Amy R. Stuck, PhD, RN

 To describe the use of home-based services in accountable care organizations (ACOs).

 Cross-sectional analysis of 2019 ACO survey.

 We analyzed surveys completed by 151 ACOs describing the characteristics of home-based care programs serving high-need, high-cost patients. We linked survey results to publicly available information about ACO characteristics, governance, and risk model participation.

 Twenty-five percent of respondent ACOs had formal home-based care programs, 25% offered occasional home visits, and 17% were actively developing new programs. Home-based primary care was the most common program type. Half of programs were established within the past 3 years. The programs utilized multidisciplinary care teams, but two-thirds had fewer than 500 visits annually. Funding sources included direct billing for services, health system subsidies, and ACO shared savings. A majority of respondents expressed interest in expanding services but were concerned about their ability to demonstrate a return on investment (ROI), which was reported as a major or moderate challenge by three-quarters of respondents.

ACOs deliver a diverse array of home-visit services including primary care, acute medical care, palliative care, care transitions, and interventions to address social determinants of health. Many services provided are not billable, and therefore ACO leaders are hesitant to fund expansions without strong evidence of ROI. Expanding Medicare ACO home-visit waivers to all risk-bearing ACOs and covering integrated telehealth services would improve the financial viability of these programs.

Many accountable care organizations (ACOs) provide home-based care to high-need patients, including primary care, acute care, support for care transitions, and interventions to address social determinants of health. However, many services are not well reimbursed, limiting program growth.

There is no prior research describing the full range of home-based care programs in ACOs.

With Medicare’s expansion of mandatory downside risk, more ACOs are considering home-based programs.

ACO managers are hesitant to expand these programs unless they can demonstrate a return on investment.

Expanding home-visit waivers to all risk-bearing Medicare ACOs and covering telehealth for patients receiving home-based primary care would improve these programs’ financial viability.

Improving care for high-need, high-cost (HNHC) individuals with multiple chronic conditions, functional limitations, and complex social needs is an issue of growing interest for policy makers, health systems, and payers.

 These individuals account for a large proportion of Medicare spending generally and a disproportionate amount of potentially preventable spending.

 The needs of HNHC individuals are frequently not well addressed by a health care system and culture that are focused on specialized and facility-based care.

 Home-based care is increasingly recognized as an approach with potential to improve outcomes and patient experience for HNHC individuals.

There are many different home-based care models: home-based primary care (HBPC), hospital at home (HAH), community paramedicine, postdischarge transitional care, and care coordination services that employ both clinical and lay personnel. These models are distinct from certified home health agency services, which are not covered in this paper.

HBPC is well established but regularly practiced by relatively few providers. In 2013, approximately 5000 primary care providers made 1.7 million home visits to Medicare fee-for-service (FFS) beneficiaries, but almost half of the visits were delivered by just 470 providers.

 More than half of Americans live more than 30 miles from any of these high-volume providers, limiting Medicare beneficiary access to HBPC services.

Medicare reimbursement affects the availability of home-based care. Medicare pays for HBPC delivered by physicians or advanced practice clinicians (APCs), but HBPC providers see far fewer patients than office-based providers because of travel time, which limits revenue. Medicare has not historically paid for HAH, but in late 2020, CMS established a waiver covering certain HAH services during the COVID-19 pandemic.

 Transitional care management services are covered by Medicare when provided by a clinician but are typically offered in a provider’s office or telephonically.

Home-based programs rely on multidisciplinary teams, and patients may be visited by nurses, social workers, paramedics, and community health workers—none of whom can bill for home visits in most circumstances. As a result, many programs rely on financial support from a hospital, medical school, or philanthropic organization.

Although not historically well reimbursed, studies have shown that HBPC,

 and home-based programs led by registered nurses or lay health workers

 can lower spending for complex patients. These savings do not accrue to providers under traditional FFS reimbursement but generate financial benefits under models like capitation that pay a fixed per-member per-month amount for covered beneficiaries.

