Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study (Preprint)

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.

A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting

“…The remaining articles were organized according the TNPS conceptual framework which grounded the articles in the TNPS clinical use scenarios to provide a basis for parity of the synthesis. The main limitations noted in these articles included generalizability, 19 20 21 22 23 24 25 26 27 28 small samples sizes, 23 26 29 30 31 32 potential deviations from methodological convention, 22 33 and feasibility. 24 34…”

“…19 23 24 33 35 Three articles described patient protection from harm, all of which focused on CDSS for medication safety. 25 27 34 Four articles described direct care use scenarios, all of which described insulin administration and a CDSS tool for insulin infusions. 20 22 29 36 Finally, one article described a hybrid situation of patient communication and monitoring, with CDSS specific to fluid management 28 ( Table 3 ).…”

“…While these strategies have decreased preventable medication errors, they have not eliminated them completely. 25 One article detailed why insulin dosing CDSS guidance was overridden. In that case, the RN did not agree with the recommendation based on the patient's clinical presentation at the time.…”

“…22 Clinician distrust and suspicion of the CDSS guidance can result in disregard and bypassing of the systems, and a subsequent decrease in user compliance. 22 25 27 There is an ongoing need for staff education and awareness regarding the limits and capabilities of health IT, 27 managing alert fatigue, 22 27 34 and clinician workarounds (e.g., manual entries, basic infusions) 33 which can be human factors that influence clinical decision making when presented with CDSS guidance.…”