporting fair/poor SRH differed statistically from 4199 (75.3%) respondents reporting good/very good/excellent SRH in terms of age, sex, ethnicity, number of comorbid conditions, DxCG scores, lifestyle behaviors, blood pressure control, and inpatient and emergency department (ED) utilization. Respondents reporting fair/poor health were more likely to have recurrent major coronary events (MCE), including death. Fair/poor SRH was consistently statistically significant when it was included as a predictor in regression modeling for poor blood pressure control, health care utilization, MCE, and all-cause mortality. Variables associated with fair/poor SRH in regression modeling included females, Hispanic ethnicity, ≥1 baseline ED visit, and DxCG score. Exercising <30 minutes per week was strongly associated with fair/poor SRH. Single-item SRH status may help identify patients with CAD at higher risk of poor blood pressure control, recurrent MCE, and death and those who may benefit from interventions to increase physical activity.
To determine whether adding selfreported health and functional status data to a diagnostic risk-score model explains additional variance in predicting inpatient admissions and costs.
Retrospective ...observational analysis.
We used data from a Health Status Questionnaire (HSQ), completed by 6407 Kaiser Permanente Northwest Medicare patients between December 2006 and October 2008. We used answers from 3 items on the HSQ: (1) General Self-rated Health score, (2) needing help with 1 or more activities of daily living, and (3) having a bothersome health condition. We calculated a DxCG relative risk score from utilization information in the year prior to the survey, using electronic medical records. We compared: (1) DxCG as the sole independent variable and (2) DxCG plus the 3 items as independent variables. We estimated area under the curve (AUC) for each model. Any inpatient admission (yes/no) and being in the top 10% of costs (in the year after survey) were the dependent variables for the first and second logistic regression models, respectively.
The 3 items explained an additional 2.8% and 4.0% of variance for inpatient admissions and top 10% of costs,respectively, in addition to the variance explained by the DxCG score alone. For DxCG alone, the AUC was 0.686 (95% confidence interval CI 0.663-0.710) and 0.741 (95% CI 0.719- 0.764), respectively, for inpatient admissions and top 10% of costs and improved to 0.709 (95% CI 0.687-0.730) and 0.770 (95% CI 0.749-0.790) when the 3 self-reported items were added.
Using self-reported health information improved the predictive power of a DxCG model to forecast inpatient admissions and patient cost-tier.
The
Dartmouth Atlas
method for examination of variation in care at the end of life was replicated by Kaiser Permanente (KP). Variation within KP was analyzed and compared with corresponding
Dartmouth ...Atlas
Hospital Referral Regions. Although KP inpatient care use rates were 25% to 30% lower and hospice use rates were higher than in the surrounding communities, there was still two- to four-fold variation in inpatient care use across KP geographic areas. Evidence suggests that more, or more intensive, care for this population is neither necessarily better nor desired by patients. If all California (CA) KP residence areas had the hospital day rate of the average of the lowest three, 2005 decedents would have had more than 50,000 fewer hospital days in their last six months of life. High-intensity care accounts for a large proportion of the overall variation in total costs for this population. This strongly reinforces the focus on appropriate intensive care unit (ICU) use in end-of-life care. Greater emphasis on palliative care approaches for patients with chronic conditions and earlier transition to the use of hospice would create a better match between the expressed desires of patients and the care they receive, thus improving member and family satisfaction as well as quality of care. In addition, earlier transition to hospice in KP could be one important tool for avoiding undesired and nonbeneficial ICU use, given the negative correlation between hospice and ICU use identified in this analysis.
Geographic variation in hospital use within KP appears to be correlated with variation in the surrounding communities, even though it is lower on average within KP than outside it. This suggests that KP resource use may be influenced at least in part by broader community practices.
The Dartmouth Atlas method for examination of variation in care at the end of life was replicated by Kaiser Permanente (KP). Variation within KP was analyzed and compared with corresponding Dartmouth ...Atlas Hospital Referral Regions. Although KP inpatient care use rates were 25% to 30% lower and hospice use rates were higher than in the surrounding communities, there was still two- to four-fold variation in inpatient care use across KP geographic areas. Evidence suggests that more, or more intensive, care for this population is neither necessarily better nor desired by patients. If all California (CA) KP residence areas had the hospital day rate of the average of the lowest three, 2005 decedents would have had more than 50,000 fewer hospital days in their last six months of life. High-intensity care accounts for a large proportion of the overall variation in total costs for this population. This strongly reinforces the focus on appropriate intensive care unit (ICU) use in end-of-life care. Greater emphasis on palliative care approaches for patients with chronic conditions and earlier transition to the use of hospice would create a better match between the expressed desires of patients and the care they receive, thus improving member and family satisfaction as well as quality of care. In addition, earlier transition to hospice in KP could be one important tool for avoiding undesired and nonbeneficial ICU use, given the negative correlation between hospice and ICU use identified in this analysis.Geographic variation in hospital use within KP appears to be correlated with variation in the surrounding communities, even though it is lower on average within KP than outside it. This suggests that KP resource use may be influenced at least in part by broader community practices.
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