A “Reverse July Effect”: Association Between Timing of Admission, Medical Team Workload, and 30-Day Readmission Rate
Abstract
Background
High teaching team workload has been associated with poor supervision and worse patient outcomes, yet it is unclear whether this association is more pronounced during the early months of the academic year when the residents are less experienced.
Objective
We examined the associations between teaching team workload, timing of admission, and the 30-day readmission rate.
Methods
In this retrospective observational study, all admissions to an urban internal medicine teaching service over a 16-month period were divided into 2 groups based on admission date: early in the academic year (July–September) or late (October–June) and further defined as being admitted to “busy” versus “less busy” teams based on number of monthly admissions. The primary outcome was 30-day readmission rate. Multivariate logistic regression was used to determine the independent association between teaching team workload and readmission rates, stratified by time of year of admission after adjustment for demographic and clinical characteristics.
Results
Of 12 118 admissions examined, 2352 (19.4%) were admitted early in the year, and 9766 (80.6%) were admitted later. After multivariate adjustment, we found that patients admitted to busy versus less busy teams in the first quarter had similar 30-day readmission rate (odds ratio [OR]adj = 1.03 [0.82–1.30]). Later year admission to a busy team was associated with increased risk of readmission after adjustment (ORadj = 1.16 [1.03–1.30]).
Conclusions
Admission to busy teams early in the year was not associated with increased odds of 30-day readmission, whereas admission later in the year to busy teams was associated with 16% increased odds of readmission.
Introduction
Of the 19.4 million hospitalizations that occur each year, 4 million patients are admitted to teaching hospitals.1 High teaching team workload has been associated with poor supervision and worse patient outcomes,2–6 including increased readmission rate,7 but it is not clear whether this effect occurs uniformly throughout the academic year.
The July phenomenon refers to a presumed drop in the quality of care in the early months of the academic year at teaching institutions, thought to be related to inexperienced residents taking on new responsibilities in July. Care by new residents on a teaching service at the beginning of the academic year has been associated with increase in medical errors,8,9 mortality,10 and inefficient care.11 In a review of the July phenomenon, Barack et al12 suggested that reducing teaching team workload in the beginning of the academic year should be considered as a measure to compensate for the July effect.12
It is not clear whether the association between high workload and poor outcomes during the early months of the academic year, when residents are new, is the same as that in the later months of the academic year, when residents are more experienced. To address this, we sought to examine the association between teaching team workload and readmission rate in the beginning of the academic year versus that in the later months of the academic year.
To determine whether high resident workload may be a factor in the July phenomenon, we examined the association between teaching team workload and readmission rate13 in the first 3 months of the academic year versus that in the remainder of the academic year. We hypothesized that the association between high team workload and increased readmissions would be strongest in the first 3 months of the academic year and less strong during the rest of the year.
Methods
Study Setting and Patients
Montefiore Medical Center is an urban academic medical center in Bronx, New York, consisting of Weiler (381 beds) and Moses hospitals (706 beds), and is affiliated with Albert Einstein College of Medicine. We examined clinical and administrative data for all patients who were admitted to the medicine teaching service from March 1, 2009, to June 30, 2010. This represented a time period during which we had full data for teaching team structure and when there was significant variation in team workload.
Teaching Teams
Each month, 18 teaching teams provide care at the 2 hospitals within the medical center. Each teaching team consists of a supervising attending, a second-year or third-year resident, and 1 or 2 interns plus no or 1 subintern. The maximum number of daily admissions was 5 for an intern and 2 for a subintern. Patient census was capped at 5 for the subintern, at 10 for the intern, and at 20 for the supervising resident. Admissions were assigned to the teams without any known bias according to an on-call schedule. Social workers and discharge planners were assigned to specific floors, whereas teaching teams covered patients throughout the hospital.
Study Design
We conducted a retrospective observational cohort study to examine the associations among timing of admissions throughout the academic year, teaching team workload, and patient readmission rates. Clinical data were extracted from a replicate of Montefiore's Clinical Information System, using Clinical Looking Glass quality improvement health care surveillance software.14
Montefiore's Institutional Review Board approved this study.
Outcomes
The primary outcome of the study was 30-day readmission rate. Thirty-day readmission was defined as any admission for any reason within 30 days after discharge to either hospital and was analyzed both as a dichotomous and time-to-event variable.
Independent Variables
Teaching team workload was based on the number of monthly admissions to a team. First, we examined the total number of admissions seen by each team each month. Next, we defined each team as a low-workload team (total admissions ≤ 49, the median for all teams) or a high-workload team (total admissions > 49).
Each admission was defined as early in the academic year (July–September) or later in the academic year (October–June).
Covariates
To address the threat of confounding, we examined patient characteristics, including demographic characteristics, comorbidities, and severity of illness indices. Patient characteristics included age, sex, race/ethnicity, and insurance (categorized as Medicare, Medicaid, commercial, or self-pay) and were analyzed as continuous or dichotomous variables. We used the Charlson comorbidity score15 as our comorbidity index. The index was determined by using International Classification of Diseases, 9th revision, diagnosis codes and was analyzed as a continuous variable. We calculated the laboratory-based acute physiology score (LAPS) to measure severity of illness. When combined with measures of comorbidity, the LAPS accounts for significant variance in inpatient mortality (C-statistic, 0.88–0.91).16,17 LAPS was calculated using 14 commonly used laboratory values and was analyzed as a continuous variable. Medical team characteristics were examined with respect to the presence of junior versus senior resident and subintern on the team.
