Profit and loss analysis for an intensive care unit (ICU) in Japan: a tool for strategic management

BACKGROUND: Accurate cost estimate and a profit and loss analysis are necessary for health care practice. We performed an actual financial analysis for an intensive care unit (ICU) of a university hospital in Japan, and tried to discuss the health care policy and resource allocation decisions that have an impact on critical intensive care. METHODS: The costs were estimated by a department level activity based costing method, and the profit and loss analysis was based on a break-even point analysis. The data used included the monthly number of patients, the revenue, and the direct and indirect costs of the ICU in 2003. RESULTS: The results of this analysis showed that the total costs of US$2,678,052 of the ICU were mainly incurred due to direct costs of 88.8$ 2,295,044. However, it was determined that the ICU required at least 1,986 patient days within one fiscal year based on a break-even point analysis. As a result, an annual deficit of US$ 383,008 has occurred in the ICU. CONCLUSION: These methods are useful for determining the profits or losses for the ICU practice, and how to evaluate and to improve it. In this study, the results indicate that most ICUs in Japanese hospitals may not be profitable at the present time. As a result, in order to increase the income to make up for this deficit, an increase of 437 patient days in the ICU in one fiscal year is needed, and the number of patients admitted to the ICU should thus be increased without increasing the number of beds or staff members. Increasing the number of patients referred from cooperating hospitals and clinics therefore appears to be the best strategy for achieving these goals

A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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: FiO₂ratio, 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 r²was 20.2% across individuals and 44.3% across units.</p> <p>Conclusions</p> <p>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.</p

A new conceptual framework for ICU performance appraisal and improvement

Purpose: This study examined the use of outcomes for the purposes of ICU evaluation and improvement. We reviewed the strengths and weaknesses of an outcomes-centered approach to intensive care unit (ICU) evaluation and present a more comprehensive conceptual framework for ICU evaluation and improvement. Materials and Methods: Data was collected from 2 sources: (1) a structured review of the literature, with relevant articles identified using Medline, and (2) 85 semistructured interviews of health care professionals (eg, physicians) and health care administrators (eg, chief executive officer). The interviewees came from 4 institutions: a 900-bed East Coast teaching medical center, a 600-bed East Coast teaching medical center, a 590-bed East Coast teaching medical center, and a 435-bed West Coast private community hospital. A nonrandomized, purposeful sample was used. Results: A conceptual framework for ICU evaluation is presented that identifies and defines 3 different types of variables: performance (eg, appropriateness of care, effectiveness of care), outcome (eg, resource use, mortality), and process (eg, timeliness of treatment, work environment). The framework emphasizes performance variables and the relationships between performance, outcome, and process of care variables, as a logical focus for ICU evaluation and improvement. Conclusions: Performance variables offer distinct advantages over outcome variables for ICU evaluation. Their use, however, will require additional development of current evaluation tools and methods. They provide the ability to identify the value an ICU adds to patient care in a hospital or to an episode of illness, and to evaluate integrated systems for providing care. Copyright 2002, Elsevier Science (USA). All rights reserved

Hospital costs in patients receiving prolonged mechanical ventilation: Does age have an impact?

Background: The aging of the population is one of the causes of the increase in healthcare costs in the past few decades. It is controversial whether chronological age alone should be used in making healthcare decisions. Objective: To determine the association between age and hospital costs in patients receiving mechanical ventilation (MV). Design: Prospective, observational study. Setting: Intensive care units at a teaching hospital. Patients: A total of 813 adults who received prolonged (≥48 hrs) mechanical ventilation. Intervention: None. Measurements: Severity of illness, comorbidities, length of stay, hospital costs, and mortality. We evaluated the independent association of age with hospital costs using linear regression. Results: Mean (±SD) age of patients was 60.4 ± 18.8 yrs. Median Acute Physiology Chronic Health Evaluation III score and probability of hospital death at intensive care unit admission were 64 and 0.31, respectively. Hospital mortality was 36%. Median total hospital costs and daily costs were $56,056 and$2,655, respectively. Older age was associated with lower total hospital costs after controlling for sex, intensive care unit type, severity of illness, length of stay, insurance type, resuscitation status, and survival. Hospital costs were significantly less in older patients in all cost departments examined, except for respiratory care and intensive care unit room costs. Conclusions: Daily and total hospital costs were lower in older patients. Decreased hospital resource use in older patients may be related to a preference for less aggressive care by older patients and their families or by healthcare providers

