Impact of a Connected Care model on 30-day readmission rates from skilled nursing facilities

Study Design and Population

We reviewed administrative and clinical data from a retrospective cohort of patients discharged to SNF from the Cleveland Clinic main campus from January 1, 2011 to December 31, 2014. We included all patients who were discharged to an SNF during the study period. Our main outcome measure was 30-day all-cause readmissions to any hospital in the Cleveland Clinic Health System (CCHS), including the main campus and 8 regional community hospitals. Study patients were followed until January 30, 2015 to capture 30-day readmissions. According to 2012 Medicare data, of CCHS patients who were readmitted within 30 days, 83% of pneumonia, 81% of major joint replacement, 72% of heart failure and 57% of acute myocardial infarction patients were readmitted to a CCHS facility. As the Cleveland Clinic main campus attracts cardiac patients from a 100+-mile radius, they may be more likely to seek care readmission near home and are not reflective of CCHS patients overall. Because we did not have access to readmissions data from non-CCHS hospitals, we excluded patients who were discharged to SNFs beyond a 25-mile radius from the main campus, where they may be more likely to utilize non-CCHS hospitals for acute hospitalization. We also excluded patients discharged to non-CCHS hospital-based SNFs, which may refer readmissions to their own hospital system. Because the Connected Care program began in December 2012, the years 2011-2012 served as the baseline period. The intervention was conducted at 7 SNFs. All other SNFs within the 25-mile radius were included as controls, except for 3 hospital-based SNFs that would be unlikely to admit patients to CCHS. We compared the change in all-cause 30-day readmission rates after implementation of Connected Care, using all patients discharged to SNFs within 25 miles to control for temporal changes in local readmission rates. Discharge to specific SNFs was determined solely by patient choice.

Data Collection

For each patient, we collected the following data that has been shown to be associated with readmissions:^16-18 demographics (age, race, sex, ZIP code), lab values on discharge (hemoglobin and sodium); hemodialysis status; medicine or surgical service; elective surgery or nonelective surgery; details of the index admission index (diagnosis-related group [DRG], Medicare severity-diagnosis-related groups [MS-DRG] weight, primary diagnosis code; principal procedure code; admission date; discharge date, length of stay, and post-acute care provider); and common comorbidities, as listed in Table 2. We also calculated each patient’s HOSPITAL^19,20 score. The HOSPITAL score was developed to predict risk of preventable 30-day readmissions,¹⁹ but it has also been validated to predict 30-day all-cause readmission rates for patients discharged to SNF.²¹ The model contains 7 elements (hemoglobin, oncology service, sodium, procedure, index type, admissions within the last year, length of stay) (supplemental Table).Patients with a high score (7 or higher) have a 41% chance of readmission, while those with a low score (4 or lower) have only a 15% chance. ²¹ We assessed all cause 30-day readmission status from CCHS administrative data. Observation patients and outpatient same-day surgeries were not considered to be admissions. For patients with multiple admissions, each admission was counted as a separate index hospitalization. Cleveland Clinic’s Institutional Review Board approved the study.

Statistical Analysis

For the 7 intervention SNFs, patient characteristics were summarized as means and standard deviations or frequencies and percentages for the periods of 2011-2012 and 2013-2014, respectively, and the 2 periods were compared using the Student t test or χ² test as appropriate.

Mixed-effects logistic regression models were used to model 30-day readmission rates. Since the intervention was implemented in the last quarter of 2012, we examined the difference in readmission rates before and after that time point. The model included the following fixed effects: SNF type (intervention or usual care), time points (quarters of 2011-2014), whether the time is pre- or postintervention (binary), and the 3-way interaction between SNF type, pre- or postintervention and time points, and patient characteristics. The model also contained a Gaussian random effect at the SNF level to account for possible correlations among the outcomes of patients from the same SNF. For each quarter, the mean adjusted readmission rates of 2 types of SNFs were calculated from the fitted mixed models and plotted over time. Furthermore, we compared the mean readmission rates of the 2 groups in the pre- and postintervention periods. Subgroup analyses were performed for medical and surgical patients, and for patients in the low, intermediate and high HOSPITAL score groups.

All analyses were performed using RStudio (Boston, Massachusetts). Statistical significance was established with 2-sided P values less than 0.05.