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

Nurses engaging with referral letters and discharge summaries: A qualitative study

AIMS

To investigate the ways that nurses engage with referral letters and discharge summaries, and the qualities of these documents they find valuable for safe and effective practice.

DESIGN

This study comprised a qualitative, case-study design within a constructivist paradigm using convenience sampling.

METHODS

Interviews were conducted with nurses to investigate their practices relating to referral letters and discharge summaries. Data collection also involved nurses' examination and evaluation of a diverse range of 10 referral letters and discharge summaries from medical records at two Australian hospitals through focus-group sessions. The data were transcribed and analysed inductively.

RESULTS

In all, 67 nurses participated in interviews or focus groups. Nurses indicated they used referral letters and discharge summaries to inform their work when caring for patients at different times throughout their hospitalisation. These documents assisted them with verbal handovers, to enable them to educate patients about their condition and treatment and to provide a high standard of care. The qualities of referral letters and discharge summaries that they most valued were language and communication, an awareness of audience and clinical knowledge, as well as balancing conciseness with comprehensiveness of information.

CONCLUSION

Nurses relied on referral letters and discharge summaries to ensure safe and effective patient care. They used these documents to enhance their verbal handovers, contribute to patient care and to educate the patient about their condition and treatment. They identified several qualities of these documents that assisted them in maintaining patient safety including clarity and conciseness of information.

IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE

It is important that referral letters and discharge summaries are written clearly, concisely and comprehensively because nurses use them as key sources of evidence in planning and delivering care, and in communicating with other health professionals in relaying goals of care and implementing treatment plans.

IMPACT

Nurses reported that they regularly used referral letters and discharge summaries as valuable sources of evidence throughout their patients' hospitalisation. The qualities of these documents which they most valued were language and communication styles, awareness of audience and clinical knowledge, as well as balancing conciseness with comprehensiveness of information. This research has important impact on the patient experience in relation to encouraging effective referral letter and discharge summary writing.

REPORTING METHOD

We have adhered to the relevant EQUATOR guidelines through the SRQR reporting method.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

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

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.

Deciphering clinical abbreviations with a privacy protecting machine learning system

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.

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

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

Assess use of abbreviations in clinical handover documents from inpatient hospital teams to general practitioners (GPs), and the interpretation of these abbreviations by GPs and hospital-based junior doctors.

METHODS

Retrospective audit of 802 discharge summaries completed during a one-week period in 2017 by a Queensland regional health service. GPs and local junior doctors then attempted interpretation of twenty relevant abbreviations.

RESULTS

99% (794) discharge summaries included abbreviations. 1612 different abbreviations were used on 16 327 occasions. The median number of abbreviations per discharge summary was 17 (range 0-86). 254 GPs and 62 junior doctors responded to a survey which found that no abbreviation was interpreted the same by all respondents. GPs and junior doctors were unable to offer any interpretation in 17.9% and 15.2% of cases respectively. GPs offered a greater range of interpretations than junior doctors, with a median of 9 and 3 different interpretations per abbreviation respectively. 94% (239) of GPs felt that the use of abbreviations in discharge summaries had the potential to impact patient care. 152 (60%) GPs 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 This article is protected by copyright. All rights reserved.

A deep database of medical abbreviations and acronyms for natural language processing

The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6–14% increase in abbreviation coverage; 28–52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations.

Measurement(s)	Controlled Vocabulary • Linguistic Form
Technology Type(s)	digital curation • data combination
Sample Characteristic - Location	United States of America

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14068949

Development and evaluation of novel ophthalmology domain-specific neural word embeddings to predict visual prognosis

OBJECTIVE

To develop and evaluate novel word embeddings (WEs) specific to ophthalmology, using text corpora from published literature and electronic health records (EHR).

MATERIALS AND METHODS

We trained ophthalmology-specific WEs using 121,740 PubMed abstracts and 89,282 EHR notes using word2vec continuous bag-of-words architecture. PubMed and EHR WEs were compared to general domain GloVe WEs and general biomedical domain BioWordVec embeddings using a novel ophthalmology-domain-specific 200-question analogy test and prediction of prognosis in 5547 low vision patients using EHR notes as inputs to a deep learning model.

RESULTS

We found that many words representing important ophthalmic concepts in the EHR were missing from the general domain GloVe vocabulary, but covered in the ophthalmology abstract corpus. On ophthalmology analogy testing, PubMed WEs scored 95.0 %, outperforming EHR (86.0 %) and GloVe (91.0 %) but less than BioWordVec (99.5 %). On predicting low vision prognosis, PubMed and EHR WEs resulted in similar AUROC (0.830; 0.826), outperforming GloVe (0.778) and BioWordVec (0.784).

CONCLUSION

We found that using ophthalmology domain-specific WEs improved performance in ophthalmology-related clinical prediction compared to general WEs. Deep learning models using clinical notes as inputs can predict the prognosis of visually impaired patients. This work provides a framework to improve predictive models using domain-specific WEs.