Currently Viewing:
The American Journal of Managed Care June 2019
Reports of the Demise of Chemotherapy Have Been Greatly Exaggerated
Bruce Feinberg, DO; Jonathan Kish, PhD, MPH; Igoni Dokubo, MD; Jeff Wojtynek, PharmD; and Kevin Lord, PhD, MHS
From the Editorial Board: Patrick H. Conway, MD, MSc
Patrick H. Conway, MD, MSc
Currently Reading
Association of Decision Support for Hospital Discharge Disposition With Outcomes
Winthrop F. Whitcomb, MD; Joseph E. Lucas, PhD; Rachel Tornheim, MBA; Jennifer L. Chiu, MPH; and Peter Hayward, PhD
Understanding Price Growth in the Market for Targeted Oncology Therapies
Jesse Sussell, PhD; Jacqueline Vanderpuye-Orgle, PhD; Diana Vania, MSc; Hans-Peter Goertz, MPH; and Darius Lakdawalla, PhD
Cancer Care Spending and Use by Site of Provider-Administered Chemotherapy in Medicare
Andrew Shooshtari, BS; Yamini Kalidindi, MHA; and Jeah Jung, PhD
Will 2019 Kick Off a New Era in Person-Centered Care?
Ann Hwang, MD; and Marc A. Cohen, PhD
Enhanced Care Coordination Improves HIV Viral Load Suppression Rates
Ross G. Hewitt, MD; Debra Williams, EdD; Richard Adule; Ira Feldman, MPS; and Moe Alsumidaie, MBA, MSF
Impact of Care Coordination Based on Insurance and Zip Code
Jennifer N. Goldstein, MD, MSc; Merwah Shinwari, BS; Paul Kolm, PhD; Daniel J. Elliott, MD, MSCE; William S. Weintraub, MD; and LeRoi S. Hicks, MD, MPH
Changing Electronic Formats Is Associated With Changes in Number of Laboratory Tests Ordered
Gari Blumberg, MD; Eliezer Kitai, MD; Shlomo Vinker, MD; and Avivit Golan-Cohen, MD
Health Insurance Design and Conservative Therapy for Low Back Pain
Kathleen Carey, PhD; Omid Ameli, MD, MPH; Brigid Garrity, MS, MPH; James Rothendler, MD; Howard Cabral, PhD; Christine McDonough, PhD; Michael Stein, MD; Robert Saper, MD, MPH; and Lewis Kazis, ScD
Improving Quality Measure Maintenance: Navigating the Complexities of Evolving Evidence
Thomas B. Valuck, MD, JD; Sarah Sampsel, MPH; David M. Sloan, PhD; and Jennifer Van Meter, PharmD

Association of Decision Support for Hospital Discharge Disposition With Outcomes

Winthrop F. Whitcomb, MD; Joseph E. Lucas, PhD; Rachel Tornheim, MBA; Jennifer L. Chiu, MPH; and Peter Hayward, PhD
The use of clinical decision support for hospital discharge disposition was associated with a reduction in spending and readmissions without negatively affecting emergency department use.

We tested a CDS tool for post–hospital discharge destination and found that following its recommendation was associated with reduced spending and readmissions, with no change in ED use, over the course of an episode encompassing a hospitalization and the ensuing 90-day postdischarge period for patients participating in the Medicare bundled payment program. For cases that were discharged to a more intense care level than the recommendation, associated spending and readmissions were greater, whereas ED use was unchanged. For cases discharged to less intense care than recommended, spending was reduced, whereas readmissions and ED use were unchanged.

