Evidence map›Paper›PMID 38055618›Full record

ArticlePaediatric anaesthesia2024

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

Arshia Aalami Harandi, Katherine McPherson, Yungtai Lo, Rodrigo Gutiérrez, Jerry Y Chao

Abstract read
In one paragraph

Article in Paediatric anaesthesia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Arshia Aalami HarandiDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, USA.
Katherine McPhersonDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, USA.ORCID 0000-0002-6606-2410
Yungtai LoDepartment of Epidemiology & Population Health (Biostatistics), Albert Einstein College of Medicine, New York, USA.
Rodrigo GutiérrezDepartment of Anesthesiology and Perioperative Medicine, Center of Advanced Clinical Research, University of Chile, Santiago, Chile.
Jerry Y ChaoDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, USA.ORCID 0000-0001-8666-7583

Funding

CTSA Administrative Supplement for Informatics Core: A novel AI/ML system to predict respiratory failure and ARDS in Covid-19 patientsUL1TR002556 · NCATS · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI KELLER, MARLA J, KIM, MIMI Y · 2018 to 2022
$26.7M
Institutional Career Development CoreKL2TR002558 · NCATS · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI MARANTZ, PAUL R · 2018 to 2022
$2.7M
Analgesic-Behavioral and Neurophysiological Correlates of Opioid-Sparing Spinal Anesthesia Compared to General Anesthesia in Human InfantsK23DA057499 · NIDA · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI CHAO, JERRY Y · 2023 to 2025
$587k
NCATS NIH HHS KL2 TR002558NCATS NIH HHS UL1 TR002556NIDA NIH HHS K23 DA057499NIDA NIH HHS L70 DA057081NIH HHS
6 · The paper itself

Abstract

BACKGROUND/

aimsTraditional manual methods of extracting anesthetic and physiological data from the electronic health record rely upon visual transcription by a human analyst that can be labor-intensive and prone to error. Technical complexity, relative inexperience in computer coding, and decreased access to data warehouses can deter investigators from obtaining valuable electronic health record data for research studies, especially in under-resourced settings. We therefore aimed to develop, pilot, and demonstrate the effectiveness and utility of a pragmatic data extraction methodology.

methodsExpired sevoflurane concentration data from the electronic health record transcribed by eye was compared to an intermediate preprocessing method in which the entire anesthetic flowsheet narrative report was selected, copy-pasted, and processed using only Microsoft Word and Excel software to generate a comma-delimited (.csv) file. A step-by-step presentation of this method is presented. Concordance rates, Pearson correlation coefficients, and scatterplots with lines of best fit were used to compare the two methods of data extraction.

resultsA total of 1132 datapoints across eight subjects were analyzed, accounting for 18.9 h of anesthesia time. There was a high concordance rate of data extracted using the two methods (median concordance rate 100% range [96%, 100%]). The median time required to complete manual data extraction was significantly longer compared to the time required using the intermediate method (240 IQR [199, 482.5] seconds vs 92.5 IQR [69, 99] seconds, p = .01) and was linearly associated with the number of datapoints (r

conclusionsWe describe a pragmatic data extraction methodology that does not require additional software or coding skills intended to enhance the ease, speed, and accuracy of data collection that could assist in clinician investigator-initiated research and quality/process improvement projects.

Indexed as

AnestheticsElectronic Health RecordsHumansAnestheticsaccuracy rateanesthesia information systems (AIMS)concordance ratedata extraction/scrapingdata scienceelectronic health record (EHR)gold standardpediatric anesthesiarigor and reproducibility in clinical research

Identifiers

PMID38055618
PMCPMC10922302

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