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A S Ash

Publications and source records attributed to A S Ash.

At least 55 records · Page 3Linked to original sources
PubMed1996

Severity measurement methods and judging hospital death rates for pneumonia.

Payers and policymakers are increasingly examining hospital mortality rates as indicators of hospital quality. To be meaningful, these death rates must be adjusted for patient severity. This research examined whether judgments about an individual hospital's risk-adjusted mortality is affected by the severity adjustment method. Data came from 105 acute care hospitals nationwide that use the Medis-Groups severity measure. The study population was 18,016 adults hospitalized in 1991 for pneumonia. Multivariable logistic models to predict in-hospital death were computed separately for 14 severity methods, controlling for patient age, sex, and diagnosis-related group (DRG). For each hospital, observed-to-expected death rates and z scores were calculated for each severity method. The overall in-hospital death rate was 9.6%. Unadjusted mortality rates for the 105 hospitals ranged from 1.4% to 19.6%. After adjusting for age, sex, DRG, and severity, 73 facilities had observed mortality rates that did not differ significantly from expected rates according to all 14 severity methods; two had rates significantly higher than expected for all 14 severity methods. For 30 hospitals, observed mortality rates differed significantly from expected rates when judged by one or more but not all 14 severity methods. Kappa analysis showed fair to excellent agreement between severity methods. The 14 severity methods agreed about relative hospital performance more often than expected by chance, but perceptions of individual hospitals' mortality rates varied using different severity adjustment methods for almost one third of facilities. Judgments about individual hospital performance using different severity adjustment approaches may reach different conclusions.

Adolescent↗
PubMed1996

The importance of comorbidities in explaining differences in patient costs.

OBJECTIVES: The authors examine to what extent comorbidities contribute to differences in patient hospital costs. METHODS: The medical record data for this study were collected from 15 metropolitan Boston hospital for 4,439 patients admitted mostly in 1985 for one of eight common conditions. Massachusetts hospital discharge abstract data for 1985 and 1993 also were used. Comorbidities were identified from the medical record for the 15-hospital data set and from discharge abstracts for all cases. Stepwise regression models were used to develop comorbidity scores. RESULTS: Across all conditions, the medical record-based comorbidity score increased the R2 value from .42 in a model with diagnosis-related groups alone to .50. In condition-specific analyses, including the comorbidity score increased the R2 by more than 50% in six of eight conditions, and was more important than several other dimensions of severity in explaining condition-specific costs. When comorbidities were determined from discharge abstract data rather than medical records, only approximately half as much comorbidity was found. Also, there was much less explanatory power: the all-condition R2 only went from .42 to .44. However, a comorbidity score developed from statewide hospital discharge abstract data was more useful in explaining variations in charges in the eight condition-specific analyses conducted on patients 65 years and older. CONCLUSIONS: Comorbidities, particularly when determined from the medical record, are important determinants of patient costs.

Adolescent↗
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

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

Diagnosis-based risk adjustment for Medicare capitation payments.

Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula.

Aged↗
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

The importance of severity of illness adjustment in predicting adverse outcomes in the Medicare population.

The importance of using risk-adjusted mortality rates to measure quality of care is well-established. However, mortality rates may be an insensitive measure of quality for surgical patients since death is a relatively rare outcome. This study used Medicare files to identify, through chart abstraction, clinical postoperative complications of four surgical procedures (n = 8126) that could serve as measures of quality. Disease-specific severity of illness models using a moderate number of clinical variables and admission MedisGroups score models computed from approximately 250 clinical variables were compared in predicting postoperative adverse events. Initial differences between the two models disappeared upon cross-validation. Validated R-squareds and C statistics from models using half the data were generally positive, suggesting that these models had real, although modest, predictive power. We have shown that severity of illness on admission plays a role in predicting adverse events of surgery. Risk-adjusted outcomes may potentially be useful in screening for quality shortfalls.

