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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.

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What is the most efficient data extraction method for quality improvement and research in cardiology a comparison of re

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

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

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 pre-determined 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.

METHODS

RESULTS

  • Patient Population

  • 327 patients admitted with high-risk non-ST-segment elevation myocardial infarction were included in the study

  • 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.

  • 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

Table 1: Determination of Contraindications and Eligible Patients for Processes of Care

BACKGROUND

*Accuracy defined as true positives plus true negatives divided by the total number of patients

  • 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

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

IMPLICATIONS

SPECIFIC AIMS

  • 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

  • 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