Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support

Background Standardization in clinical documentation can increase efficiency and can save time and resources. Objective The objectives of this work are to compare documentation forms for acute coronary syndrome (ACS), check for standardization, and generate a list of the most common data elements using semantic form annotation with the Unified Medical Language System (UMLS). Methods Forms from registries, studies, risk scores, quality assurance, official guidelines, and routine documentation from four hospitals in Germany were semantically annotated using UMLS. This allowed for automatic comparison of concept frequencies and the generation of a list of the most common concepts. Results A total of 3710 forms items from 86 sources were semantically annotated using 842 unique UMLS concepts. Half of all medical concept occurrences were covered by 60 unique concepts, which suggests the existence of a core dataset of relevant concepts. Overlap percentages between forms were relatively low, hinting at inconsistent documentation structures and lack of standardization. Conclusions This analysis shows a lack of standardized and semantically enriched documentation for patients with ACS. Efforts made by official institutions like the European Society for Cardiology have not yet been fully implemented. Utilizing a standardized and annotated core dataset of the most important data concepts could make export and automatic reuse of data easier. The generated list of common data elements is an exemplary implementation suggestion of the concepts to use in a standardized approach.

Section: Introductionmentioning

confidence: 99%

Common Data Elements for Acute Coronary Syndrome: Analysis Based on the Unified Medical Language System

Kentgen¹,

Varghese²,

Samol³

et al. 2019

“…This study is among the first to integrate smart infusion pump information with EHR data to analyze the most error-prone phases of the medication use process, recognizing that linkage of complex data has its challenges [ 4 ]. Smart pump data lack clinical usefulness without appropriate identification of both patient information and medication being used at the time of infusion.…”

Section: Discussionmentioning

confidence: 99%

“…Electronic health records (EHRs) are the central focus of many efforts in clinical research and quality improvement given their role as the primary clinical data source [ 1 - 4 ]. Despite their granularity, the data heavily rely on manual input and are prone to human errors [ 3 , 4 ]. Many digital devices have been used in clinical environments, and they provide additional sources of data for understanding health care processes, a form of real-world data from clinical settings.…”

Section: Introductionmentioning

confidence: 99%

“…Integration of SPRs with EHR data requires advanced informatics technologies and is not without significant challenges [ 4 ]. Most health care institutions that use smart pumps have not fully integrated them into a closed-loop system, which would permit automatic linkage of order data in the EHRs to administration information from the pumps.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Lingren

Huth

et al. 2020

Self Cite

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 incorporated with prescribing data from EHRs, smart pump records (SPRs) are capable of shedding light on actions that take place during the medication use process. However, harmoniz-ing the 2 sources is hindered by multiple technical challenges, and the data quality and utility of SPRs have not been fully realized. Objective This study aims to evaluate the quality and utility of SPRs incorporated with EHR data in detecting medication administration errors. Our overarching hypothesis is that SPRs would contribute unique information in the med-ication use process, enabling more comprehensive detection of discrepancies and potential errors in medication administration. Methods We evaluated the medication use process of 9 high-risk medications for patients admitted to the neonatal inten-sive care unit during a 1-year period. An automated algorithm was developed to align SPRs with their medica-tion orders in the EHRs using patient ID, medication name, and timestamp. The aligned data were manually re-viewed by a clinical research coordinator and 2 pediatric physicians to identify discrepancies in medication ad-ministration. The data quality of SPRs was assessed with the proportion of information that was 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 understand the detec-tion power and capabilities of SPR and EHR data. Results Approximately 70% of the SPRs contained valid patient IDs and medication names, making them feasible for data integration. After combining the 2 sources, the investigative team reviewed 2307 medication orders with 10,575 medication administration records (MARs) and 23,397 SPRs. A total of 321 MAR and 682 SPR dis-crepancies were identified, with vasopressors showing the highest discrepancy rates, followed by narcotics and total parenteral nutrition. Compared with EHR MARs, substantial dosing discrepancies were more commonly detectable using the SPRs. The concordance analysis showed little overlap between MAR and SPR discrepan-cies, with most discrepancies captured by the SPR data. Conclusions We integrated smart infusion pump information with EHR data to analyze the most error-prone phases of the medication lifecycle. The findings suggested that SPRs could be a more reliable 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.

“…This is in contrast to current methods of detection, which usually rely on sampling strategies followed by selective manual review of records or by reviewing the output from voluntary reporting [13,[15][16][17]. In our prior work [12,[20][21][22], discrepancies were identified when an algorithm detected a difference between the dosage intended to be delivered (prescriber's orders) and how it was documented as being delivered (MAR data). A dosing-related MAE was defined as any discrepancy between the medication dose or infusion rate administered to a patient and the dose/rate prescribed by physicians during patient care.…”

Section: Introductionmentioning

confidence: 99%

The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis

Kirkendall¹,

Huth²,

Rauenbuehler³

et al. 2020

Self Cite

Background As a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site. Objective The purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions. Methods The MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized. Results Compared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit. Conclusions The execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe’s fit at new sites.