Aged↗
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

Postoperative adverse events of cholecystectomy in the Medicare population.

We explored the use of postoperative adverse events of cholecystectomy as possible screens for poor quality of care. Retrospective analysis of clinical data abstracted from hospital charts between 1985-1986 was conducted on a random sample of 3,182 cholecystectomy cases. Severity of illness models were developed predicting adverse events following cholecystectomy in patients with and without bile duct exploration. Outcome measures included 17 nonfatal adverse events and death within 30 days of admission. Adverse event rates were 23.2% for cases with bile duct exploration and 14.4% for cases without bile duct exploration. Cross-validated R-squareds and C-statistics showed that models had real, although modest, predictive power. We conclude that clinically meaningful adverse events of cholecystectomy can be successfully identified through chart abstraction.

Aged↗
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↗
PubMed1993

Predicting the occurrence of adverse events after coronary artery bypass surgery.

OBJECTIVE: To determine whether adverse events occurring after coronary artery bypass surgery in Medicare patients can be predicted from clinical variables representing illness severity at admission. DESIGN: Retrospective analysis of clinical data abstracted from hospital charts, with development and validation using half-samples of the database. A logistic model was developed using illness severity at admission to predict the occurrence of an adverse event after bypass surgery. SETTING: Hospitals in seven states. PATIENTS: Random sample of 2213 Medicare patients 65 years of age or more who underwent bypass surgery between January 1985 and June 1986. OUTCOME MEASURE: The occurrence of death within 30 days of admission or any of 13 nonfatal postoperative adverse events (for example, myocardial infarction, congestive heart failure, and wound infection). RESULTS: Thirty-three percent of patients had one or more postoperative adverse events or died within 30 days of admission. Mortality within 30 days of admission was 6.6%; each adverse event was associated with increased mortality (range, 7.5% to 66.7%). Admission predictors of the occurrence of an adverse event included a history of bypass surgery, emergent surgery, a history of chronic obstructive pulmonary disease, the presence of an infiltrate on admission chest radiograph, a pulse of 110 beats/min or more, age, blood urea nitrogen of 10.7 mmol/L (30 mg/dL) or more, acute myocardial infarction at admission, and a history of myocardial infarction; the presence of one- or two-vessel disease was negatively associated with the occurrence of an adverse event. The model c-statistic was 0.64. CONCLUSIONS: Severity of illness at admission has modest predictive power with respect to adverse-event occurrence in Medicare patients who undergo bypass surgery.

Aged↗
PubMed1992

Predicting in-hospital mortality. A comparison of severity measurement approaches.

Hospital mortality statistics derived from administrative data may not adjust adequately for patient risk on admission. Using clinical data collected from the medical record, this study compared the ability of six models to predict in-hospital death, including one model based on administrative data (age, sex, and principal and secondary diagnoses), one on admission MedisGroups score, and one on an approximation of the Acute Physiology Score (APS) from the revised Acute Physiology and Chronic Health Evaluation (APACHE II), as well as three empirically derived models. The database from 24 hospitals included 16,855 cases involving five medical conditions, with an overall in-hospital mortality rate of 15.6%. The administrative data model fit least well (R-squared values ranged from 1.9-5.5% across the five conditions). Admission MedisGroups score and the proxy APS score did better, with R-squared values ranging from 4.9% to 25.9%. Two empirical models based on small subsets of explanatory variables performed best (R-squared values ranged from 18.5-29.9%). The preceding models had the same relative performances after cross-validation using split samples. However, the high R-squared values produced by the full empirical models (using 40 or more explanatory variables) were not preserved when they were cross-validated. Most of the predictive clinical findings were general physiologic measures that were similar across conditions; only a fifth of predictors were condition-specific. Therefore, an efficient approach to risk-adjusting in-hospital mortality figures may involve adding a small subset of condition-specific clinical variables to a core group of acute physiologic variables. The best predictive models employ condition-specific weighting of even the generic clinical findings.

Databases, Factual↗