Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists

Zhou, Li; Blackley, Suzanne V.; Kowalski, Leigh T.; Doan, Raymond; Acker, Warren W.; Landman, Adam B.; Kontrient, Evgeni; Mack, David; Meteer, Marie; Bates, David W.; Goss, Foster R.

doi:10.1001/jamanetworkopen.2018.0530

Cited by 57 publications

(39 citation statements)

References 36 publications

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“…The last row represents results for all analyzed reports overall. The four most frequently added words overall (one, none, left, right) are marked in italics reported overall error rates of up to 7.4% [20]. Even though speech recognition solutions improved over the years, error detection still solely depends on proofreading.…”

Section: Discussionmentioning

confidence: 99%

Revealing the most common reporting errors through data mining of the report proofreading process

et al. 2020

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Objectives To investigate the most common errors in residents’ preliminary reports, if structured reporting impacts error types and frequencies, and to identify possible implications for resident education and patient safety. Material and methods Changes in report content were tracked by a report comparison tool on a word level and extracted for 78,625 radiology reports dictated from September 2017 to December 2018 in our department. Following data aggregation according to word stems and stratification by subspecialty (e.g., neuroradiology) and imaging modality, frequencies of additions/deletions were analyzed for findings and impression report section separately and compared between subgroups. Results Overall modifications per report averaged 4.1 words, with demonstrably higher amounts of changes for cross-sectional imaging (CT: 6.4; MRI: 6.7) than non-cross-sectional imaging (radiographs: 0.2; ultrasound: 2.8). The four most frequently changed words (right, left, one, and none) remained almost similar among all subgroups (range: 0.072–0.117 per report; once every 9–14 reports). Albeit representing only 0.02% of analyzed words, they accounted for up to 9.7% of all observed changes. Subspecialties solely using structured reporting had substantially lower change ratios in the findings report section (mean: 0.2 per report) compared with prose-style reporting subspecialties (mean: 2.0). Relative frequencies of the most changed words remained unchanged. Conclusion Residents’ most common reporting errors in all subspecialties and modalities are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). Structured reporting reduces overall error rates, but does not affect occurrence of the most common errors. Increased error awareness and measures improving report correctness and ensuring patient safety are required. Key Points • The two most common reporting errors in residents’ preliminary reports are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). • Structured reporting reduces the overall the error frequency in the findings report section by a factor of 10 (structured reporting: mean 0.2 per report; prose-style reporting: 2.0) but does not affect the occurrence of the two major errors. • Staff radiologist review behavior noticeably differs between radiology subspecialties.

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Section: Discussionmentioning

confidence: 99%

Revealing the most common reporting errors through data mining of the report proofreading process

et al. 2020

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“…Although significant EPR technological developments, such as artificial intelligence, natural speech analysis and integration with wearable devices, are expected, only a few of the articles reviewed discuss these advances [161][162][163]. To reap the next generation of benefits, values associated more closely with digital technology ethics and digitisation in the wider societal context require more consideration in, and could help inform, the EPR arena.…”

Section: Future Challengesmentioning

confidence: 99%

Examination and diagnosis of electronic patient records and their associated ethics: a scoping literature review

Jacquemard¹,

Doherty

Fitzsimons³

2020

BMC Med Ethics

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Background: Electronic patient record (EPR) technology is a key enabler for improvements to healthcare service and management. To ensure these improvements and the means to achieve them are socially and ethically desirable, careful consideration of the ethical implications of EPRs is indicated. The purpose of this scoping review was to map the literature related to the ethics of EPR technology. The literature review was conducted to catalogue the prevalent ethical terms, to describe the associated ethical challenges and opportunities, and to identify the actors involved. By doing so, it aimed to support the future development of ethics guidance in the EPR domain. Methods: To identify journal articles debating the ethics of EPRs, Scopus, Web of Science, and PubMed academic databases were queried and yielded 123 eligible articles. The following inclusion criteria were applied: articles need to be in the English language; present normative arguments and not solely empirical research; include an abstract for software analysis; and discuss EPR technology. Results: The medical specialty, type of information captured and stored in EPRs, their use and functionality varied widely across the included articles. Ethical terms extracted were categorised into clusters 'privacy', 'autonomy', 'risk/ benefit', 'human relationships', and 'responsibility'. The literature shows that EPR-related ethical concerns can have both positive and negative implications, and that a wide variety of actors with rights and/or responsibilities regarding the safe and ethical adoption of the technology are involved. Conclusions: While there is considerable consensus in the literature regarding EPR-related ethical principles, some of the associated challenges and opportunities remain underdiscussed. For example, much of the debate is presented in a manner more in keeping with a traditional model of healthcare and fails to take account of the multidimensional ensemble of factors at play in the EPR era and the consequent need to redefine/modify ethical norms to align with a digitally-enabled health service. Similarly, the academic discussion focuses predominantly on bioethical values. However, approaches from digital ethics may also be helpful to identify and deliberate about current and emerging EPR-related ethical concerns.

