Being A Lean Mean Discharging Machine

Emma Misra1, Hiral Choksi, MD, FHM, FACP2, 1SSM Health St. Louis University Hospital, St. Louis, MO; 2Saint Louis University, MO

Meeting: Hospital Medicine 2018; April 8-11; Orlando, Fla.

Abstract number: 247

Categories: Innovations, Quality Improvement, Uncategorized

Keywords: , , , ,

Background: Long discharge times (DT), (the time from discharge order to patient leaving room), have detrimental impacts on any hospital. Apart from causing dissatisfaction among patients and their families who are waiting to go home, prolonged DT also increases wait times for patients being admitted from the ED. Delayed admissions pursuant to late discharges, caused by a complex process, over-burdens the meager evening staff. With a daily high census of patients, discharging patients timely and safely is of utmost priority.

Purpose: Our 356 bed Academic Medical Center and Level 1 Trauma Center averages 1630 discharges per month, of which 35% are from the General Internal Medicine (GIM) Division; averaging DT of 324 minutes. We launched a multidisciplinary quality improvement initiative to decrease our DT and increase number of discharges before noon and 3 pm for the GIM teams. The multidisciplinary team applied principles of Lean A3 Problem Solving to design corrective interventions.

Description: Following interventions were trialed on the pilot unit:1 Resident teams to communicate daily with patients’ nurses, social worker, case manager regarding plan of care
2 Residents to enter discharge order during unit rounds
3 Follow up appointments scheduled by discharge scheduler instead of residents
4 Charge nurse to monitor patients’ needs on discharge scorecard starting from day of admission
5 Designated discharge wheelchair per unit
6 Cab vouchers for patients waiting more than 3 hours for family pick up
7 Removed redundant step of nursing ‘discharge time out’

Results: On evaluation, the total number of discharge process steps reduced by 50% (85 to 42) (Fig.1). The pilot unit, pre-intervention, averaged 42 discharges per month (n) with average DT (x) of 559 minutes (SD=1486.3, Median=194.4). Post interventions DT improved by 73%, statistical significance of p=0.0009 (n=45, x=149.2, SD=119.6, Median=120). On comparison with another unit of similar patient demographic (n=43, x=268.2, SD=339, Median=178.4), during the post intervention period, DT was significantly lower (p=0.0239). Eliminating nursing ‘discharge time out’ redundant step reduced 35 minutes, on average, from DT. Discharges before 3 pm steadily increased from 33% to 59% (p=0.062) post interventions. However, discharges before noon did not significantly improve (from 8% to 17%).

Conclusions: Our initiative decreased DT by nearly 7 hours and consequently the length of stay. Hence, a multidisciplinary approach backed by lean methodology makes discharge process efficient and replicable in all units of the hospital.

IMAGE 1: Fig. 1 – Discharge Process Flow Map Post Intervention

IMAGE 2: Fig. 2 – Tables Showing Discharge Times and Discharges before Noon and 3 pm Post Intervention

To cite this abstract:

Misra, E; Choksi, H. Being A Lean Mean Discharging Machine. Abstract published at Hospital Medicine 2018; April 8-11; Orlando, Fla. Abstract 247. Accessed May 24, 2019.

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