Risk prediction of emergency department revisit 30 days post discharge: a prospective study.
BACKGROUND
Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization.


RESULTS
A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns.


CONCLUSIONS
Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.
HBI Solutions Inc., Palo Alto, California, United States of America; Department of Surgery, Stanford University, Stanford, California, United States of America.
Shiying
S
Hao
HBI Solutions Inc., Palo Alto, California, United States of America.
Bo
B
Jin
Department of Pediatrics, Stanford University, Stanford, California, United States of America.
Andrew Young
AY
Shin
HBI Solutions Inc., Palo Alto, California, United States of America.
Yifan
Y
Zhao
HBI Solutions Inc., Palo Alto, California, United States of America.
Chunqing
C
Zhu
Department of Surgery, Stanford University, Stanford, California, United States of America.
Zhen
Z
Li
HBI Solutions Inc., Palo Alto, California, United States of America.
Zhongkai
Z
Hu
HBI Solutions Inc., Palo Alto, California, United States of America.
Changlin
C
Fu
HBI Solutions Inc., Palo Alto, California, United States of America.
Jun
J
Ji
Department of Statistics, Stanford University, Stanford, California, United States of America; Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
Yong
Y
Wang
Department of Surgery, Stanford University, Stanford, California, United States of America.
Yingzhen
Y
Zhao
HBI Solutions Inc., Palo Alto, California, United States of America.
Dorothy
D
Dai
HealthInfoNet, Portland, Maine, United States of America.
Devore S
DS
Culver
HealthInfoNet, Portland, Maine, United States of America.
Shaun T
ST
Alfreds
HealthInfoNet, Portland, Maine, United States of America.
Todd
T
Rogow
HBI Solutions Inc., Palo Alto, California, United States of America.
Frank
F
Stearns
Department of Surgery, Stanford University, Stanford, California, United States of America.
Karl G
KG
Sylvester
HBI Solutions Inc., Palo Alto, California, United States of America.
Eric
E
Widen
Department of Surgery, Stanford University, Stanford, California, United States of America.
Xuefeng B
XB
Ling
D004632:Emergency Medical Services; D005260:Female; D006801:Humans; D008281:Maine; D008297:Male; D016347:Medical Records Systems, Computerized; D008962:Models, Theoretical; D011446:Prospective Studies; D012189:Retrospective Studies; D012307:Risk Factors; D013997:Time Factors