Optimizing Heparin Quality Assurance Utilizing Electronic Data Abstraction

Background: Heparin is a high-risk medication with significant variability across patients. Systematic data analysis can help hospitals improve heparin management, ensuring safe and effective anticoagulation. An opportunity exists to create a more efficient data collection process, allowing hospitals to streamline quality assurance programs. Objective: To assess the agreement between manual and electronic data abstraction for heparin quality assurance. Methods: This is a single-center, observational cohort study that evaluated patients who received therapeutic unfractionated heparin from September to November 20, 2023. Patients treated for less than 24 hours were excluded. Data were collected manually from pharmacist monitoring forms and the electronic medical record; electronic data abstraction was queried from an institutional data warehouse. The primary outcome was agreement in percentage of patients achieving a therapeutic aPTT within 24 hours. Secondary outcomes included agreement on time to therapeutic aPTT, agreement on time to therapeutic or supratherapeutic aPTT, and clinical outcomes. Results: The study included 288 patients. Manual data collection indicated 44.1% of patients were therapeutic within 24 hours, whereas electronic data collection showed 46.9% (kappa = 0.86). The kappa value for agreement of therapeutic or supratherapeutic aPTT within 24 hours was substantial (kappa = 0.69), with manual data showing 61.5% of patients therapeutic within 24 hours compared with 73.3% in electronic data. However, poor agreement was found when identifying subsequent heparin boluses (kappa = 0.13) and new venous thromboembolism cases (kappa = −0.01). Conclusions and Relevance: The metrics from the 2 data collection methods varied in reliability, ranging from highly consistent to poorly aligned. A hybrid approach, integrating manual and reliable electronic methods, has been implemented at our institution to improve efficiency. Further studies are needed to assess generalizability, and enhance electronic data capture for clinical outcomes.