Towards Understanding ASR Error Correction for Medical Conversations

Mani, Anirudh; Palaskar, Shruti; Konam, Sandeep

doi:10.18653/v1/2020.nlpmc-1.2

Cited by 21 publications

(21 citation statements)

References 13 publications

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“…After screening the titles and abstracts of these articles, we assessed 144 full-text articles for eligibility. We included 20 articles [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] for our analysis (Fig. 1 and Supplementary Table 2).…”

Section: Study Selectionmentioning

confidence: 99%

“…1 and Supplementary Table 2). Of these, ten were conference proceedings [19][20][21]23,27,28,32,38 , seven were workshop proceedings 22,26,29,[34][35][36][37] , two were journal articles 24,25 , and three were Arxiv preprints 30,31,33 .…”

Section: Study Selectionmentioning

confidence: 99%

“…Although all 20 studies aimed to decrease the administrative burden of clinical documentation in some way, the specific approaches and the setting differed greatly among studies. Three studies focused on improving the ASR for clinical conversations as the first step towards accurately extracting information from them 19,21,36 . Eleven studies chose to manually transcribe the conversations and performed NLP tasks on the transcripts 20,22,24,25,27,[30][31][32]34,35,40 .…”

Section: Setting and Research Phasementioning

confidence: 99%

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The digital scribe in clinical practice: a scoping review and research agenda

et al. 2021

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The number of clinician burnouts is increasing and has been linked to a high administrative burden. Automatic speech recognition (ASR) and natural language processing (NLP) techniques may address this issue by creating the possibility of automating clinical documentation with a “digital scribe”. We reviewed the current status of the digital scribe in development towards clinical practice and present a scope for future research. We performed a literature search of four scientific databases (Medline, Web of Science, ACL, and Arxiv) and requested several companies that offer digital scribes to provide performance data. We included articles that described the use of models on clinical conversational data, either automatically or manually transcribed, to automate clinical documentation. Of 20 included articles, three described ASR models for clinical conversations. The other 17 articles presented models for entity extraction, classification, or summarization of clinical conversations. Two studies examined the system’s clinical validity and usability, while the other 18 studies only assessed their model’s technical validity on the specific NLP task. One company provided performance data. The most promising models use context-sensitive word embeddings in combination with attention-based neural networks. However, the studies on digital scribes only focus on technical validity, while companies offering digital scribes do not publish information on any of the research phases. Future research should focus on more extensive reporting, iteratively studying technical validity and clinical validity and usability, and investigating the clinical utility of digital scribes.

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Section: Study Selectionmentioning

confidence: 99%

Section: Study Selectionmentioning

confidence: 99%

Section: Setting and Research Phasementioning

confidence: 99%

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The digital scribe in clinical practice: a scoping review and research agenda

et al. 2021

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“…Furthermore, recently, there has been additional work on the analysis of medical conversation. [57][58][59][60][61] However, the corresponding models and methodologies for these studies are not publicly available for evaluation and comparison in this project.…”

Section: Background and Significancementioning

confidence: 99%

Natural Language Processing for Automated Annotation of Medication Mentions in Primary Care Visit Conversations

Ganoe

Barr

et al. 2021

Preprint

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Objectives: The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. Materials and Methods: Eight clinicians contributed to a dataset of 85 clinic visit transcripts, and ten transcripts were randomly selected from this dataset as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System (UMLS) controlled vocabulary to generate a list of medication candidates in the transcribed text, and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations. Results: Sixty-five transcripts with 1,121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9%. Discussion: Our medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software. Conclusion: Integration of our annotation system with clinical recording applications has the potential to improve patients' understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes.

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“…Dictation may have applications in many fields. One with many idiosyncratic challenges is medical dictation, where ASR systems are used to help medical personnel take notes and generate medical records (Miner et al, 2020;Mani et al, 2020). This poses challenges in the support of domain-specific jargon, which we will discuss in subsection 2.2.…”

Section: Horizontals: Asr Applicationsmentioning

confidence: 99%

Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future

2021

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Where have we been, and where are we going? It is easier to talk about the past than the future. These days, benchmarks evolve more bottom up (such as papers with code). There used to be more top-down leadership from government (and industry, in the case of systems, with benchmarks such as SPEC). Going forward, there may be more top-down leadership from organizations like MLPerf and/or influencers like David Ferrucci, who was responsible for IBM's success with Jeopardy, and has recently written a paper suggesting how the community should think about benchmarking for machine comprehension. Tasks such as reading comprehension become even more interesting as we move beyond English. Multilinguality introduces many challenges, and even more opportunities.

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Towards Understanding ASR Error Correction for Medical Conversations

Cited by 21 publications

References 13 publications

The digital scribe in clinical practice: a scoping review and research agenda

The digital scribe in clinical practice: a scoping review and research agenda

Natural Language Processing for Automated Annotation of Medication Mentions in Primary Care Visit Conversations

Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future

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