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Biomedical subjects

L I Iezzoni

Publications and source records attributed to L I Iezzoni.

At least 37 records · Page 2Linked to original sources
PubMed1998

Validation of relative value scale for congenital heart operations.

BACKGROUND: To determine the validity of the newly assigned work relative value unit (RVU) scale for surgical procedures for congenital heart disease, we measured its relationship to length of hospital stay, total hospital charges, and mortality. METHODS: We identified cases by the presence of ICD-9-CM codes in nine statewide, administrative hospital discharge abstract databases for 1992. Computer algorithms were generated to assign RVUs to individual cases. Spearman correlation coefficients between work and practice expense RVUs and median length of hospital stay, total hospital charges, and in-hospital mortality were determined, as well as parameter estimates from linear and logistic regression. RESULTS: Using data from 5,192 cases involving 34 surgical procedures for congenital heart disease, higher work RVUs were associated with longer lengths of hospital stay (rs = 0.72, p < 0.0001), higher total hospital charges (rs = 0.81, p < 0.0001), and higher in-hospital mortality (rs = 0.45, p = 0.01). A 5-point increase in the relative value scale was associated with an increase in the length of stay by a multiplicative factor of 1.3 (p < 0.0001); total hospital charges by 1.5 (p < 0.0001); and the odds of in-hospital death by 1.9 (p < 0.0001). Findings were similar for practice expense RVUs, as work and practice expense RVUs were highly correlated (rs = 0.93, p < 0.0001). CONCLUSIONS: The group of work RVUs for surgical procedures for congenital heart defects are reasonable relative measures, on average, of physician work for these procedures, thus supporting the use of this scale to determine physician reimbursement. Practice expense RVUs may not be an independent measure for these procedures.

Cardiac Surgical Procedures↗
PubMed1998

Predicting in-hospital deaths from coronary artery bypass graft surgery. Do different severity measures give different predictions?

OBJECTIVES: Severity-adjusted death rates for coronary artery bypass graft (CABG) surgery by provider are published throughout the country. Whether five severity measures rated severity differently for identical patients was examined in this study. METHODS: Two severity measures rate patients using clinical data taken from the first two hospital days (MedisGroups, physiology scores); three use diagnoses and other information coded on standard, computerized hospital discharge abstracts (Disease Staging, Patient Management Categories, all patient refined diagnosis related groups). The database contained 7,764 coronary artery bypass graft patients from 38 hospitals with 3.2% in-hospital deaths. Logistic regression was performed to predict deaths from age, age squared, sex, and severity scores, and c statistics from these regressions were used to indicate model discrimination. Odds ratios of death predicted by different severity measures were compared. RESULTS: Code-based measures had better c statistics than clinical measures: all patient refined diagnosis related groups, c = 0.83 (95% C.I. 0.81, 0.86) versus MedisGroups, c = 0.73 (95% C.I. 0.70, 0.76). Code-based measures predicted very different odds of dying than clinical measures for more than 30% of patients. Diagnosis codes indicting postoperative, life-threatening conditions may contribute to the superior predictive power of code-based measures. CONCLUSIONS: Clinical and code-based severity measures predicted different odds of dying for many coronary artery bypass graft patients. Although code-based measures had better statistical performance, this may reflect their reliance on diagnosis codes for life-threatening conditions occurring late in the hospitalization, possibly as complications of care. This compromises their utility for drawing inferences about quality of care based on severity-adjusted coronary artery bypass graft death rates.

Coronary Artery Bypass↗
PubMed1997

The risks of risk adjustment.