Because of the observational nature of the study, it is difficult to definitively state the reasons for lower spending or readmission reductions when following the tool’s recommendation. It is possible that the recommendation-concordant group—who went home more often and to postacute facilities less often (Table 1)—recovered more successfully because they were in the home environment. The hazards of postacute facilities (eg, falls, delirium, infection, poor nutrition, decreased mobility, deconditioning29,30) may play a role in increased readmissions and ED use, although the evidence on the impact of home care versus alternative locations on health outcomes is inconclusive.31 Alternatively, in select patients, such as those undergoing elective total joint replacement, a home discharge has been associated with lower readmissions.32

Similarly, the reasons for findings associated with less intense and more intense discharge decisions are speculative. It is conceivable that for many patients, receiving less intense posthospital care than the tool recommends is in actuality the appropriate care level, thus explaining why spending is lower and readmissions and ED use were no different from those of the recommendation-concordant group. For patients receiving more intense care, which may in part be driven by patient/caregiver preferences, one may argue that higher spending and readmissions are explained by patient factors; however, the propensity model was designed to adjust for such factors.

For patients discharged to a less intense level of care than recommended, because readmissions and ED use were unchanged in that group, it is likely that if the discharge team’s judgment supports the decision to discharge to a less intense level, such a decision is safe and appropriate. Conversely, if a more intense level of care is felt to be required, the team should consider this study’s findings of higher readmissions and ED use and carefully consider the purported benefits of the decision.

The results of the study also showed that in the comparison of cases receiving CDS tool testing—regardless of whether the recommendation was followed—versus no testing, there was no statistically significant change in spending and no change in readmission rate or ED use. It is likely that providers were selective about who received the tool, namely that those tested had longer hospital lengths of stay and higher rates of dual enrollment, suggesting they may be sicker and more likely in need of additional care after discharge. This is relevant because even with the propensity model, it is difficult to adjust for all the differences between the tested and untested, yet the potentially sicker tested group did not have worse outcomes.

It should be stated that the intention of the CDS tool is to inform the discharge planning team’s evaluation regarding the factors influencing discharge destination, with a final decision arrived at with input from the patient/caregiver and the judgment of the team. Discussion and evaluation by the team of the tool’s data elements—including measures of independence, availability of a capable caregiver, and postacute needs—along with other details of each particular case can form the basis of a structured process yielding a final decision. The adoption of such a process for evaluating patients’ postdischarge destination may help hospitals looking to improve the precision with which various postacute services and settings are recommended.


The study was limited to the use of observational data. Thus, if there are unmeasured confounders associated with the decision to follow the CDS tool’s recommendation, or to use the tool at all, there may be uncorrected bias in the results. Because providers may exercise discretion as to who receives the CDS tool, selection bias may be a significant factor in differences between the tested and untested groups. Also, the study used only Medicare Part A and Part B claims data in its outcomes analysis. It is possible that clinical data would have improved the propensity model and increased the relevance of the outcomes. It is also possible that there were differences in spending not reflected in Part A and Part B claims, such as out-of-pocket spending or that associated with supplemental insurance, that were not measured.

The population in the study was limited to patients 65 years or older. We cannot rule out the possibility that the impact of the test will differ among younger patients. Moreover, the analysis of recommendation concordance is confounded by the fact that providers’ discharge decisions are potentially affected by the tool’s recommendations.


This study demonstrated an association between concordance with a CDS algorithm and decreased 90-day episode spending and readmissions, with no adverse effect on postdischarge ED visits. The study is an example of an innovative approach to care redesign under a bundled payment model. Because bundled payments create an incentive to critically evaluate decisions affecting discharge destination, the development and implementation of the CDS tool can be viewed as a result of a new payment incentive.


The authors acknowledge the valuable contributions of Georgine Schmidt, RN; Susana Hall, RN, MBA; Benjamin Record, BA; Luann Tammany, PT, MBA; and Steve Wiggins, MBA, to the manuscript. All are affiliated with Remedy Partners.

Author Affiliations: Remedy Partners (WFW, JLC, PH), Norwalk, CT; Village Care of New York (RT), New York, NY; Vital Statistics, LLC (JEL), Chapel Hill, NC.

Source of Funding: None.

