A discussion about how emergency department (ED) health information exchange (HIE) implementations affect lengths of stay (LOS) in MA hospitals.

1. Does Health Information
Exchange Move Patients Through
Emergency Departments Faster?
Discussant: Peiyin Hung
(University of Minnesota)
Workshop on Health IT and Economics 2014
Alexandria, VA
October 10, 2014

2. Summary-Variables
Key Variables
Outcome Length of stay defined as duration
between admission and discharge
Treatment Health information exchange in a
given year 2009,2010, 2011
Compared to patients in hospitals
without HIE in all years
Crowdedness Hourly dummy variable indicating
above or below 75% of annual census
Severity Dummy variable indicating 2 or
higher Charlson Comorbidity Index
2

3. Summary-Identification
Main Model: Difference-in-difference model to
evaluate average treatment effects on means of LOS
Hierarchical model with the patient-visit as
unit of analysis
3

4. Primary Questions
Q1: How patients flow in ED?
Q2: Which clinical conditions matter?
Q3: How was patient clustering in a
hospital addressed?
Q4: Is it possible that high-efficiency
hospitals are more amenable to HIE
adoptions?
Q5: Year of HIE adoption vs. Number of
years adopted
4

5. Patient Flows in Emergency
Departments
Registratio
n/Check in
Triage
Handover/
Assessment
Examination
Inpatient
Admission
Referral/
Discharge
Treatment
5

6. Length of Stay
by Clinical Condition and Severity
Average Length of Stay in Emergency
Department (2010)
Hours)
(12
Stays of Length Obstetric Patients Chest Pain Patients 18
5.1
8.2
Low-risk
(Vaginal
Delivery)
High-risk
(Cesarean
Delivery)
Low-risk High-risk
6

7. Repeated ED Visits
Hospital
A
Patient 1
Visit 1
Visit 2
Patient 2 Visit 1
Hospital
B
Patient 3
Visit 1
Visit 2
Visit 3
Patient 4
Visit 1
Visit 2
7

8. Reminder of
Identification Method
Main Model: Difference-in-difference model to
evaluate average treatment effects on means of LOS
Hierarchical model with the patient-visit as
unit of analysis
8

9. Other Identification
Suggestions
• HIE implementers by number of years
adopted
• Ordinal Severity Scores
– AHRQ’s Emergency Severity Index
• Patients visits nested in a patient and
clustered in a hospital
– Generalized Estimating Equations
• Multicollinearity between covariates
– E.g. Community income and patient
income
• Stratify by clinical conditions
– Which condition would need more information? 9

10. Future Extensions
• Distinct treatment effects on LOS
– Lagged effects (how many years would
significant effects in place? By how much?)
– Hospital inpatient and ED integration
– Effects by exchangeability of scope
• Within and outside of hospital systems
• Ambulatory providers in and outside of a system
– Effects by discharged vs. admitted patients
• Other intermediate effects
– Simultaneous effects of HIE on quality and
volume on efficiency
10