The Association between the Quality of Inpatient Care and Early Readmission

Early readmission has great appeal as an indicator of hospital quality; it is common and costly. Depending on the diagnosis, 5% to 29% of adults are readmitted within a month of a medical-surgical stay [1] and 25% of Medicare expenditures for inpatient care are for readmissions within 60 days [2]. Hospital utilization databases make early readmission easy to tabulate. Some early readmissions are probably preventable. These features explain why early readmission has been used to screen for quality of care by payers (such as Medicare) and by multihospital systems (such as the Department of Veterans Affairs), since the early 1980s. However, a process-outcome link between early readmission and the quality of care during the previous hospitalization has not been well established. Only 10 primary data studies [3-12] have examined the process of inpatient care directly and compared early readmission rates of patients whose hospital care was substandard with patients whose care was normative; results from these studies differ. For example, we [3, 4] previously found, as did Reed and colleagues [5], that patients who had changes in their medication regimen just before discharge were more likely to be readmitted within a month or less. However, in a casecontrol study of 292 patients from 50 Veterans Affairs hospitals, Ludke and colleagues [6] detected no differences in the adequacy of discharge planning or in medical stability at discharge between patients who were and were not readmitted within 14 days. A single-hospital retrospective cohort study by Hayward and colleagues [7] showed that quality-of-care ratings of the index stay did not differ between patients who were and those who were not readmitted within 28 days. Early readmission is widely used as a quality-of-care indicator, although associations between it and the antecedent care process are not well established. We sought to determine whether the quality of inpatient care was associated with unplanned readmission within 14 days. Methods Participants Patients enrolled in our casecontrol study were men discharged from a hospital stay for treatment of diabetes, chronic obstructive lung disease, or heart failure at a convenience sample of 12 participating Veterans Affairs hospitals in the southern United States between 1 October 1987 and 30 September 1989. Three hospitals were large, urban, referral centers affiliated with medical schools. Five hospitals were medium sized; all but 1 of these had a medical school affiliation. The remaining 4 hospitals were small and had no or limited affiliations. Cases were men with an unplanned readmission to any Veterans Affairs hospital within 14 days of discharge from an index stay. Controls were men who did not have an unplanned readmission to a Veterans Affairs hospital within 14 days. The index stay was defined as the first hospitalization occurring during the 24-month period. The sampling frame was created using the computerized hospital discharge database of the Veterans Affairs medical system, the Patient Treatment File, which contains records of all hospital stays throughout the 159-hospital Veterans Affairs medical system. Three diagnosis-specific samples were created, with no patient appearing twice. Table 1 shows how samples were derived and lists reasons for ineligibility. Men discharged from an index stay with diabetes, chronic obstructive lung disease, or heart failure listed as the primary diagnosis were considered potentially eligible (International Classification of Disease codes available on request from the authors). All patients readmitted for any reason to any Veterans Affairs hospital within 14 days of the index discharge were placed on the list of potential eligible participants. Three patients who were not readmitted were randomly selected for each readmitted patient, and this 3:1 match provided a balance between the costs of chart review and statistical efficiency and power. The result of this sampling strategy was that patients who were not readmitted were group-matched to readmitted patients using hospital, diagnosis, and fixed 6-month period of discharge. Table 1. Selection of Participants The lists of potentially eligible patients were sent to each hospital. Personnel in records departments retrieved the charts, and study personnel traveled to each hospital to review them. We were interested only in those patients whose reason for admission was an exacerbation of one of the three conditions [diabetes, heart failure, and chronic obstructive lung disease] that we were studying, not those in whom one of the conditions was a comorbid illness. This distinction cannot be made accurately from computerized hospital-discharge abstracts. Therefore, before a patient was enrolled, his chart was examined to see if he met the final two eligibility criteria: 1) an admission presentation with evidence of an exacerbation of the conditions in question and 2) a discharge summary indicating that the condition was the primary reason for admission. Review of the Process of Care The process of care during the index stay was reviewed using a set of explicit, unit-weighted (0 or 1) process-of-care criteria specific for each diagnosis. (Criteria sets and instructions for use are available from the authors.) The criteria development and scoring processes have been described previously [13]. The sets were developed by panels composed of expert physicians and covered all elements of essential technical care of the hospitalized patient. Items were divided into three categories: criteria for the admission workup (history, physical examination, and initial tests), criteria for evaluation and treatment during the stay, and criteria for readiness for discharge. Many of the criteria were formulated as if, then statements, and a given criterion could be applicable or not applicable. The number of applicable criteria is a proxy for need for care. An adherence score, expressed as the percentage of applicable criteria that were met, was computed for each of the three categories of criteria. Each segment of the hospital stay was scored individually because different segments might influence the probability of readmission in a different manner. The same physician and physician assistant served as chart reviewers for the entire study. Inter-rater reliability was assessed for each criterion using the statistic [14]. To account for the problem of a low score despite a high percentage of agreement, we categorized the criteria based on whether there was high or low percentage of agreement, and we eliminated criteria for which the reviewers had an observed percentage of agreement lower than that expected by chance and a score of less than 0.20 that persisted after training. The final diabetes criteria set included 47 admission-workup criteria, 42 evaluation and treatment criteria, and 11 readiness-for-discharge criteria. Respective numbers of criteria were 44, 19, and 10 for heart failure and 54, 21, and 9 for obstructive lung disease. The reviewers traveled as a team to each site. One served as the administrative reviewer, and the other served as the quality reviewer. After ascertaining that the patient met the enrollment criteria, the administrative reviewer extracted data on severity of illness at admission using the Acute Physiology, Age, and Chronic Health Evaluation II (APACHE II) scoring method [15]; on comorbidity count (a list of 11 conditions with standardized definitions); and, in readmitted patients, on whether the readmission was planned or unplanned. The chart was then given to the quality reviewer, who applied the process-of-care criteria. The quality reviewer was kept blinded to the readmission status of the patient. Blinding is important because if reviewers know that an adverse outcome has occurred, they may rate the antecedent process of care as substandard [16]. Data on race, marital status, and number of admissions in the previous 2 years were obtained from the Patient Treatment File. Readmission The occurrence of readmission was tabulated from the Patient Treatment File and was therefore 100% complete for readmissions to any of the 159 Veterans Affairs hospitals. Readmissions to non-Veterans Affairs hospitals were not present in the Patient Treatment File and therefore could not be tabulated. Statistical Analysis Analyses were done separately for each disease. Bivariate comparisons were made using the Student t-test for continuous variables and the chi-square test for categorical variables. Student t-tests were two-tailed, and statistical significance was determined by a P value of less than 0.05. Multiple logistic regression [17] was used to estimate the unique effect of adherence scores on the probability of readmission, after controlling for patient demographic, illness-severity, and need-for-care variables as indicated by the number of applicable criteria. Because the number of criteria in each category differed for each of the three diseases, the numbers of applicable items were converted to standard normal variables to make the scales comparable. Conditional logistic regression was done to account for the matching of hospital and time period inherent in the design. The results were the same as those achieved with unconditional logistic regression, which indicated that hospital and time period were not confounding variables and that it was desirable to dissolve the matching. Unconditional logistic regression has advantages in the assessment of model fit because some regression diagnostic statistics (for example, Hosmer-Lemeshow deciles of risk) are not available for conditional logistic analysis. We examined all models and confirmed that the continuous variables conformed to a linear gradient. We assessed model fit by the Hosmer-Lemeshow deciles of risk statistic, by the log-likelihood chi-square analysis, and by the c-index (which corresponds to the area under the receiver-operating characteristic curve). The data d