Critical care is the care of patients at high risk for a life-threatening deterioration, such as those with myocardial infarction, acute respiratory failure, trauma, or severe sepsis. Although provided in diverse settings, critical care is often also defined by the location of care delivery—a critical care unit, commonly an intensive care unit (ICU) or coronary care unit (CCU)—that is defined by the intensive monitoring, high nurse to patient ratio, and availability of invasive technology for organ support. Over the last 20 years there has been a dramatic rise in the use of intensive care in the United States. From 1985 to 2005, critical care beds increased by 36 percent (69,300–93,995), whereas noncritical care beds decreased by 35 percent (820,300–534,414). (Halpern, Pastores, and Greenstein 2004; Halpern et al. 2007; Halpern and Pastores 2009). Because care provided in the ICU is costly, critical care spending is a major contributor to the rapidly escalating health care costs in the United States and now represents ∼1 percent of the U.S. gross domestic product.(Halpern and Pastores 2009). The rise in ICU costs may result, in part, from specific differences in practice patterns at individual hospitals. Certain hospitals may overuse ICUs by admitting patients either with no meaningful chance of recovery, or, conversely, patients who do not require life-sustaining therapies (Zimmerman et al. 1995; Rosenthal et al. 1998; Barnato et al. 2007). Financial incentives for both hospitals and physicians due to reimbursement for intensive care may also drive variation in practice (Garland et al. 2006). In addition, relative overuse of the ICU may result from understaffing of general medical/surgical floors in pursuit of lower costs for common noncritical illness diagnosis-related groups (DRGs); such an understaffed floor might be unable to care for modest acuity patients (requiring a lower threshold for transfer to the ICU) or might be late to detect incipient critical illness or fail to rescue such patients.(Ghaferi, Birkmeyer, and Dimick 2009) To address the etiology and consequences of under or overuse, researchers and policy makers must first measure the extent to which varying use of intensive care is present. One way to indirectly measure the potential for overuse is to examine how critical care practice varies across hospitals. The decision to admit a patient to the ICU and provide critical care is complex, and results from the interaction of specific characteristics of the patient (e.g., illness severity or comorbidity), and specific organizational and cultural aspects of a hospital. The fraction of patients receiving care in an ICU will vary between hospitals not only because of variation in patients' needs for life-sustaining therapies, the decision to admit individual patients, but also because of compositional differences between hospitals in the array of services they offer, some of which may have different requirements for ICU care (e.g., caring for advanced burns or cardiac surgery). A simple conceptual model that integrates these factors is shown in the Supplemental Digital Content (eFigure S1), drawing on the work of Andersen and Aday (Andersen et al. 1987) and Penchansky and Thomas (1981). Our primary goal is to understand the extent to which variation between hospitals in the use of intensive care is explained by patients and hospital factors. We further wish to understand the extent to which hospital-to-hospital variation is a feature of objective differences between hospitals, particularly with regard to their case mix and offering of specialty services, as opposed to more idiosyncratic local cultural variation in use of the ICU for observationally equivalent patients. This last category of variation—whereby comparable patients may be treated differently merely as a function of where they are hospitalized—is the area of greatest policy interest. For example, the existence of wide variation in the use of intensive care across hospitals that is not attributable to measured patient or hospital differences may suggest that some hospitals are using critical care disproportionately. By then identifying hospital outliers, researchers and policy makers can focus their efforts on select hospitals—taking advantage of this variation as natural laboratories in hospital process. To address these questions, we used all-payer State Inpatient Data (SID) from the Agency for Healthcare Research and Quality (AHRQ) to measure variation in ICU use across hospitals without accounting for any known differences. Using multilevel regression analysis, we adjust for known patient differences to determine the proportion of total variation that remained due to hospital-level factors. We add patient factors, including case mix adjustment, in a priori fashion, to understand the impact of more granular adjustment. We then adjust for several measured hospital characteristics to determine the residual hospital-level variation, which was independent of known patient and hospital characteristics (Merlo et al. 2005a). To further understand whether hospital-level variation in intensive care is consistent across more homogeneous diagnoses or procedures, we evaluate our models in patients hospitalized with acute myocardial infarction, pneumonia, congestive heart failure, or surgery for colorectal cancer. We chose these diagnoses because they are common, have a reasonable likelihood of requiring ICU admission, and are reliably identified using administrative data.