Medication Regimen Extraction from Medical Conversations

Selvaraj, Sai P.; Konam, Sandeep

doi:10.1007/978-3-030-53352-6_18

Cited by 13 publications

(16 citation statements)

References 11 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%

“…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 . Five studies used input data representative of the input of an implemented digital scribe (ASR transcripts or chat dialogs) 26,28,33,37,38 .…”

Section: Setting and Research Phasementioning

confidence: 99%

See 2 more Smart Citations

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%

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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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“…9 A recent study described a proof-of-concept digital scribe, with evaluation limited to eight doctor-patient conversations. 13 Machine learning research related to digital scribes includes clinical speech recognition, 14,15 extraction of clinical information from transcripts of medical conversations, [16][17][18][19] and summarization of medical conversations to generate medical notes. 20 While existing work has focused on machine learning to advance digital scribe research, no work to date has explored the relationship between what is exchanged between a doctor and a patient during a consultation and relevancy to the documentation of the encounter.…”

Section: Introductionmentioning

confidence: 99%

Identifying relevant information in medical conversations to summarize a clinician-patient encounter

Quiroz¹,

Laranjo²,

Kocaballı

et al. 2020

Health Informatics J

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To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions—such as digital scribes—must focus on identifying the 20% relevant information for automatically generating consultation summaries.

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Medication Regimen Extraction from Medical Conversations

Cited by 13 publications

References 11 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

Identifying relevant information in medical conversations to summarize a clinician-patient encounter

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