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Biomedical subjects

L I Iezzoni

Publications and source records attributed to L I Iezzoni.

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

Discharge planning: comparison of patients and nurses' perceptions of patients following hospital discharge.

Planning for hospital discharge is an important component of nursing. Results are presented of a study to determine how well primary nurses predict the functional ability of their patients following discharge and to assess whether patients and nurses agree about their patients' understanding of the post-discharge treatment plan. Comparing nurses' predictions with patients' reports of functional status 2 months following discharge, we found that nurses consistently underestimate the functional ability of their patients. Comparing nurses' perceptions of their patients' understanding of their post-discharge treatment plan with patients' reports about their understanding, significant differences were found between nurses' perceptions and patients' reports. Nurses' perceptions were that patients were much more knowledgeable than their patients reported. These preliminary data suggest that hospital discharge planning is an area for further investigation and intervention. Nurses should explore new paradigms for patient education as lengths of hospital stay decrease and care shifts from acute care to community care.

Activities of Daily Living↗
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

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

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

Predicting in-hospital mortality. The importance of functional status information.

Monitoring risk-adjusted outcomes is the centerpiece of efforts to ensure health care quality. Because data collection is expensive, questions arise concerning what information is essential to adjust for risk. This investigation used retrospective analysis of existing, computerized clinical databases containing laboratory test results, information on chronic coexisting conditions, and nursing evaluations of functional status to predict in-hospital mortality. We studied persons admitted to one tertiary teaching hospital between 1987 and 1992 for cerebrovascular disease or pneumonia. Predictive models for each of the conditions were developed using logistic regression; the results were validated with split samples. We compared the predictive value of the nursing functional status assessments and the clinical laboratory data. For each study condition, the functional status data had as much prognostic information as the laboratory data. Specifically, a nurse's report that a patient required total assistance for bathing was the best single predictor of in-hospital mortality in the models for patients with either cerebrovascular disease or pneumonia. If hospitals admit patients with different levels of functional impairment, it is important to account for these differences before comparing outcomes across facilities. Assessments of functional status are a simple, inexpensive measure that may have considerable value.

Activities of Daily Living↗
PubMed1995

Costs of potential complications of care for major surgery patients.

We examined computerized hospital discharge abstract data from 372,680 major surgery patients admitted to 404 California acute care hospitals in 1988 to identify potential complications of care. At least one potential in-hospital complication occurred for 10.8% of patients. Patients with complications were older and more likely to die in-hospital (9.4% compared to 1.0%, P < 0.0001). On average, patients with complications had longer stays (13.5 versus 5.4 days, p < 0.0001) and higher total charges ($30,896 versus $9,239, p < 0.0001). After adjusting for demographic, clinical, and hospital factors, patients with potential complications averaged $16,023 higher total hospital charges than uncomplicated patients. Complications were associated with 96.6% (95% confidence interval = 95.2%, 98.0%) higher hospital charges after adjusting for these factors. Across all patients, complications were related to over $647 million in additional total hospital charges for these major surgery patients.

California↗
PubMed1995

Use of follow-up services by patients referred from a walk-in unit: how can patient compliance be improved?

Timely use of necessary follow-up services is an important dimension of ambulatory care quality. Using a hospital-based walk-in center, this study identified patients who were referred for follow-up care and examined factors related to compliance with these referrals. The participants were 696 adults seen in a hospital-based walk-in unit between June 1, 1992, and December 1, 1992. Patients completed a self-administered questionnaire including questions about sociodemographic characteristics, prior use of health services, and the Medical Outcomes Study (MOS) 36-Item Health Survey. Medical findings, follow-up recommendations, insurance status, and compliance with follow-up referrals were ascertained using chart review, the hospital's computing system, and clinic records. Fifty percent of the patients were referred for follow-up medical care; 55% of these complied with follow-up referrals. Factors associated with referral for follow-up care included older age, inability to afford a physician, longer duration of chief complaint, the patient's belief that follow-up care would be needed, and worse MOS pain score. The most important factor associated with compliance with follow-up referral was scheduling appointments while patients were still in the walk-in unit. Patients with such scheduled appointments were almost 10 times more likely than others to receive follow-up (adjusted odds ratio = 9.6, 95% confidence interval = 4.4-21.2). The most important step a provider can take to improve compliance with follow-up referral is to schedule appointments before patients are sent home. This should presumably improve quality of ambulatory care.

