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D Teres

Publications and source records attributed to D Teres.

At least 37 records · Page 2Linked to original sources
PubMed1996

The relationship between age and the use of DNR orders in critical care patients. Evidence for age discrimination.

OBJECTIVE: To determine whether a relationship exists between the use of do-not-resuscitate (DNR) orders in the intensive care unit and the age of the patient after controlling for the severity of illness. METHODS: Patients from the Mortality Probability Model database, which includes 6103 patients in 4 large hospitals, and from a second database, which includes 3226 additional patients in 25 hospitals, were analyzed through logistic regression seeking a relationship between age and DNR use. Adult medical and surgical intensive care units from 27 hospitals in the United States were included. RESULTS: In the Mortality Probability Model database, 11.4% of the patients had DNR orders written. In the group of patients younger than 65 years, 8% had DNR orders. This percentage climbed rapidly with age. For age ranges of 65 to younger than 75 years, 75 to younger than 85 years, and 85 years or older, the percentage of patients who had a DNR order was 11.2%, 18.9%, and 32.6%, respectively. Similar results were found in the second database: 5.4% of patients had DNR orders and, again, the rise in the use of DNR orders was associated with increased age. For patients younger than 75 years, 4.2% had DNR orders. For the older groups, 75 to younger than 85 years and 85 years and older, the rates were 8.8% and 15.4%, respectively. Logistic regression was used to control for severity of illness; when compared with patients younger than 65 years, patients 75 to younger than 85 years were 50% more likely to have DNR orders written and patients 85 years or older were 140% more likely to have DNR orders written. CONCLUSIONS: Older patients (> or = 75 years old) are significantly more likely than younger patients to have DNR orders written even after the severity of illness is controlled as a confounding variable. This association suggests age discrimination and becomes stronger as patient age increases.

Adult↗
PubMed1996

Factors affecting the performance of the models in the Mortality Probability Model II system and strategies of customization: a simulation study.

OBJECTIVES: To examine the impact of hospital mortality and intensive care unit (ICU) size on the performance of the Mortality Probability Model II system for use in quality assessment, and to examine the ability of model customization to produce accurate estimates of hospital mortality to characterize patients by severity of illness for clinical trials. DESIGN: Prospective evaluation of model performance, using retrospective data. SETTING: Data for the simulation were assembled from six adult medical and surgical ICUs in Massachusetts and New York. PATIENTS: Consecutive admissions (n = 4,224) to the Massachusetts and New York ICUs were studied. The mortality rate in the database was 18.7%. INTERVENTIONS: A computer simulation of several different hospital mortality rates and ICU sample sizes, using a multicenter database of consecutive ICU admissions, was utilized. We simulated 20 different mortality rates by randomly changing the outcomes at hospital discharge from "survived" to "deceased" and from "deceased" to "survived". Four sample size simulations used 75%, 50%, 25%, and 10% of the database. Ten replications of each mortality rate and samples size were constructed, and model calibration and discrimination were assessed for each replication. Model coefficients were customized, using logistic regression. MEASUREMENTS AND MAIN RESULTS: Vital status at hospital discharge was the outcome measure among the ICU patient population. Model performance was assessed using the Hosmer-Lemeshow C statistic for calibration, and the area under the receiver operating characteristic curve for discrimination. Goodness-of-fit tests and receiver operating characteristic curve areas demonstrated that the models were sensitive to differences in hospital mortality, indicating that they are useful quality assurance tools. Goodness-of-fit tests were more sensitive than the receiver operating characteristic curve areas. The further the hospital mortality rate diverged from the original rate, the worse the performance of the model. Sample size had an impact on these results. The smaller the sample size, the less likely the model was to perform poorly. Model coefficients were successfully customized to demonstrate that improved model performance can be achieved when necessary for clinical trial stratification. CONCLUSION: Mortality Probability Model II models can be used to assess quality of care in ICUs, but the size of the sample should be considered when assessing calibration and discrimination.

Adult↗
PubMed1996

Effect of changing patient mix on the performance of an intensive care unit severity-of-illness model: how to distinguish a general from a specialty intensive care unit.

