SUPPLEMENTAL DIGITAL CONTENT
Identifying Repeat Imaging
Patients receiving CT, ultrasound, and chest x-ray imaging were identified using the
HCUP Clinical Classification Software (CCS). We created three dependent variables – one for
each imaging modality – that captured whether the visit included potentially redundant imaging.
This process employed the HCUP Clinical Classification Software (CCS), a tool for grouping
procedures, identified by ICD-9-CM procedure codes, into clinically meaningful groups for
analysis.1 Because not all imaging within the same modality would be clinically equivalent, we
restricted repeat testing to only those studies performed on the same body region (represented by
each row in eTable 1). For CT scans, we used the three CCS categories that identify CT scans of
specific body regions: head, abdomen, and chest. We supplemented these three categories by
creating a fourth for “spine CT” using CPT codes for cervical, thoracic, and lumbar spine CTs.
Similarly, we used the CCS category for abdominal ultrasound, the most common in EDs, and
created a second and third, using CPT codes for pelvis and extremity vein ultrasound studies.
Because x-rays have a great deal of variability in their body region applications we focused
solely on the chest, the most common x-ray obtained in the ED setting.
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eTable 1. Imaging procedure codes used to capture repeat utilization.
Imaging modality Body Region
Computed
Tomography
head
abdomen
chest
spine
HCUP Clinical
Classification
Software codes
177
179
178
CPT codes used to construct
additional categories
72125-72133 (cervical,
thoracic, lumbar spine)
X-rays
(radiographs)
chest
183
Ultrasounds
abdomen
194
pelvis
76801-76819, 76830,
76856, 76815, 76856
93970, 93971, 93965
extremity veins
Methods
Using ordinary least squares with ED fixed effects and ED trends, we estimated the
following linear probability model.
𝑅𝑒𝑝𝑒𝑎𝑡𝐼𝑚𝑎𝑔𝑒𝑖ℎ𝑡 = 𝛽1 𝐻𝐼𝐸ℎ𝑡 + 𝜂𝑆𝑖𝑧𝑒ℎ + 𝜽𝑿𝑖ℎ𝑡 + 𝜏𝑡 + 𝛿𝑠𝑡 + 𝛾ℎ + 𝜆ℎ 𝑡 + 𝜀𝑖ℎ𝑡
The dependent variable 𝑅𝑒𝑝𝑒𝑎𝑡𝐼𝑚𝑎𝑔𝑒𝑖ℎ𝑡 is an indicator variable that takes on a value of 1 if
patient i received a repeat image in ED h during year t, and is otherwise 0. The model controls
for the number of annual discharges for ED h, 𝑆𝑖𝑧𝑒ℎ , and for a vector of patient-level
characteristics, 𝑿𝑖ℎ𝑡 , which includes age, sex, race (black or not black), uninsured status, days
since the previous visit to an unaffiliated ED and Charlson index score. The model also has a
year fixed effect, 𝜏𝑡 , a coefficient specific to California in year t, 𝛿𝑠𝑡 , an ED fixed effect, 𝛾ℎ , and an EDspecific trend coefficient, 𝜆ℎ , multiplied by the continuous trend variable t. The term 𝜀𝑖ℎ𝑡 represents
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unobserved patient-specific factors that contribute to repeat imaging in unaffiliated EDs. In order to
estimate 𝛽1 , the true effect of 𝐻𝐼𝐸, with OLS the assumption must be met that 𝐻𝐼𝐸ℎ𝑡 is not correlated
with 𝜀𝑖ℎ𝑡 for all t, conditional on the covariates in the model. However, it is permissible for 𝐻𝐼𝐸ℎ𝑡 to be
correlated with the ED fixed effect, 𝛾ℎ . In other words, if the assumption of the exogeneity of 𝐻𝐼𝐸ℎ𝑡 has
been met, estimation of the effect of HIE will not be biased even if time-invariant ED characteristics, such
as an ED's reputation for quality, influences patient selection into an HIE participant.