Statistical Analysis
Patients admitted early in the academic year versus later in the year were compared with respect to demographic characteristics, comorbidities, severity of illness, and length of stay by using t tests and χ2 and rank sum tests, as appropriate. To assess univariate associations among timing of admission, team workload, and 30-day readmission, we compared rates of these outcomes among groups by using χ2 tests. Logistic regression models were constructed to determine the independent association between assignment to a less busy team and readmission versus assignment to a more busy team and readmission, stratified by time of year, after adjustment for demographic and clinical characteristics. Finally, we constructed survival curves to examine the time to readmission for admissions early versus later in the academic year, using the Kaplan-Meier method. Survival curves for admissions assigned to low-workload teams versus those assigned to high-workload teams were compared using log-rank tests. STATA/IC version 10.0 software (Stata Corp, College Station, TX) was used for all statistical analysis and data management.
Results
Patient Populations
Of 12 118 admissions examined, 2352 (19%) were admitted early in the academic year (July–September), and 9766 (81%) were admitted later in the academic year (October–June). Patients admitted during the first quarter were similar to those patients admitted during the rest of the year with respect to age, sex, race/ethnicity, insurance, clinical characteristics (LAPS and Charlson score), and mean length of stay. Patients admitted later in the year had a slightly higher 90-day prior admissions rate (19% versus 24%, respectively, P < .001; table 1).
Medical Teams Characteristics
During the study period, 273 medical teams were providing care. Medical teams had similar characteristics at the beginning versus later in the academic year regarding the presence of junior residents (38% versus 36%, respectively, P = .76) and subinterns (62% versus 71%, respectively, P = .18; table 2).
Readmission Rates
Early in the academic year, high-workload teams and lower-workload teams had similar readmission rates (15.3% versus 14.9%, respectively, P = .82). However, later in the academic year, high-workload teams had a significantly higher readmission rate than lower-workload teams (17% versus 14%, respectively, P = .04; figure, table 3). After multivariate adjustment for age, LAPS, Charlson score, and 90-day prior admissions, the odds of 30-day readmission early in the academic year for high-workload teams were similar to those for lower-workload teams (odds ratio [OR] = 1.03, 95% confidence interval [CI] 0.82–1.30). However, high team workload later in the academic year was associated with increased risk of 30-day readmission (OR = 1.16, 95% CI 1.03–1.30) after adjustment for age, LAPS, Charlson score, and prior admissions (table 4).
Citation: Journal of Graduate Medical Education 6, 1; 10.4300/JGME-D-13-00014.1
Discussion
Our study showed that high teaching team workload was associated with increased risk of 30-day readmission later in the academic year but not early in the academic year. Contrary to our hypothesis that inexperienced residents would be particularly susceptible to high workload in the early months of the academic year, we found the opposite effect. Our findings do not support the notion that teaching team workload should be adjusted down during the early months of the academic year to decrease readmission rates.
In a recent comprehensive review of 38 studies of the July phenomenon, Young et al18 found that care in July was associated with increased mortality in 22% of the studies,11,19 increased morbidity in 17% of studies,20–23 and decreased efficiency in 37% of studies,24,25 whereas most studies did not find evidence of the July effect. In addition, there is no consensus on the mechanisms causing the July effect. Our study found a “reverse July effect,” with teaching apparently more susceptible to high workload later in the academic year.
The heterogeneity of results of studies of the July phenomenon and our evidence of a “reverse July effect” suggests that there may be processes in place that tend to offset the relative inexperience of trainees on teaching teams in July. In a study by Carnahan,26 teaching teams were compared to hospitalist teams during 4 academic year quarters, and it was found that during the fourth academic year quarter, teaching teams had more patient-related adverse outcomes that were not seen in the other quarters. The authors concluded that the notion of a July phenomenon was not supported by their study and that a relative increase in adverse effects at the end of the academic year might be explained by either resident burnout or decrease in supervision. Other possible explanations of the apparent “reverse July effect” include the fact (1) that stronger or more senior residents and/or attending physicians might be assigned to duty at the beginning of academic year; (2) that new residents might compensate for the lack of systems knowledge early in the academic year by being more diligent and thorough than later in a year; and (3) that supervising attending physicians might become less vigilant as the year progressed, as interns attained greater confidence and autonomy in handling medical cases.
Our study has limitations. First, it was conducted at 1 academic institution, and results may not be generalizable to other training programs. Second, we were able to collect meaningful data for a 16-month period, and the trend we observed may not be generalizable to other years. Also, we were not able to directly measure the quality of resident supervision or the quality of care. Although a 30-day readmission rate is a reasonable global measure of quality,13 it reflects patient factors outside of residents' control, including comorbidities,27 postdischarge environment,28 health literacy,29 and socioeconomic status.30 Finally, the study was observational, and the results may be confounded if there were differences in the patient groups being compared. We tried to minimize the threat of confounding by adjusting for covariates in a broad range of domains, including patient demographics, comorbidities, severity of illness, and previous admissions.
Conclusion
During the first quarter of the academic year, admission to a busier team is not associated with increased odds of 30-day readmission, suggesting there is a July effect, at least for patient readmissions. In contrast, during the rest of the academic year, admission to busier teams is associated with slightly increased risk for 30-day readmission.
Readmission Rates During First Academic Year Quartile Versus Rest of Academic Year to Low-Workload Versus High-Workload Teams
Author Notes
Both authors are with the Department of Medicine, Division of Hospital Medicine, Albert Einstein College of Medicine, Montefiore Medical Center. Yelena Averbukh, MD, is Assistant Professor; and William Southern, MD, MS, is Associate Professor.
Funding: The authors report no external funding source for this study.
This research was presented at the Annual Meeting of the Society of Hospital Medicine, San Diego, CA, April 2012.
The authors would like to thank William Lee for help with extracting data from the Clinical Looking Glass information system.
