The Impact of Implementing Electronic
Health Records on the Occurrence of
Medical Errors in Hospitals
Ann F. Chou, PhD, MPH
Robert C. Wild, MS, MPH, CPH
Steven Mattachione, JD
Robn Green, MPH
Robert H. Roswell, MD
College of Public Health and College of Medicine
The University of Oklahoma Health Sciences Center
AcademyHealth, June 2009
1
Background

To Err is Human, Institute of Medicine,
2000


Estimated 44,000-98,000 Americans die
each year from preventable medical errors
Crossing the Quality Chasm, Institute of
Medicine, 2001

Recommended the use of health information
technology as a transforming strategy to
reduce errors
2
Key Strategies for Improving Quality
of Care
National voluntary quality
campaigns
39
Stronger regulatory
oversight of providers
50
Financial incentives for
improved quality
51
Public reporting of
provider performance
59
67
Accelerating IT adoption
0
10
20
30
40
50
60
70
80
Percent
Source: Commonwealth Fund/Modern Healthcare Opinion Leaders Survey, 2007
3
Benefits of Health IT Adoption



Patient safety and quality
 Reduction in medical errors or adverse events
 Reduction in redundant testing/duplicative services
 Timely preventive/screening services
 Promotion of evidence based medicine
Inpatient savings
 Shortened length of stay (LOS)
 Efficiency savings
Outpatient savings
 Efficiency savings
 Enhanced billing productivity
4
Current Health IT Implementation


In a survey of 2,758 physicians, 13%
utilize a basic electronic record system but
only 4% are using a fully functional
system in their offices.1
In a survey of 3,049 hospitals, 7.6% have
a basic system and only 1.5% have a
comprehensive electronic health system.2
1
DesRoches CM, Campbell EG, Sowmya R, et al. Electronic health records in ambulatory care-A national survey of
physicians. New Engl J Med. 2008; 359:50-60.
2
Jha AK, DesRoches CM, Campbell EG, et al. Use of electronic health records in U.S. hospitals. New Engl J Med. 2009; 360:
1628-38.
5
Objective

The goal of this study is to understand
the impact of implementing electronic
health records (EHR) on the likelihood of
an adverse medical event (AME)
occurrence.
6
Data

2006 Oklahoma data sources:





156,630 discharges from 50 ambulatory surgical
centers
520,829 inpatient hospitalizations from 137
hospitals
283,700 outpatient discharges from 96 hospitals
161 health care facilities in the Oklahoma Annual
Cooperative Hospital Survey
Patients were linked with facility level
information. The final dataset included 529,332
patients from 117 facilities.
7
Measures: Outcome Variables

From 2006 ICD-9 Codes:

Medical misadventures to patient during
surgical and medical care (E870-876)

Medication errors (E850-858)

Other surgical or procedural errors without
mention of misadventure as the cause of
abnormal reaction of patient or
complications (E878-E879)
8
Measures: Covariates


Fully implemented EHR v. Paper-based
system or Partially implemented EHR
Patient demographics







Gender
Race/ethnicity
Age groups
Length of stay in days
Weekend admission
Comorbidity
Hospital characteristics



Ownership status
Network/Joint venture
Bed size
9
Analysis

Hierarchical generalized linear modeling
(HGLM) estimated with Generalized
Estimating Equations (GEE) that allowed for
the clustering of observations within
hospitals was used to investigate
associations between the occurrence of
AMEs and EHR implementation, controlling
for patient and hospital level characteristics.
10
Descriptive Statistics: Patients

Gender



Female: 315,918 (59.7%)
Male: 213,414 (40.3%)
Age Group





<1 year: 57,890 (10.9%)
1-19: 36,814 (7.0%)
20-44: 120,013 (22.7%)
45-64: 125,396 (23.7%)
65+: 189,219 (35.8%)

Race/Ethnicity




White:
431,562 (81.5%)
African American:
43,041 (8.1%)
Native American:
22,462 (4.2%)
Other:
32,267 (6.1%)
11
Descriptive Statistics: Patients

