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Biomedical subjects

L I Iezzoni

Publications and source records attributed to L I Iezzoni.

At least 73 records · Page 4Linked to original sources
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

Identifying complications of care using administrative data.

The Complications Screening Program (CSP) is a method using standard hospital discharge abstract data to identify 27 potentially preventable in-hospital complications, such as post-operative pneumonia, hemorrhage, medication incidents, and wound infection. The CSP was applied to over 1.9 million adult medical/surgical cases using 1988 California discharge abstract data. Cases with complications were significantly older and more likely to die, and they had much higher average total charges and lengths of stay than other cases (P < 0.0001). For most case types, 13 chronic conditions, defined using diagnosis codes, increased the relative risks of having a complication after adjusting for patient age. Cases at larger hospitals and teaching facilities generally had higher complication rates. Logistic regression models to predict complications using demographic, administrative, clinical, and hospital characteristics variables, had modest power (C statistics = 0.64 to 0.70). The CSP requires further evaluation before using it for purposes other than research.

Adult↗
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↗
PubMed1994

Chronic conditions and risk of in-hospital death.

OBJECTIVE: This study examined the relationship of in-hospital death and 13 conditions likely to have been present prior to the patient's admission to the hospital, defined using secondary discharge diagnosis codes. DATA SOURCES AND STUDY SETTING: 1988 California computerized hospital discharge abstract data, including 24 secondary diagnosis coding slots, from all general, acute care hospitals. STUDY DESIGN: The odds ratio for in-hospital death associated with each of 13 chronic conditions was computed from a multivariable logistic regression using patient age and all chronic conditions to predict in-hospital death. DATA EXTRACTION: All 1,949,276 general medical and surgical admissions of persons over 17 years of age were included. Patients were assigned to four groups according to the mortality rate of their reason for admission; some analyses separated medical and surgical hospitalizations. PRINCIPAL FINDINGS: Overall mortality was 4.4 percent. For all cases, mortality varied by chronic condition, ranging from 5.3 percent for coronary artery disease to 18.6 percent for nutritional deficiencies. The odds ratios associated with the presence of a chronic condition were generally highest for patients in the rare mortality group. Although chronic conditions were more commonly listed for medical patients, the associated odds ratios were generally higher for surgical patients, particularly in lower mortality groups. CONCLUSIONS: Studies examining death rates need to consider the influence of chronic conditions. Chronic conditions had a particularly significant association with the likelihood of death for admission types generally associated with low mortality rates and for surgical hospitalizations. The accuracy and completeness of discharge diagnoses require further study, especially relating to chronic illnesses.

Adolescent↗
PubMed1994

Using administrative data to screen hospitals for high complication rates.

Medicare's Peer Review Organizations (PROs) now are required to work with hospitals to improve patient outcomes. Which hospitals should be targeted? We used 1988 California discharge data to identify hospitals with higher-than-expected rates of complications in six adult, medical-surgical patient populations. Relative hospital complication rates generally were correlated across clinical areas, although correlations were lower between medical and surgical case types. Higher relative rates of complications were associated with larger size, major teaching facilities, and provision of open heart surgery, as well as with coding more diagnoses per case. Complication rates generally were not related significantly to hospital mortality rates as calculated by the Health Care Financing Administration. Different hospitals may be chosen for quality review depending on the method used to identify poor outcomes.

Aged↗
PubMed1992

A description and clinical assessment of the Computerized Severity Index.

Recent initiatives expanding health care data networks have increasingly emphasized severity of illness information, both to improve fairness of hospital payment and to assist in widespread assessment of hospital and physician quality. The Computerized Severity Index (CSI), one of the newest severity tools to generate interest, is disease specific and produces scores from 1 to 4 at both the disease and overall patient levels. Severity is defined as "the treatment difficulty presented to physicians due to the extent and interactions of patient's diseases." The clinical logic of the severity rating system is readily available through the "severity matrices" associated with over 820 disease groups. Questions exist about the CSI's dependence on diagnostic coding and the qualitative nature of some of the clinical criteria. More study is required to assess the utility of the CSI for various health policy purposes.

Baltimore↗
PubMed1992

A method for screening the quality of hospital care using administrative data: preliminary validation results.

