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Improved Cost and Utilization Among Medicare Beneficiaries Dispositioned From the ED to Receive Home Health Care Compared With Inpatient Hospitalization
James Howard, MD; Tyler Kent, BS; Amy R. Stuck, PhD, RN; Christopher Crowley, PhD; and Feng Zeng, PhD
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Improved Cost and Utilization Among Medicare Beneficiaries Dispositioned From the ED to Receive Home Health Care Compared With Inpatient Hospitalization

James Howard, MD; Tyler Kent, BS; Amy R. Stuck, PhD, RN; Christopher Crowley, PhD; and Feng Zeng, PhD
A retrospective analysis of Medicare claims was used to study emergency department (ED) dispositions, specifically evaluating inpatient admissions compared with home health referrals.

In a comparison of patients transitioned from the ED with 1 of 5 conditions, those patients transitioned to home health care had overall cost and utilization advantages compared with those admitted to the inpatient setting. When costs were compared for the index episode plus the 90-day follow-up period, the lower costs of the home health cohort were highly statistically significant. When episode of care and follow-up period are considered, the cost delta reached $7495 per patient. This number could represent a significant cost reduction opportunity for Medicare and for patients. In this study, the inpatient cohort had 21,608 patients per 9-month period. Annually, this accounts for 576,213 patients across the nation who might have similar admission profiles. If acute home-based care options were available to all emergency physicians across the nation for just these 5 conditions, this could account for an annual savings of $3.7 billion in total costs, $3 billion in Medicare savings, and $520 million in patient OOP expenses. This suggests that programs that support more acute care at home could have a major impact on lowering healthcare costs.

Finally, our research shows that moving patients to home health care directly from the ED could result in a reduction in utilization and associated costs. Although we have considered the patient’s and the payer’s financial perspective in this analysis, one must also look at the hospital’s point of view. In general, inpatient admissions for the DRGs in this study may not be of major value to hospitals, as the DRG weights from the Medicare fiscal year 2015 final rule are all less than 1, with the highest geometric mean LOS at 3.8 days and arithmetic mean LOS at 4.5 days.27 Hospitals offering clinicians and patients a home-based care alternative could create an increased hospital capacity for planned procedure-based admissions that could enhance revenue and reduce overcrowding in the hospital as well as in the ED. Thus, the transition to home health care from the ED, when appropriate, would support a viable financial model from the perspectives of the patient, payer, hospital, and home health agency, assuming that the home health agencies are being appropriately reimbursed for these conditions, in the right setting.28


This research sought to corroborate the improvement in outcomes related to cost and utilization that were previously demonstrated in the substitutive hospital model for Medicare Advantage and Medicaid patients.7 We used purely operational information from the Medicare FFS 5% claims so we could get a firm measurement of the cost, as carrier claims are available only in the 5% data set. Our model looks nationally at beneficiaries who were transitioned to home health care directly from the ED. We used an artificial construct, because currently, on a national scale, transitions of patients to home health care from the ED are a rare occurrence relative to inpatient admissions.

All factors within the claims data that we could abstract were used to control for acuity, comorbidity, geolocation, and other demographic factors. Our acuity and comorbidity control factors included using DRGs that did not contain MCCs, a LOS of less than 4 days in the inpatient group, CCI scores, revenue center codes, ICD-9 procedure codes, and HCPCS codes that would not require hospitalization. In addition, we included patient age, gender, payer status, hospitalization history, principal ICD-9 diagnosis by CCS category, age eligibility, and exclusion based on metastatic cancer and stays in the intensive care unit. Although we used propensity scores to balance these characteristics across the 2 cohorts, we recognize that physicians face more variables when they make the decision to disposition a patient to home health care or to admit a patient to the hospital. We did not attempt to control such differences in the study, as they are not present within the claims data set.


This analysis of Medicare claims data compared a small population of patients transitioned directly from the ED to home health care with patients transitioned from the ED to inpatient hospitalization, both with selected conditions. The home health cohort had statistically significant lower costs as well as reduced readmissions at a significant level. The favorable outcomes for this small number of patients transitioned from ED to home health care, derived from the Medicare FFS claims data set, suggest that home-based alternatives to hospitalization, using the home health benefit, are worthy of further exploration and testing in real-world scenarios. Developing processes to support an ED to home health care disposition option may benefit risk-bearing organizations such as accountable care organizations and Medicare Advantage participants. Although the scale and scope of this study is limited, the possibility of financial and utilization benefits supports continued research in this area.

Author Affiliations: Scripps Memorial Hospital (JH), Encinitas, CA; Gary and Mary West Health Institute (TK, ARS, CC), La Jolla, CA; La Jolla Pharmaceutical Company (FZ), San Diego, CA.

Source of Funding: This work was solely funded by the Gary and Mary West Health Institute.

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 (JH, TK, ARS, CC, FZ); acquisition of data (JH, TK, FZ); analysis and interpretation of data (JH, TK, ARS, CC, FZ); drafting of the manuscript (JH, ARS, FZ); critical revision of the manuscript for important intellectual content (JH, TK, ARS, CC); statistical analysis (JH, TK, FZ); obtaining funding (FZ); and administrative, technical, or logistic support (TK, ARS).

Send Correspondence to: Tyler Kent, BS, Gary and Mary West Health Institute, 10350 N Torrey Pines Rd, La Jolla, CA 92037. Email:

1. Schneider EC, Sarnak DO, Squires D, Shah A, Doty MM. Mirror, Mirror 2017: international comparison reflects flaws and opportunities for better U.S. health care. The Commonwealth Fund website. Published July 2017. Accessed March 28, 2018.

2. Medical cost trend: behind the numbers 2018. PwC website. Published June 2017. Accessed March 28, 2018.

3. Office of the Actuary, National Health Statistics Group. National health care spending in 2016. CMS website. Accessed January 30, 2019.

