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Biomedical subjects

L I Iezzoni

Publications and source records attributed to L I Iezzoni.

At least 91 records · Page 5Linked to original sources
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↗
PubMed1990

Using administrative diagnostic data to assess the quality of hospital care. Pitfalls and potential of ICD-9-CM.

Diagnostic information within administrative data bases is generally in the form of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. The purpose of this article is to introduce ICD-9-CM, to review its strengths and limitations, and to suggest ways that it can be used through administrative files for assessing the quality of hospital care.

Disease↗
PubMed1989

Using severity information for quality assessment: a review of three cases by five severity measures.

Five severity measurement systems--APACHE II, Computerized Severity Index, Disease Staging (both Q-Scale and clinical criteria version), MedisGroups, and Patient Management Categories--were applied to three clinical cases. Severity results and recommendations for quality review were then compared and contrasted. Systems that define severity based on resource need produce different impressions than those that define severity in more clinical terms. In-depth quality review is generally suggested when severity scores later in the hospital stay are higher than at admission. Recommendations for review may be automatic or tied to other factors. Some systems use normative data, based on observed severity patterns and patient outcomes, to guide decisions about quality reviews.

Adolescent↗
PubMed1988

A clinical assessment of MedisGroups.

Interest has focused recently on measuring severity of illness, both to improve the fairness of diagnosis related group-based reimbursement and to facilitate judgments about hospital quality of care. MedisGroups is a prominent, proprietary severity-measurement system, recently mandated for use by all Pennsylvania hospitals. We reviewed MedisGroups and its key clinical findings. MedisGroups produces admission scores, from 0 through 4, indicating increasing risk of imminent organ failure. Score computation is independent of diagnosis, but many key clinical findings are disease specific and require particular diagnostic technologies. Using a database including patients 65 years of age and older from 24 hospitals, we found that fewer than 1% of patients with scores of 0 or 1 died in-hospital, compared with 60% of those with scores of 4. Questions remain about the impact of the procedural nature of many key clinical findings and the independence from diagnosis. Further study is needed to determine the utility of MedisGroups for policy purposes.

Algorithms↗
PubMed1988

Coding of acute myocardial infarction. Clinical and policy implications.

STUDY OBJECTIVE: To evaluate the appropriateness of diagnostic coding of acute myocardial infarction across teaching and nonteaching hospitals. DESIGN: Retrospective review of a random sample of medical records to find evidence of the occurrence and active treatment of acute myocardial infarction during the admission. SETTING: Five tertiary teaching, five other teaching, and five nonteaching hospitals in metropolitan Boston. CASES: Random sample of hospital admissions assigned a discharge diagnosis of acute myocardial infarction between October 1984 and September 1985. MEASUREMENT AND MAIN RESULTS: Of the 1003 cases reviewed, 260 did not meet the clinical criteria for acute myocardial infarction. At tertiary hospitals, 175 (41.7%) failed to qualify, compared with 25 (9.1%) at nonteaching facilities. In a large fraction of the disqualified cases, the patients had been admitted to exclude the diagnosis of acute myocardial infarction; although explicitly "ruled out," an acute myocardial infarction code was assigned. Sixty-six cases from teaching hospitals did not qualify because the patient had been admitted only for coronary angiography after an uneventful postmyocardial infarction course. Almost one-third of these patients had had their infarcts from 5 to 8 weeks before the angiography admission. CONCLUSIONS: Cases with an inappropriate discharge diagnosis of acute myocardial infarction may be concentrated in teaching hospitals. This finding could have implications for Medicare's diagnosis-related group payment system and governmental and other research efforts that use these data for such purposes as drawing inferences about the quality of hospital care.

Boston↗
PubMed1988

Admission MedisGroups score and the cost of hospitalizations.

Concerns about the insensitivity of Medicare's diagnosis-related groups (DRGs) to illness severity heightened interest in the potential of alternative patient classification systems to improve the fairness of hospital reimbursement. This article examines the ability of admission MedisGroups score to explain the costs of hospital stays. The database contained 54,112 patients 65 years or older discharged in 28 high-frequency DRGs from 1984 to the middle of 1986 from 24 hospitals across the country. Admission MedisGroups score alone explained 3% of costs using trimmed data. Addition of admission MedisGroups score to DRGs modestly improved ability to predict differences in cost: for trimmed data, DRGs alone explained 52% of the variation in costs, compared with 55% when admission MedisGroups score was added. Within individual DRGs, the explanatory power of admission MedisGroups score ranged from 0% to 21%. The level of explanatory power was not related to the spread of cases across admission MedisGroups scores within DRG. No consistent clinical pattern explained these differences across DRGs.