Regression results
In eTables 2 - 4, below, we present the full set of regression result from the base model,
ED fixed effects model, and ED fixed effects and trends model. The results only for the
coefficient estimate on HIE are presented for all three imaging modalities in Table 3 of the
paper.
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eTable 2. HIE and the percentage of CTs that are repeated among patients visiting unaffiliated EDs.
HIE
Charlson index
Uninsured
Race (black)
Female
Days since prior visit
Age
Age ( ≤ 30 years)
Age (> 30 years)
ED Size†
Total discharges
≤ 15,000
Total discharges
15,001 to 18,000
Total discharges
> 18,000
Year Fixed Effects
State-Year Fixed Effects
ED trends
Observations
Base model*
ED Fixed Effects*
-5.51
(-9.96, 1.06)
0.04
(-0.47, 0.57)
-0.42
(-1.02, 0.19)
-2.40
(-3.38, -1.42)
-2.27
(-2.71, -1.84)
-0.18
(-0.22, -0.15)
-3.81
(-10.59, 2.97)
-0.40
(-0.80, 0.01)
-0.45
(-0.92, 0.03)
-2.28
(-2.85, -1.72)
-2.40
(-2.81, -2.00)
-0.19
(-0.19, -0.16)
-8.69
(-14.71, -2.67)
-0.45
(-0.84, -0.06)
-0.33
(-0.79, 0.13)
-2.26
(-2.81, -1.71)
-2.37
(-2.77, -1.97)
-0.19
(-0.22, -0.16)
0.29
(0.22, 0.35)
-0.23
(-0.31, -0.16)
0.31
(0.24, 0.37)
-0.25
(-0.32, -0.18)
0.30
(0.24, 0.37)
-0.25
(-0.32, -0.17)
-0.11
(-0.62, 0.40)
-0.24
(-2.91, 3.39)
0.57
(0.11, 1.02)
-1.40
(-2.70, -0.10)
6.90
(4.97, 8.83)
-1.34
(-1.79, -0.90)
-1.42
(-4.26, 1.43)
5.88
(2.55, 9.21)
1.42
(0.61, 2.24)
Included
Included
Included
Included
137,483
137,483
ED Fixed Effects
with ED trends*
Included
Included
Included
137,483
Notes: *95% confidence intervals, which account for clustering by ED, are reported in parentheses. † Total
discharges were rescaled by 10,000 for ease of interpreting the results.
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eTable 3. HIE and the percentage of ultrasounds that are repeated among patients visiting unaffiliated
EDs..
Base model*
HIE
Charlson index
Uninsured
Race (black)
Female
Days since prior visit
Age
Age ( ≤ 30 years)
Age (> 30 years)
ED Size†
Total discharges
≤ 15,000
Total discharges
15,001 to 18,000
Total discharges
> 18,000
Year Fixed Effects
State-Year Fixed Effects
ED trends
Observations
ED Fixed Effects*
ED Fixed Effects
with ED trends*
-9.33
(-14.50, -4.16)
-3.19
(-4.18, -2.21)
-2.95
(-4.02, -1.89)
3.80
(1.08, 6.53)
8.81
(7.31, 10.31)
-0.18
(-0.23, -0.13)
-3.71
(-13.25, 5.84)
-3.29
(-4.19, -2.39)
-3.20
(-3.96, -2.45)
2.30
(1.22, 3.37)
8.20
(6.75, 9.65)
-0.19
(-0.23, -0.15)
-9.15
(-17.23, -1.06)
-3.29
(-4.20, -2.38)
-3.14
(-3.92, -2.37)
2.32
(1.23, 3.40)
8.12
(6.65, 9.58)
-0.18
(-0.22, -0.14)
-0.11
(-0.21, -0.01)
-0.19
(-0.30, -0.08)
-0.08
(-0.16, 0.01)
-0.20
(-0.30, -0.10)
-0.09
(-0.17, -0.01)
-0.19
(-0.30, -0.09)
0.67
(-0.27, 1.61)
2.04
(-4.79, 8.87)
-0.36
(-1.40, 0.68)
-0.93
(-2.93, 1.07)
13.28
(6.10, 20.46)
-2.93
(-3.57, -2.28)
-2.35
(-5.18, 0.47)
5.54
(-7.19, 18.27)
1.08
(-0.29, 2.45)
Included
Included
Included
Included
62,984
62,984
Included
Included
Included
62,984
Notes: *95% confidence intervals, which account for clustering by ED, are reported in parentheses. † Total
discharges were rescaled by 10,000 for ease of interpreting the results.
