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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.