Using a consensus-based definition, the prevalence, hospital mortality, and costs of chronic critical illness are substantial. Chronic critical illness is particularly common in the elderly although in very old patients the prevalence declines, in part because of an increase in early mortality among potentially eligible patients.

Background Intensive care unit (ICU) telemedicine is an increasingly common strategy for improving the outcome of critical care, but its overall impact is uncertain. Objectives To determine the effectiveness of ICU telemedicine in a national sample of hospitals and quantify variation in effectiveness across hospitals. Research design We performed a multi-center retrospective case-control study using 2001–2010 Medicare claims data linked to a national survey identifying United States hospitals adopting ICU telemedicine. We matched each adopting hospital (cases) to up to 3 non-adopting hospitals (controls) based on size, case-mix and geographic proximity during the year of adoption. Using ICU admissions from 2 years before and after the adoption date, we compared outcomes between case and control hospitals using a difference-in-differences approach. Results 132 adopting case hospitals were matched to 389 similar non-adopting control hospitals. The pre- and post-adoption unadjusted 90-day mortality was similar in both case hospitals (24.0% vs. 24.3%, p=0.07) and control hospitals (23.5% vs. 23.7%, p<0.01). In the difference-in-differences analysis, ICU telemedicine adoption was associated with a small relative reduction in 90-day mortality (ratio of odds ratios: 0.96, 95% CI = 0.95–0.98, p<0.001). However, there was wide variation in the ICU telemedicine effect across individual hospitals (median ratio of odds ratios: 1.01; interquartile range 0.85–1.12; range 0.45–2.54). Only 16 case hospitals (12.2%) experienced statistically significant mortality reductions post-adoption. Hospitals with a significant mortality reduction were more likely to have large annual admission volumes (p<0.001) and be located in urban areas (p=0.04) compared to other hospitals. Conclusions Although ICU telemedicine adoption resulted in a small relative overall mortality reduction, there was heterogeneity in effect across adopting hospitals, with large-volume urban hospitals experiencing the greatest mortality reductions.

Discharge practices bias in-hospital ICU mortality measures in a way that disadvantages large hospitals. Accounting for discharge bias will prevent these hospitals from being unfairly disadvantaged in public reporting and pay-for-performance.

LTACHs vary widely in mortality rates, underscoring the need to better understand the sources of this variation and improve the quality of care for patients requiring long-term ventilator weaning.

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Rationale: Current mortality-based critical care performance measurement focuses on intensive care unit (ICU) admissions as a single group, conflating low-severity and high-severity ICU patients for whom performance may differ and neglecting severely ill patients treated solely on hospital wards.Objectives: To assess the relationship between hospital performance as measured by risk-standardized mortality for severely ill ICU patients, less severely ill ICU patients, and severely ill patients outside the ICU.Methods: Using a statewide, all-payer dataset from the Pennsylvania Healthcare Cost Containment Council, we analyzed discharge data for patients with nine clinical conditions with frequent ICU use. Using a validated severity-of-illness measure, we categorized hospitalized patients as either high severity (predicted probability of in-hospital death in top quartile) or low severity (all others). We then created three mutually exclusive groups: high-severity ICU admissions, low-severity ICU admissions, and high-severity ward patients. We used hierarchical logistic regression to generate hospital-specific 30-day riskstandardized mortality rates for each group and then compared hospital performance across groups using Spearman's rank correlation. Measurements and Main Results:We analyzed 87 hospitals with 22,734 low-severity ICU admissions (mean per hospital, 261 6 187), 10,991 high-severity ICU admissions (mean per hospital, 126 6 105), and 6,636 high-severity ward patients (mean per hospital, 76 6 48). We found little correlation between hospital performance for high-severity ICU patients versus low-severity ICU patients (r = 0.15; P = 0.17). There were 29 hospitals (33%) that moved up or down at least two quartiles of performance across the ICU groups. There was weak correlation between hospital performance for highseverity ICU patients versus high-severity ward patients (r = 0.25; P = 0.02). There were 24 hospitals (28%) that moved up or down at least two quartiles of performance across the high-severity groups.Conclusions: Hospitals that perform well in caring for highseverity ICU patients do not necessarily also perform well in caring for low-severity ICU patients or high-severity ward patients, indicating that risk-standardized mortality rates for ICU admissions as a whole offer only a narrow window on a hospital's overall performance for critically ill patients.

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Public reporting of ICU in-hospital mortality rates was associated with changes in discharge patterns but no change in risk-adjusted mortality.