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eTable 4. Changes in the percentage of chest x-ray that are repeated among patients visiting unaffiliated
EDs.
Base model*
HIE
Charlson index
Uninsured
Race (black)
Female
Days since prior visit
Age
Age ( ≤ 30 years)
Age (> 30 years)
ED Size†
Total discharges
≤ 15,000
Total discharges
15,001 to 18,000
Total discharges
> 18,000
Year Fixed Effects
State-Year Fixed Effects
ED trends
Observations
ED Fixed Effects*
ED Fixed Effects
with ED trends*
-9.03
(-15.91., -2.15)
8.28
(7.17, 9.40)
-0.56
(-1.24, 0.12)
1.08
(-0.47, 2.62)
-1.55
(-2.00, -1.11)
-0.15
(-0.19, -0.11)
-8.65
(-14.24, -3.07)
7.64
(6.72, 8.57)
-0.53
(-1.07, 0.00)
1.26
(0.58, 1.94)
-1.66
(-2.04, -1.27)
-0.16
(-0.19, -0.12)
-13.03
(-18.35, -7.72)
7.57
(6.64, 8.50)
-0.42
(-0.96, 0.13)
1.29
(0.62, 1.97)
-1.64
(-2.03, -1.26)
-0.16
(-0.19, -0.12)
-0.10
(-0.18, -0.02)
0.23
(0.14, 0.31)
-0.06
(-0.12, -0.01)
0.19
(0.13, 0.25)
-0.07
(-0.12, -0.02)
0.20
(0.14, 0.26)
0.03
(-0.75, 0.82)
-0.75
(-7.81, 6.30)
0.96
(-0.37, 2.28)
-1.23
(-2.52, 0.06)
10.29
(5.50, 15.09)
-1.07
(-1.61, -0.52)
-0.37
(-2.89, 2.14)
8.76
(1.22, 16.30)
3.76
(2.90, 4.61)
Included
Included
Included
Included
152,408
152,408
Included
Included
Included
152,408
Notes: *95% confidence intervals, which account for clustering by ED, are reported in parentheses. † Total
discharges were rescaled by 10,000 for ease of interpreting the results.
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Average adopter trend
To investigate the pattern of larger marginal effect estimates for HIE from regressions
with ED specific trends in comparison to estimates from the ED fixed effects regressions, we
examined regressions with a single linear trend variable for HIE adopters. Estimates of the
coefficients for these trends gives us an adjusted average of the trend coefficients for EDs that
adopted HIE. In eTable 5, below, we present results from these fixed effects regression models.
The results show a negative marginal effect of HIE across all three modalities, similar to all other
regressions concerning second visits to unaffiliated EDs. Moreover, the estimate on the linear
adopter trend is positive and statistically significant at the 95% level for all 3 imaging modalities.
These results indicate that the average trend in repeat imaging was steeper for EDs initiating HIE
relative to EDs that never participated in HIE, and thus that these HIE adopters had more "room
for improvement" in terms of repeat imaging relative to non-adopters of HIE.
eTable 5. Changes in percentage of images repeated among patients revisiting unaffiliated EDs,
fixed effects with adopter trend
Repeat CT
Scans*
HIE
Adopter trend
Observations
Repeat
Ultrasounds*
- 6.8
(-11.8, -1.8)
3.5
(2.0, 5.0)
137,483
-6.0
(-13.9, 2.0)
2.9
(0.5, 5.3)
62,984
Repeat Chest Xrays*
- 11.7
(-16.0, -7.4)
3.4
(1.5, 5.3)
152,408
Notes: *95% confidence intervals, which account for clustering by ED, are reported in parentheses. Covariates
included in each regression: Charlson index, black race, female, age, days since index ED visit, uninsured status,
annual number of ED discharges, year dummies, state-year dummies.