OBJECTIVE: To analyze the effects of patient mix diversity on performance of an intensive care unit (ICU) severity-of-illness model. DESIGN: Multiple patient populations were created using computer simulations. A customized version of the Mortality Probability Model (MPM) II admission model was used to ascertain probabilities of hospital mortality. Performance of the model was assessed using discrimination (area under the receiver operating characteristic curve) and calibration (goodness-of-fit testing). SETTING: Intensive care units. PATIENTS: Data were collected from 4,224 ICU patients from two Massachusetts hospitals (Baystate Medical Center, Springfield, MA; University of Massachusetts Medical Center, Worcester, MA) and two New York hospitals (Albany Medical Center, Albany, NY; Ellis Hospital, Schenectady, NY). INTERVENTIONS: Random samples were taken from a database. The percentage of patients with each model disease characteristic was varied by assigning weights (ranging from 0 to 10) to patients with a disease characteristic. Three simulations were run for each of 15 model variables at each of 16 weights, totaling 720 simulations. MEASUREMENTS AND MAIN RESULTS: The area under the receiver operating characteristic curve and model fit were assessed in each random sample. Removing patients with a given disease characteristic did not affect discrimination or calibration. Increasing frequency of patients with each disease characteristic above the original frequency caused discrimination and calibration to deteriorate. Model fit was more robust to increases in less frequently occurring patient conditions. From the goodness-of-fit test, a critical percentage for each admission model variable was determined for each disease characteristic, defined as the percentage at which the average p value for the test over the three replications decreased to < .10. CONCLUSIONS: The concept of critical percentages is potentially clinically important. It might provide an easy first step in checking applicability of a given severity-of-illness model and in defining a general medical-surgical ICU. If the critical percentages are exceeded, as might occur in a highly specialized ICU, the model would not be accurate. Alternative modeling approaches might be to customize the model coefficients to the population for more accurate probabilities or to develop specialized models. The MPM approach remained robust for a large variation in patient mix factors.

Aged↗
PubMed1996

Resource use implications of do not resuscitate orders for intensive care unit patients.

This study describes the use of do not resuscitate (DNR) orders for ICU patients in four northeastern U.S. teaching hospitals and investigates the relationship between DNR orders and length of stay. The use of detailed data from the mortality probability model (MPM II) study on 6,290 consecutive ICU admissions to general adult medical and surgical ICUs during 1989 through 1991 allows us to control for severity of illness and the time during the ICU stay at which the DNR order was entered. About 12.8% of patients were DNR during their ICU stay, including more than half of nonsurvivors. The percentage of patients with DNR was higher for older and more severely ill patients. Most DNR orders were issued after 72 h in the ICU, but many were issued during the first ICU day. Nonsurvivors with early (first 24 h) DNR had shorter mean and median ICU and hospital stays than the comparison group of non-DNR patients. The percentage of patients with very long ICU (> 30 d) and hospital (> 60 d) stays was smaller among DNR patients. The use of DNR orders, particularly early in the ICU stay, may be associated with significant resource use reduction for an identifiable group of patients.

Adult↗
PubMed1995

Customized probability models for early severe sepsis in adult intensive care patients. Intensive Care Unit Scoring Group.

OBJECTIVE: To develop customized versions of the Simplified Acute Physiology Score II (SAPS II) and the 24-hour Mortality Probability Model II (MPM II) to estimate the probability of mortality for intensive care unit patients with early severe sepsis. DESIGN AND SETTING: Logistic regression models developed for patients with severe sepsis in a database of adult medical and surgical intensive care units in 12 countries. PATIENTS: Of 11,458 patients in the intensive care unit for at least 24 hours, 1130 had severe sepsis based on criteria of the American College of Chest Physicians and the Society of Critical Care Medicine (systemic inflammatory response syndrome in response to infection, plus hypotension, hypoperfusion, or multiple organ dysfunction). RESULTS: In patients with severe sepsis, mortality was higher (48.0% vs 19.6% among other patients) and 28-day survival was lower. The customized SAPS II was well calibrated (P = .92 for the goodness-of-fit test) and discriminated well (area under the receiver operating characteristic [ROC] curve, 0.78). Performance in the validation sample was equally good (P = .85 for the goodness-of-fit test; area under the ROC curve, 0.79). The customized MPM II was well calibrated (P = .92 for the goodness-of-fit test) and discriminated well (area under the ROC curve, 0.79). Performance in the validation sample was equally good (P = .52 for the goodness-of-fit test; area under the ROC curve, 0.75). The models are independent of each other; either can be used alone to estimate the probability of mortality of patients with severe sepsis. CONCLUSIONS: Customization provides a simple technique to apply existing models to a subgroup of patients. Accurately assessing the probability of hospital mortality is a useful adjunct for clinical trials.

