Search PubMed⌕ Search

Biomedical subjects

M Shwartz

Publications and source records attributed to M Shwartz.

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

Does severity explain differences in hospital length of stay for pneumonia patients?

OBJECTIVES: In the USA, the role of patient severity in determining hospital resource use has been questioned since Medicare adopted prospective hospital payment based on diagnosis-related groups (DRGs). Exactly how to measure severity, however, remains unclear. We examined whether assessments of severity-adjusted hospital lengths of stay (LOS) varied when different measures were used for severity adjustment. METHODS: The complete study sample included 18,016 patients receiving medical treatment for pneumonia at 105 acute care hospitals. We studied 11 severity measures, nine based on patient demographic and diagnosis and procedure code information and two derived from clinical findings from the medical record. For each severity measure, LOS was regressed on patient age, sex, DRG, and severity score. Analyses were performed on trimmed and untrimmed data. Trimming eliminated cases with LOS more than three standard deviations from the mean on a log scale. RESULTS: The trimmed data set contained 17,976 admissions with a mean (S.D.) LOS of 8.9 (6.1) days. Average LOS ranged from 5.0-11.8 days among the 105 hospitals. Using trimmed data, the 11 severity measures produced R-squared values ranging from 0.098-0.169 for explaining LOS for individual patients. Across all severity measures, predicted average hospital LOS varied much less than the observed LOS, with predicted mean hospital LOS ranging from about 8.4-9.8 days. DISCUSSION: No severity measure explained the two-fold differences among hospitals in average LOS. Other patient characteristics, practice patterns, or institutional factors may cause the wide differences across hospitals in LOS.

Centers for Medicare and Medicaid Services, U.S.↗
PubMed1996

High hospital admission rates and inappropriate care.

This study tests whether the rate of inappropriate hospital admissions is high in areas with high medical admission rates. Seventy small geographic areas were formed by grouping Massachusetts ZIP codes by similarity of hospital use. Appropriateness of hospital admission was measured both by applying the Appropriateness Evaluation Protocol and by applying physicians' judgment to the medical records of patients age sixty-five and older who were admitted for treatment of a medical condition in 1990-1992. No relationship between hospital admission rate and inappropriate admission rate was found, which calls into question the common assumption that areas with higher hospital use have more inappropriate use of hospital care.

Aged↗
PubMed1996

Judging hospitals by severity-adjusted mortality rates: the influence of the severity-adjustment method.

OBJECTIVES: This research examined whether judgments about a hospital's risk-adjusted mortality performance are affected by the severity-adjustment method. METHODS: Data came from 100 acute care hospitals nationwide and 11880 adults admitted in 1991 for acute myocardial infarction. Ten severity measures were used in separate multivariable logistic models predicting in-hospital death. Observed-to-expected death rates and z scores were calculated with each severity measure for each hospital. RESULTS: Unadjusted mortality rates for the 100 hospitals ranged from 4.8% to 26.4%. For 32 hospitals, observed mortality rates differed significantly from expected rates for 1 or more, but not for all 10, severity measures. Agreement between pairs of severity measures on whether hospitals were flagged as statistical mortality outliers ranged from fair to good. Severity measures based on medical records frequently disagreed with measures based on discharge abstracts. CONCLUSIONS: Although the 10 severity measures agreed about relative hospital performance more often than would be expected by chance, assessments of individual hospital mortality rates varied by different severity-adjustment methods.

Adult↗
PubMed1996

Judging hospitals by severity-adjusted mortality rates: the case of CABG surgery.

In many health care marketplaces, outcomes assessment is central to monitoring quality while controlling costs. Comparing outcomes across providers generally requires adjustment for patient severity. For mortality rates and other adverse outcomes comparisons, severity adjustment ideally aims to control for patient characteristics prior to the health care intervention. A variety of severity methodologies, specifically for hospitalized patients, are commercially available. Some have been adopted by state or regional initiatives for publicly comparing hospital outcomes. We applied 14 common severity measures to the same data set to determine whether judgments about risk-adjusted hospital death rates are sensitive to the specific severity method. We examined 7,765 patients undergoing coronary artery bypass graft (CABG) surgery at 38 hospitals. Unadjusted death rates ranged from 0% to 11.2% across hospitals. Comparisons of relative hospital performance were relatively insensitive to the severity adjustment method.