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15 day, and 60 day windows for a second visit
We examined the sensitivity of our main results to alternative windows of time within
which to second ED visits are observed. In particular we examined regression estimates from
models specified exactly as those of the main models presented in the paper, but with three
alternative windows for second ED visits: 15 days and 60 days. In eTable 6 below we report
results from these alternative regressions with fixed effects and fixed effects with ED specific
trends. We note that the signs of the coefficients are identical to the findings with a 30 day
window, and the coefficient magnitudes, and statistical significance are very similar to those
results as well, among each of the alternative windows for a second visit. We also note that the
estimated magnitudes are larger with the shorter window periods. This is consistent with the
expectation that access through HIE to prior test results is likely to have greater clinical
relevance for second visits that occur closer to the date of the index visit.
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eTable 6. Alternative maximum periods for second visit and changes in the percentage of repeat images
15 Days
ED Fixed
Effects*
HIE and Repeat
CT Scans
HIE and Repeat
Ultrasounds
HIE and Repeat
Chest X-rays
- 4.3
(-11.3, 2.7)
- 5.1
(-15.2, 5.0)
- 9.2
(-15.1, -3.4)
ED Fixed
Effects with
ED Trends*
- 9.5
(-15.8, -3.2)
-10.2
(-19.1, -1.3)
- 13.2
(-19.0, -7.4)
60 Days
ED Fixed
Effects*
- 3.8
(-9.8, 2.2)
- 2.8
(-12.3, 6.7)
- 8.4
(-13.7, -3.1)
ED Fixed
Effects with
ED Trends*
- 8.4
(-13.7, -3.0)
-7.8
(-15.9, 0.3)
- 12.6
(-17.6, -7.6)
Notes: Each cell of the tables presents estimates of HIE's marginal effect from a different regression. *95% confidence intervals, which account for clustering by
ED, are reported in parentheses. Covariates included in each regression: Charlson index, black race, female, age, days since index ED visit, uninsured status,
annual number of ED discharges, year dummies, state-year dummies.
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Falsification Exercise
In eTable 5 below, we present the results for the falsification regressions, including the
base models, the models with ED fixed effects, and ED fixed effects plus trends for the three
imaging modalities. These models were specified identically as the corresponding main models
reported in the paper, the results of which are presented in Table 3.
eTable 7. Falsification exercise: HIE and the percentage of imaging repeated in the same ED.
Base Model*
HIE and Repeat
CT Scans
0.2
(-1.4, 1.8)
ED Fixed
Effects*
-0.1
(-1.1, 0.9)
ED Fixed
Effects with
ED Trends*
0.4
(-0.6, 1.4)
N = 388,788
HIE and Repeat
Ultrasounds
-4.3
(-6.6, -2.1)
1.6
(-1.4, 4.7)
2.2
(-1.7, 6.1)
N = 183,771
HIE and Repeat
Chest X-rays
3.1
(-2.5, 8.7)
0.0
(-1.2, 1.2)
1.2
(-1.5, 3.8)
N = 474,502
Notes: Each cell of the table presents estimates of HIE's marginal effect from a different regression. *95%
confidence intervals, which account for clustering by ED, are reported in parentheses. Covariates included in each
regression: Charlson index, black race, female, age, days since index ED visit, uninsured status, annual number of
ED discharges, year dummies, state-year dummies.
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REFERENCES
1. Elixhauser A, Steiner C, Palmer L. Clinical Classifications Software (CCS), 2013. U.S.
Agency for Healthcare Research and Quality. [accessed November 19, 2012] Available:
http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp
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