Adult↗
PubMed1995

Outcome prediction for individual intensive care patients: useful, misused, or abused?

Probabilities of hospital mortality provide meaningful information in many contexts, such as in discussions of patient prognosis by intensive care physicians, in patient stratification for analysis of clinical trial data by researchers, and in hospital reimbursement analysis by insurers. Use of probabilities as binary predictors based on a cut point can be misleading for making treatment decisions for individual patients, however, even when model performance is good overall. Alternative models for estimating severity of illness in intensive care unit (ICU) patients, while demonstrating good agreement for describing patients in the aggregate, are shown to differ considerably for individual patients. This suggests that identifying patients unlikely to benefit from ICU care by using models must be approached with considerable caution.

Bias↗
PubMed1995

A comparison of intensive care unit utilization in Alberta and western Massachusetts.

OBJECTIVE: To analyze differences in intensive care unit (ICU) utilization between a Canadian province and a U.S. area. DESIGN: Retrospective data analysis of hospital discharge data and existing data from an international study of severity of illness in ICU patients. SETTING: Administrative data for the province of Alberta and the four counties of western Massachusetts for the years 1990 to 1991 were used. Detailed data on consecutive ICU admissions from two Alberta hospitals, one western Massachusetts hospital, and 24 other U.S. hospitals for 3 months in 1991 were used. MEASUREMENTS AND MAIN RESULTS: ICU use and hospital mortality rates were compared for 50,030 hospital admissions divided into 11 patient groups. ICU days per million population were two to three times as great in western Massachusetts as in Alberta. The primary reason was higher ICU incidence (percent of hospitalized patients treated in the ICU) rather than a difference in hospital admission rate or length of ICU stay. ICU incidence in western Massachusetts was significantly higher in ten of 11 patient groups--for the coronary bypass surgery group, there was no difference. The hospital mortality rate in western Massachusetts was similar to, or higher than, the mortality rate in Alberta. In Alberta, a much higher proportion of ICU patients received mechanical ventilation. For elective surgery patients, the ICU severity of illness was lower in western Massachusetts and in other U.S. hospitals than in Alberta. CONCLUSIONS: Western Massachusetts hospitalized patients are more likely to be treated in an ICU than are similar patients in Alberta. There is no evidence that the greater ICU utilization in western Massachusetts led to a lower hospital mortality rate.

Aged↗
PubMed1994

Mortality probability models for patients in the intensive care unit for 48 or 72 hours: a prospective, multicenter study.

OBJECTIVE: To develop models in the Mortality Probability Model (MPM II) system to estimate the probability of hospital mortality at 48 and 72 hrs in the intensive care unit (ICU), and to test whether the 24-hr Mortality Probability Model (MPM24), developed for use at 24 hrs in the ICU, can be used on a daily basis beyond 24 hrs. DESIGN: A prospective, multicenter study to develop and validate models, using a cohort of consecutive admissions. SETTING: Six adult medical and surgical ICUs in Massachusetts and New York adjusted to reflect 137 ICUs in 12 countries. PATIENTS: Consecutive admissions (n = 6,290) to the Massachusetts/New York ICUs were studied. Of these patients, 3,023 and 2,233 patients remained in the ICU and had complete data at 48 and 72 hrs, respectively. Patients < 18 yrs of age, burn patients, coronary care patients, and cardiac surgical patients were excluded. OUTCOME MEASURE: Vital status at the time of hospital discharge. RESULTS: The models consist of five variables measured at the time of ICU admission and eight variables ascertained at 24-hr intervals. The 24-hr model demonstrated poor calibration and discrimination at 48 and 72 hrs. The newly developed 48- and 72-hr models--MPM48 and MPM72--contain the same 13 variables and coefficients as the MPM24. The models differ only in their constant terms, which increase in a manner that reflects the increasing probability of mortality with increasing length of stay in the ICU. These constant terms were adjusted by a factor determined from the relationship between the data from the six Massachusetts and New York ICUs and a more extensive data set, from which the ICU admission Mortality Probability Model (MPM0) and MPM24 were developed. This latter data set was assembled from ICUs in 12 countries. The MPM48 and MPM72 calibrated and discriminated well, based on goodness-of-fit tests and area under the receiver operating characteristic curve. CONCLUSIONS: Models developed for use among ICU patients at one time period are not transferable without modification to other time periods. The MPM48 and MPM72 calibrated well to their respective time periods, and they are intended for use at specific points in time. The increasing constant terms and associated increase in the probability of hospital mortality exemplify a common clinical adage that if a patient's clinical profile stays the same, he or she is actually getting worse.