Applying a computerized algorithm to administrative data to help assess the quality of hospital care is intriguing. As Iezzoni and colleagues point out, there are major differences of opinion as to the worth of such efforts. This article significantly advances the state of the art in using administrative data to screen for potential quality-of-care problems. In addition, this work on identifying complications of care goes well beyond the emphasis of many government organizations on hospital mortality rates. One question, however, not raised in the paper is: What is a practical upper limit to the sensitivity and specificity in comparing computerized screen results with the consensus judgments of a group of independent physicians? Advanced statistical techniques (such as bootstrapping) might be used to estimate the stability of consensus judgments by physician groups. When the judgments of two groups of physicians are compared with each other, the resulting sensitivity and specificity will not be .99! In addition, more training of members of the physician panels would probably have increased interrater reliability. While acknowledging this problem, the researchers' detailed analysis of the panel results is intriguing and represents a model for such studies. It is hoped that the authors will follow up on the avenues opened here. Furthermore, what degree of accuracy is necessary to identify facilities with higher-than-expected rates of complications? The authors discuss problems involved in using administrative data to target hospitals and departments for more costly in-depth reviews of quality. It is hoped that the promising findings that are reported here will be validated in other studies. Certainly their algorithms should find a ready audience in insurers and hospitals willing to try them out. Finally, should we expect additional research to lead to improvement in the authors' algorithms? I believe the algorithms will prove difficult to improve upon; but perhaps we should not worry about this. At some point, however, the cost of trying to identify and correct quality problems in "minimally outlier" hospitals will exceed the benefits, particularly given alternative uses for the funds. Might we now be close the the "flat of the curve" in the development of such systems for identification of quality problems? This issue should be discussed much further in future studies.

Abstracting and Indexing↗
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↗
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

Admission and mid-stay MedisGroups scores as predictors of hospitalization charges.

This study examines the ability of MedisGroups, a severity measure based on clinical data abstracted from the medical record, to predict hospitalization charges. MedisGroups measures severity both on admission and approximately 1 week into the hospital stay. The data base contained 23,361 admissions of Medicare beneficiaries in six conditions from 836 hospitals in seven states between January 1985, and May 1986. In all six conditions, higher admission and mid-stay severity scores were generally associated with higher charges. Across the six conditions, the R2 values for predicting charges using diagnosis-related group (DRG) class ranged from 0.06 to 0.32 using trimmed data. Adding admission MedisGroups scores to DRG class produced R2 values ranging from 0.09 to 0.33, while adding mid-stay scores yielded R2 values from 0.15 to 0.41, and adding both admission and mid-stay scores produced R2 levels ranging from 0.17 to 0.42. Very little of the superior predictive power of the mid-stay score could be attributed to its serving as a proxy for length of stay.

Diagnosis-Related Groups↗
PubMed1991

Admission and mid-stay MedisGroups scores as predictors of death within 30 days of hospital admission.

We examined the ability of MedisGroups, a severity measure based on clinical data abstracted from the medical record, to predict mortality 30 days following admission. MedisGroups measures severity both on admission and approximately one week into the hospital stay. The data base was a random sample of 20,985 admissions of Medicare beneficiaries with one of six conditions from 833 hospitals in seven states between January 1985 and May 1986. In all six conditions, higher admission and mid-stay severity scores were generally associated with higher risk of death. Across the six conditions, the R-squared values for predicting death using admission scores ranged from 0.01 to 0.16; R-squared values using mid-stay scores ranged from 0.03 to 0.34; and R-squared values from combining admission and mid-stay scores ranged from 0.05 to 0.41. Admission MedisGroups score was thus only modestly predictive of 30-day mortality. While the mid-stay score was more powerful, it may not be an appropriate severity adjuster to screen for quality using hospital mortality rates because it could be influenced by substandard care.

Cholecystectomy↗
PubMed1991

Purpose of admission and resource use during cancer hospitalizations.

This study examined the role of purpose of admission (POA) in hospitalizations for lung, colon, and breast cancers, using the 1985 20-percent Medicare provider analysis and review file. Six POA categories were created from discharge abstract data. Average hospitalization charges, per diem charges, length of stay, and rates of death varied significantly by POA (p < .001). Rural and small hospitals were more likely to admit patients for palliation, while urban and large hospitals admitted relatively more patients for active interventions (p < .0001). POA and indicators of case complexity added only modestly to the ability of diagnosis-related groups to predict hospitalization charges.

Aged↗
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↗