Improving Patient Flow: Analysis of an Initiative to Improve Early Discharge
BACKGROUND: Discharge delays adversely affect hospital bed availability and thus patient flow.
OBJECTIVE: We aimed to increase the percentage of early discharges (EDCs; before 11 am). We hypothesized that obtaining at least 25% EDCs would decrease emergency department (ED) and postanesthesia care unit (PACU) hospital bed wait times.
DESIGN: This study used a pre/postintervention retrospective analysis.
SETTING: All acute care units in a quaternary care academic children’s hospital were included in this study.
PATIENTS: The patient sample included all discharges from the acute care units and all hospital admissions from the ED and PACU from January 1, 2014, to December 31, 2016.
INTERVENTION: A multidisciplinary team identified EDC barriers, including poor identification of EDC candidates, accountability issues, and lack of team incentives. A total of three successive interventions were implemented using Plan–Do-Check-Act (PDCA) cycles over 10 months between 2015 and 2016 addressing these barriers. Interventions included EDC identification and communication, early rounding on EDCs, and modest incentives.
MEASUREMENTS: Calendar month EDC percentage, ED (from time bed requested to the time patient left ED) and PACU (from time patient ready to leave to time patient left PACU) wait times were measured.
RESULTS: EDCs increased from an average 8.8% before the start of interventions (May 2015) to 15.8% after interventions (February 2016). Using an interrupted time series, both the jump and the slope increase were significant (3.9%, P = .02 and 0.48%, P < .01, respectively). Wait times decreased from a median of 221 to 133 minutes (P < .001) for ED and from 56 to 36 minutes per patient (P = .002) for PACU.
CONCLUSION: A multimodal intervention was associated with more EDCs and decreased PACU and ED bed wait times.
© 2019 Society of Hospital Medicine
Measures
Our primary outcome was percentage of EDCs (based on the time the patient left the room) across acute care. Secondary outcome measures were median wait times for an inpatient bed from the ED (time bed requested to the time patient left the ED) and the average PACU wait time (time the patient is ready to leave the PACU to time the patient left the PACU) per admitted patient. We also assessed balancing measures, including discharge satisfaction, seven-day readmission rates, and LOS. We obtained the mean discharge satisfaction score from the organization’s Press Ganey survey results across acute care (the three discharge questions’ mean – “degree … you felt ready to have your child discharged,” “speed of discharge process …,” and “instructions… to care for your child…”). We obtained seven-day readmission rates from acute care discharges using the hospital’s regularly reported data. We assessed patient characteristics, including sex, age, case mix index (CMI; >2 vs <2), insurance type (nongovernment vs government), day of discharge (weekend vs weekday), and LOS from those patients categorized as inpatients. Complete patient characteristics were not available for observation (InterQual® criteria) status patients.
Analysis
We used descriptive statistics to describe the inpatient population characteristics by analyzing differences when EDC did and did not occur using chi-square and the Mann–Whitney U tests. Patients with missing data were removed from analyses that incorporated patient factors.
To assess our primary outcome, we used an interrupted time series analysis assessing the percentage of EDC in the total population before any intervention (May 2015) and after the last intervention (March 2016). We used the Durbin–Watson statistic to assess autocorrelation of errors in our regression models. As we had only patient characteristics for the inpatient population, we repeated the analysis including only inpatients and accounting for patient factors significantly associated with EDC.
As units and physician teams had differential exposure to the interventions, we performed a subanalysis (using interrupted time series) creating groups based on the combination of interventions to which a patient’s discharge was exposed (based on unit and physician team at discharge). Patient discharges from group 1 (medical patients on Units C, D, and E) were exposed to all three interventions, group 2 patient discharges (medical patients on Units A and B) were exposed to interventions 2 and 3, group 3 (cardiology, hematology/oncology, surgical patients on Units A and B) were exposed to intervention 3, and group 4 (surgical, cardiology, hematology/oncology patients on Units C, D, and E) were exposed to interventions 1 and 3 (Figure 1). Interrupted time series models were fit using the R Statistical Software Package.20
Because of seasonal variation in admissions, we compared secondary outcomes and balancing measures over similar time frames in the calendar year (January to September 2015 vs January to September 2016) using the Mann–Whitney U test and the unpaired t-test, respectively.
The project’s primary purpose was to implement a practice to improve the quality of care, and therefore, the Stanford Institutional Review Board determined it to be nonresearch.