Author Disclosures: Drs Whitcomb and Hayward report current employment with and stock options in Remedy Partners. Ms Chiu reports previous employment with Remedy Partners. Ms Tornheim reports previous employment with and stock options in Remedy Partners. Dr Lucas reports receiving payment from Remedy Partners for involvement in the preparation of this manuscript.

Authorship Information: Concept and design (WFW, RT, PH); acquisition of data (WFW, JLC, PH); analysis and interpretation of data (WFW, RT, JEL, JLC, PH); drafting of the manuscript (WFW, JEL); critical revision of the manuscript for important intellectual content (WFW, JEL, PH); statistical analysis (JEL, PH); administrative, technical, or logistic support (WFW, RT, JLC, PH); and supervision (PH).

Address Correspondence to: Winthrop F. Whitcomb, MD, Remedy Partners, 800 Connecticut Ave, 3rd Floor, Norwalk, CT 06854. Email:

1. Huckfeldt PJ, Mehrotra A, Hussey PS. The relative importance of post-acute care and readmissions for post-discharge spending. Health Serv Res. 2016;51(5):1919-1938. doi: 10.1111/1475-6773.12448.

2. Chandra A, Dalton MA, Holmes J. Large increases in spending on postacute care in Medicare point to the potential for cost savings in these settings. Health Aff (Millwood). 2013;32(5):864-872. doi: 10.1377/hlthaff.2012.1262.

3. Mechanic R. Post-acute care—the next frontier for controlling Medicare spending. N Engl J Med. 2014;370(8):692-694. doi: 10.1056/NEJMp1315607.

4. Newhouse JP, Garber AM. Geographic variation in Medicare services. N Engl J Med. 2013;368(16):1465-1468. doi: 10.1056/NEJMp1302981.

5. Cost requirements: PY2019. CMS Quality Payment Program website. Accessed May 3, 2018.

6. Unsworth CA. Selection for rehabilitation: acute care discharge patterns for stroke and orthopedic patients. Int J Rehabil Res. 2001;24(2):103-114.

7. Lockery SA, Dunkle RE, Kart CS, Coulton CJ. Factors contributing to the early rehospitalization of elderly people. Health Soc Work. 1994;19(3):182-191.

8. Bohannon RW, Lee N, Maljanian R. Postadmission function best predicts acute hospital outcomes after stroke. Am J Phys Med Rehabil. 2002;81(10):726-730. doi: 10.1097/01.PHM.0000027422.46697.A7.

9. Ekstrand E, Ringsberg KA, Pessah-Rasmussen H. The Physiotherapy Clinical Outcome Variables Scale predicts length of hospital stay, discharge destination and future home facility in the acute comprehensive stroke unit. J Rehabil Med. 2008;40(7):524-528. doi: 10.2340/16501977-0210.

10. van der Zwaluw CS, Valentijn SAM, Nieuwenhuis-Mark R, Rasquin SM, van Heugten CM. Cognitive functioning in the acute phase poststroke: a predictor of discharge destination? J Stroke Cerebrovasc Dis. 2011;20(6):549-555. doi: 10.1016/j.jstrokecerebrovasdis.2010.03.009.

11. Gambier N, Simoneau G, Bihry N, et al. Efficacy of early clinical evaluation in predicting direct home discharge of elderly patients after hospitalization in internal medicine. South Med J. 2012;105(2):63-67. doi: 10.1097/SMJ.0b013e318242d74d.

12. Jette DU, Stilphen M, Ranganathan VK, Passek SD, Frost FS, Jette AM. AM-PAC “6-Clicks” functional assessment scores predict acute care hospital discharge destination. Phys Ther. 2014;94(9):1252-1261. doi: 10.2522/ptj.20130359.

13. Bundled Payments for Care Improvement (BPCI) initiative: general information. CMS website. Updated April 17, 2019. Accessed April 29, 2018.

14. Graf C. Functional decline in hospitalized older adults. Am J Nurs. 2006;106(1):58-67.

15. Hartigan I. A comparative review of the Katz ADL and the Barthel Index in assessing the activities of daily living of older people. Int J Older People Nurs. 2007;2(3):204-212. doi: 10.1111/j.1748-3743.2007.00074.x.

