Automated Extraction of Structured Data from Text Notes in the Electronic Medical Record

“…While our tool does require some minimal effort from clinical staff, this is largely within normal clinical workflow and remains far less than what is required for manual extraction. Finally, though our tool was built using the Epic EHR, multiple EHRs have similar discrete data macro functionalities which could allow a similar tool to be developed in different systems both in the United States and Europe 17 , 22 .…”

Section: Discussionmentioning

confidence: 99%

“…Many EHRs contain functions that allow for automated capture of structed data from EHR text notes 17 . We worked with an Epic analyst to develop a tool using this EHR functionality to capture total ADR (tADR), or the proportion of all colonoscopies regardless of indication in which at least one adenoma is found.…”

Section: Methodsmentioning

confidence: 99%

Leveraging electronic medical record functionality to capture adenoma detection rate

Jones

Scott

Espinoza

et al. 2022

Sci Rep

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Measuring the adenoma detection rate (ADR) is critical to providing quality care, however it is also challenging. We aimed to develop a tool using pre-existing electronic health record (EHR) functions to accurately and easily measure total ADR and to provide real-time feedback for endoscopists. We utilized the Epic EHR. With the help of an Epic analyst, using existing tools, we developed a method by which endoscopy staff could mark whether an adenoma was detected for a given colonoscopy. Using these responses and all colonoscopies performed by the endoscopist recorded in the EHR, ADR was calculated in a report and displayed to endoscopists within the EHR. One endoscopist piloted the tool, and results of the tool were validated against a manual chart review. Over the pilot period the endoscopist performed 145 colonoscopies, of which 78 had adenomas. The tool correctly identified 76/78 colonoscopies with an adenoma and 67/67 of colonoscopies with no adenomas (97.4% sensitivity, 100% specificity, 98% accuracy). There was no difference in ADR as determined by the tool compared to manual review (53.1% vs. 53.8%, p = 0.912). We successfully developed and pilot tested a tool to measure ADR using existing EHR functionality.

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“…Finally, though our tool was built using the Epic® EHR, multiple EHRs have similar discrete data macro functionalities which could allow a similar tool to be developed in different systems. (17,20) Further work is needed to validate this tool among a greater proportion of endoscopists and ideally among multiple centers using the Epic® EHR. Adjusting the structure of the Reporting Workbench algorithm by utilizing other macro data tools like additional SmartLists or owsheets within the EHR may also allow for capture of additional data such as colonoscopy indication, prep quality and even allow for use of a similar tool to capture QIs in other endoscopic arenas.…”

Section: Discussionmentioning

confidence: 99%

“…Many EHRs contain functions that allow for automated capture of structed data from EHR text notes. (17) We worked with an Epic® analyst to develop a tool using this EHR functionality to capture total ADR (tADR), or the proportion of all colonoscopies regardless of indication in which at least one adenoma is found. Total ADR has previously been shown to correlate with true ADR.…”

Section: Methodsmentioning

confidence: 99%

Leveraging Electronic Medical Record Functionality to Capture Adenoma Detection Rate

Jones

Scott

Espinoza

et al. 2021

Preprint

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IntroductionMeasuring the adenoma detection rate (ADR) is critical to providing quality care, however it is also challenging. We aimed to develop a tool using pre-existing electronic health record (EHR) functions to accurately and easily measure total ADR and to provide real-time feedback for endoscopists. MethodsWe utilized the Epic® EHR. With the help of an Epic® analyst, using existing tools, we developed a method by which endoscopy staff could mark whether an adenoma was detected for a given colonoscopy. Using these responses and all colonoscopies performed by the endoscopist recorded in the EHR, ADR was calculated in a report and displayed to endoscopists within the EHR. One endoscopist piloted the tool, and results of the tool were validated against a manual chart review.ResultsOver the pilot period the endoscopist performed 145 colonoscopies, of which 78 had adenomas. The tool correctly identified 76/78 colonoscopies with an adenoma and 67/67 of colonoscopies with no adenomas (97.4% sensitivity, 100% specificity, 98% accuracy). There was no difference in ADR as determined by the tool compared to manual review (53.1% vs. 53.8%, p=0.912).ConclusionsWe successfully developed and pilot tested a tool to measure ADR using existing EHR functionality.

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Automated Extraction of Structured Data from Text Notes in the Electronic Medical Record

Cited by 4 publications

References 4 publications

Potential accuracy of prehospital NIHSS‐based triage for selection of candidates for acute endovascular stroke therapy

Potential accuracy of prehospital NIHSS‐based triage for selection of candidates for acute endovascular stroke therapy

Leveraging electronic medical record functionality to capture adenoma detection rate

Leveraging Electronic Medical Record Functionality to Capture Adenoma Detection Rate

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