JMIR Publications

ABSTRACT

Background:

At present, electronic health records (EHRs) are the central focus of clinical informatics given their role as the primary source of clinical data. Despite their granularity, the EHR data heavily rely on manual input and are prone to human errors. Many other sources of data exist in the clinical setting, including digital medical devices such as smart infusion pumps. When incorpo-rated with prescribing data from the EHRs, smart pump records (SPR) are capable of shedding light on actions taking place during the medication use process. Nevertheless, harmonizing the two sources is hindered by multiple technical challenges, and the usability and utility of SPRs have not been realized.

Objective:

In this study we incorporated SPRs with EHR data to evaluate their usability and utility in detecting medication administration errors. Our overarching hypothesis was that SPRs would contribute unique information in the medication use process, enabling more comprehensive detection of discrepancies and potential errors in medication administration.

Methods:

We evaluated the medication use process of nine high-risk medications for patients admitted to the neonatal intensive care unit during a one-year period. An automated algorithm was developed to align SPRs with their medication orders in the EHRs using patient ID, medication name and timestamp. The aligned data were manually reviewed by a clinical research coordinator and two pediatric physicians to identify discrepancies in medication administration. The usability of SPRs was assessed with the proportion of usable data that were linked to valid EHR orders. To evaluate their utility, we compared the frequency and severity of discrepancies captured by the SPR and EHR data respectively. A novel concordance assessment was also developed to under-stand the detection power and capabilities of SPR and EHR data.

Results:

Approximately 70% of the SPRs contained valid patient IDs and medication names, making them usable for data integration. After combing the two sources, the investigative team re-viewed 2307 medication orders with 10575 medication administration records (MARs) and 23397 SPRs. A total of 321 MAR and 682 SPR discrepancies were identified, with vasopressors showing the highest discrepancy rates, followed by narcotics and total parenteral nutrition. Compared to EHR MARs, substantial dosing discrepancies were more detectable using the SPRs. The concordance analysis showed little overlap between MAR and SPR discrepancies, with most discrepancies captured by the SPR data.

Conclusions:

We integrated smart infusion pump information with EHR data to make visible the most error-prone phases of the medication lifecycle. The findings suggested that SPRs could be a more re-liable data source for medication error detection. Ultimately, it is imperative to integrate SPR information with EHR data to fully detect and mitigate medication administration errors in the clinical setting.