Accountable care organizations (ACOs) are well positioned to improve care for HNHC populations. ACOs are provider groups that take responsibility for caring for a specified set of beneficiaries. They are eligible to share in savings if they manage spending below a prospective budget target. Many ACOs have data systems and processes to identify high-risk beneficiaries and establish care plans to coordinate services for complex patients.

 Prior research suggests that a majority of ACOs make home-based care transition visits, primarily conducted by nonphysician staff.

 This paper offers a more comprehensive assessment of home-based care in ACOs.

We conducted a national survey of ACOs to assess the prevalence and characteristics of home-based care programs. It targeted ACOs participating in the Medicare Shared Savings Program (MSSP) or Next Generation ACO (NGACO) model as of July 1, 2019, that remained in the program during 2020 (N = 505).

A web-based survey was sent to ACOs in September 2019. Weekly email reminders were sent to nonrespondents for the first 4 weeks, and biweekly reminders were sent over the next 4 months. Survey results were merged with information on Medicare ACO characteristics. Descriptive analysis of survey results focuses on ACOs with 1 or more home-based initiatives.

We received 151 completed surveys for a 30% response rate. Respondents were larger, were more likely to be affiliated with a health system, and had more Medicare ACO experience than nonrespondents (

). Forty-four percent of respondents were enrolled in a Medicare ACO track with downside risk compared with 40% of all Medicare ACOs.

Two-thirds of ACOs reported some home-visit activity: 25% had formal programs, 25% provided occasional home visits, and 17% were actively developing programs.

This paper focuses on the 40 ACOs with formal home-visit initiatives. Fifty-eight percent of them participated in a Medicare ACO arrangement with downside risk vs 40% for all ACOs. They were also more likely to participate in commercial and Medicare Advantage risk contracts than other respondents (Table 1). ACOs with home-visit programs were larger and had more Medicare ACO experience than other ACOs.

The 40 ACOs reported 49 distinct home-based initiatives that focused on home-based primary care (37%), care coordination (24%), care transitions support (13%), addressing social needs (13%), and acute hospital-level services (11%), as shown in 

. Respondents provided other services to supplement their program’s principal function including care coordination (70%), addressing social determinants (65%), care transition support (59%), palliative care (41%), and acute urgent care services (24%).

Half of the programs were established between 2017 and 2019, and one-third of them reported 500 or more home visits annually (Table 2). Duration of services ranged from a single home visit (24%), 30 days or less (13%), 1 to 6 months (42%), to longer than 6 months (9%).

Most home-visit programs utilized multidisciplinary teams. Key staff included physicians (48% of programs), nurse practitioners (65%), registered nurses (85%), and social workers (70%). Approximately one-third reported using pharmacists, paramedics, or community health workers (Table 2).

The sustainability of home-visit models depends on funding. Major sources of program funding included billed services (54% of programs), shared savings from value-based contracts (52%), and institutional support (46%). Fifteen percent reported grant funding, and 7% reported direct support from health plans.

Respondents were asked whether they considered 6 specific issues as major, moderate, minor, or insignificant challenges (data not shown). The most common major challenge was demonstrating return on investment (ROI) (45%), followed by engaging physicians to refer patients for home-based services (30%). The most common major or moderate challenges were finding community resources for patients with social needs (72%), demonstrating ROI (70%), physician engagement to refer patients (60%), and targeting the right patients (58%).

Our survey shows that ACOs are involved in a diverse range of home-based initiatives. ACOs’ programs differed in size, staff mix, type of care provided, duration of services, and funding sources. Half of the programs were established in the past 3 years. This may reflect increasing pressure to develop initiatives that moderate spending growth as more ACOs enter risk contracts. ACOs that reported home-based programs were more likely to participate in payment models with downside risk, suggesting that more ACOs may adopt home-based care as CMS expands mandatory risk requirements.

Forty percent of respondents reported plans to expand their home-based programs, whereas 38% percent reported they would expand only if they could demonstrate positive ROI. However, measuring ROI was identified as a major challenge by 45% of programs and a moderate challenge by 26%. This is critical because many organizations are reluctant to invest in new programs designed for cost savings rather than revenue generation.