Adult↗
PubMed1994

A method for assessing the clinical performance and cost-effectiveness of intensive care units: a multicenter inception cohort study.

OBJECTIVES: To present an approach for assessing intensive care unit (ICU) performance which takes into account both economic and clinical performance while adjusting for severity of illness. To present a graphic display which permits comparisons among a group of hospitals. DESIGN: A multicenter, inception cohort study. SETTING: Twenty-five ICUs in U.S. hospitals that participated in the European and North American Study of Severity Systems for ICU Patients. PATIENTS: Consecutive patients (n = 3,397) admitted to ICUs in participating hospitals between September 30, 1991 and December 27, 1991. Excluded were coronary care patients, burn patients, cardiac surgery patients and patients aged < 18 yrs. MEASUREMENTS AND MAIN RESULTS: The clinical performance index is the difference between observed hospital survival rate and survival rate predicted by the Mortality Probability Model measuring severity of illness at ICU admission. The economic performance (resource use) measure is a length of stay index, Weighted Hospital Days, which weights ICU days more heavily than non-ICU days. The economic performance index is the difference between actual mean resource use and the resource use predicted by a regression including severity of illness and percent of surgical patients. Both the clinical and economic performance indices are standardized to show how far a particular hospital is from the overall mean and are graphed together. Most of the 25 hospitals lie within 1 SD of the mean on both clinical and economic performance scales. The graph makes it easy to identify those hospitals that are outside this range. There is no evidence of a trade-off between high clinical performance and high economic performance; i.e., it is possible to achieve both. CONCLUSIONS: Cross-indexing of clinical and economic ICU performance is easy to calculate. It has potential as a research and evaluation tool used by physicians, hospital administrators, payers, and others.

Adult↗
PubMed1994

Why severity models should be used with caution.

There are now two validated time points for predicting hospital mortality of ICU patients--at admission and at 24 hours. The best purposes include evaluation of high clinical performance ICUs and for patients being enrolled in clinical trials. For the latter purpose, the model must be calibrated in the individual hospital to ensure that the model is applicable. This can be estimated by using goodness-of-fit testing. There are fewer uses for physiology scores and increased emphasis on converting scores to probabilities. For individual patient application, the model should be demonstrated to have high discrimination, as measured by the area under the receiver operating characteristic curve, and high calibration, as defined by goodness-of-fit testing. Although models have improved substantially and are now based on much larger databases, there is considerable uncertainty in their application for insurance purposes, triage, regulatory applications, sanctions against individual physicians, and cost containment. Current models may not adequately describe important ICU conditions such as adult respiratory distress syndrome and multi-organ dysfunction occurring after 24 hours into ICU care. For family discussions regarding prognosis of individual patients, ICU severity models must be used cautiously at admission or after 24 hours, with the understanding of the strengths and weakness of estimating probabilities of hospital mortality. The mathematical link between physiology score and estimation of hospital mortality is established only for the time point of 24 hours after ICU admission. Calibration and discrimination of the admission and 24-hour models also must be performed within each hospital in which individual probabilities are presented to families. It may be possible to customize a probability model such as MPM to achieve a high level of calibration at the individual hospital level.

Bias↗
PubMed1993

Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients.

OBJECTIVE: To revise and update models in the Mortality Probability Model (MPM II) system to estimate the probability of hospital mortality among 19,124 intensive care unit (ICU) patients that can be used for quality assessment within and among ICUs. DESIGN AND SETTING: Models developed and validated on consecutive admissions to adult medical and surgical ICUs in 12 countries. PATIENTS: A total of 12,610 patients for model development, 6514 patients for model validation. Patients younger than 18 years and burn, coronary care, and cardiac surgery patients were excluded. OUTCOME MEASURE: Vital status at hospital discharge. RESULTS: The admission model, MPM0, contains 15 readily obtainable variables. In developmental and validation samples it calibrated well (goodness-of-fit tests: P = .623 and P = .327, respectively, where a high P value represents good fit between observed and expected values) and discriminated well (area under the receiver operating characteristic curve = 0.837 and 0.824, respectively). The 24-hour model, MPM24 (developed on 10,357 patients still in the ICU at 24 hours), contains five of the admission variables and eight additional variables easily ascertained at 24 hours. It also calibrated well (P = .764 and P = .231 in the developmental and validation samples, respectively) and discriminated well (area under the receiver operating characteristic curve = 0.844 and 0.836 in the developmental and validation samples, respectively). CONCLUSIONS: Among severity systems for intensive care patients, the MPM0 is the only model available for use at ICU admission. Both MPM0 and MPM24 are useful research tools and provide important clinical information when used alone or together.

