Currently Viewing:
The American Journal of Managed Care May 2018
Impact of Emergency Physician–Provided Patient Education About Alternative Care Venues
Pankaj B. Patel, MD; David R. Vinson, MD; Marla N. Gardner, BA; David A. Wulf, BS; Patricia Kipnis, PhD; Vincent Liu, MD, MS; and Gabriel J. Escobar, MD
Monitoring the Hepatitis C Care Cascade Using Administrative Claims Data
Cheryl Isenhour, DVM, MPH; Susan Hariri, PhD; and Claudia Vellozzi, MD, MPH
Delivery of Acute Unscheduled Healthcare: Who Should Judge Whether a Visit Is Appropriate (or Not)?
Adam Sharp, MD, MSc, and A. Mark Fendrick, MD
Impact of Formulary Restrictions on Medication Intensification in Diabetes Treatment
Bruce C. Stuart, PhD; Julia F. Slejko, PhD; Juan-David Rueda, MD; Catherine Cooke, PharmD; Xian Shen, PhD; Pamela Roberto, PhD; Michael Ciarametaro, MBA; and Robert Dubois, MD
Characteristics and Medication Use of Veterans in Medicare Advantage Plans
Talar W. Markossian, PhD, MPH; Katie J. Suda, PharmD, MS; Lauren Abderhalden, MS; Zhiping Huo, MS; Bridget M. Smith, PhD; and Kevin T. Stroupe, PhD
Currently Reading
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
Changes in Specialty Care Use and Leakage in Medicare Accountable Care Organizations
Michael L. Barnett, MD, MS, and J. Michael McWilliams, MD, PhD
Increasing Hepatitis C Screening in a Large Integrated Health System: Science and Policy in Concert
Carla V. Rodriguez, PhD; Kevin B. Rubenstein, MS; Benjamin Linas, MD; Haihong Hu, MS; and Michael Horberg, MD
Nevada's Medicaid Expansion and Admissions for Ambulatory Care–Sensitive Conditions
Olena Mazurenko, MD, PhD; Jay Shen, PhD; Guogen Shan, PhD; and Joseph Greenway, MPH
Introduction of Cost Display Reduces Laboratory Test Utilization
Kim Ekblom, MD, PhD, and Annika Petersson, MSc, PhD

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.

Some baseline characteristics of the 638 Medicare FFS beneficiaries in the postintervention treatment group, such as gender and age, were similar to benchmarks for the national Medicare population, but other characteristics indicate that the treatment group had more healthcare needs than the general population (eAppendix C). The Hierarchical Condition Category risk score for the treatment group was 2.47, indicating that the group could be expected to have Medicare spending more than double the national average over the next year.13 The prevalence of chronic obstructive pulmonary disease, chronic kidney disease, and congestive heart failure in the treatment group was more than twice the national average. Treatment group members also had high service use and spending. The treatment group beneficiaries had, on average, 1092 hospitalizations and 406 ED visits per 1000 beneficiaries, and their Medicare spending averaged $6603 per month in the quarter before enrollment.

In both the pre- and postintervention cohorts, the treatment and comparison beneficiaries were well matched on individual-level characteristics at baseline, including demographics, health status, chronic conditions, reason for the hospitalization leading to eligibility for enrollment in the care transitions program, and health service use and spending 1 year before discharge (eAppendix C).

As shown in the Table, the follow-up ambulatory care visit rate in the 14 days following discharge was 73.5%, 5.9 percentage points higher than the regression-adjusted rate for the comparison group. This DID estimate was not statistically significant (90% CI, –1.6 to 13.4; P = .194).

The treatment group’s 30-day unplanned readmission rate following discharge was 11.6%, 1.9 percentage points higher than the comparison group’s after regression adjustment. This was a large difference (18.9%), but the large standard error means that the unfavorable impact was estimated imprecisely (90% CI, –3.6 to 7.3; P = .578).

The treatment group averaged 229 all-cause inpatient admissions per 1000 beneficiaries per quarter over the first 2 quarters following the beneficiary’s qualifying discharge, which was estimated to be 72 admissions fewer than the comparison group (90% CI, –149 to 4; P = .121), a statistically insignificant difference of about 24%.

The treatment group rate of outpatient ED visits within 6 months after discharge was similar to the comparison group rate (after regression adjustment); however, the DID was not estimated precisely (difference, –19; 90% CI, –111 to 73; P = .735).

Medicare parts A and B spending for the treatment group averaged $2992 per beneficiary per month over the first 2 quarters following the beneficiary’s discharge, which was estimated to be $1333 lower than regression-adjusted spending for the comparison group. This DID estimate is statistically significant (90% CI, –$2078 to –$589; P = .003) and large (31% lower than the adjusted comparison group’s spending). The treatment group’s spending was higher than the comparison group’s during the preintervention period, but lower in the intervention period, leading to the large DID estimate.

Decreases in spending for inpatient claims accounted for 55% of the reduction in total spending. Specifically, regression-adjusted inpatient spending was $729 lower than the comparison group’s spending (90% CI, –$1234 to –$225; P = .017), whereas spending for noninpatient claims was $604 lower (90% CI, –$968 to –$239; P = .006).


