Theera-Ampornpunt N, Speedie SM, Du J, Park YT, Kijsanayotin B, Connelly DP. Impact of prior clinical information in an EHR on care outcomes of emergency patients. Paper presented at: Biomedical and Health Informatics - From Foundations to Applications to Policy. AMIA 2009 Annual Symposium; 2009 Nov 14-18; San Francisco, CA. Based on Theera-Ampornpunt N, Speedie SM, Du J, Park YT, Kijsanayotin B, Connelly DP. Impact of prior clinical information in an EHR on care outcomes of emergency patients. AMIA Annu Symp Proc. 2009 Nov:634-8. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815461/

1. Impact of Prior Clinical
Information in an EHR on C
I f ti i Care
Outcomes of Emergency Patients
AMIA 2009 November 16, 2009
Nawanan Th
N Theera‐Ampornpunt, MD, MS
A t MD MS
Stuart M. Speedie, PhD
Jing Du, MPH
Young‐Taek Park, MPH
Boonchai Kijsanayotin, MD, PhD
Donald P. Connelly, MD, PhD
Donald P. Connelly, MD, PhD
1

2. Background
• Continuity of care is critical to healthcare
y
quality & efficiency
• Existence of prior history enhances
continuity of care, potentially improving
quality & efficiency
– Preventing redundant tests
– Helpful past diagnoses
– Allergies & medication lists fewer errors
2

3. Emergency Departments (ED)
• Prone to errors because of
– Urgent nature
– Limited patient information
– Time & resource constraints
• Critical transition point from ambulatory to
emergency & inpatient settings
• Limited/unreliable self reported history
self-reported
• 32% of ED visits had information gaps
which can lead to prolonged ED stay
hi h l dt l d t
Stiell A et al. CMAJ. 2003;169:1023-8.
3

4. Mixed Effects of Electronic Records
• Information access via HIE leads to more
ED visits & hospitalizations among
medically indigent adults
y g
Vest JR. J Med Syst. 2009;33:223-31.
• Automated records in inpatient setting
p g
associated with decreased mortality &
costs but no effect on LOS and increased
CHF complications
Amarasingham R et al. Arch Intern Med. 2009;169:108-14.
4

5. Study Objective
• To evaluate impact of p
p prior clinical
information readily available in an EHR on
q
quality & efficiency of care in ED
y y
• Focus: 3 chronic diseases
– Congestive heart failure (CHF)
– Diabetes
– A th
Asthma
5

6. Methods
• Site: 3 large, metro hospital EDs
• Time Period: Jun. 2006 - Jun. 2007
• Data Source: Billing & clinical information
g
systems data
• Index Visit: First ED visit in the time period
• Internal Patients: Those with at least one
substantive encounter in the health system’s
EHR prior to the index visit
• External Patients: Those without such an
encounter prior to the index visit
t i t th i d i it
6

7. Outcome Variables
• Duration of ED visit (hours)
( )
• Hospitalization
• Hospital length of stay in days (LOS)
• Inpatient mortality
• Number of lab test orders
• Number of diagnostic procedures
g
(mostly imaging studies)
7

8. Hypotheses
Internal patients (having prior clinical information
in an EHR) will exhibit
• Shorter ED visit durations
• A lower hospitalization rate
• Shorter hospital LOS
p
• A lower inpatient mortality rate
• Fewer lab test orders
• Fewer procedure orders
than external patients
p
8

9. Analysis
• Logistic regression for mortality &
hospitalization
• Generalized linear model for ED &
inpatient LOS
• Count data models (Poisson, negative
binomial, hurdle regression) for counts
of lab test orders and procedures
• All models adjusted for gender, age,
and comorbidities (Charlson index)
9

10. Descriptive Statistics
Characteristic Site 1 Site 2 Site 3
N 1,957 2,050 2,136
Mean age (years) ± SD 57.8 22.6
57 8 ± 22 6 50.9 20.0
50 9 ± 20 0 58.3 22.8
58 3 ± 22 8
% females 59.2% 52.3% 62.2%
Mean Charlson index ± SD 1.6 ± 1.3 1.1 ± 0.5 1.3 ± 0.7
% internal patients 70.8%
70 8% 85.0%
85 0% 47.4%
47 4%
Hospitalization rate 27.9% 47.2% 61.6%
10