Measuring ROI is complicated by the financial structure of shared savings models. The MSSP establishes spending targets for each ACO while paying FFS for the care provided. Even if home-visit programs generate savings, the ACOs won’t benefit unless they generate sufficient overall savings. Historically, 30% to 50% of MSSP ACOs earn shared savings in any given year. These payments aren’t made until 9 months after the performance year ends, so ACOs need to fund new programs with their own capital.

 The business case is stronger with prospective monthly payments per beneficiary, which is an option under Medicare’s new ACO Realizing Equity, Access, and Community Health (REACH) model (formerly called Direct Contracting).

During the COVID-19 pandemic, Medicare began reimbursing for a wide range of telehealth services. Continued telehealth coverage beyond the pandemic could be a game-changer for HBPC because providers could reduce travel time and see more patients through a mix of virtual and in-person care. Although frail older patients often have difficulty with telehealth technologies,

 practices can send medical assistants or paramedics to patients’ homes to set up video visits and assist in exams using remotely enabled devices.

Although physicians and APCs can bill for home-based services, personnel like paramedics and nurses cannot. Medicare has been more willing to pay for services from nonclinician staff when organizations accept financial risk for the total cost of care. The NGACO and ACO REACH models offer waivers that authorize payment for up to 9 postdischarge home visits by licensed clinical staff under the general supervision of a physician.

This study has several limitations. First, the survey response rate was low (30%). Second, respondents were larger and more likely to participate in risk contracts, so our results may not be representative of all ACOs. Finally, ACOs may have added or dropped home-visit initiatives since completing the survey because of the COVID-19 pandemic.

Home-based programs are a promising strategy to improve care for high-need patients while reducing preventable hospital and emergency care. Policy changes to strengthen the financial viability of home-based care for ACOs include expanding home-visit waivers to MSSP ACOs taking downside risk; expanding the new HAH waiver, now available only to acute care hospitals, to independent medical groups; and paying for telehealth in conjunction with HBPC services on a permanent basis. These steps could help ACOs lower rates of preventable acute care services for HNHC patients and provide more personalized care in the comfort of their homes.

This work was conducted with funding from the West Health Institute. The authors thank the 151 ACOs whose participation in the survey made this paper possible.

 Brandeis University (REM, JP), Waltham, MA; Institute for Accountable Care (REM, JP), Washington, DC; The West Health Institute (ARS, CC), La Jolla, CA.

 Mr Mechanic is a board member of Atrius Health. Dr Perloff works for the Institute for Accountable Care, where the study was conducted. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (REM, JP, ARS, CC); acquisition of data (REM, JP); analysis and interpretation of data (REM, JP, ARS, CC); drafting of the manuscript (REM, JP, ARS, CC); critical revision of the manuscript for important intellectual content (REM, JP, ARS, CC); and statistical analysis (JP).

 Robert E. Mechanic, MBA, Brandeis University, 415 South St, MS035, Waltham, MA 02454. Email: mechanic@brandeis.edu.

1. Hayes SL, Salzberg CA, McCarthy D, et al. High-need, high-cost patients: who are they and how do they use health care? 

2. Figueroa JF, Joynt Maddox KE, Beaulieu N, Wild RC, Jha A. Concentration of potentially preventable spending among high-cost Medicare subpopulations: an observational study. 

. 2017;167(10):706-713. doi:10.7326/M17-0767

3. Leff B, Carlson CM, Saliba D, Ritchie C. The invisible homebound: setting quality-of-care standards for home-based primary and palliative care. 

. 2015;34(1):21-29. doi:10.1377/hlthaff.2014.1008

4. Boling PA, Leff B. Comprehensive longitudinal health care in the home for high-cost beneficiaries: a critical strategy for population health management. 

. 2014;62(10):1974-1976. doi:10.1111/jgs.13049

5. Yao N, Ritchie C, Camacho F, Leff B. Geographic concentration of home-based medical care providers. 

. 2016;35(8):1404-1409. doi:10.1377/hlthaff.2015.1437

6. Acute hospital care at home individual waiver only. CMS. Accessed May 4, 2021. https://qualitynet.cms.gov/acute-hospital-care-at-home

7. Desai NR, Smith KL, Boal J. The positive financial contribution of home-based primary care programs: the case of the Mount Sinai Visiting Doctors. 

