The Association of ICU Acuity With Outcomes of Patients at Low Risk of Dying.

OBJECTIVE: Many ICU patients do not require critical care interventions. Whether aggressive care environments increase risks to low-acuity patients is unknown. We evaluated whether ICU acuity was associated with outcomes of low mortality-risk patients. We hypothesized that admission to high-acuity ICUs would be associated with worse outcomes. This hypothesis was based on two possibilities: 1) high-acuity ICUs may have a culture of aggressive therapy that could lead to potentially avoidable complications and 2) high-acuity ICUs may focus attention toward the many sicker patients and away from the fewer low-risk patients. DESIGN: Retrospective cohort study. SETTING: Three hundred twenty-two ICUs in 199 hospitals in the Philips eICU database between 2010 and 2015. PATIENTS: Adult ICU patients at low risk of dying, defined as an Acute Physiology and Chronic Health Evaluation-IVa-predicted mortality of 3% or less. EXPOSURE: ICU acuity, defined as the mean Acute Physiology and Chronic Health Evaluation IVa score of all admitted patients in a calendar year, stratified into quartiles. MEASUREMENTS AND MAIN RESULTS: We used generalized estimating equations to test whether ICU acuity is independently associated with a primary outcome of ICU length of stay and secondary outcomes of hospital length of stay, hospital mortality, and discharge destination. The study included 381,997 low-risk patients. Mean ICU and hospital length of stay were 1.8 ± 2.1 and 5.2 ± 5.0 days, respectively. Mean Acute Physiology and Chronic Health Evaluation IVa-predicted hospital mortality was 1.6% ± 0.8%; actual hospital mortality was 0.7%. In adjusted analyses, admission to low-acuity ICUs was associated with worse outcomes compared with higher-acuity ICUs. Specifically, compared with the highest-acuity quartile, ICU length of stay in low-acuity ICUs was increased by 0.24 days; in medium-acuity ICUs by 0.16 days; and in high-acuity ICUs by 0.09 days (all p < 0.001). Similar patterns existed for hospital length of stay. Patients in lower-acuity ICUs had significantly higher hospital mortality (odds ratio, 1.28 [95% CI, 1.10-1.49] for low-; 1.24 [95% CI, 1.07-1.42] for medium-, and 1.14 [95% CI, 0.99-1.31] for high-acuity ICUs) and lower likelihood of discharge home (odds ratio, 0.86 [95% CI, 0.82-0.90] for low-, 0.88 [95% CI, 0.85-0.92] for medium-, and 0.95 [95% CI, 0.92-0.99] for high-acuity ICUs). CONCLUSIONS: Admission to high-acuity ICUs is associated with better outcomes among low mortality-risk patients. Future research should aim to understand factors that confer benefit to patients with different risk profiles.

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

OBJECTIVES: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts. DESIGN: We performed clustering analysis using data from patients' hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population. SETTING: Kaiser Permanente Northern California, a healthcare delivery system serving 3.9 million members. PATIENTS: ICU patients 18 years old or older with an ICU admission between January 1, 2012, and December 31, 2012, at one of 21 Kaiser Permanente Northern California hospitals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables. CONCLUSIONS: A machine learning approach revealed important differences between empirically derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients' shared needs.

The Financial Effect of Value-Based Purchasing and the Hospital Readmissions Reduction Program on Safety-Net Hospitals in 2014: A Cohort Study.

BACKGROUND: Medicare's value-based purchasing (VBP) and the Hospital Readmissions Reduction Program (HRRP) could disproportionately affect safety-net hospitals. OBJECTIVE: To determine whether safety-net hospitals incur larger financial penalties than other hospitals under VBP and HRRP. DESIGN: Cross-sectional analysis. SETTING: United States in 2014. PARTICIPANTS: 3022 acute care hospitals participating in VBP and the HRRP. MEASUREMENTS: Safety-net hospitals were defined as being in the top quartile of the Medicare disproportionate share hospital (DSH) patient percentage and Medicare uncompensated care (UCC) payments per bed. The differences in penalties in both total dollars and dollars per bed between safety-net hospitals and other hospitals were estimated with the use of bivariate and graphical regression methods. RESULTS: Safety-net hospitals in the top quartile of each measure were more likely to be penalized under VBP than other hospitals (62.9% vs. 51.0% under the DSH definition and 60.3% vs. 51.5% under the UCC per-bed definition). This was also the case under the HRRP (80.8% vs. 69.0% and 81.9% vs. 68.7%, respectively). Safety-net hospitals also had larger payment penalties ($115 900 vs. $66 600 and $150 100 vs. $54 900, respectively). On a per-bed basis, this translated to $436 versus $332 and $491 versus $314, respectively. Sensitivity analysis setting the cutoff at the top decile rather than the top quartile decile led to similar conclusions with somewhat larger differences between safety-net and other hospitals. The quadratic fit of the data indicated that the larger effect of these penalties is in the middle of the distribution of the DSH and UCC measures. LIMITATION: Only 2 measures of safety-net status were included in the analyses. CONCLUSION: Safety-net hospitals were disproportionately likely to be affected under VBP and the HRRP, but most incurred relatively small payment penalties in 2014. PRIMARY FUNDING SOURCE: Patient-Centered Outcomes Research Institute.

Safety-net hospitals more likely than other hospitals to fare poorly under Medicare's value-based purchasing.

Medicare's value-based purchasing (VBP) program potentially puts safety-net hospitals at a financial disadvantage compared to other hospitals. In 2014, the second year of the program, patient mortality measures were added to the VBP program's algorithm for assigning penalties and rewards. We examined whether the inclusion of mortality measures in the second year of the program had a disproportionate impact on safety-net hospitals nationally. We found that safety-net hospitals were more likely than other hospitals to be penalized under the VBP program as a result of their poorer performance on process and patient experience scores. In 2014, 63 percent of safety-net hospitals versus 51 percent of all other sample hospitals received payment rate reductions under the program. However, safety-net hospitals' performance on mortality measures was comparable to that of other hospitals, with an average VBP survival score of thirty-two versus thirty-one among other hospitals. Although safety-net hospitals are still more likely than other hospitals to fare poorly under the VBP program, increasing the weight given to mortality in the VBP payment algorithm would reduce this disadvantage.

California safety-net hospitals likely to be penalized by ACA value, readmission, and meaningful-use programs.

The Affordable Care Act includes provisions to increase the value obtained from health care spending. A growing concern among health policy experts is that new Medicare policies designed to improve the quality and efficiency of hospital care, such as value-based purchasing (VBP), the Hospital Readmissions Reduction Program (HRRP), and electronic health record (EHR) meaningful-use criteria, will disproportionately affect safety-net hospitals, which are already facing reduced disproportionate-share hospital (DSH) payments under both Medicare and Medicaid. We examined hospitals in California to determine whether safety-net institutions were more likely than others to incur penalties under these programs. To assess quality, we also examined whether mortality outcomes were different at these hospitals. Our study found that compared to non-safety-net hospitals, safety-net institutions had lower thirty-day risk-adjusted mortality rates in the period 2009-11 for acute myocardial infarction, heart failure, and pneumonia and marginally lower adjusted Medicare costs. Nonetheless, safety-net hospitals were more likely than others to be penalized under the VBP program and the HRRP and more likely not to meet EHR meaningful-use criteria. The combined effects of Medicare value-based payment policies on the financial viability of safety-net hospitals need to be considered along with DSH payment cuts as national policy makers further incorporate performance measures into the overall payment system.