11. Outcomes: Internal vs External Pts.
Outcome Site 1 Site 2 Site 3
Inpatient Mortality
Hospitalization C A
ED Visit Duration A
Inpatient LOS D A
Count of
C D A
Lab Orders
Count of
D A
Procedure Orders
A - Asthma C - Congestive Heart Failure D - Diabetes
Significant in hypothesized direction
Significant opposite hypothesized direction 11

12. Conclusions
• There is some evidence that prior
p
clinical information in an EHR is
associated with improvement in certain
p
outcomes
• But...effects not consistent
– Across study sites
– Across disease groups
• Study shows mixed effects
12

13. Discussion
• Possible underlying organizational
y g g
characteristics contributing to
inconsistent effects
– Organizational structures, policies,
workflows, and provider practice styles
– Differences in how IT is used
– Different patient demographics
p g p
• Variation of effects for different
diseases
13

14. Limitations
• Limited availability of potential
y p
confounders in secondary data
• Pattern of information access and use
by ED physicians not captured
• Heterogeneity among study sites may
also contribute to the observed mixed
effects
14

15. Preliminary Results from
Second Round of Data (not in p p )
( paper)
• Expanded timeframe, larger sample size
• Cl
Cleaner data and availability of additional d t
d t d il bilit f dditi l data
about confounding variables (e.g. race, insurance
status,
status marital status)
• Analysis currently available for 1 study site (Site 3)
• Most results are significant in hypothesized
directions
15 15

16. Acknowledgments
• Bryan Dowd, Bonnie Westra, Kevin
Peterson, and D i l R h f
P d Daniel Routhe from
University of Minnesota
• St ff from 3 participating health systems
Staff f ti i ti h lth t
• Project’s board members
• Thi project was f d d i part under
This j t funded in t d
grant number UC1 HS16155 from the
Agency for Healthcare Research and
Quality, Department of Health and Human
Services.
16

17. Outcomes: Internal vs External Pts.
Inpatient Inpatient
Patient Hospitalization ED LOS
Site Mortality
y LOS
Subgroup
S bgro p Odds R ti
Odd Ratio Change
Ch
Odds Ratio Change
1 CHF 0.21 0.82 1.10 0.91
Diabetes 0.63 1.16 1.03 0.70
Asthma N/A 1.42 1.05 1.09
2 CHF 1.56 0.76 1.00 1.09
Diabetes 0.66 0.72 1.10 1.07
Asthma 0.33 0.70 1.08 1.09
3 CHF 0.59 0.35 1.02 1.00
Diabetes 0.57
0 57 1.37
1 37 0.99
0 99 1.17
1 17
Asthma N/A 1.68 1.11 1.21
Cell values represent change in odds or LOS by that factor for internal (vs external) patients
Bold significant at p ≤ 0.05
g
Significant in hypothesized direction
Significant opposite hypothesized direction 17

18. Outcomes: Internal vs External Pts.
Change in Change in Count of
Count of Lab Orders Procedure Orders
Patient
Site Poisson/ Poisson/
Subgroup Logit Logit
Negative Negative
Part Part
Binomial Part Binomial Part
1 CHF 0.70 0.85 0.89
Diabetes 0.71 1.00 1.01
Asthma 0.74 1.06 1.07
2 CHF 0.92 1.21 0.94
Diabetes 1.05
1 05 0.94
0 94 1.31
1 31 0.71
0 71
Asthma 1.09 0.98 1.24 0.47
3 CHF 0.94 0.98 0.46 0.90
Diabetes 0.92 0.97 0.94 0.87
Asthma 0.54 0.99 0.71 1.11
Whether a specific outcome has 1 or 2 parts depends on the best fit count data model
Cell values for the logit part represent odds ratio (internal vs external pts.) of having zero count
Cell values for the Poisson or negative binomial part represent change in count by that factor
for internal patients among patients with positive counts
Bold significant at p ≤ 0.05
Hypothesized direction 18
Opposite hypothesized direction