Long-term mortality and quality of life after prolonged mechanical ventilation

Objective: To describe and identify factors associated with mortality rate and quality of life 1 yr after prolonged mechanical ventilation. Design: Prospective, observational cohort study with patient recruitment over 26 months and follow-up for 1 yr. Setting: Intensive care units at a tertiary care university hospital. Patients: Adult patients receiving prolonged mechanical ventilation. Interventions: None. Measurements and Main Results: We measured mortality rate and functional status, defined as the inability to perform instrumental activities of daily living (IADLs) 1 yr following prolonged mechanical ventilation. The study enrolled 817 patients. Their median age was 65 yrs, 46% were women, and 44% were alive at 1 yr. Median ages at baseline of 1-yr survivors and nonsurvivors were 53 and 71 yrs, respectively. At the time of admission to the hospital, survivors had fewer comorbidities, lower severity of illness score, and less dependence compared with nonsurvivors. Severity of illness on admission to the intensive care unit and prehospitalization functional status had a significant association with short-term mortality rate, whereas age and comorbidities were related to long-term mortality. Fifty-seven percent of the surviving patients needed caregiver assistance at 1 yr of follow-up. The odds of having IADL dependence at 1-yr among survivors was greater in older patients (odds ratio 1.04 for 1-yr increase in age) and those with IADL dependence before hospitalization (odds ratio 2.27). Conclusions: Mortality rate after prolonged mechanical ventilation is high. Long-term mortality rate is associated with older age and poor prehospitalization functional status. Many survivors needed assistance after discharge from the hospital, and more than half still required caregiver assistance at 1 yr. Interventions providing support for caregivers and patients may improve the functional status and quality of life of both groups and thus need to be evaluated

Is There a July Phenomenon?: The Effect of July Admission on Intensive Care Mortality and LOS in Teaching Hospitals

BACKGROUND: It has been suggested that inexperience of new housestaff early in an academic year may worsen patient outcomes. Yet, few studies have evaluated the “July Phenomenon,” and no studies have investigated its effect in intensive care patients, a group that may be particularly susceptible to deficiencies in management stemming from housestaff inexperience. OBJECTIVE: Compare hospital mortality and length of stay (LOS) in intensive care unit (ICU) admissions from July to September to admissions during other months, and compare that relationship in teaching and nonteaching hospitals, and in surgical and nonsurgical patients. DESIGN, SETTING, AND PATIENTS: Retrospective cohort analysis of 156,136 consecutive eligible patients admitted to 38 ICUs in 28 hospitals in Northeast Ohio from 1991 to 1997. RESULTS: Adjusting for admission severity of illness using the APACHE III methodology, the odds of death was similar for admissions from July through September, relative to the mean for all months, in major (odds ratio [OR], 0.96; 95% confidence interval [95% CI], 0.91 to 1.02; P = .18), minor (OR, 1.02; 95% CI, 0.93 to 1.10; P = .66), and nonteaching hospitals (OR, 0.96; 95% CI, 0.91 to 1.01; P = .09). The adjusted difference in ICU LOS was similar for admissions from July through September in major (0.3%; 95% CI, −0.7% to 1.2%; P = .61) and minor (0.2%; 95% CI, −0.9% to 1.4%; P = .69) teaching hospitals, but was somewhat shorter in nonteaching hospitals (−0.8%; 95% CI, −1.4% to −0.1%; P = .03). Results were similar when individual months and academic years were examined separately, and in stratified analyses of surgical and nonsurgical patients. CONCLUSIONS: We found no evidence to support the existence of a July phenomenon in ICU patients. Future studies should examine organizational factors that allow hospitals and residency programs to compensate for inexperience of new housestaff early in the academic year