CONTEXT: Risk adjustment is essential before comparing patient outcomes across hospitals. Hospital report cards around the country use different risk adjustment methods. OBJECTIVES: To examine the history and current practices of risk adjusting hospital death rates and consider the implications for using risk-adjusted mortality comparisons to assess quality. DATA SOURCES AND STUDY SELECTION: This article examines severity measures used in states and regions to produce comparisons of risk-adjusted hospital death rates. Detailed results are presented from a study comparing current commercial severity measures using a single database. It included adults admitted for acute myocardial infarction (n=11880), coronary artery bypass graft surgery (n=7765), pneumonia (n=18016), and stroke (n=9407). Logistic regressions within each condition predicted in-hospital death using severity scores. Odds ratios for in-hospital death were compared across pairs of severity measures. For each hospital, z scores compared actual and expected death rates. RESULTS: The severity measure called Disease Staging had the highest c statistic (which measures how well a severity measure discriminates between patients who lived and those who died) for acute myocardial infarction, 0.86; the measure called All Patient Refined Diagnosis Related Groups had the highest for coronary artery bypass graft surgery, 0.83; and the measure, MedisGroups, had the highest for pneumonia, 0.85 and stroke, 0.87. Different severity measures predicted different probabilities of death for many patients. Severity measures frequently disagreed about which hospitals had particularly low or high z scores. Agreement in identifying low- and high-mortality hospitals between severity-adjusted and unadjusted death rates was often better than agreement between severity measures. CONCLUSIONS: Severity does not explain differences in death rates across hospitals. Different severity measures frequently produce different impressions about relative hospital performance. Severity-adjusted mortality rates alone are unlikely to isolate quality differences across hospitals.

Benchmarking↗
PubMed1997

Assessing quality using administrative data.

Administrative data result from administering health care delivery, enrolling members into health insurance plans, and reimbursing for services. The primary producers of administrative data are the federal government, state governments, and private health care insurers. Although the clinical content of administrative data includes only the demographic characteristics and diagnoses of patients and codes for procedures, these data are often used to evaluate the quality of health care. Administrative data are readily available, are inexpensive to acquire, are computer readable, and typically encompass large populations. They have identified startling practice variations across small geographic areas and-supported research about outcomes of care. Many hospital report cards (which compare patient mortality rates) and physician profiles (which compare resource consumption) are derived from administrative data. However, gaps in clinical information and the billing context compromise the ability to derive valid quality appraisals from administrative data. With some exceptions, administrative data allow limited insight into the quality of processes of care, errors of omission or commission, and the appropriateness of care. In addition, questions about the accuracy and completeness of administrative data abound. Current administrative data are probably most useful as screening tools that highlight areas in which quality should be investigated in greater depth. The growing availability of electronic clinical information will change the nature of administrative data in the future, enhancing opportunities for quality measurement.

Abstracting and Indexing↗
PubMed1997

Patient-physician communication at hospital discharge and patients' understanding of the postdischarge treatment plan.

BACKGROUND: The quality of discharge planning is an important determinant of patient outcomes following hospital discharge. Patients often report inadequate discussion prior to discharge regarding major elements of the postdischarge treatment plan, including medication and daily activities. OBJECTIVE: To determine whether this apparent lack of communication might be the result of differing perceptions on the part of patients and physicians regarding the patients' understanding of the treatment plan. METHODS: We surveyed 99 patients and their attending physicians. All patients had been discharged recently from an academic medical center with the diagnosis of acute myocardial infarction or pneumonia. We asked both patients and physicians about time spent prior to discharge discussing the postdischarge treatment plan and the patients' understanding of this plan. McNemar test was used to determine whether responses of patients and physicians differed. RESULTS: Physicians reported spending more time discussing postdischarge care than did patients (P = .10). Physicians believed that 89% of patients understood the potential side effects of their medications, but only 57% of patients reported that they understood (P < .001). Similarly, physicians believed that 95% of patients understood when to resume normal activities, while only 58% of patients reported that they understood (P < .001). CONCLUSIONS: Physicians overestimate patients' understanding of the postdischarge treatment plan. Steps should be taken to improve communication about postdischarge treatment.

Adult↗
PubMed1997

Differences in procedure use, in-hospital mortality, and illness severity by gender for acute myocardial infarction patients: are answers affected by data source and severity measure?

OBJECTIVES: According to some studies, women with heart disease receive fewer procedures and have higher in-hospital death rates than men. These studies vary by data source (hospital discharge abstract versus detailed clinical information) and severity measurement methods. The authors examined whether evaluations of gender differences for acute myocardial infarction patients vary by data source and severity measure. METHODS: The authors considered 10 severity measures: four using clinical medical record data and six using discharge abstracts (diagnosis and procedure codes). The authors studied all 14,083 patients admitted in 1991 for acute myocardial infarction to 100 hospitals nationwide, examining in-hospital death and use of coronary angiography, coronary artery bypass graft surgery (CABG), and percutaneous transluminal coronary angioplasty (PTCA). Logistic regression was used to calculate odds ratios for death and procedure use for women compared with men, controlling for age and each of the severity scores. RESULTS: After adjusting only for age, women were significantly more likely than men to die and less likely to receive CABG and coronary angiography. Severity measures provided different assessments of whether women were sicker than men; for all cases, clinical data-based MedisGroups rated women's severity compared with men's, whereas four code-based severity measures viewed women as sicker. After adjusting for severity and age, women were significantly more likely than men to die in-hospital and less likely to receive coronary angiography and CABG; women and men had relatively equal adjusted odds ratios of receiving PTCA. Odds ratios reflecting gender differences in procedure use and death rates were similar across severity measures. CONCLUSIONS: Comparisons of severity-adjusted in-hospital death rates and invasive procedure use between men and women yielded similar findings regardless of data source and severity measure.