Adult↗
PubMed1993

Nosocomial pneumonia during stress ulcer prophylaxis with cimetidine and sucralfate.

BACKGROUND: Recent studies have questioned the use of histamine (H2) receptor antagonist in stress ulcer prophylaxis because of an increased incidence of nosocomial pneumonia and subsequent death. DESIGN: This prospective randomized study compared prophylaxis with cimetidine vs sucralfate. SETTING: Medical/surgical intensive care unit in Springfield, Mass. PATIENTS: One hundred fourteen patients were enrolled. INTERVENTIONS: Cimetidine, administered as a primed continuous infusion using a 300-mg bolus followed by 37.5 mg/h, was compared with sucralfate, administered via nasogastric tube, at a dosage of 1 g every 6 hours suspended in 20 mL of sterile water. MAIN OUTCOME MEASURES: End points of the study included nosocomial pneumonia, gastrointestinal hemorrhage, and death. RESULTS: Fifty-six patients were randomized to receive cimetidine and their rate of pneumonia was 12.5%; upper gastrointestinal hemorrhage, 3.6%; and mortality, 33.9%. Fifty-eight patients were given sucralfate, and their rate of pneumonia was 13.8%; upper gastrointestinal hemorrhage, 3.4%; and mortality, 37.9%. There were no significant differences between these study end points. In patients who had pneumonia, 80% of isolates were aerobic gram-negative bacilli. CONCLUSIONS: These observations suggest that the rate of nosocomial pneumonia is not increased in patients in the intensive care unit who receive prophylaxis with cimetidine to prevent stress ulcer bleeding.

Administration, Oral↗
PubMed1993

Trends from the United States with end of life decisions in the intensive care unit.

OBJECTIVE: To describe the changes that have occurred in the United States since medicine has moved away from a paternalistic model to one that promotes patient autonomy and self-determination. To discuss the implications for cardiopulmonary resuscitation (CPR) and the increasing use of when not to perform CPR and other life-sustaining therapies. To describe the various interpretations of the ritual term Do-Not-Resuscitate (DNR) and to introduce the concept of futility in the context of non-beneficial over-treatment and discriminatory under-treatment. SETTING: Selected clinical, philosophical and public policy literature and two illustrative case examples. RESULTS: 1. There is no longer a mandate to perform CPR on all dying patients, even though the Council on Ethical and Judicial Affairs of the American Medical Association in 1991 said that the only restrictions should be in patients with an irreversible terminal condition or when the physician writes the order, DNR. 2. The DNR order usually requires the informed refusal of CPR by the patient or family. There is only minimal support for a unilateral decision even for patients with far advanced disease. 3. DNR is often the first step in the negotiated process of forgoing care in the ICU. There are multiple interpretations of DNR both in and outside of the ICU. 4. Health Proxy is the latest attempt to have a person clarify his/her wishes and preferences by naming a decision maker, if the individual losses mental capacity. 5. Although ethical principles seem well established, there are inconsistent interpretations and practices at the bedside in the United States in part due to the restructuring of the relationship between physicians and patients, providers and consumers/clients. 6. Objective severity scores such as Apache III, SAPS II, MPM II are generally not applicable for individual patient end-of-life decisions. CONCLUSIONS: Although Health Proxy in its current formulation has been disappointing, there is a clear trend for wider application of DNR and for more active discussions about withholding or forgoing other life-sustaining therapies. DNR has a different interpretation late into the ICU course (> 72 h) than when applied at or shortly after ICU admission. Late in the ICU course, it has been decided by the medical team and family or surrogate decision maker/Health Proxy that the patient has failed or is in the process of failing aggressive ICU therapy. Early use of DNR may be related to limitations based on pre-existing chronic or subacute disease burden or an unwillingness to proceed with a full ICU course of therapy. It is unclear how Ethics Committees, risk management and hospital administrators, national practice guidelines, governmental sponsored health care reform will interface with the highly complex individual patient--physician--family--Health Proxy interface as practiced in the United States. Dialogue between the Society of Critical Care Medicine and the European Society of Critical Care Medicine and among interested physicians could provide a format for a multi-cultural context to discuss end of life issues in the ICU setting.

