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Kansas City, MO, United States

Cerner Corporation is a supplier of health care information technology solutions, services, devices and hardware. Cerner solutions optimize processes for health care organizations. These solutions are currently licensed by approximately 9,300 facilities around the world, including more than 2,650 hospitals, 3,750 physician practices, 40,000 physicians, 500 ambulatory facilities, 800 home health facilities, 40 employer sites, and 1,600 retail pharmacies. As of December 2013, the company had more than 14,200 employees globally. Wikipedia.


Kramer A.A.,Cerner Corporation | Kramer A.A.,University of Kansas | Higgins T.L.,Baystate Medical Center | Higgins T.L.,Tufts University | Zimmerman J.E.,George Washington University
Critical Care Medicine | Year: 2013

OBJECTIVE:: To examine the association between ICU readmission rates and case-mix-adjusted outcomes. DESIGN:: Retrospective cohort study of ICU admissions from 2002 to 2010. SETTING:: One hundred five ICUs at 46 United States hospitals. PATIENTS:: Of 369,129 admissions, 263,082 were first admissions that were alive at ICU discharge and candidates for readmission. INTERVENTIONS:: None. MEASUREMENTS AND MAIN RESULTS:: The median unit readmission rate was 5.9% (intraquartile range 5.1%-7.0%). Across all admissions, hospital mortality for patients with and without readmission was 21.3% vs. 3.6%, mean ICU stay 4.9 days vs. 3.4 days, and hospital stay 13.3 days vs. 4.5 days, respectively. We stratified ICUs according to their readmission rate: high (>7%), moderate (5%-7%), and low (<5%) rates. Observed and case-mix-adjusted hospital mortality, ICU and hospital lengths of stay were examined by readmission rate strata. Observed outcomes were much worse in the high readmission rate units. But after adjusting for patient and institutional differences, there was no association between level of unit readmission rate and case-mix-adjusted mortality. The difference between observed and predicted mortality was-0.4%, 0.4%, and-1.1%, for the high, medium, and low readmission rate strata, respectively. Additionally, the difference between observed and expected ICU length of stay was approximately zero for the three strata. CONCLUSIONS:: Patients readmitted to ICUs have increased hospital mortality and lengths of stay. After case-mix adjustment, there were no significant differences in standardized mortality or case-mix-adjusted lengths of stay between units with high readmission rates compared to units with moderate or low rates. The use of readmission as a quality measure should only be implemented if patient case-mix is taken into account. © 2013 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins. Source


Kramer A.A.,Cerner Corporation | Zimmerman J.E.,George Washington University
BMC Medical Informatics and Decision Making | Year: 2010

Background. Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay. Methods. We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model. Results. The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO2: FiO 2ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r2 was 20.2% across individuals and 44.3% across units. Conclusions. A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay. © 2010 Kramer and Zimmerman; licensee BioMed Central Ltd. Source


Kramer A.A.,Cerner Corporation | Zimmerman J.E.,Cerner Corporation | Zimmerman J.E.,George Washington University
Critical Care Medicine | Year: 2011

OBJECTIVES: To assess variations in case-mix-adjusted hospital and intensive care unit length of stay and to examine the relationship between intensive care unit and hospital stay. DESIGN: Retrospective cohort study. SETTING: Sixty-nine intensive and cardiac care units in 23 U.S. hospitals during 2002 to 2008. PATIENTS: Intensive care unit admissions (202,300) who met inclusion criteria. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We obtained hospital and intensive care unit characteristics and patient demographic, clinical, diagnostic, and physiologic variables, mortality, and lengths of stay. We developed and validated a model to assess case-mix-adjusted hospital stay and modified and updated a previously validated model to assess adjusted intensive care unit stay. We used these models to compare observed and expected hospital and intensive care unit stay for each patient by calculating the observed minus expected length of stay. Mean observed intensive care unit stay was 4.33 days and mean predicted intensive care unit stay was 4.09 days (5.9-hr difference); mean observed hospital stay was 9.93 days and mean predicted hospital stay was 9.52 days (9.7-hr difference). Observed minus expected intensive care unit and hospital length of stay were significantly shorter (p <.01) at one intensive care unit and significantly longer (p <.01) at nine intensive care units. There was a correlation between hospital and intensive care unit observed minus expected length of stay across individuals (R =.40), which was much stronger across units (R =.76). CONCLUSIONS: Case-mix-adjusted benchmarks for hospital and intensive care unit stays reveal substantial differences in unit efficiency. Hospital and intensive care unit stays are strongly correlated at the patient and unit level, suggesting that there are causal factors common to both. Copyright © 2011 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins. Source