Insurance Coverage





Commercial:
151,380 (28.6%)
Medicare:
207,831 (39.3%)
Medicaid:
110,966 (21%)
Uninsured:
34,031 (6.4%)
Other:
25,124 (4.8%)

Co-morbidities





None: 42,138 (8%)
1-5: 263,527 (49.8%)
6-10: 169,465 (32%)
11-15: 54,202 (10.2%)
Mean Length of Stay
4.42 days
12
Descriptive Statistics: Hospitals

Hospital Has Electronic Health Record (EHR)



Type of Organization




Government: 36 (30.8%)
Not for profit: 30 (25.6%)
For-profit: 37 (31.6%)
Hospital Participates in



Fully implemented: 9 (7.7%)
Partially implemented: 22 (18.8%)
A network: 23 (19.7%)
Joint Venture: 25 (21.4%)
Mean Bedsize: 119.6
13
Results
Variables
Odds Ratio (95% Confidence Interval)
Misadventure
Medication Error
Procedural
0.41* (0.20, 0.83)
0.61* (0.45, 0.82)
0.72 (0.34, 1.51)
Gender: Female
1.07 (0.91, 1.25)
0.87* (0.80, 0.96)
0.85* (0.81, 0.90)
African American
0.64* (0.43, 0.95)
0.99 (0.83, 1.17)
0.97 (0.81, 0.90)
Native American
0.95 (0.62, 1.45)
1.09 (0.88, 1.35)
1.02 (0.94, 1.10)
0.99 (0.42, 2.31)
24.4* (12.24, 48.83)
2.44* (1.86, 3.19)
20-44
5.49* (2.04, 14.78)
10.36* (5.38, 19.96)
2.82* (1.78, 4347)
45-64
7.02* (2.62, 18.85)
8.45* (4.92, 14.50)
3.79* (2.28, 6.31)
65+
4.99* (1.90, 13.05)
3.46* (2.07, 5.79)
3.07* (1.93, 4.90)
Fully implemented EHR
Patient Demographics
Age (<1 year of age)
1-19
* Statistically significant
14
Results
Variables
Odds Ratio (95% Confidence Interval)
Misadventure
Medication Error
Procedural
Weekend Admission
0.52* (0.42, 0.65)
1.65* (1.41, 1.93)
0.77* (0.73, 0.81)
Length of stay
1.01* (1.01, 1.02)
0.85* (0.81, 0.88)
1.01* (1.00, 1.02)
Number of comorbidities
1.07* (1.05, 1.09)
1.20* (1.17, 1.22)
1.08* (1.06, 1.10)
For-Profit
3.21* (1.27, 8.09)
0.59* (0.39, 0.90)
3.29* (1.30, 8.33)
Not for profit
1.94* (1.04, 3.64)
0. 93 (0.66, 1.32)
1.77 (0.73, 4.28)
2.10* (1.20, 3.68)
0. 91 (0.64, 1.29)
1.98 (0.92, 4.24)
Hospital Ownership
(Government)
Hospital is joint venture
* Statistically significant
15
Discussion

Findings showed that AMEs were less likely
to occur if an EHR has been fully
implemented in comparison to no or partially
EHR implementation.

Although the capital costs have often been
cited as a primary barrier for EHR
implementation, the reduction in errors
presents a business case of return on
investments in support of wider EHR
adoption and use.
16
Limitations

ICD-9 codes not designed to capture all
medical errors

Reliance on provider and organization
report of medical errors

Lack of clinical details in administrative
datasets

Lack of determination of when the AME
occurred during hospitalization
17
Implications



A systems approach to prevent AMEs is
warranted, with the implementation of clinical
information systems serving as a key strategy to
reduce these errors.
Better patient safety and quality, along with
stabilization of rising US health care costs may
be dependent upon wider implementation of
electronic records systems.
Despite the current economic situation, efforts to
increase adoption of EHRs will be prominent in
the next several years.
18
Acknowledgements

Funded by:


Agency for Healthcare Research and
Quality
Oklahoma State Department of Health
Binitha Kunnel
 Kelly Baker

19
THANK YOU
 Ann F. Chou
(405) 271-2115 ext 4
 ann-chou@ouhsc.edu

20