Transcript ACC_05_poster

What is the Most Efficient Data Extraction Method for Quality Improvement and Research in Cardiology?:
A Comparison of REMIND Artificial Intelligence Software vs. Manual Chart Abstraction for Determining ACC/AHA Guideline Adherence in
Non-ST Elevation Acute Coronary Syndromes
Ali F. Sonel, MD, C. Bernie Good, MD MPH, Harsha Rao, MD, Alanna Macioce, BS, Lauren J. Wall, BS, Radu Stefan Niculescu, MS,
Sahtyakama Sandilya, PhD, Phan Giang, PhD, Sriram Krishnan, PhD, Prasad Aloni, MS, MBA, Bharat Rao, PhD
ABSTRACT
Introduction: Manual extraction of data for Quality Improvement is tedious,
requiring significant individual training and careful attention to the HIPAA
Privacy Rule. Automated chart abstraction is an alternative approach that saves
time and costs. We compared manual chart abstraction from an electronic
medical record (VA CPRS EMR System) to automated extraction using the
REMIND artificial intelligence software in 327 consecutive patients admitted
with unstable angina or non-ST elevation myocardial infarction.
Methods: All patient features required by ACC/AHA guidelines for determining
eligibility for class I recommendations to use ACE inhibitors and glycoprotein
IIb/IIIa treatment were extracted by both methods. Manual extraction was
carried out by well-trained, qualified chart abstractors with prior experience in
manual chart abstraction. When both extraction results were identical, the result
was assumed correct. Disagreements were manually adjudicated based on predetermined definitions.
Results: Manual extraction and data entry required 136 hours compared to 3
hours using the Siemens REMIND software. A total of 2289 data elements were
identified, with agreement in 1912 (84%) and disagreement in 377, involving
2.5-35% of patients for various parameters. REMIND was found to be correct in
215/377 disagreements (57%) and manual extraction was correct in the
remaining 43% (162). Based on adjudication, guideline adherence for ACE
inhibitor and glycoprotein IIb/IIIa receptor antagonist use were 58.5% and
38.2% respectively. REMIND identified adherence at 55.7% and 38.2%
respectively, which was more accurate than guideline adherence determined by
manual extraction (64.8% and 33.3%).
Conclusions: REMIND can assess ACC/AHA guideline adherence at least as
accurately as manual chart abstraction. Use of REMIND for Quality
Improvement and research can result in significant savings, better resource
utilization, and may improve data extraction quality.
BACKGROUND
•Research and quality improvement projects
involve large amounts of data collection through
review of medical records
•Manual data collection requires a significant
amount of training and is time consuming
•Automated data extraction methods could save
time and improve resource utilization
•Little is known about the accuracy of automated
systems for record extraction
SPECIFIC AIMS
•Compare the accuracy of data collection in a large
and complex medical record set using manual
extraction and REMIND automated extraction tool
•Compare the level of adherence to ACC/AHA
guideline recommendations for treatment of non-ST
elevation acute coronary syndromes (ACS) using
manual extraction and REMIND automated data
extraction tool
Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System and the Cardiovascular Institute, University of Pittsburgh Pittsburgh, PA, Siemens Medical Solutions, USA, Malvern, PA
METHODS
Patient Population
•327 patients admitted with high-risk non-ST-segment elevation
myocardial infarction were included in the study
RESULTS
• Complete data extraction required 176 hours of manual extraction, compared to 4.5 hours
with REMIND automated extraction
Table 3: Accuracy* of Compliance Assessment with REMIND Compared to Manual Extraction
Data Collection
•Records were extracted from VA CPRS Electronic Medical Record
System
•Manual extraction of predefined variables was performed by a trained
abstractor with expertise in ACS data abstraction for research purposes
•An artificial intelligence model developed by Siemens, the REMIND
automated data extraction tool, was used to extract the same
information electronically
•Medical information required to determine eligibility and the
presence of absence of contraindications for Class I treatment
recommendations in the ACC/AHA guidelines was collected for the
following medications:
•Aspirin in all patients
•Beta-blockers in all patients
•Angiotensin converting enzyme (ACE) inhibitors or angiotensin
receptor blockers (ARB) in patients with diabetes mellitus,
congestive heart failure, left ventricular dysfunction or hypertension
•Glycoprotein IIb/IIIa receptor antagonists in patients in whom an
early invasive management strategy is planned
Data Analysis
•We compared the results of the two methods for accuracy
•When both extraction methods were in agreement, the result was
assumed to be correct. When extracted results differed, disagreements
were manually adjudicated based on pre-determined definitions, using
the source documents of each extraction method
• Accuracy was defined as the number of patients where there was
agreement with adjudication as to whether the patient was compliant
or not, divided by the total number of patients in the study
•Compliance is defined as the number of patients eligible and not
contraindicated to that medication, actually received the medication,
divided by the number of patients who are eligible and has no
contraindication to that medication.
Table 1: Determination of Contraindications and Eligible Patients for Processes of Care
Patients with Contraindications for
Processes of Care (%)
ACCURACY (%) N=327
Ideal Patients for
Processes of Care (%)
TREATMENT
(N=327)
REMIND
MANUAL
ADJUDICATED
REMIND
MANUAL
ADJUDICATED
Aspirin
82 (25%)
77 (24%)
86 (26%)
245 (75%)
250 (77%)
241 (74%)
Beta Blockers
229 (70%)
ACE
Inhibitors/ARB
142 (43%)
Glycoprotein
IIb/IIIa Receptor
Antagonists
99 (30%)
186 (57%)
128 (39%)
233 (71%)
152 (46%)
98 (30%)
146 (45%)
141 (43%)
125 (38%)
94 (29%)
TREATMENT
REMIND
MANUAL
Aspirin
319 (97%)
314 (96%)
Beta Blockers
319 (97%)
316 (97%)
ACE Inhibitors/ARB
300 (92%)
310 (95%)
Glycoprotein IIb/IIIa Receptor
Antagonists
300 (92%)
290 (89%)
125 (38%)
*Accuracy defined as true positives plus true negatives divided by the total number of patients
93 (28%)
106 (32%)
103 (32%)
79 (24%)
86 (26%)
CONCLUSION
•REMIND can determine ACC/AHA guideline adherence for non-ST-elevation
acute coronary syndromes at least as accurately as manual chart abstraction.
Table 2: Assessment of Compliance with Guideline Recommended Therapies
COMPLIANCE BY
REMIND
EXTRACTION (%)
COMPLIANCE BY
MANUAL
EXTRACTION (%)
COMPLIANCE
FOLLOWING
ADJUDICATION (%)
202/245 (83%)
220/250 (88%)
206/241 (85%)
Beta Blockers
76/98 (78%)
116/141 (82%)
76/94 (81%)
ACE Inhibitors/ARB
101/146 (69%)
98/125 (78%)
95/125 (76%)
Glycoprotein IIb/IIIa
Receptor Antagonists
36/103 (35%)
30/79 (38%)
34/86 (40%)
TREATMENT
Aspirin
IMPLICATIONS
•Use of REMIND for quality improvement and research related applications in
facilities with electronic medical records can result in significant savings and
better resource utilization.
•Use of REMIND can enable evaluation of very large sets of medical
information that would otherwise be impractical by manual extraction