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Structured panendoscopy reports improve report completeness and documentation time
by
Breuer, Thomas
, Ernst, Benjamin Philipp
, Potthast, Georg Long Fei
, Holderried, Martin
, Wolpert, Stephan Matthias
, Bärhold, Friederike
, Becker, Sven
, Pummerer, Lotte
in
631/67
/ 631/67/2322
/ Artificial intelligence
/ Clinical medicine
/ Deep learning
/ Digitalization
/ Documentation
/ Documentation - methods
/ Documentation - standards
/ Endoscopy
/ Endoscopy - methods
/ Female
/ Free text reports
/ Gastroscopy
/ Hospitals
/ Humanities and Social Sciences
/ Humans
/ Male
/ Middle Aged
/ multidisciplinary
/ Otolaryngology
/ Otorhinolaryngology
/ Panendoscopy
/ Patients
/ Physicians
/ Questionnaires
/ Readability
/ Regression analysis
/ Science
/ Science (multidisciplinary)
/ Software
/ Structured reporting
/ Surgeons
/ Surgery
/ Surveys and Questionnaires
/ Time Factors
/ Usability
/ User satisfaction
2025
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Structured panendoscopy reports improve report completeness and documentation time
by
Breuer, Thomas
, Ernst, Benjamin Philipp
, Potthast, Georg Long Fei
, Holderried, Martin
, Wolpert, Stephan Matthias
, Bärhold, Friederike
, Becker, Sven
, Pummerer, Lotte
in
631/67
/ 631/67/2322
/ Artificial intelligence
/ Clinical medicine
/ Deep learning
/ Digitalization
/ Documentation
/ Documentation - methods
/ Documentation - standards
/ Endoscopy
/ Endoscopy - methods
/ Female
/ Free text reports
/ Gastroscopy
/ Hospitals
/ Humanities and Social Sciences
/ Humans
/ Male
/ Middle Aged
/ multidisciplinary
/ Otolaryngology
/ Otorhinolaryngology
/ Panendoscopy
/ Patients
/ Physicians
/ Questionnaires
/ Readability
/ Regression analysis
/ Science
/ Science (multidisciplinary)
/ Software
/ Structured reporting
/ Surgeons
/ Surgery
/ Surveys and Questionnaires
/ Time Factors
/ Usability
/ User satisfaction
2025
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Structured panendoscopy reports improve report completeness and documentation time
by
Breuer, Thomas
, Ernst, Benjamin Philipp
, Potthast, Georg Long Fei
, Holderried, Martin
, Wolpert, Stephan Matthias
, Bärhold, Friederike
, Becker, Sven
, Pummerer, Lotte
in
631/67
/ 631/67/2322
/ Artificial intelligence
/ Clinical medicine
/ Deep learning
/ Digitalization
/ Documentation
/ Documentation - methods
/ Documentation - standards
/ Endoscopy
/ Endoscopy - methods
/ Female
/ Free text reports
/ Gastroscopy
/ Hospitals
/ Humanities and Social Sciences
/ Humans
/ Male
/ Middle Aged
/ multidisciplinary
/ Otolaryngology
/ Otorhinolaryngology
/ Panendoscopy
/ Patients
/ Physicians
/ Questionnaires
/ Readability
/ Regression analysis
/ Science
/ Science (multidisciplinary)
/ Software
/ Structured reporting
/ Surgeons
/ Surgery
/ Surveys and Questionnaires
/ Time Factors
/ Usability
/ User satisfaction
2025
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Structured panendoscopy reports improve report completeness and documentation time
Journal Article
Structured panendoscopy reports improve report completeness and documentation time
2025
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Overview
Even today, surgical reports are usually dictated in a free text form (FTR), leading to a wide range in report-quality. This study investigated the use of a fully structured panendoscopy report (SR) compared to FTRs. 64 panendoscopies were performed by three experienced head and neck surgeons. The surgical reports were created as both FTRs and SRs, which were examined regarding time to completion and content using a multilevel regression analysis. User satisfaction was evaluated using a questionnaire. There was no significant difference in time to complete the SRs compared to FTRs. The completeness ratings of SRs were significantly higher than for FTRs (81% vs. 66%,
p
< 0.001), leading to increased report quality. Overall user satisfaction was higher for SRs than for conventional FTRs (VAS 8.1 vs. 3.5,
p
< 0.001). The SRs proved to be fast to complete and more comprehensive with a higher completeness of content. Participating surgeons indicated that they preferred SRs over FTRs because of their advantages in terms of structure, guidance for inexperienced residents and non-native speakers. The data stratification also enables secondary data use to further develop deep learning algorithms in patient care and research.
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