Johnson A.E.W.,University of Oxford | Kramer A.A.,Cerner Corporation | Clifford G.D.,University of Oxford
Critical Care Medicine | Year: 2013

OBJECTIVES:: Severity of illness scores have gained considerable interest for their use in predicting outcomes such as mortality and length of stay. The most sophisticated scoring systems require the collection of numerous physiologic measurements, making their use in real-time difficult. A severity of illness score based on a few parameters that can be captured electronically would be of great benefit. Using a machine-learning technique known as particle swarm optimization, we attempted to reduce the number of physiologic parameters collected in the Acute Physiology, Age, and Chronic Health Evaluation IV system without losing predictive accuracy. DESIGN:: Retrospective cohort study of ICU admissions from 2007 to 2011. SETTING:: Eighty-six ICUs at 49 U.S. hospitals where an Acute Physiology, Age, and Chronic Health Evaluation IV system had been installed. PATIENTS:: 81,087 admissions, of which 72,474 did not have any missing values. INTERVENTIONS:: None. MEASUREMENTS AND MAIN RESULTS:: Machine-learning algorithms were used to come up with the minimal set of variables that were capable of yielding an accurate severity of illness score: the Oxford Acute Severity of Illness Score. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score were developed on admissions during 2007-2009 and validated on admissions during 2010-2011. The most parsimonious Oxford Acute Severity of Illness Score consisted of seven physiologic measurements, elective surgery, age, and prior length of stay. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score achieved an area under the receiver operating characteristic curve of 0.88 and calibrated well. CONCLUSIONS:: A reduced severity of illness score had discrimination and calibration equivalent to more complex existing models. This was accomplished in large part using machine-learning algorithms, which can effectively account for the nonlinear associations between physiologic parameters and outcome. Copyright © 2013 by the Society of Critical Care Medicine and Lippincott. Source


Kramer A.A.,Cerner Corporation | Higgins T.L.,Tufts University | Zimmerman J.E.,George Washington University
Critical Care Medicine | Year: 2012

Objective: To examine which patient characteristics increase the risk for intensive care unit readmission and assess the association of readmission with case-mix adjusted mortality and resource use. Design: Retrospective cohort study. Setting: Ninety-seven intensive and cardiac care units at 35 hospitals in the United States. Patients: A total of 229,375 initial intensive care unit admissions during 2001 through 2009 who met inclusion criteria. Interventions: None. Measurements and Main Results: For Patients who were discharged alive and candidates for readmission, we compared the characteristics of those with and without a readmission. A multivariable logistic regression analysis was used to identify potential patient-level characteristics that increase the risk for subsequent readmission. We also evaluated case-mix adjusted outcomes by comparing observed and predicted values of mortality and length of stay for Patients with and without intensive care unit readmission. Among 229,375 first admissions that met inclusion criteria, 13,980 (6.1%) were eventually readmitted. Risk factors associated with the highest multivariate odds ratio for unit readmission included location before intensive care unit admission, age, comorbid conditions, diagnosis, intensive care unit length of stay, physiologic abnormalities at intensive care discharge, and discharge to a step-down unit. After adjustment for risk factors, Patients who were readmitted had a four-fold greater probability of hospital mortality and a 2.5-fold increase in hospital stay compared to Patients without readmission. Conclusions: Intensive care readmission is associated with patient factors that reflect a greater severity and complexity of illness, resulting in a higher risk for hospital mortality and a longer hospital stay. To improve patient safety, physicians should consider these risk factors when making intensive care discharge decisions. Because intensive care unit readmission correlates with more complex and severe illness, readmission rates require case-mix adjustment before they can be properly interpreted as quality measures. © 2012 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins. Source

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