. 2008;56(4):744-749. doi:10.1111/j.1532-5415.2007.01641.x

8. De Jonge KE, Jamshed N, Gilden D, Kubisiak J, Bruce SR, Taler G. Effects of home-based primary care on Medicare costs in high-risk elders. 

. 2014;62(10):1825-1831. doi:10.1111/jgs.12974

9. Edes T, Kinosian B, Vuckovic NH, Nichols LO, Becker MM, Hossain M. Better access, quality, and cost for clinically complex veterans with home-based primary care. 

. 2014;62(10):1954-1961. doi:10.1111/jgs.13030

10. Conley J, O’Brien CW, Leff BA, Bolen S, Zulman D. Alternative strategies to inpatient hospitalization for acute medical conditions: a systematic review. 

. 2016;176(11):1693-1702. doi:10.1001/jamainternmed.2016.5974

11. Coleman EA, Parry C, Chalmers S, Min SJ. The care transitions intervention: results of a randomized controlled trial. 

. 2016;166(17):1822-1828. doi:10.1001/archinte.166.17.1822

12. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. 

. 1999;281(7):613-620. doi:10.1001/jama.281.7.613

13. Kangovi S, Mitra N, Grande D, Long JA, Asch DA. Evidence-based community health worker program addresses unmet social needs and generates positive return on investment. 

. 2020;39(2):207-213. doi:10.1377/hlthaff.2019.00981

14. Ruiz S, Snyder LP, Rotondo C, Cross-Barnet C, Colligan EM, Giuriceo K. Innovative home visit models associated with reductions in costs, hospitalizations, and emergency department use. 

. 2017;36(3):425-432. doi:10.1377/hlthaff.2016.1305

15. Fraze TK, Beidler LB, Briggs ADM, Colla CH. Translating evidence into practice: ACOs’ use of care plans for patients with complex health needs. 

. 2021;36(1):147-153. doi:10.1007/s11606-020-06122-4

16. Fraze TK, Beidler LB, Briggs ADM, Colla CH. ‘Eyes in the home’: ACOs use home visits to improve care management, identify needs, and reduce hospital use. 

. 2019;38(6):1021-1027. doi:10.1377/hlthaff.2019.00003.

17. Bleser WK, Saunders RS, Winfield L, et al. ACO serious illness care: survey and case studies depict current challenges and future opportunities. 

. 2019;38(6):1011-1020. doi:10.1377/hlthaff.2019.00013

18. Latus-Olaifa O, Norman GJ, Kurliand M, et al. Not yet ready for prime time: video visits in a home-based primary care program. 

. 2019;67(10):2202-2215. doi:10.1111/jgs.16064

19. Next Generation ACO post-discharge and care management home visit waivers. CMS. May 2021. Accessed June 1, 2021. https://innovation.cms.gov/files/x/nextgenaco-pd-caremgmt-homevisit-waivers.pdf

Articles

Accountable care organizations (ACOs) deliver a diverse array of home-based services, but many of the services are not reimbursed. ACOs may not expand these programs without strong evidence of cost savings.

Characteristics of Home-Based Care Provided by Accountable Care Organizations

As the shift from volume to value in healthcare expands, efforts to develop alternatives to hospitalization are gaining momentum. This study explores home health care initiated directly from the emergency department (ED) using the Medicare-reimbursed home health benefit as a potential alternative to hospitalization. We address barriers to home-based care by comparing costs and utilization of care for older adults dispositioned to home health care versus hospital admission.

We conducted a retrospective institutional and carrier claims analysis of 5% of total Medicare fee-for-service beneficiaries from January 2012 through December 2013 using 2 cohorts: patients treated in the hospital following an ED visit (inpatient) and patients treated at home following an ED visit (home health). Patients had 1 of the following: congestive heart failure, chronic obstructive pulmonary disease, urinary tract infection, pneumonia, or cellulitis.

