Uncovering Discrepancies in IV Vancomycin Infusion Records between Pump Logs and EHR Documentation

Abstract

Background Infusion start time, completion time, and interruptions are the key data points needed in both area under the concentration–time curve (AUC)- and trough-based vancomycin therapeutic drug monitoring (TDM). However, little is known about the accuracy of documented times of drug infusions compared with automated recorded events in the infusion pump system. A traditional approach of direct observations of infusion practice is resource intensive and impractical to scale. We need a new methodology to leverage the infusion pump event logs to understand the prevalence of timestamp discrepancies as documented in the electronic health records (EHRs). Objectives We aimed to analyze timestamp discrepancies between EHR documentation (the information used for clinical decision making) and pump event logs (actual administration process) for vancomycin treatment as it may lead to suboptimal data used for therapeutic decisions. Methods We used process mining to study the conformance between pump event logs and EHR data for a single hospital in the United States from July to December 2016. An algorithm was developed to link records belonging to the same infusions. We analyzed discrepancies in infusion start time, completion time, and interruptions. Results Of the 1,858 infusions, 19.1% had infusion start time discrepancy more than ± 10 minutes. Of the 487 infusion interruptions, 2.5% lasted for more than 20 minutes before the infusion resumed. 24.2% (312 of 1,287) of 1-hour infusions and 32% (114 of 359) of 2-hour infusions had over 10-minute completion time discrepancy. We believe those discrepancies are inherent part of the current EHR documentation process commonly found in hospitals, not unique to the care facility under study. Conclusion We demonstrated pump event logs and EHR data can be utilized to study time discrepancies in infusion administration at scale. Such discrepancy should be further investigated at different hospitals to address the prevalence of the problem and improvement effort.

Designing the User Interface of a Nitroglycerin Dose Titration Decision Support System: User-Centered Design Study

“…Tung et al (2022) evaluated timestamp discrepancies between EHR documentation and pump event logs (actual administration process) for vancomycin infusion. 44 The researchers used process mining to examine the conformance between pump event logs and EHR data for a single hospital in the United States. 44 An algorithm was developed to link records of the same infusions, and discrepancies in infusion start time, completion time, and interruptions were analyzed.…”

“…44 The researchers used process mining to examine the conformance between pump event logs and EHR data for a single hospital in the United States. 44 An algorithm was developed to link records of the same infusions, and discrepancies in infusion start time, completion time, and interruptions were analyzed. Of the 1,858 infusions, approximately 20% had an infusion start time discrepancy of more than AE10 minutes.…”

“…The researchers suggested these discrepancies are inherent parts of the current EHR documentation process commonly found in hospitals, not unique to the study site. 44 A potential solution to reducing the discrepancy in documentation can be to have interoperable systems. Suppose a dose accepted on nitro DSS is automatically documented in the MAR section of the EHR; this could eliminate the need to document it in the EHR and potentially reduce the time discrepancies.…”

Mapping Injection Order Messages to Health Level 7 Fast Healthcare Interoperability Resources to Collate Infusion Pump Data

“…We then verified whether the infusion pump settings could be matched with the injection order information. This was not a study of a "smart pump" [7][8][9][10][11] that automatically adjusts the drug speed and dosage; instead, we collected information from the infusion pump and used it for a injection order matching process. Although the linkage of data with smart pumps is innovative, it is not practical to obtain approval from the Japanese regulatory agency, the Pharmaceuticals and Medical Devices Agency.…”