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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
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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.
Subsequently, the algorithm was embedded in the convener’s proprietary software platform used by bundled payment program operating personnel to identify and manage patients during the bundle episode. These users—personnel at the convener’s BPCI episode–initiating hospitals—were trained in the use of the tool, which involved a webinar, a user’s manual, and targeted face-to-face and remote individualized education and support. Training addressed how and when to populate the components of the algorithm and also the importance of creating a process to discuss the tool’s inputs and recommendation with the interdisciplinary discharge team, including the patient/surrogate decision maker. Although all episode-initiating hospitals were encouraged to use the tool, only a subset did so, which was at the discretion of the organizations’ leadership.

Data: Medicare

All Part A and Part B claims for the convener’s population of fee-for-service Medicare BPCI patients encompassing acute hospitalization and the subsequent 90-day period occurring between January 1, 2016, and March 31, 2017, were accessed. After deleting claims with incomplete information, there were 148,385 episodes available for analysis. During the same time period, a subset of 15,887 patients in this population were tested using the CDS tool. Our analytic data set consists of Medicare claims data on 132,498 episodes that did not receive the CDS tool and Medicare claims data plus CDS testing results for the 15,887 episodes that did receive the CDS tool.

For each episode, we examined the following outcomes: allowed payment amounts (spending), discharge disposition (home, home health agency, or postacute facility), readmission within 90 days, and postdischarge ED visits not associated with a readmission within 90 days of hospital discharge.

Propensity Model

We used propensity modeling with inverse probability weighting (IPW) for each analysis26,27; propensity to follow the CDS recommendation was used in the concordant versus discordant analysis, and propensity to use the test was used in the intention-to-treat (ITT) analysis (tested vs untested). We used logistic regression for both propensity models. The following variables were included as independent variables: length of stay, race, sex, dual-enrolled status (binary), prior system utilization (the number of days spent as a hospital inpatient in the year prior to the index hospitalization), entered through the ED, clinical episode name, Medicare Severity Diagnosis Related Group tier (no comorbid complication, comorbid complication, major comorbid complication), and primary diagnosis code. In all analyses, we used IPW to compute average treatment effect among the treated (ATT) estimates. Although these variables were all selected a priori without first examining their relationship to the probability of testing, most of them are significantly associated with it.

Statistical Approach

We performed 2 analyses of the effect of the CDS tool’s use on patient outcomes: concordant versus discordant and tested versus untested.

Concordant Versus Discordant

Concordant was defined as discharge disposition agreeing with the CDS tool’s recommendation, whereas discordant was defined as discharge disposition disagreeing with the recommendation. We used IPW regression (spending) and logistic regression (ED use and readmissions) to test for the effect of agreeing with the CDS tool recommendation on outcomes.

Within this analysis, we also examined the impact of more intense and less intense levels of care. In an attempt to control for hospital-level differences, we performed a post hoc analysis of CDS-concordant versus CDS-discordant cases, showing results for when the hospital is controlled for as a main effect.

A more intense level of care is defined as either (1) the CDS tool recommends home and the actual disposition was either home health agency or postacute facility or (2) the CDS tool recommends home health agency and the actual disposition was postacute facility. A less intense level of care is defined as either (1) the CDS tool recommends postacute facility and the actual disposition was either home health agency or home or (2) the CDS recommends home health agency and the actual disposition was home. In these models we included the impact of more or less intense levels of care on the outcomes.


 
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