Frequency, comprehension and attitudes of physicians towards abbreviations in the medical record

Hamiel, Uri; Hecht, Idan; Nemet, Achia; Pe’er, Liron; Man, Vitaly; Hilely, Assaf; Achiron, Asaf

doi:10.1136/postgradmedj-2017-135515

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…The EHR corpus was marred by irregular spelling, irregular abbreviations, typographical errors, grammatical errors, and other irregularities absent from the OMIM corpus and the Textbook corpus. Others have noted the high frequency of irregular abbreviations, spelling, grammatical, and other writing errors in the clinical notes created by physicians (68)(69)(70)(71)(72) The general approach of the writers of the OMIM corpus was brevity. OMIM writers tended to use lists of clinical concepts such as "the patient had optic disk pallor, miosis, anisocoria, and a relative afferent pupil defect."…”

Section: Discussionmentioning

confidence: 99%

“…The lower accuracy for recognition of signs and symptoms in the EHR corpus (physician notes) deserves further comment. One way to improve automated recognition of signs and symptoms in physician notes is to encourage them to use structured rather than unstructured documentation ( 73 ). However, given physician burnout associated with clinical documentation ( 74 ), and physician distaste for structured documentation ( 75 ), it seems unlikely that physicians will adopt structured documentation for recording signs and symptoms.…”

Section: Discussionmentioning

confidence: 99%

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Enhanced neurologic concept recognition using a named entity recognition model based on transformers

Azizi

Hier

Wunsch

2022

Front. Digit. Health

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Although deep learning has been applied to the recognition of diseases and drugs in electronic health records and the biomedical literature, relatively little study has been devoted to the utility of deep learning for the recognition of signs and symptoms. The recognition of signs and symptoms is critical to the success of deep phenotyping and precision medicine. We have developed a named entity recognition model that uses deep learning to identify text spans containing neurological signs and symptoms and then maps these text spans to the clinical concepts of a neuro-ontology. We compared a model based on convolutional neural networks to one based on bidirectional encoder representation from transformers. Models were evaluated for accuracy of text span identification on three text corpora: physician notes from an electronic health record, case histories from neurologic textbooks, and clinical synopses from an online database of genetic diseases. Both models performed best on the professionally-written clinical synopses and worst on the physician-written clinical notes. Both models performed better when signs and symptoms were represented as shorter text spans. Consistent with prior studies that examined the recognition of diseases and drugs, the model based on bidirectional encoder representations from transformers outperformed the model based on convolutional neural networks for recognizing signs and symptoms. Recall for signs and symptoms ranged from 59.5% to 82.0% and precision ranged from 61.7% to 80.4%. With further advances in NLP, fully automated recognition of signs and symptoms in electronic health records and the medical literature should be feasible.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Enhanced neurologic concept recognition using a named entity recognition model based on transformers

Azizi

Hier

Wunsch

2022

Front. Digit. Health

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“…Subspecialty abbreviations are better understood by doctors working in the same field of work, 9,10,18 evidenced by a study on interpretation of ophthalmology abbreviations by Hamiel et al 19 . In this study, less than half of surveyed non‐ophthalmologists were able to correctly interpret 10% or more of ophthalmology abbreviations found in medical records, and no non‐ophthalmologists were able to interpret them all 19 …”

Section: Introductionmentioning

confidence: 56%

“…International literature has previously indicated that there is incomplete understanding of abbreviations by doctors, with doctors correctly interpreting only 37–73% of abbreviations across various specialties 14–17 . Subspecialty abbreviations are better understood by doctors working in the same field of work, 9,10,18 evidenced by a study on interpretation of ophthalmology abbreviations by Hamiel et al 19 . In this study, less than half of surveyed non‐ophthalmologists were able to correctly interpret 10% or more of ophthalmology abbreviations found in medical records, and no non‐ophthalmologists were able to interpret them all 19 …”

Section: Introductionmentioning

confidence: 99%

Danger in discharge summaries: abbreviations create confusion for both author and recipient

Turner

Coverdale

2022

Internal Medicine Journal

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Background: The transition from hospital inpatient care to medical care in the community is a high-risk period for adverse events. Inadequate communication, including low-quality or unavailable discharge summaries, has been shown to impact patient care.Aims: To assess use of abbreviations in clinical handover documents from inpatient hospital teams to general practitioners (GP), and the interpretation of these abbreviations by GP and hospital-based junior doctors.Methods: This is a retrospective audit of 802 discharge summaries completed during a 1-week period in 2017 by a Queensland regional health service. GP and local junior doctors then attempted interpretation of 20 relevant abbreviations.Results: A total of 99% (794) discharge summaries included abbreviations. A total of 1612 different abbreviations was used on 16 327 occasions. The median number of abbreviations per discharge summary was 17 (range 0-86). A total of 254 GP and 62 junior doctors responded to a survey, which found that no abbreviation was interpreted the same by all respondents. GP and junior doctors were unable to offer any interpretation in 17.9% and 15.2% of cases respectively. GP offered a greater range of interpretations than junior doctors, with a median of 9 and 3 different interpretations per abbreviation respectively. A total of 94% (239) of GP felt that the use of abbreviations in discharge summaries had the potential to impact patient care. A total of 152 (60%) GP felt that time spent clarifying abbreviations in discharge summaries could be excessive.Conclusions: Abbreviations are often used in discharge summaries, yet poorly understood. This has the potential to impact patient care in the transition period after hospitalisation.

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“…The number of abbreviations that prior methods have evaluated varies from 13 to 1116 15 – 18 , with separate models typically developed for each abbreviation. Many studies include only abbreviations that are “ambiguous” (i.e., multiple long forms for the abbreviation) 18 , although unambiguous abbreviations can be difficult even for physicians in other specialties to discern 19 , 20 . To detect abbreviations in text, prior research focuses on heuristics, such as string matching of abbreviations like “ivf,” rather than machine learning 20 – 22 .…”

Section: Introductionmentioning

confidence: 99%

Deciphering clinical abbreviations with a privacy protecting machine learning system

et al. 2022

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Physicians write clinical notes with abbreviations and shorthand that are difficult to decipher. Abbreviations can be clinical jargon (writing “HIT” for “heparin induced thrombocytopenia”), ambiguous terms that require expertise to disambiguate (using “MS” for “multiple sclerosis” or “mental status”), or domain-specific vernacular (“cb” for “complicated by”). Here we train machine learning models on public web data to decode such text by replacing abbreviations with their meanings. We report a single translation model that simultaneously detects and expands thousands of abbreviations in real clinical notes with accuracies ranging from 92.1%-97.1% on multiple external test datasets. The model equals or exceeds the performance of board-certified physicians (97.6% vs 88.7% total accuracy). Our results demonstrate a general method to contextually decipher abbreviations and shorthand that is built without any privacy-compromising data.

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Frequency, comprehension and attitudes of physicians towards abbreviations in the medical record

Cited by 9 publications

References 18 publications

Enhanced neurologic concept recognition using a named entity recognition model based on transformers

Enhanced neurologic concept recognition using a named entity recognition model based on transformers

Danger in discharge summaries: abbreviations create confusion for both author and recipient

Deciphering clinical abbreviations with a privacy protecting machine learning system

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