Aged↗
PubMed1987

Changes in payment policies: impact on physicians' office testing.

Recent changes in payment policies include powerful pecuniary incentives to move care from expensive hospital settings to cheaper outpatient sites. Physicians face competition from a growing number of alternative providers in the diagnostic testing marketplace. Given that a concurrent trend involves aggressive utilization review with stiff penalties for noncompliance, physicians are challenged to practice appropriate restraint in ordering and performing tests.

Clinical Laboratory Techniques↗
PubMed1987

Case classification and quality of care: issues to consider before making the investment.

Hospitals are considering adoption of patient classification systems, in part, because of their desire to create a clinically meaningful data base for quality assurance and monitoring. But before they make the substantial financial investment such a system requires, hospital personnel should be sure it is appropriate for their institution. The system should be reliable, medically meaningful, and useful in differentiating cases in which care was poor. The classification components should be easily available from the hospital's standard information. The cost of the system and its impact on staff should also be considered.

Costs and Cost Analysis↗
PubMed1986

The impact of reimbursement changes on doctors' office testing.

Most of the recent changes that have revolutionized the way health care is financed include powerful pecuniary incentives to move care out of the hospital and into such outpatient settings as doctors' offices. However, many of these changes also include aggressive utilization reviews with stiff penalties for noncompliance. Thus, although doctors' office testing will continue to expand, this growth may bring more external oversight into doctors' offices.

Ambulatory Care↗
PubMedDate not supplied

Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality?

OBJECTIVE: Incomplete coding of secondary diagnoses may bias assessments of patient risks of poor outcomes using administrative health care databases, most of which allow only five diagnoses. The Medicare program is expanding the number of possible diagnoses from five to nine, aiming to improve coding completeness. We examined the impact of having more diagnosis codes available on assessments of risk of death. DESIGN: We used 1988 computerized hospital discharge abstract data from California, which allow up to 25 diagnoses per discharge, to select a sample of hospitalized patients and assessed the relationship between the presence of 29 specific secondary diagnoses and the risk of in-hospital death. SETTING: Nonfederal acute-care hospitals in California. STUDY POPULATION: All patients at least 65 years of age who were hospitalized for stroke, pneumonia, acute myocardial infarction, or congestive heart failure in California in 1988 (N = 162,790). MAIN OUTCOME MEASURES: Relative risk of death for each specific secondary diagnosis. RESULTS: Many conditions that on a clinical basis would be expected to increase the risk of death, such as adult-onset diabetes mellitus, previous myocardial infarction, angina, and ventricular premature beats, were associated with a lower risk of in-hospital death. CONCLUSIONS: Bias against coding of chronic or comorbid conditions on the computerized discharge abstracts of patients who die best explains these results. Efforts to improve diagnosis coding completeness solely by increasing the number of available coding spaces may not succeed.

Aged↗
PubMedDate not supplied

Predicting in-hospital mortality for stroke patients: results differ across severity-measurement methods.

OBJECTIVE: To see whether severity-adjusted predictions of likelihoods of in-hospital death for stroke patients differed among severity measures. METHODS: The study sample was 9,407 stroke patients from 94 hospitals, with 916 (9.7%) in-hospital deaths. Probability of death was calculated for each patient using logistic regression with age-sex and each of five severity measures as the independent variables: admission MedisGroups probability-of-death scores; scores based on 17 physiologic variables on admission; Disease Staging's probability-of-mortality model; the Seventy Score of Patient Management Categories (PMCs); and the All Patient-Refined Diagnosis Groups (APR-DRGs). For each patient, the odds of death predicted by the severity measures were compared. The frequencies of seven clinical indicators of poor prognosis in stroke were examined for patients with very different odds of death predicted by different severity measures. Odds ratios were considered very different when the odds of death predicted by one severity measure was less than 0.5 or greater than 2.0 of that predicted by a second measure. RESULTS: MedisGroups and the physiology scores predicted similar odds of death for 82.2% of the patients. MedisGroups and PMCs disagreed the most, with very different odds predicted for 61.6% of patients. Patients viewed as more severely III by MedisGroups and the physiology score were more likely to have the clinical stroke findings than were patients seen as sicker by the other severity measures. This suggests that MedisGroups and the physiology score are more clinically credible. CONCLUSIONS: Some pairs of severity measures ranked over 60% of patients very differently by predicted probability of death. Studies of severity-adjusted stroke outcomes may produce different results depending on which severity measure is used for risk adjustment.

Adolescent↗