 Propensity score—weighted regression was used to measure the total cost of care for 90 days post index visit, hospital admissions/readmissions, and ED revisits.

Total 90-day costs were lower for the home health cohort than for the inpatient cohort ($13,012 vs $20,325; 

 <.0001). The home health cohort also had lower hospital admissions/readmissions (23.7% vs 33.0%; odds ratio, 1.535; 

 <.0001) compared with the inpatient cohort. Although the home health cohort had fewer ED revisits, the difference was not statistically significant.

The findings suggest that risk-bearing healthcare organizations could use home-based alternatives to hospital admission as a means of providing high-quality care at a lower cost.

The American Journal of Accountable Care. 2019;7(1):10-16

Healthcare spending in the United States exceeded $3.3 trillion in 2016, which translates into an average of $10,348 per capita annually.

 Despite some slowing in growth in 2016, experts still predict an unsustainable spending trajectory.

 Of total healthcare spending, hospital-based care accounts for the largest percentage at 32%, or $1.056 trillion. In 2016, hospital costs increased by 4.6%, which, although 1% slower than the growth in 2015,

 is still unsustainable. Because of this unsustainable growth in healthcare costs and poor value for the amount we spend, major efforts are underway nationally to shift Medicare from a volume-based system to a value-based system. Due to the high cost of hospitalization, alternatives to hospital admission have become a focal point of that shift.

Providing hospital-level care at home as an alternative to hospitalization is showing promising results in value-based care and may become a significant asset to a chronic care model in a senior service line portfolio.

 A growing body of evidence suggests that providing higher-intensity acute care in the home achieves the quadruple aim of improving population health, lowering costs, and improving patient and provider experience.

 This particular type of hospital-level care at home has demonstrated significantly better outcomes for selected patients compared with standard inpatient hospitalization, including comparable mortality,

 In late 2017, the Physician-Focused Payment Technical Advisory Committee recommended that the secretary of HHS implement the hospital-at-home model as an advanced payment model.

Despite evidence that home-based acute care models are cost-effective and safe, the scaling and sustainability of these models has been limited by the historical lack of payment reimbursements by Medicare parts A and B.

 The objective of this paper is to address this issue by investigating the potential for Medicare cost savings and reductions in utilization when providing home-based acute care. This study is unique in that it explores the implications of using the Medicare home health benefit to pay for the delivery of needed care after an emergency department (ED) visit, as opposed to the typical stand-alone hospital substitute model described in the literature. We compare the costs and utilization for seniors transitioned from the ED to home health care versus admission to the hospital from the ED. Data were analyzed to explore (1) whether care delivered in the home following an ED visit has lower costs than that delivered in a hospital and (2) whether ED and hospital utilization are reduced in the 90 days following an episode of care for similar patients. Answers to these 2 questions have important implications for policy makers and the medical community, as adoption of acute home-based care innovations is currently limited in the United States.

We conducted a retrospective analysis of 5% of Medicare claims data using carrier and institutional claims from January 2012 through December 2013. Patients included in the analysis were 65 years or older and had visits originating in the ED with a principal diagnosis, identified by Clinical Classifications Software (CCS) groupings, of congestive heart failure (CHF), pneumonia, urinary tract infection (UTI), chronic obstructive pulmonary disease (COPD), or cellulitis. Patients with end-stage renal disease (ESRD) and metastatic cancer were excluded. Patients needed to be enrolled in Medicare fee-for-service (FFS) continuously as age-eligible beneficiaries for 6 months before the index ED visit and 3 months after ED or hospital discharge.

To answer our research questions, we constructed 2 cohorts: (1) patients admitted to hospitals from the ED (inpatient cohort) and (2) patients dispositioned from the ED to home health care (home health cohort). 

 shows how we created these cohorts and indexed the visits. For the inpatient cohort, the index episode of care was defined as the hospital stay. For the home health cohort, the index episode of care was defined as the home health stay.