Adolescent↗
PubMed1996

Do severity measures explain differences in length of hospital stay? The case of hip fracture.

OBJECTIVE: To examine whether judgments about hospital length of stay (LOS) vary depending on the measure used to adjust for severity differences. DATA SOURCES/STUDY SETTING: Data on admissions to 80 hospitals nationwide in the 1992 MedisGroups Comparative Database. STUDY DESIGN: For each of 14 severity measures, LOS was regressed on patient age/sex, DRG, and severity score. Regressions were performed on trimmed and untrimmed data. R-squared was used to evaluate model performance. For each severity measure for each hospital, we calculated the expected LOS and the z-score, a measure of the deviation of observed from expected LOS. We ranked hospitals by z-scores. DATA EXTRACTION: All patients admitted for initial surgical repair of a hip fracture, defined by DRG, diagnosis, and procedure codes. PRINCIPAL FINDINGS: The 5,664 patients had a mean (s.d.) LOS of 11.9 (8.9) days. Cross-validated R-squared values from the multivariable regressions (trimmed data) ranged from 0.041 (Comorbidity Index) to 0.165 (APR-DRGs). Using untrimmed data, observed average LOS for hospitals ranged from 7.6 to 23.9 days. The 14 severity measures showed excellent agreement in ranking hospitals based on z-scores. No severity measure explained the differences between hospitals with the shortest and longest LOS. CONCLUSIONS: Hospitals differed widely in their mean LOS for hip fracture patients, and severity adjustment did little to explain these differences.

Aged↗
PubMed1995

Predicting who dies depends on how severity is measured: implications for evaluating patient outcomes.

OBJECTIVE: To determine whether assessments of illness severity, defined as risk for in-hospital death, varied across four severity measures. DESIGN: Retrospective cohort study. SETTING: 100 hospitals using the MedisGroups severity measure. PATIENTS: 11 880 adults managed medically for acute myocardial infarction; 1574 in-hospital deaths (13.2%). MEASUREMENTS: For each patient, probability of death was predicted four times, each time by using patient age and sex and one of four common severity measures: 1) admission MedisGroups scores for probability of death scores; 2) scores based on values for 17 physiologic variables at time of admission; 3) Disease Staging's probability-of-mortality model; and 4) All Patient Refined Diagnosis Related Groups (APR-DRGs). Patients were ranked according to probability of death as predicted by each severity measure, and rankings were compared across measures. The presence or absence of each of six clinical findings considered to indicate poor prognosis in patients with myocardial infarction (congestive heart failure, pulmonary edema, coma, low systolic blood pressure, low left ventricular ejection fraction, and high blood urea nitrogen level) was determined for patients ranked differently by different severity measures. RESULTS: MedisGroups and the physiology score gave 94.7% of patients similar rankings. Disease Staging, MedisGroups, and the physiology score gave only 78% of patients similar rankings. MedisGroups and APR-DRGs gave 80% of patients similar rankings. Patients whose illnesses were more severe according to MedisGroups and the physiology score were more likely to have the six clinical findings than were patients whose illnesses were more severe according to Disease Staging and APR-DRGs. CONCLUSIONS: Some pairs of severity measures assigned very different severity levels to more than 20% of patients. Evaluations of patient outcomes need to be sensitive to the severity measures used for risk adjustment.

Adult↗
PubMed1995

Who gets repeat screening mammography: the role of the physician.

To determine rates of, and explore physician factors associated with, repeat mammography, administrative data for 791 women aged 50 years and older were examined. Three-fourths of the women (73%) received repeat mammography (i.e., a second mammogram was obtained within six to 18 months of the first). Provider factors associated with higher repeat mammography rates were: being a woman, practicing in the women's health group rather than the general internal medicine service, and being a fellow or an attending physician (p-values < 0.01). Patients of women attendings/fellows had higher repeat mammography rates than did those of men attendings/fellows, men residents, and women residents. Characteristics (gender, level of training) of providers strongly influence their patients' screening behavior.

Boston↗
PubMed1995

Using severity-adjusted stroke mortality rates to judge hospitals.