Angioplasty, Balloon, Coronary↗
PubMed1996

When walking fails.

Explore the source record for details and available documents.

Adaptation, Psychological↗
PubMed1996

100 apples divided by 15 red herrings: a cautionary tale from the mid-19th century on comparing hospital mortality rates.

In 1863, Florence Nightingale argued that London hospitals were dangerous, especially compared with provincial facilities. She bolstered this contention with statistics published in William Farr's Registrar-General report which claimed that 24 London hospitals had mortality rates exceeding 90%, whereas rural hospitals had an average mortality rate of 13%. Farr had calculated mortality rates by dividing the total number of patients who died throughout the year by the number of inpatients on a single day. When calculated as the annual number of deaths divided by the total number of inpatients during the year, the mortality rate of London hospitals was 10%. A raucous debate erupted in the London medical press over how best to calculate hospital mortality rates. Critics claimed that Farr had not adjusted for differences in severity of illness between urban and rural hospitals and that his figures would mislead the public. Farr and Nightingale, in turn, criticized the poor quality of hospital data. This story reinforces the need to understand the methodologic derivation of statistics intended to compare provider quality.

England↗
PubMed1996

Using severity measures to predict the likelihood of death for pneumonia inpatients.

OBJECTIVE: To see whether predictions of patients, likelihood of dying in-hospital differed among severity methods. DESIGN: Retrospective cohort. PATIENTS: 18,016 persons 18 years of age and older managed medically for pneumonia; 1,732 (9.6%) in-hospital deaths. METHODS: Probability of death was calculated for each patient using logistic regression with age, age squared, sex, and each of five severity measures as the independent variables: 1) admission MedisGroups probability of death scores; 2) scores based on 17 admission physiologic variables; 3) Disease Staging's probability of mortality model; the Severity Score of Patient Management Categories (PMCs); 4) and the All Patient Refined Diagnosis-Related Groups (APR-DRGs). Patients were ranked by calculated probability of death; 5) rankings were compared across severity methods. Frequencies of 14 clinical findings considered poor prognostic indicators in pneumonia were examined for patients ranked differently by different methods. RESULTS: MedisGroups and the physiology score predicted a similar likelihood of death for 89.2% of patients. In contrast, the three code-based severity methods rated over 25% of patients differently by predicted likelihood of death when compared with the rankings of the two clinical data-based methods [MedisGroups and the physiology score]. MedisGroups and the physiology score demonstrated better clinical credibility than the three severity methods based on discharge abstract data. CONCLUSIONS: Some pairs of severity measures ranked over 25% of patients very differently by predicted probability of death. Results of outcomes studies may vary depending on which severity method is used for risk adjustment.

Adolescent↗
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

Financial pressure and competition. Changes in hospital efficiency and cost-shifting behavior.

Using data from the American Hospital Association and the Medicare program, the authors analyzed the effects of financial pressure and market competition on changes in several measures of performance of 1,435 acute care hospitals between 1987 and 1989. Over the observation period, the least profitable hospitals constrained their growth in total expenses to half that for the most profitable hospitals (13.3% versus 27.6%) by limiting the growth of their staffs and their total assets. These changes were associated with a reduction in inefficiency of 1.8% (11.2%) compared with a very slight increase in inefficiency for the highest profit group. Similarly, hospitals in highly competitive markets controlled expenses relative to those in the least competitive areas. However, they also experienced slower revenue growth than did less competitive hospitals so that, in relative terms, their profit rates fell. The authors found no evidence to suggest that financial pressures created by either low profits or market competition resulted in hospitals engaging in cost-shifting. The authors conclude that health care reforms or market forces that put financial pressures on hospitals can result in cost-containment and improved efficiency without significant cost-shifting.

Cost Allocation↗
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↗