To construct an inpatient cohort comparable with the home health cohort in terms of the potential to be dispositioned to care in the home, we selected patients with diagnosis-related groups (DRGs) with and without comorbidities or complications (CCs) but without major comorbidities or complications (MCCs). The following DRG codes were included: 292, heart failure and shock with CCs; 293, heart failure and shock without CCs/MCCs; 194, simple pneumonia and pleurisy with CCs; 195, simple pneumonia and pleurisy without CCs/MCCs; 690, kidney and urinary tract infections without MCCs; 191, COPD with CCs; 192, COPD without CCs/MCCs; and 603, cellulitis without MCCs.

Patients in the inpatient cohort also needed to have a LOS of fewer than 4 days. Additionally, inpatient cohort designees could not have a procedure code that may have required an overnight stay.

To generate a list of codes representing events that normally would require an overnight hospital stay, we first tabulated all 

International Classification of Diseases, Ninth Revision

) procedure codes, revenue center codes, and Healthcare Common Procedure Coding System (HCPCS) codes associated with hospitalizations for CHF, pneumonia, UTI, COPD, and cellulitis. Then, 2 healthcare practitioners, an emergency physician and a critical care nurse (J.H. and A.R.S.), each with clinical and research experience, independently examined these codes to identify procedures that conservatively would require a hospital overnight stay. Their resulting list of exclusion codes were then presented to a second emergency physician, not connected to the research team, for review. A final list was developed by these 3 clinicians based on discussion and consensus. A complete list of exclusion codes is listed in the 

Patients assigned to the home health cohort were those transitioned directly to home health care following an ED visit. To be included in this cohort, patients needed to have a visit by a home health provider within 2 days of an ED visit with CHF, pneumonia, UTI, COPD, or cellulitis as the principal diagnosis by CCS grouping in both the ED and the home health claims. The 2-day limit was imposed to ensure that only home health visits immediately after ED visits were included and no other claims from other providers were processed. To ensure that the 2 cohorts were comparable, we applied the same exclusion criteria to the home health cohort as we applied to the inpatient cohort, including procedure codes, revenue center codes, and HCPCS codes, and exclusion of any patient with metastatic cancer or Medicare eligibility due to ESRD.

This research compares the costs and utilization for the 2 cohorts. Cost variables include the amount of out-of-pocket (OOP) dollars the patient paid and the amount of Medicare reimbursement, including total reimbursements for the episode of care and costs in the 90 days following the index visit. The cost for the index episode of care included all facility fees, physician fees, and durable medical equipment. The 90-day follow-up costs included all costs from inpatient, outpatient, skilled nursing facility, home health, and hospice claims from both institutional and carrier claims. We used 2 common hospital measures to track utilization within 90 days of the index visit: (1) percent of inpatient hospital readmissions and (2) percent of ED revisits.

Regression models were used to compare the costs and utilization of the ED/hospital for the 2 cohorts. Ideally, the characteristics of these cohorts should be comparable. To account for differences among member profiles of the 2 cohorts, we used propensity scoring to balance the characteristics of the cohorts. The propensity score indicates the probability of a patient being in a group after all observable characteristics are controlled for. Through this technique, researchers can conveniently control for the probability of belonging in the case group (home health) or the control group (inpatient) and create a quasi-experimental design.

Typically, a logistic model is used to generate a propensity score. In this research, a simple logistic model was problematic in generating propensity scores because the sample sizes of the patient cohorts were imbalanced. The number of observations in the inpatient cohort was about 50 times the number of observations in the home health cohort. Ideally, the probability of being in a group should be higher than 0.1 because the logistic model may underestimate rare events.

 To address this issue, we first drew a 10% random sample from the inpatient cohort, then we combined this 10% random sample with the home health cohort and used a logistic model to calculate the probability of being in the home health cohort.

 Covariates for the propensity score model included age, gender, Medicaid eligibility, geographic locations, types of disease, a history of hospitalization, and Charlson Comorbidity Index (CCI) score.

We used a propensity score—weighted regression to evaluate costs and utilization of services.

