Effect of Expansion of Abbreviations and Acronyms on Patient Comprehension of Their Health Records

Liu, Lisa Grossman; Russell, David; Turchioe, Meghan Reading; Myers, Annie; Vawdrey, David K.; Creber, Ruth Masterson

doi:10.1001/jamanetworkopen.2022.12320

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Given new US legislation that mandates clinical notes be shared with all patients electronically, it is increasingly important to make this information understandable and useful to the more than 50 million patients who currently have access to their medical notes 2 . In a recent study, patient comprehension of 10 common medical abbreviations was found to be 62%, whereas expanding these abbreviations boosted comprehension to 95% 3 . Even clinicians face difficulty deciphering clinical notes due to idiosyncratic terminology and acronyms across clinical specialties and local parlance 4 .…”

Section: Introductionmentioning

confidence: 95%

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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Section: Introductionmentioning

confidence: 95%

Deciphering clinical abbreviations with a privacy protecting machine learning system

et al. 2022

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“…[3]. Minimizing the clarity issue with abbreviations could save a large portion of those documented 5% of lives, and researchers Liu et al showed that expanding the definitions of abbreviations overwhelmingly increased the patient comprehension in regards to their health records [4].…”

Section: Introductionmentioning

confidence: 99%

Findings on Ad Hoc Contractions

Choi

Taghva

2023

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Abbreviations are often overlooked, since their frequency and acceptance are almost second nature in everyday communication. Business names, handwritten notes, online messaging, professional domains, and different languages all have their own set of abbreviations. The abundance and frequent introduction of new abbreviations cause multiple areas of overlaps and ambiguity, which mean documents often lose their clarity. We reverse engineered the process of creating these ad hoc abbreviations and revealed some preliminary statistics on what makes them easier or harder to define. In addition, we generated candidate definitions for which it proved difficult for a word sense disambiguation model to select the correct definition.

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Diagnostic terms psychiatrists prefer to use for common psychotic and personality disorders

Cohen

Öngür

Babb

et al. 2022

Journal of Psychiatric Research

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Effect of Expansion of Abbreviations and Acronyms on Patient Comprehension of Their Health Records

Cited by 6 publications

References 4 publications

Deciphering clinical abbreviations with a privacy protecting machine learning system

Deciphering clinical abbreviations with a privacy protecting machine learning system

Findings on Ad Hoc Contractions

Diagnostic terms psychiatrists prefer to use for common psychotic and personality disorders

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