This telephonic intervention decreased Medicare parts A and B spending substantially (by nearly one-third) during the first 6 months after beneficiaries’ enrollment, driven in part by a decrease in inpatient spending. AGH expected cost reductions to occur through decreases in the readmission rate and the number of ED visits, and it set a goal to obtain a 20% reduction for these 2 measures. However, tests for these outcomes, as well as for a decrease in the number of admissions and an increase in the rate of ambulatory care follow-up within 14 days of discharge, did not yield statistically significant results. The lack of observed effects may be due to imprecision in the estimates; AGH’s expected impacts fall within the 90% CIs.

Although other studies have found that care transitions programs can improve outcomes, this study was unique in both the size of the impacts relative to the low-touch (and low-cost) telephonic intervention and the location in a small rural healthcare system—a setting not often represented in such studies. The estimated $5.4 million in savings from the transitional care component well exceeded the $1.1 million HCIA award. The effects coincided with successful implementation of the program, including process improvements throughout the program to accommodate patients’ needs. Thus, this promising program model merits further testing, and hospitals looking to implement a care transitions program, particularly in a rural setting, might consider implementing AGH’s program model. 


Many individuals contributed to this study. The authors would specifically like to acknowledge the important contributions to this report made by Andrew McGuirk, Sandi Nelson, Ken Peckham, and Lauren Vollmer of Mathematica Policy Research, who skillfully processed and helped analyze Medicare claims and enrollment data.

Author Affiliations: Mathematica Policy Research (KK, LMB, RS, GGP, BG, LB, KAS, SDH, LM), Ann Arbor, MI, Cambridge, MA, Princeton, NJ, and Washington, DC; Center for Medicare & Medicaid Innovation (CMMI), CMS (TJD), Baltimore, MD.

Source of Funding: Evaluation funded by CMS, CMMI, contract number HHSM-500-2010-000261/HHSM-500-T0015. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of HHS or any of its agencies.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (KK, LMB, GGP, BG, LB, KAS, TJD, LM); acquisition of data (RS); analysis and interpretation of data (KK, LMB, RS, GGP, BG, LB, KAS, SDH); drafting of the manuscript (KK, LMB, RS, GGP, SDH); critical revision of the manuscript for important intellectual content (KK, LMB, GGP, SDH, TJD, LM); statistical analysis (KK); obtaining funding (BG, LM); administrative, technical, or logistic support (KK, GGP, BG, LB, KAS, TJD, LM); and supervision (KK, GGP, LM).

Address Correspondence to: Keith Kranker, PhD, Mathematica Policy Research, PO Box 2393, Princeton, NJ 08543-2393. Email:

1. Health Care Innovation Challenge: cooperative agreement: initial announcement. CMS website. Published November 14, 2011. Accessed April 2, 2018.

2. Coleman EA, Boult C; American Geriatrics Society Health Care Systems Committee. Improving the quality of transitional care for persons with complex care needs. J Am Geriatr Soc. 2003;51(4):556-557. doi: 10.1046/j.1532-5415.2003.51186.x.

3. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520-528. doi: 10.7326/0003-4819-155-8-201110180-00008.

4. Peikes D, Lester RS, Gilman B, Brown R. The effects of transitional care models on re-admissions: a review of the current evidence. Generations. 2012;36(4):44-55.

5. Feltner C, Jones CD, Cené CW, et al. Transitional care interventions to prevent readmissions for persons with heart failure: a systematic review and meta-analysis. Ann Intern Med. 2014;160(11):774-784. doi: 10.7326/M14-0083.

6. Phillips CO, Wright SM, Kern DE, Singa RM, Shepperd S, Rubin HR. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta-analysis. JAMA. 2004;291(11):1358-1367. doi: 10.1001/jama.291.11.1358.

7. Vedel I, Khanassov V. Transitional care for patients with congestive heart failure: a systematic review and meta-analysis. Ann Fam Med. 2015;13(6):562-571. doi: 10.1370/afm.1844.

8. Linden A, Butterworth SW. A comprehensive hospital-based intervention to reduce readmissions for chronically ill patients: a randomized controlled trial. Am J Manag Care. 2014;20(10):783-792.

9. Toth M, Holmes M, Van Houtven C, Toles M, Weinberger M, Silberman P. Rural Medicare beneficiaries have fewer follow-up visits and greater emergency department use postdischarge. Med Care. 2015;53(9):800-808. doi: 10.1097/MLR.0000000000000401.

10. Kind AJ, Jensen L, Barczi S, et al. Low-cost transitional care with nurse managers making mostly phone contact with patients cut rehospitalization at a VA hospital. Health Aff (Millwood). 2012;31(12):2659-2668. doi: 10.1377/hlthaff.2012.0366.

11. Gilman B, Hoag S, Moreno L, et al. Evaluation of Health Care Innovation Awards (HCIA): Primary Care Redesign Programs: First Annual Report. Princeton, NJ: Mathematica Policy Research; 2014. CMS website. Accessed December 15, 2016.

12. Hansen BB. Full matching in an observational study of coaching for the SAT. J Am Stat Assoc. 2004;99(467):609-618. doi: 10.1198/016214504000000647.

13. Pope GC, Kautter J, Ellis RP, et al. Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev. 2004;25(4):119-141.
Copyright AJMC 2006-2018 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up

Sign In

Not a member? Sign up now!