Adult↗
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↗
PubMed1995

In-hospital mortality for surgical repair of congenital heart defects: preliminary observations of variation by hospital caseload.

OBJECTIVE: To examine the impact of hospital caseload on in-hospital mortality for pediatric congenital heart surgery. DESIGN: Population-based, retrospective cohort study. SETTING: Acute care hospitals in California and Massachusetts. PATIENTS: Children undergoing surgery for congenital heart disease, identified by the presence of procedure codes indicating surgical repair of a congenital heart defect in computerized statewide hospital discharge abstract databases. Cases were grouped into four categories based on the complexity of the procedure. MAIN OUTCOME MEASURES: Adjusted odds ratios (OR) for in-hospital death were estimated using generalized estimating equations that account for the intra-institutional correlation among patients. RESULTS: A total of 2833 cases at 37 centers were identified. Compared with centers performing > 300 cases per year, after controlling for patient characteristics, centers performing < 10 cases per year had an OR for in-hospital death of 7.7 (95% confidence interval (CI) [1.6-37.8]); 10 to 100 cases, OR = 2.9 (95% CI [1.6-5.3]); 101 to 300 cases, OR = 3.0 (95% CI [1.8-4.9]). Independent risk factors for mortality included procedure complexity category (P < .0001), use of cardiopulmonary bypass (P < .0001), young age at surgery (P = .001), and transfer from another acute care hospital (P < .0001). Few differences were found by hospital caseload in length of stay or total hospital charges. CONCLUSIONS: For children with a congenital heart defect who underwent surgery in California in 1988 or Massachusetts in 1989, the risk of dying in-hospital was much lower if the surgery was performed at an institution performing > 300 cases annually. This study was limited by the absence of clinical detail in discharge abstract databases. If these findings are corroborated by other studies, health care delivery strategies that direct children requiring surgical correction of congenital heart defects to high-volume centers may substantially reduce overall mortality.

California↗
PubMed1994

Using risk-adjusted outcomes to assess clinical practice: an overview of issues pertaining to risk adjustment.

Increasingly, health care providers are being evaluated and held accountable for their patients' outcomes, ranging from the costs to service consumption to death. To be meaningful, the outcomes under scrutiny must be important to patients or to the health care system as a whole, relatively common, and linked temporally and causally to the care provided. In addition, outcomes findings should be adjusted for patient risk factors, with the goal of accounting for pertinent clinical characteristics before drawing inferences about the effectiveness or quality of care. Risk adjustment "levels the playing field" in comparing outcomes across providers. Although this concept is straightforward, performing clinically credible risk adjustment is difficult, especially given the widespread data constraints. In this article, I review the major issues involved in performing risk adjustment for health care outcomes studies.

Health Services Research↗
PubMed1994

Widespread assessment of risk-adjusted outcomes: lessons from local initiatives.

BACKGROUND: Controlling costs while maintaining quality is a crucial element of national health care reform. Prompted generally by concerns of local businesses, states and regions around the country have already initiated such dual efforts. The central component of these activities is the examination of risk-adjusted outcomes. ISSUES: Publicizing comparisons across providers and using these data for payer contracting have prompted several concerns among many providers, primarily involving reservations about the validity of the risk-adjustment methodologies and the fairness of the outcomes data reports. In contrast, business leaders recognize potential limitations in the data but argue that some information is better than none. They believe that these efforts have already saved money by forcing providers to compete and to justify unusually high costs. Troubling questions arise about whether publicizing their outcomes may prompt providers to avoid especially sick and high-risk patients. Although common themes emerge, each of these initiatives to monitor risk-adjusted outcomes has had to address specific local concerns. The form that these local initiatives take is influenced particularly by relationships between the business and provider communities and their respective visions of how to motivate change in the health care sector. SUMMARY: In some regions, businesses and providers have worked collaboratively, whereas in others relationships have been strained. One major problem in making objective decisions about risk-adjusted outcomes methods is the lack of clear evidence about whether such information truly measures quality of care. Whether a standard national quality monitoring program will satisfy local demands remains unclear.

Consumer Advocacy↗