https://www.ajmc.com/journals/ajac/2019/2019-vol7-n1/improved-cost-and-utilization-among-medicare-beneficiaries-dispositioned-from-the-ed-to-receive-home-health-care-compared-with-inpatient-hospitalization
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

Healthcare spending in the United States exceeded $3.3 trillion in 2016, which translates into an average of $10,348 per capita annually.1 Despite some slowing in growth in 2016, experts still predict an unsustainable spending trajectory.2 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 2015,3 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.4 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.5-15 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,5,6 improved mortality,6,7 similar readmissions,7 decreased readmissions,8 decreased length of stay (LOS),7,9,10 significant cost reduction,6-11 improved functional recovery,12,13 overall positive provider evaluations,12 lower levels of family member stress,14 and increased patient satisfaction.6,7,15 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.16

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.17 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.

METHODS

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). Table 1 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 (ICD-9) 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 eAppendix (available at ajmc.com).

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.
Outcome Variables

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.18

Analytical Models

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.19,20

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.21,22 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.23 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.24-26 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.25

RESULTS

Descriptive Statistics

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 (P = .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 Figure 1. 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 (P <.0001).

Figure 2 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; P <.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) (P = .2349).

Figure 3 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.

Regression Results

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 (P <.0001).

The ORs for ED revisits and inpatient hospitalization within the 90-day period are reported in Table 2. Overall, compared with the home health cohort, patients in the inpatient cohort were more likely to be readmitted to the hospital (OR, 1.535; P <.0001). The OR for ED visits in the 90-day follow-up period was not statistically significant, at 1.806 (P = .2128).
DISCUSSION

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

Limitations

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.

CONCLUSIONS

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.
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