Findings from the implementation of a validated readmission predictive tool in the discharge workflow of a medical intensive care unit.
BACKGROUND
Provider decisions about patients to be discharged from the intensive care unit (ICU) are often based on subjective intuition, sometimes leading to premature discharge and early readmission. The Stability and Work Load Index for Transfer (SWIFT) score, as a risk stratification tool, has moderate ability to predict patients at risk of ICU readmission.


OBJECTIVE
To describe findings following the incorporation of the SWIFT score into the discharge workflow of a medical ICU.


METHODS
The study involved 5,293 consecutive patients discharged alive from the medical ICU of an academic medical center. The SWIFT score and associated percentage risk for readmission were incorporated into daily rounds for purpose of discharge decision-making. We measured readmission rates before and after implementation and observed changes in provider discharge decisions for individual patients after SWIFT discussions.


RESULTS
Baseline (n = 1,906) and implementation (n = 1,938) cohorts differed with respect to APACHE III scores (P = 0.03). In the implementation cohort, 26.2% of subjects had SWIFT scores greater than 15 and thus were predicted to have a higher risk of unplanned readmissions. In this high-risk group, 25% had SWIFT discussed in their discharge planning. There was modification of provider discharge decisions in 108 (30%) of cases in which the SWIFT was discussed. SWIFT score values above a prespecified cutoff of 15 were associated with physician tendency to prolong ICU stay or to discharge to a monitored setting (P < 0.001). There was no difference in 24-hour or 7-day readmission rates between the baseline and implementation cohorts (1.9 vs. 2.4%, P = 0.24; 6.5 vs. 7.4%, P = 0.26, respectively) even after adjustment for severity of illness.


CONCLUSIONS
Using the SWIFT score as an adjunct to clinical judgment, physicians modified their discharge decisions in one-third of subjects. Introducing such tools into the discharge workflow may present change management challenges that limit the evaluation of their impact on readmission rates and other relevant ICU outcomes.
1 Division of Critical Care Medicine, Geisinger Medical Center, Danville, Pennsylvania.
Uchenna R
UR
Ofoma
Subhash
S
Chandra
Rahul
R
Kashyap
Vitaly
V
Herasevich
Adil
A
Ahmed
Ognjen
O
Gajic
Brian W
BW
Pickering
Christopher J
CJ
Farmer
D005260:Female; D005500:Follow-Up Studies; D006305:Health Status Indicators; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration*; D007902:Length of Stay / Q000639:trends; D008297:Male; D008875:Middle Aged; D010351:Patient Discharge / Q000592:standards*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D057188:Workflow
care transitions; quality; readmissions; risk stratification