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The American Journal of Managed Care June 2019
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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
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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.
ABSTRACT

Objectives: To assess the association of a clinical decision support (CDS) algorithm for hospital discharge disposition with spending, readmissions, and postdischarge emergency department (ED) use.

Study Design: A retrospective study in a cohort of fee-for-service Medicare patients 65 years or older linked to a database of patients receiving CDS.

Methods: We evaluated (1) patients whose discharge disposition was concordant with the CDS recommendation versus those whose disposition was not and (2) patients receiving CDS for discharge disposition versus those not receiving CDS, regardless of concordance. Outcomes were spending over a 90-day episode, 90-day readmissions, and postdischarge ED utilization not associated with a readmission.

Results: Analysis of concordant versus discordant cases showed decreased spending for concordant cases ($860 savings; 95% CI, $162-$1558; P = .016), a decrease in readmissions (adjusted odds ratio [OR], 0.920; 95% CI, 0.850-0.995; P = .038), and no change in rate of postdischarge ED use (adjusted OR, 0.990; 95% CI, 0.882-1.110; P = .858). Analysis of patients receiving CDS versus not receiving CDS showed no significant difference in spending ($221 savings; 95% CI, –$115 to $557; P = .198), ED use (adjusted OR, 0.959; 95% CI, 0.908-1.012; P = .128), or readmission rate (adjusted OR, 1.004; 95% CI, 0.966-1.043; P = .840).

Conclusions: Following the recommendation of a CDS algorithm for hospital discharge disposition was associated with lower spending, fewer readmissions, and no change in ED use over a 90-day episode of care.

Am J Manag Care. 2019;25(6):288-294
Takeaway Points

Following the recommendation of a clinical decision support (CDS) algorithm for hospital discharge disposition was associated with lower spending and reduced readmissions with no change in emergency department (ED) use.
  • A CDS algorithm incorporating cognition, ambulation, activities of daily living, capable caregiver availability, skilled therapy needs, and skilled nursing needs was evaluated in 15,887 patients participating in Medicare’s bundled payment program.
  • Following the algorithm’s recommended level of care (home vs home with home health agency vs postacute facility) was associated with an $860 decrease in spending, fewer readmissions, and unchanged postdischarge ED use over a 90-day episode compared with those patients for whom the recommended level of care was not followed.
  • The judicious use of postacute care resources by the hospital discharge team can be enhanced by using a CDS algorithm.
Spending on postacute care accounts for a substantial portion of overall healthcare costs and is growing faster than other spending categories.1,2 For conditions like pneumonia, chronic obstructive pulmonary disease, heart failure, and joint replacement, Medicare spends nearly as much in the 30 days after discharge as it does during hospitalization.3 Moreover, postacute costs are associated with large geographic variation across the United States, with three-fourths of all regional variation in Medicare spending attributable to postacute care spending.4

Achieving the judicious, appropriate use of resources following hospitalization can be a critical success factor for organizations participating in full risk capitation and in payment models such as accountable care organizations and bundled payments. Additionally, Medicare spending per beneficiary—a measure of spending encompassing the 30 days subsequent to hospitalization—is publicly reported and tied to financial incentives for all US Inpatient Prospective Payment System hospitals as part of Hospital Value-Based Purchasing and is a component of the physician payment formula under the Merit-based Incentive Payment System.5

Despite growing pressure for hospitals to develop a systematic approach to using postacute care for all patients, most published studies examining such approaches address a single diagnosis, such as stroke or joint replacement; describe a method that may be too complex and time-consuming for widespread use; or rely on information that is unavailable early in the hospitalization, when factors influencing discharge destination may be modified.6-12

We sought to build a clinical decision support (CDS) algorithm to assist hospital discharge planning teams in identifying the most appropriate discharge care level while avoiding untoward effects such as increases in readmissions, emergency department (ED) use, and overall spending. To assess the algorithm, as a convener in CMS’ Bundled Payments for Care Improvement initiative (BPCI),13 we accessed Medicare claims data for acute hospitalizations and the subsequent 90-day period.

We evaluated the effect of the algorithm on spending, 90-day readmissions, and postdischarge 90-day ED use in cases in which discharge disposition was concordant or discordant with the algorithm’s recommendation and in which the algorithm was or was not used (regardless of concordance).

METHODS

Setting

The setting of this study was acute hospitalization and the 90-day period following discharge, encompassing patient care in the home, home with a home health agency (hereafter written simply as home health agency), and postacute facility (including skilled nursing facilities, inpatient rehabilitation facilities, and long-term acute care hospitals).

Instrument

We developed a proprietary CDS tool incorporating an algorithm to help teams determine an appropriate level of care following hospital discharge. Inputs to the algorithm were ambulatory status; ability to perform activities of daily living; cognitive status; availability of a capable caregiver; postacute physical, occupational, and speech therapy needs; and postacute skilled nursing needs. In developing the CDS tool, we reviewed the literature to identify patient-level factors to serve as the basis of decision support.14-19 Caregiver support was also recognized as critical to effective discharge planning.20-24 We convened experts in home health, postacute facility care, and hospital care in a series of working sessions to finalize factors driving a decision to discharge patients to 1 of 3 options. Other potentially informative factors, including comorbidities, polypharmacy, environmental factors (eg, stairs, home modifications), and social determinants, were intentionally omitted to increase the tool’s ease of use. After assessing the tool for user requirements and reproducibility of results across users, we created a scoring system based on the identified inputs that yielded a recommendation for 1 of 3 postdischarge care intensity levels: home, home health agency, or postacute facility.

A pilot was then conducted, using an analysis of 1537 BPCI patients to whom the CDS tool was retrospectively applied. This pilot yielded a proposal that made recommendations as follows (results shown as pilot vs controls): More patients go home or to a home health agency (home, 50.6% [95% CI, 47.7%-53.6%] vs 36.1% [95% CI, 32.8%-39.5%]; home health agency, 32.5% [95% CI, 29.1%-35.1%] vs 26.0% [95% CI, 16.8%-24.4%]), and fewer go to a postacute facility (16.9% [95% CI, 13.1%-20.8%] vs 43.3% [95% CI, 40.2%-46.5%]). The tool’s performance was then evaluated using risk-adjusted regression models created to predict the rate of 90-day readmissions for patients discharged to home, a home health agency, and a postacute facility. These rates were compared with observed 90-day readmissions and found to be not statistically different across the 3 discharge dispositions: home (32.4% [95% CI, 30.4%-34.4%] vs 32.8% [95% CI, 29.5%-36.0%]), home health agency (33.1% [95% CI, 31.1%-35.1%] vs 32.0% [95% CI, 27.5%-36.4%]), and postacute facility (33.3% [95% CI, 31.4%-35.3%] vs 31.7% [95% CI, 28.7%-34.7%]).25


 
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