 The propensity score weighting method was used for 2 reasons. First, there were relatively few observations in the home health cohort. Using propensity score matching further risked reducing the number of observations in the home health cohort. In addition, research has shown that propensity score weighting is more robust to misspecifications of regression models. It is more likely to generate an unbiased estimate of treatment effect, even when the model is not specified correctly.

A summary of the descriptive statistics by cohort, weighted and unweighted (available in eAppendix), shows that before propensity score weighting, observations in the 2 cohorts were different in several ways, including demographics, comorbidities, Medicaid eligibility, and geographical locations. Upon weighting, cohorts were more evenly balanced. The differences in age groups, average age, gender, Medicaid status, principal diagnosis, CCI score, and history of hospitalization became statistically nonsignificant. The only remaining statistical differences were geographical location and having a comorbid cancer diagnosis. Most important, once these groups were weighted, they had similar propensity scores: 0.0198 for inpatient versus 0.0200 for home health (

 = .812). This means that after weighting, patients in the inpatient cohort had near identical opportunities to be in the home health cohort.

The average actual OOP costs for the patient, average Medicare payment, and total average reimbursement for the index episode and the 90-day follow-up period are shown in 

. The average reimbursements for the home health cohort were much lower than those for the inpatient cohort. The average Medicare reimbursements for the home health cohort were $986 for patient OOP costs, $12,025 for the episode, and $13,012 for the 90-day total. For the inpatient cohort, reimbursements were $1965, $18,248, and $20,325, respectively (

 summarizes the 90-day inpatient hospital readmissions and ED revisits. Patients in the home health cohort had lower inpatient hospital readmissions at 23.7% (n = 84) compared with 32.8% (n = 5790) readmissions for patients in the inpatient cohort (odds ratio [OR], 3.796; 

 <.0001). There were no statistically significant differences in 90-day ED revisits between the home health cohort at 39.0% (n = 138) and the inpatient cohort at 42.7% (n = 7496) (

provides least squares mean linear regression modeled average cost differences for the index episode and the 90-day follow-up period: OOP, Medicare reimbursements, and total reimbursements comparing the inpatient and home health cohorts.

Propensity score—weighted regressions were used to model the impact of discharges to home health as opposed to inpatient admissions from the ED. The average OOP costs to the patient, Medicare payment, and total reimbursement difference for the index episode and 90-day follow-up were $975, $6338, and $7425, respectively, which means that the inpatient cohort costs were greater than those of the home health cohort with statistical significance (

The ORs for ED revisits and inpatient hospitalization within the 90-day period are reported in 

. Overall, compared with the home health cohort, patients in the inpatient cohort were more likely to be readmitted to the hospital (OR, 1.535;

<.0001). The OR for ED visits in the 90-day follow-up period was not statistically significant, at 1.806 (

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.

 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.

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.

 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, 

 procedure codes, and HCPCS codes that would not require hospitalization. In addition, we included patient age, gender, payer status, hospitalization history, principal 

 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.

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.

This work was solely funded by the Gary and Mary West Health Institute.

 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.

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).

Tyler Kent, BS, Gary and Mary West Health Institute, 10350 N Torrey Pines Rd, La Jolla, CA 92037. Email: tkent@westhealth.org.

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. commonwealthfund.org/interactives/2017/july/mirror-mirror. Published July 2017. Accessed March 28, 2018.

2. Medical cost trend: behind the numbers 2018. PwC website. pwc.com/us/en/health-industries/health-research-institute/behind-the-numbers/reports/hri-behind-the-numbers-2018.pdf. Published June 2017. Accessed March 28, 2018.

3. Office of the Actuary, National Health Statistics Group. National health care spending in 2016. CMS website. cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/NHE-Presentation-Slides.pdf. 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. 

. 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. 

. 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. 

. 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. 

. 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. 

. 2008;56(3):493-500. doi: 10.1111/j.1532-5415.2007.01562.x.

A retrospective analysis of Medicare claims was used to study emergency department (ED) dispositions, specifically evaluating inpatient admissions compared with home health referrals.

Improved Cost and Utilization Among Medicare Beneficiaries Dispositioned From the ED to Receive Home Health Care Compared With Inpatient Hospitalization