Cardiopulmonary Resuscitation↗
PubMed1993

Civilian triage in the intensive care unit: the ritual of the last bed.

OBJECTIVE: To evaluate the numerous problems that exist when there is an acute shortage of trained critical care nurses, no triage officer is available or designated, there is no cooperation among intensive care units (ICUs) or alternative sites, or there is excessive political or financial pressure applied to maintain a referral practice or to fill all the beds, or limited ability to divert ambulances to other hospitals. The Joint Commission on Accreditation of Health Care organizations now mandates a written policy: "when patient load exceeds optimal operational capacity" (1992). DATA SOURCES/STUDY SELECTION: Selected clinical, philosophical, and public policy literature on the subject of triage. DATA SYNTHESIS/DATA EXTRACTION: 1) An ICU medical director, designee, or supervisory nurse should be empowered as the gatekeeper/triage officer. 2) The basis for regulating admission, discharge, or triage from the special care unit should be medical suitability (from a utilitarian or egalitarian point of view). During high-level triage when all ICU patients are receiving active therapy, these decisions should override the individual primary physician-patient relationship. 3) The guidelines should follow the "congestive heart failure" treatment analogy: a) preload reduction: hold high-risk patients in the postanesthesia care unit or Emergency Room, postpone surgery, hold transfers in outlying ICUs; b) improve cardiac performance: increase efficiency and decrease workload per patient by performing fewer invasive procedures and transporting fewer patients for abdominal computed tomography scans; c) afterload reduction: keep unstable patients in the postanesthesia care unit, send sicker patients to intermediate care units, send "stable" ventilator-dependent patients to general medical/surgical units, and transfer or resolve issues regarding "hopeless" patients. CONCLUSIONS: It is necessary to have public disclosure of the broader issues related to high-level triage. The first issue is recognition that there are periods of time when ICU capacity is exceeded or skilled critical care nurse availability is reduced. The next issue is the decision of who is best suited to make complex and dynamic triage decisions and what kind of oversight should be provided. Other issues relate to whether there should be patient or family consent, and what to do about patients receiving marginal benefit or who are considered hopeless or unsalvageable, yet the family or surrogate decision maker (or perhaps one of the consultants) wants to continue active care in the ICU. In the conflict between individual and community rights and benefits, there should be a nonlitigious approach when a patient is harmed during these periods of high census or limited capacity. In recognition of these complex issues (including potential conflicts among ICUs, hospital administration, individual physicians, and the various medical and surgical programs feeding patients into special care units), the Society of Critical Care Medicine has organized a Task Force on the legal and ethical justification for triage.

Bed Occupancy↗
PubMed1993

Using severity measures to describe high performance intensive care units.

This article describes the use of various scores and probabilities to clinically categorize patients in the adult intensive care unit. Some of the limitations of these severity measures are reviewed including variable definitions, timing of measurements, and whether models can be used for individual patients. Also, this article discusses how probability models may be used to compare similar types of intensive care units using standardized clinical and cost performance indices.

Hospital Mortality↗
PubMed1992

Resource utilization among intensive care patients. Managed care vs traditional insurance.

BACKGROUND: There is considerable evidence that members of managed care organizations use fewer hospital resources than patients covered by traditional health insurance. While intensive care might seem to be an unlikely setting for such differences to exist, the relationship between health coverage and use of intensive care has not been examined. METHODS: We conducted a cross-sectional analysis of consecutive intensive care unit admissions at a regional tertiary care teaching hospital. Patients in managed care plans (n = 159) and with traditional insurance (n = 389) were compared with respect to length of stay, hospital charges, charges for specific services, and use of mechanical ventilation. The analysis controlled for severity of illness, as measured by the Mortality Probability Model, case mix, and mortality. The whole sample as well as subsamples representing medical, emergency surgery, and elective surgery patients were examined. RESULTS: The managed care group, on average, had short stays (both hospital and intensive care unit), lower charges, and less use of mechanical ventilation than the traditionally insured group. Average differences of about 30% to 40% were observed. The finding held for the whole sample as well as the medical and emergency surgery subsamples. The differences were more pronounced in the patients with lowest severity of illness. CONCLUSION: Even in a setting where there would appear to be relatively little room for discretion in treatment decisions, incentives associated with type of health insurance seemed to affect resource use.

Cost Control↗