Leveraging Electronic Health Records to
I
Improve
Hospital
H
i l Performance:
P f
The Role of Management
Academy Health ARM
June 8, 2014
Julia Adler-Milstein, Kirstin Scott, Ashish Jha
The EHR
EHR-Performance
Performance Paradox

On the one hand…


On the other hand…


Studies from individual institutions reveal substantial quality
and efficiency gains from EHRs
Growing number of national studies that fail to find a
consistent relationship between EHR adoption and improved
performance
Key question
K
ti iis: under
de what
hat co
conditions
ditio s do EHRs lead to
improved performance?
The EHR
EHR-Performance
Performance Paradox

Evidence is beginning to emerge on what else is required

Dranove, Forman, Goldfarb, Greenstein (2012)


Examine the relationship between EHR adoption and operating costs
Key findings


Time effect: short-term increase
Conditions matter: long-term
g
decrease for some;; increase for others



Availability of technology skills in the local labor market
Internal IT expertise appears to have little impact on the relationship
between EHR adoption and costs
What other types of organizational competencies help
ensure that EHRs  performance improvement?
The EHR
EHR-Performance
Performance Paradox


Degree to which an organization is well-managed is likely
to be an important facilitator of IT-enabled performance
improvement

IT is a tool that requires concerted and thoughtful application

Without this, IT can make performance worse by introducing
complexity
l i that
h organization
i i iis not equipped
i d to manage
Research
R
h Question:
Q
ti
D
Does
management quality
li modify
dif
the relationship between EHR adoption and cost and quality
outcomes?
Data

Hospital HIT adoption:
AHA IT supplement (2009)

Hospital characteristics:
AHA annual survey (2009)

Hospital
p management:
g
From Sadun,, Bloom,,Van Reenen ((2010))

Hospital outcomes:
MedPAR (2010)
Data – AHA

AHA IT Supplement is administered to all U.S. hospitals
annually

Asks about adoption of various IT functionalities
C ll t d M
Collected
March
h – September
S t b 2009
2009, with
ith 69% response rate
t

Measure:


Hospital had the functionalities that comprise at least a “basic” EHR




EHR adoption
Results viewing
CPOE for
f medications
di i
Clinical documentation
AHA Annual Survey

Additional hospital characteristics: size, teaching status,
ownership, system-affiliated, urban location, & % Medicare
Data – Management

Random sample of 325 AHA hospitals (2009)

Double-blind phone interviews

Typically department or unit managers in cardiology or orthopedics

Validated tool with 20 questions scored on 1 (poor) to 5 (excellent) scale

4 dimensions of hospital management:

Operations: do companies use techniques like lean manufacturing, and do they document
process improvements and the rationale behind introductions of improvements?

Monitoring:
M
it i
h
how
wellll d
do companies
i monitor
it what
h t goes on iinside
id their
th i fifirms and
d use thi
this
for continuous improvement?

Targets: do companies set the right targets, track the right outcomes, and take
appropriate
i
action
i if the
h two are iinconsistent?
i
?

People: are companies promoting and rewarding employees based on performance, and
trying to hire and keep their best employees?
Data – Hospital Performance (AMI)

For all fee-for-service Medicare patients with a primary
diagnosis off AMI in 2010,
20 0 we calculated the following
f
riskadjusted hospital-level measures:

Length-of-stay (LOS)

30-day mortality

Payment per discharge
Analysis

Analytic sample: 191 U.S. acute-care hospitals in both management sample
and 2009 that responded to AHA IT supplement in 2009

C
Cross-sectional
i l analyses
l
(OLS)
(OLS), with
i h non-response weights
i h


Base Model: Average management score, EHR, and hospital controls

Interacted Model: with EHR*management
*
score interaction

Robustness Tests:

I
Interview-level
i
l l controls
l

Individual EHR components

Individual management components
Graph predicted margins at 0.50 increments of management score
Summary Statistics:
EHR Adopters vs
vs. Non
Non-Adopters
Adopters
At least basic
No basic EHR
EHR
(N=23)
Mean
(N=168)
Std
Std.
Mean
Dev.
Std
Std.
P
P-
Dev.
Value
Risk-Adjusted Length of Stay (days)
5.13
2.30
4.86
1.96
0.599
AMI
0.19
0.21
0.19
0.17
0.971
14,440
8,394
12,298
7,268
0.264
3.18
0.65
2.99
0.53
0.191
OUTCOMES
FOCAL
PREDICTOR
30-day Mortality
Payment per Discharge ($)
Management Composite Score
Summarized OLS Regression Results
OUTCOME:
MODEL:
EHR
MGMT
EHR X
MGMT
LENGTH OF STAY
30-DAY MORTALITY
PAYMENT
Base
Interaction
Base
Interaction
Base
Interaction
(1)
(2)
(3)
(4)
(5)
(6)
0.011
4.62*
0.03
0.19
184.59
24,417.89**
[0.50]
[2.55]
[0.05]
[0.20]
[1,968.95]
[10,329.61]
-0.42
-0.14
-0.05
-0.04
-508.82
976.70
[0.30]
[0.30]
[0.03]
[0.04]
[1,207.09]
[1,021.58]
-1.48*
-0.05
-7,786.74**
[0.77]
[0.06]
[3,140.69]
Coefficients
C
ffi i t from
f
OLS regressions
i
th
thatt iinclude
l d hhospital
it l characteristics.
h
t i ti
Brackets contain robust standard errors; *** p<0.01, ** p<0.05, * p<0.10
Predictive Margins: Length of Stay
EHR
No EHR
9
Poorly
Managed:
EHR – 6.9
No EHR – 5.0
8
7
Day
ys
6
5
4
3
Well Managed:
EHR – 3.7
No EHR – 4.7
2
1
0
1
1.5
2
2.5
3
3.5
Management Score
4
4.5
5
Predictive Margins: Mortality
EHR
No EHR
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
1
1.5
2
2.5
3
3.5
Management Score
4
4.5
5
Predictive Margins: Payment
Days
s
EHR
No EHR
9
8
7
6
5
4
3
2
1
0
1
1.5
2
2.5
3
3.5
Management Score
4
4.5
5
Limitations

Small sample
p

Not all management data from cardiology units

Associations not causal relationships
Associations,

Pre HITECH & meaningful use
Summary – Key Findings

Consistent with other studies,, no direct relationship
p between
EHR adoption and hospital performance

However, the quality of the management appears to modify the
relationship between EHR adoption and hospital performance



For efficiency related measures: LOS and payment
Some evidence for mortality (likely underpowered)
Suggests that improving hospital management may be a critical
f
factor
to ensure that
h EHRs
EHR iimprove hhospital
i l productivity
d i i
Summary – Potential Mechanisms
Motivation for adopting
EHRs…
Well-managed
g
Poorly-managed
g
Performance improvement
External pressures
(e.g., HITECH)
Relationships with frontline Engaged partners
staff…
Top-down, disconnected
Use of advanced
functionalities…
Limited – “basic” use only
Clinical decision support in
pparticular
Regular, ongoing
Use of EHR data for
pperformance measurement measurement
and monitoring…
Sporadic or none
Thank you!