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“…In 6 von 10 Studien (siehe ▶ Tab. 2) zeigte sich bei der Analyse der Fehlerrate die Spracherkennungstechnologie im Vergleich zu einem Schreibdienst oder der Texteingabe als nicht überlegen [23], [24], [25], [26], [27], [28], [29], [30]. Die mit Spracherkennungstechnologie erstellten medizinischen Berichte enthielten im Durchschnitt der Studien eine um den Faktor 1,5 erhöhte Fehlerrate als bei Erstellung durch eine Schreibkraft, was mit einer Verlängerung der Doku- mentationszeit einhergehen kann [19].…”

Section: Analyse Der Genauigkeit Und Der Fehlerrateunclassified

Medizinische Spracherkennung im stationären und ambulanten Einsatz – Eine systematische Übersicht

Zuchowski

Pashayeva

Wohlrab

2020

Gesundh ökon Qual manag

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Zusammenfassung Einleitung Die medizinische Dokumentation dient neben der Sicherung einer ordnungsgemäßen Behandlung auch der umfänglichen Aufklärung des Patienten. Gleichsam nimmt sie einen großen Anteil der Arbeitszeit von Gesundheitsprofessionen in Anspruch. Häufig ist die zeitnahe Fertigstellung der Dokumentation durch den zunehmenden Zeitdruck im klinischen Alltag zusätzlich erschwert. Um den Zeitaufwand für die medizinische Dokumentation zu reduzieren, werden auch im ärztlichen Dienst immer häufiger Spracherkennungstechnologien in Einsatz gebracht. Dennoch sind die Auswirkungen dieser medizinischen Spracherkennung auf Bearbeitungszeit und Qualität der Dokumentation zum aktuellen Zeitpunkt noch wenig erforscht. Die bestehende Literatur erfasst bis dato nicht in ausreichendem Maße alle Möglichkeiten des aktuellen technologischen Standes. Ziel dieser Übersichtsarbeit ist daher, den aktuellen Forschungsstand zu den Auswirkungen und Folgen des Einsatzes von digitaler Spracherkennung auf Arbeitsprozesse der Gesundheitsprofessionen zu analysieren. Dazu wurden alle seit dem Jahr 2000 veröffentlichten Studien der jeweiligen medizinischen Fachbereiche berücksichtigt. Methode Die Autoren führten eine Literaturrecherche unter Verwendung der Datenbanken Medline via PubMed und Google Scholar durch. Die Datenbanken wurden nach den folgenden Stichwörtern durchsucht: „speech recognition“, „voice recognition“, „medical“ und „healthcare“. Unter der Annahme, dass sich erst die ab dem Jahr 2000 entwickelten Spracherkennungstechnologien bezüglich ihrer Genauigkeit für den medizinischen Bereich eignen, wurden Studien erst ab diesem Zeitpunkt in der vorliegenden Übersichtsarbeit berücksichtigt. Ergebnisse Insgesamt lieferten die 29 für diese Analyse herangezogenen Studien sehr differente Ergebnisse. Im Vergleich zu alternativen Diktatserviceleistungen, konnte sich die Spracherkennungstechnologie als zeit- und kosteneffizienter erweisen. Im Vergleich zur Texteingabe zeigte sich die Spracherkennung in den betrachteten Studien jedoch nicht überlegen, was mehrheitlich an der geringeren beobachteten Genauigkeit der Spracherkennung lag. Schlussfolgerung Der Einsatz von digitaler Spracherkennung im klinischen Betrieb bietet einige Vorteile bezüglich der reinen Dokumentationszeit. Die vorhandenen qualitativen Probleme in der genauen semantischen Umsetzung führen jedoch in den vorliegenden Studien noch nicht zu einer eindeutigen Evidenz der Vorteilhaftigkeit dieser Technologie. Aus diesem Grund besteht darüber hinaus Bedarf an weiteren Untersuchungen auf diesem Forschungsfeld.

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Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists

Cited by 57 publications

References 36 publications

Revealing the most common reporting errors through data mining of the report proofreading process

Revealing the most common reporting errors through data mining of the report proofreading process

Examination and diagnosis of electronic patient records and their associated ethics: a scoping literature review

Medizinische Spracherkennung im stationären und ambulanten Einsatz – Eine systematische Übersicht

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Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists

Cited by 57 publications

References 36 publications

Revealing the most common reporting errors through data mining of the report proofreading process

Revealing the most common reporting errors through data mining of the report proofreading process

Examination and diagnosis of electronic patient records and their associated ethics: a scoping literature review

﻿Medizinische Spracherkennung im stationären und ambulanten Einsatz – Eine systematische Übersicht

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Medizinische Spracherkennung im stationären und ambulanten Einsatz – Eine systematische Übersicht