Mortality rates are commonly used to judge hospital performance. In comparing death rates across hospitals, it is important to control for differences in patient severity. Various severity tools are now actively marketed in the United States. This study asked whether one would identify different hospitals as having higher- or lower-than-expected death rates using different severity measures. We applied 11 widely-used severity measures to the same database containing 9407 medically-treated stroke patients from 94 hospitals, with 916 (9.7%) in-hospital deaths. Unadjusted hospital mortality rates ranged from 0 to 24.4%. For 27 hospitals, observed mortality rates differed significantly from expected rates when judged by one or more, but not all 11, severity methods. The agreement between pairs of severity methods for identifying the worst 10% or best 50% of hospitals was fair to good. Efforts to evaluate hospital performance based on severity-adjusted, in-hospital death rates for stroke patients are likely to be sensitive to how severity is measured.

Adolescent↗
PubMed1995

An integer programming model to limit hospital selection in studies with repeated sampling.

OBJECTIVE: We describe an integer programming model that, for studies requiring repeated sampling from hospitals, can aid in selecting a limited set of hospitals from which medical records are reviewed. STUDY SETTING: The model is illustrated in the context of two studies: (1) an analysis of the relationship between variations in hospital admission rates across geographic areas and rates of inappropriate admissions; and (2) a validation of computerized algorithms that screen for complications of hospital care. STUDY DESIGN: Common characteristics of the two studies: (1) hospitals are classified into categories, e.g., high, medium, and low; (2) the classification process is repeated several times, e.g., for different medical conditions; (3) medical records are selected separately for each iteration of the classification; and (4) for budgetary and logistical reasons, reviews must be concentrated in a relatively small subset of hospitals. DATA COLLECTION/EXTRACTION METHODS. In each study, hospitals are ranked based on analysis of hospital discharge abstract data. CONCLUSIONS: The model is useful for identifying a subset of hospitals at which more intensive reviews will be conducted.

Bias↗
PubMed1994

Small area variations in hospitalization rates: how much you see depends on how you look.

This research investigates the degree that estimates of the magnitude of small area variations in hospitalization rates depend on both the estimation method and the number of years of data used. Hospital discharge abstracts for patients 65 and older from acute care hospitals in Massachusetts from 1982 to 1987 were analyzed. The SCV statistic, the approach used in many current small area variation studies, and empirical Bayes (EB), an approach that adjusts more fully for the effect of random variation, were compared. EB estimates based on 3 years of data were best able to predict future area-specific hospitalization rates. Compared to EB estimates using 3 years of data, the SCV statistic with 1 year of data overestimated the median amount of systematic variation by over 70% for the 68 conditions studied; with 3 years of data, the SCV overestimated the median by 55%. Regardless of method, the same conditions were identified as relatively more variable and the same geographic areas were found to have higher than expected hospitalization rates. The magnitude of differences in hospitalization rates depends on how the data are analyzed and how many years of data are used. Hospitalization rates across small geographic areas may vary substantially less than reported previously.

Aged↗
PubMed1994

Risk adjustment methods can affect perceptions of outcomes.

When comparing outcomes of medical care, it is essential to adjust for patient risk, including severity of illness. A variety of severity measures exist, but perceptions of outcomes may differ depending on how severity is defined. We used two severity-adjustment approaches to demonstrate that comparisons of outcomes across subgroups of patients can vary dramatically depending on how severity is assessed. We studied two approaches: model 1 was the admission MedisGroups score; model 2 was computed from age and 12 chronic conditions defined by diagnosis codes. Although common summary measures of model performance (R-squared and C) both suggested that model 1 is a better predictor of in-hospital death than model 2, the weaker model consistently produced more accurate expectations by payer class and age group. Using model 1 for severity adjustment suggested that Medicare patients did substantially worse than expected and Medicaid patients substantially better. In contrast, use of model 2 found Medicare patients doing as expected, but Medicaid patients faring poorly.

Adolescent↗
PubMed1992

The utility of severity of illness information in assessing the quality of hospital care. The role of the clinical trajectory.

