Inpatient disposition classification for the creation of hospital surge capacity: a multiphase study.
BACKGROUND
The ability to provide medical care during sudden increases in patient volume during a disaster or other high-consequence event is a serious concern for health-care systems. Identification of inpatients for safe early discharge (ie, reverse triage) could create additional hospital surge capacity. We sought to develop a disposition classification system that categorises inpatients according to suitability for immediate discharge on the basis of risk tolerance for a subsequent consequential medical event.


METHODS
We did a warfare analysis laboratory exercise using evidence-based techniques, combined with a consensus process of 39 expert panellists. These panellists were asked to define the categories of a disposition classification system, assign risk tolerance of a consequential medical event to each category, identify critical interventions, and rank each (using a scale of 1-10) according to the likelihood of a resultant consequential medical event if a critical intervention is withdrawn or withheld because of discharge.


RESULTS
The panellists unanimously agreed on a five-category disposition classification system. The upper limit of risk tolerance for a consequential medical event in the lowest risk group if discharged early was less than 4%. The next categories had upper limits of risk tolerance of about 12% (IQR 8-15%), 33% (25-50%), 60% (45-80%) and 100% (95-100%), respectively. The expert panellists identified 28 critical interventions with a likelihood of association with a consequential medical event if withdrawn, ranging from 3 to 10 on the 10-point scale.


CONCLUSIONS
The disposition classification system allows conceptual classification of patients for suitable disposition, including those deemed safe for early discharge home during surges in demand. Clinical criteria allowing real-time categorisation of patients are awaited.
Kelen
Gabor D
GD
Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD 21287, USA. gkelen@jhmi.edu
Kraus
Chadd K
CK
McCarthy
Melissa L
ML
Bass
Eric
E
Hsu
Edbert B
EB
Li
Guohua
G
Scheulen
James J
JJ
Shahan
Judy B
JB
Brill
Justin D
JD
Green
Gary B
GB
D001509:Bed Occupancy; D003658:Decision Making, Computer-Assisted; D004189:Disaster Planning; D004632:Emergency Medical Services / Q000458:organization & administration*; D006801:Humans; D007297:Inpatients / Q000145:classification*; D010351:Patient Discharge; D011367:Professional Staff Committees / Q000458:organization & administration*; D018570:Risk Assessment / Q000379:methods* / Q000458:organization & administration; D012720:Severity of Illness Index; D014218:Triage / Q000379:methods* / Q000458:organization & administration