16. Katz S. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J Am Geriatr Soc. 1983;31(12):721-727. doi: 10.1111/j.1532-5415.1983.tb03391.x.

17. Katz S, Down TD, Cash HR, Grotz RC. Progress in the development of the index of ADL. Gerontologist. 1970;10(1):20-30.

18. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged. The index of ADL: a standardized measure of biological and psychosocial function. JAMA. 1963;185(12):914-919. doi: 10.1001/jama.1963.03060120024016.

19. Kresevic DM. Assessment of physical function. In: Boltz M, Capezuti E, Fulmer T, Zwicker D, eds. Evidence-Based Geriatric Nursing Protocols for Best Practice. 4th ed. New York, NY: Springer Publishing Company, LLC; 2012:89-103.

20. Kane RL. Finding the right level of posthospital care: “we didn’t realize there was any other option for him.” JAMA. 2011;305(3):284-293. doi: 10.1001/jama.2010.2015.

21. D’Souza MF, Davagnino J, Hastings S, Sloane R, Kamholz B, Twersky J. Preliminary data from the Caring for Older Adults and Caregivers at Home (COACH) program: a care coordination program for home-based dementia care and caregiver support in a Veterans Affairs Medical Center. J Am Geriatr Soc. 2015;63(6):1203-1208. doi: 10.1111/jgs.13448.

22. Black BS, Johnston D, Rabins PV, Morrison A, Lyketsos C, Samus QM. Unmet needs of community-residing persons with dementia and their informal caregivers: findings from the maximizing independence at home study. J Am Geriatr Soc. 2013;61(12):2087-2095.

23. Discharge planning proposed rule focuses on patient preferences [news release]. Baltimore, MD: CMS; October 29, 2015. Accessed May 3, 2019.

24. Medicare benefit policy manual: chapter 7—home health services [40: covered services under a qualifying home health plan of care]. CMS website. Updated March 22, 2019. Accessed May 3, 2019.

25. Care at the Right Location (CARL). Remedy Partners website. Accessed April 29, 2018.

26. Curtis LH, Hammill BG, Eisenstein EL, Kramer JM, Anstrom KJ. Using inverse probability-weighted estimators in comparative effectiveness analyses with observational databases. Med Care. 2007;45(10 suppl 2):S103-S107. doi: 10.1097/MLR.0b013e31806518ac.

27. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observation studies. Stat Med. 2015;34(28):3661-3679. doi: 10.1002/sim.6607.

28. Protection of human subjects, subpart A: basic HHS policy for protection of human research subjects: to what does this policy apply? 46 CFR §46.101(b)(5) (2009). Accessed May 3, 2019.

29. Hakkarainen TW, Arbabi S, Willis MM, Davidson GH, Flum DR. Outcomes of patients discharged to skilled nursing facilities after acute care hospitalizations. Ann Surg. 2016;263(2):280-285. doi: 10.1097/SLA.0000000000001367.

30. Wysocki A, Butler M, Kane RL, Kane RA, Shippee T, Sainfort F. Long-term services for older adults: a review of home and community-based services versus institutional care. J Aging Soc Policy. 2015;27(3):255-279. doi: 10.1080/08959420.2015.1024545.

31. Boland L, Légaré F, Perez MM, et al. Impact of home care versus alternative locations of care on elder health outcomes: an overview of systematic reviews. BMC Geriatr. 2017;17(1):20. doi: 10.1186/s12877-016-0395-y.

32. Dummit LA, Kahvecioglu D, Marrufo G, et al. Association between hospital participation in a Medicare bundled payment initiative and payments and quality outcomes for lower extremity joint replacement episodes. JAMA. 2016;316(12):1267-1278. doi: 10.1001/jama.2016.12717.
Copyright AJMC 2006-2020 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