4. Siu AL, Spragens LH, Inouye SK, Morrison RS, Leff B. The ironic business case for chronic care in the acute care setting. Health Aff (Millwood). 2009;28(1):113-125. doi: 10.1377/hlthaff.28.1.113.

5. Vianello A, Savoia F, Pipitone E, et al. “Hospital at home” for neuromuscular disease patients with respiratory tract infection: a pilot study. Respir Care. 2013;58(12):2061-2068. doi: 10.4187/respcare.02501.

6. Shepperd S, Doll H, Angus RM, et al. Avoiding hospital admission through provision of hospital care at home: a systematic review and meta-analysis of individual patient data. CMAJ. 2009;180(2):175-182. doi: 10.1503/cmaj.081491.

7. Cryer L, Shannon SB, Van Amsterdam M, Leff B. Costs for ‘hospital at home’ patients were 19 percent lower, with equal or better outcomes compared to similar inpatients. Health Aff (Millwood). 2012;31(6):1237-1243. doi: 10.1377/hlthaff.2011.1132.

8. Aimonino Ricauda N, Tibaldi V, Leff B, et al. Substitutive “hospital at home” versus inpatient care for elderly patients with exacerbations of chronic obstructive pulmonary disease: a prospective randomized, controlled trial. J Am Geriatr Soc. 2008;56(3):493-500. doi: 10.1111/j.1532-5415.2007.01562.x.

9. Montalto M. The 500-bed hospital that isn’t there: the Victorian Department of Health review of the Hospital in the Home program. Med J Aust. 2010;193(10):598-601. doi: 10.5694/j.1326-5377.2010.tb04070.x.

10. Leff B, Burton L, Mader SL, et al. Hospital at home: feasibility and outcomes of a program to provide hospital-level care at home for acutely ill older patients. Ann Intern Med. 2005;143(11):798-808. doi: 10.7326/0003-4819-143-11-200512060-00008.

11. Frick KD, Burton LC, Clark R, et al. Substitutive Hospital at Home for older persons: effects on costs. Am J Manag Care. 2009;15(1):49-56.

12. Marsteller JA, Burton L, Mader SL, et al. Health care provider evaluation of a substitutive model of hospital at home. Med Care. 2009;47(9):979-985. doi: 10.1097/MLR.0b013e31819c93fc.

13. Leff B, Burton L, Mader SL, et al. Comparison of functional outcomes associated with hospital at home care and traditional acute hospital care. J Am Geriatr Soc. 2009;57(2):273-278. doi: 10.1111/j.1532-5415.2008.02103.x.

14. Leff B, Burton L, Mader SL, et al. Comparison of stress experienced by family members of patients treated in hospital at home with that of those receiving traditional acute hospital care. J Am Geriatr Soc. 2008;56(1):117-123. doi: 10.1111/j.1532-5415.2007.01459.x.

15. Leff B, Burton L, Mader S, et al. Satisfaction with hospital at home care. J Am Geriatr Soc. 2006;54(9):1355-1363. doi: 10.1111/j.1532-5415.2006.00855.x.

16. Proposal submissions: Physician-Focused Payment Model Technical Advisory Committee. Deliberation and voting completed: report to the Secretary being drafted. HHS Office of the Assistant Secretary for Planning and Evaluation website. Accessed April 23, 2017.

17. Leff B. Defining and disseminating the hospital-at-home model. CMAJ. 2009;180(2):156-157. doi: 10.1503/cmaj.081891.

18. Bundled Payments for Care Improvement (BPCI) initiative: general information. CMS website. Accessed October 24, 2017.

19. Fu AZ, Dow WH, Liu GG. Propensity score and difference-in-difference methods: a study of second-generation antidepressant use in patients with bipolar disorder. Health Serv Outcomes Res Methodol. 2007;7(1-2):23-38. doi: 10.1007/s10742-006-0016-x.

20. Rosenbaum PR, Rubin DB. The central role of propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41-55. doi: 10.1093/biomet/70.1.41.

21. Baser O. Too much ado about propensity score models? comparing methods of propensity score matching. Value Health. 2006;9(6):377-385. doi: 10.1111/j.1524-4733.2006.00130.x.

22. King G, Zeng L. Logistic regression in rare events data. Polit Anal. 2001;9(2):23-38. doi: 10.1093/oxfordjournals.pan.a004868.

23. Zeng F, An JJ, Scully R, Barrington C, Patel BV, Nichol MB. The impact of value-based benefit design on adherence to diabetes medications: a propensity score-weighted difference in difference evaluation. Value Health. 2010;13(6):846-852. doi: 10.1111/j.1524-4733.2010.00730.x.

24. Hirano K, Imbens GW. Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization. Health Serv Outcomes Res Methodol. 2001;2(3-4):259-278. doi: 10.1023/A:1020371312283.

25. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study [erratum in Stat Med. 2017;36(14):2320. doi: 10.1002/sim.7231]. Stat Med. 2004;23(19):2937-2960. doi: 10.1002/sim.1903.

26. Rubin DB. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Serv Outcomes Res Methodol. 2001;2(3-4):169-188. doi: 10.1023/A:1020363010465.

27. FY 2017 Final Rule and Correction Notice tables. Table 5: list of MS-DRGs, relative weighting factors and geometric and arithmetic mean length of stay. CMS website. Accessed April 23, 2018.

28. Medicare Payment Advisory Commission. Implementing a unified payment system for post-acute care. In: Medicare Payment Advisory Commission. Report to the Congress: Medicare and the Health Care Delivery System. Washington, DC: Medicare Payment Advisory Commission; 2017:3-25. Accessed April 23, 2018.
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