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The American Journal of Managed Care May 2018
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Rural Hospital Transitional Care Program Reduces Medicare Spending
Keith Kranker, PhD; Linda M. Barterian, MPP; Rumin Sarwar, MS; G. Greg Peterson, PhD; Boyd Gilman, PhD; Laura Blue, PhD; Kate Allison Stewart, PhD; Sheila D. Hoag, MA; Timothy J. Day, MSHP; and Lorenzo Moreno, PhD
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Rural Hospital Transitional Care Program Reduces Medicare Spending

Keith Kranker, PhD; Linda M. Barterian, MPP; Rumin Sarwar, MS; G. Greg Peterson, PhD; Boyd Gilman, PhD; Laura Blue, PhD; Kate Allison Stewart, PhD; Sheila D. Hoag, MA; Timothy J. Day, MSHP; and Lorenzo Moreno, PhD
A telephonic transitional care program at a rural hospital reduced postdischarge Medicare spending by 31% and reduced inpatient spending for Medicare fee-for-service beneficiaries.

The treatment group for our analysis included 638 Medicare FFS patients who had an AGH PCP and were discharged from AGH during the HCIA funding period (February 2013-May 2015). We defined the treatment group using intent-to-treat criteria and thus included some patients who did not participate in the program because they declined to participate or could not be reached by the care transitions care coordinator after 3 attempts. (Data indicate that 396 of the treatment group members, or 62%, were actually enrolled in the intervention.) eAppendices A and B (available at provide additional details on sample selection, data availability, and sample sizes. The intervention also targeted other patients, including those enrolled in Medicaid, Medicare managed care, or commercial insurance, but data limitations precluded them from being included in the study.

Each treatment beneficiary was matched to 1 to 4 comparison beneficiaries. The comparison group was selected using exact matching and propensity score matching techniques,12 and it included 2232 FFS Medicare beneficiaries who were discharged during the same time frame from either Peninsula Regional Medical Center (PRMC) or AGH but did not have an AGH provider (so the beneficiaries were not contacted by the nurse care coordinator). PRMC is a larger hospital than AGH, but it was selected as a comparison because it is located just 30 miles from AGH in Salisbury, Maryland, a city of about 30,000; participated in Maryland’s global payment model, like AGH; and did not implement the care transitions component.

To support the DID analyses, we also measured outcomes for 226 patients with an AGH PCP who were discharged from AGH in a 1-year period before the intervention began (July 2011-June 2012) and 1008 matched comparison beneficiaries from the same time frame. The pre- and postintervention cohorts included different patients—a potential study limitation.

Data and Outcomes

Using Medicare FFS parts A and B claims data, we measured 5 outcomes: (1) the percentage of beneficiaries with an ambulatory care follow-up visit with a primary care or specialist physician within 14 days of the discharge that qualified the patient for the treatment or comparison group, (2) the percentage of beneficiaries with an unplanned readmission within 30 days of discharge, (3) the average number of all-cause readmissions within 6 months after discharge, (4) the average number of outpatient ED visits within 6 months after discharge, and (5) average Medicare parts A and B spending in the 6 months after discharge. These 5 confirmatory outcomes were prespecified based on being of the highest interest to CMMI. We later added 2 exploratory outcomes: average inpatient and noninpatient Medicare spending.

Statistical Analyses

Impact estimates measured the differences in postdischarge outcomes between the treatment group patients during the intervention period and matched comparison beneficiaries, minus the differences in postdischarge outcomes between treatment group patients discharged in a 1-year period before the intervention began and matched comparison beneficiaries.

We used linear regression models to implement the DID framework, adjusting for patient-level covariates. The covariates included patients’ demographics, chronic conditions, and service use and spending 0 to 3 months and 4 to 12 months before enrollment, as well as indicators for each matched set and treatment status. The DID estimate was the coefficient for an interaction of a beneficiary’s treatment status with an indicator for being in the postintervention cohort. Weighted regression models were estimated to account for many-to-one matching, and inference was based on bootstrap standard errors. See eAppendix A for additional details.

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