This research explored whether differentiating patients whose severity of illness worsened, improved, or remained the same over the hospital stay is a good screen for quality of care. The hypothesis was that substandard care is more likely to occur among patients who have worsened. Severity was measured using the Computerized Severity Index (CSI) and MedisGroups in 233 patients who had experienced acute myocardial infarction and 279 who had undergone coronary artery bypass graft who were admitted to four New England hospitals in 1987. Deaths and patients with discharge diagnoses indicating iatrogenic events and complications were oversampled. Potential quality problems were identified through explicit screening criteria applied by nurse researchers and implicit physician reviews. Acute myocardial infarction patients who worsened had higher rates of potential quality problems than other patients (CSI, P = 0.06; MedisGroups, P = 0.01). For the CSI, the 49.4% of patients who worsened captured 70.6% of the potentially substandard care; for MedisGroups, the 35.6% of patients who worsened also encompassed 70.6% of the problematic cases. For coronary artery bypass graft, results varied depending on how severity and quality were defined. The CSI performed better using implicit physician review to identify problematic care (P = 0.00), capturing 76.5% of substandard cases among the 41.6% of patients who worsened. In contrast, MedisGroups did better using explicit quality screens (P = 0.04), grouping 60.5% of the problematic cases among the 47.0% of patients who worsened. After removing in-hospital deaths from consideration, a worsening trajectory was generally associated with a higher fraction of potential quality problems among live discharges. This preliminary study suggests that examining changes in illness severity may be a useful screen for substandard hospital care, but its utility could vary by condition and by how quality problems are defined.

Coronary Artery Bypass↗
PubMed1991

Using utilization review information to improve hospital efficiency.

Hospitals are currently under great pressure to improve the efficiency of internal operations without sacrificing quality of care. In the rush to do this, they often overlook an extremely useful source of information that already exists--data routinely collected as part of the utilization review (UR) process. This article describes a system using UR data for management purposes that was developed in a large urban teaching hospital. The components described are: (1) data collected systematically by trained reviewers applying the Appropriateness Evaluation Protocol; (2) software for data collection using inexpensive, highly portable computers; and (3) formats for reporting UR findings to hospital administrators and physicians. Information derived from UR in the study hospital is discussed, as well as factors to be considered in adapting some or all of the system's components in other hospitals.

Data Collection↗
PubMed1991

The role of severity information in health policy debates: a survey of state and regional concerns.

Severity of illness measurement has recently dominated many regional health policy debates, and some states now require severity ratings for inpatients. We summarize results of a telephone survey of regional activities involving severity data. Parties use severity information either to evaluate hospital resource use or to assist in comparing quality of hospital care. For quality assessment, various constituencies frequently specify different goals for the severity information. Business representatives commonly believe that it can quantify hospital performance and help them target cost-effective providers; in contrast, providers view severity information only as a screen for substandard care, suggesting areas requiring detailed examination.

Cost-Benefit Analysis↗
PubMed1990

Illness severity and costs of admissions at teaching and nonteaching hospitals.

This research examined the hypothesis that greater severity of illness explains the higher costs of hospitalizations at teaching compared with nonteaching hospitals. Medical records of 4439 cases within eight common conditions were reviewed at five tertiary teaching, five other teaching, and five nonteaching hospitals in metropolitan Boston, Mass. We assessed acute physiologic status, severity of the principal diagnosis, comorbidities, and functional status. The principal diagnosis was more severe for teaching hospital patients in four conditions, but few significant differences were found for the other severity dimensions by condition. Across all conditions combined, except for functional status, severity was significantly higher at teaching hospitals, but the absolute differences were small. After adjusting for diagnosis related groups, costs were higher at tertiary teaching compared with other teaching and nonteaching hospitals. Further adjusting for severity and other patient characteristics explained 18% (90% confidence interval, 4 to 33) of the higher costs at tertiary compared with nonteaching hospitals.

Boston↗
PubMed1986

The frequency of bitewing radiographs.

A model for use in analyzing the implications of different rates of caries incidence and progression for the timing of bitewing radiographs was developed. Estimates of progression rates and incidence patterns were derived from an analysis of serial bitewing radiographs. A time schedule for taking the next radiographs was determined so that carious lesions would be detected before radiolucencies reach the inner half of the dentin. For asymptomatic persons with extensive exposure to fluorides and no unrestored enamel lesions on the last radiographs, bitewing films could be scheduled every 2.5 to 3 years. For persons with little exposure to fluorides or with many early enamel lesions or at least one deep enamel lesion that has not been restored, radiographs should be performed every 6 months to 1 year.

Adolescent↗