Evaluating automated electronic case report form data entry from electronic health records

Abstract

This version may be subject to change during the production process.

A pragmatic methodology to extract anesthetic and physiological data from the electronic health record

“…There was one discordant end‐tidal sevoflurane concentration value in our sample of data extraction that was later confirmed to be an error in the manual method. The discordance was observed in the hundredths decimal place and potentially attributable to a keystroke error or analyst fatigue 1,4–9 …”

“…There was one discordant end-tidal sevoflurane concentration value in our sample of data extraction that was later confirmed to be an error in the manual method. The discordance was observed in the hundredths decimal place and potentially attributable to a keystroke error or analyst fatigue 1,[4][5][6][7][8][9]. Errors in data transcription employing either manual 8 or intermediate preprocessing methods such as the one described in this manuscript can arise for a number of different reasons.…”

“…Two common examples of anesthetic data of interest include (1) pharmacological drug administration such as expired end-tidal sevoflurane concentration and (2) physiological hemodynamic data such as mean arterial blood pressure. In practice settings not utilizing paper anesthetic records, these data are now typically recorded continuously in the electronic health record (EHR) anesthesia information systems (AIMS) [1][2][3] and can be accessed and gathered post hoc by using a variety of methods that range from a manual data extraction approach from patients' EHR flowsheets-a process traditionally involving a human data analyst(s) visually reading and transcribing minute-by-minute anesthetic-physiological data by hand onto a separate spreadsheet software program, a time-consuming and error-prone process [4][5][6][7][8][9] -to more technically complicated data extraction approaches involving computer coding in languages such as Structured Query Language (SQL) that require additional expertise and resources. [10][11][12][13] Data can be accessed by relying on the support of (1) helpful intradepartmental tech savvy clinicians, (2) EHR company staff with computer coding expertise to increase access in a more scalable fashion, (3) data scientists in larger academic practices, and (4) through independent direct access to a variety of monitored variables in a perioperative data warehouse.…”

Fitness-for-use of Retrospective Multicenter Electronic Health Records to Conduct Outcome Analysis for Pediatric Ulcerative Colitis

“…Widespread EHR adoption has increased interest in using real-world data to conduct novel outcome research and generate real-world evidence at a lower cost than traditional clinical trials. [ 6 ] The National Patient-Centered Clinical Research Network (PCORnet) began with an initial $100 million investment from the Patient-Centered Outcomes Research Institute, aiming to conduct clinical and translational research faster and at lower cost by harnessing the power of “real-world” EHR data. The establishment of PCORnet was guided by a set of aims and requirements for each of the clinical research network and one of highest priorities was for EHR data from the different healthcare organizations and systems within each clinical research network to be harmonized and interoperable using PCORnet’s CDM.…”

Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic

“…Because EHRs are often the primary data source that clinical research coordinators use when populating CRFs, it is theoretically possible to automate the data extraction process and automatically populate many variables within a trial's CRFs. However, due to the complexity of healthcare, the intricacies of EHR documentation practices, and the mismatch between the periodically documented variables present in an EHR and the authoritative (and sometimes multifaceted) variables required by CRFs, automating this task (outside of retrospective proof of concept studies 9,10 ) has mainly been unattainable.…”