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

Auto-expansion software prompting reduces abbreviation use in electronic hospital discharge letters: an observational pre- and post-intervention study

BACKGROUND

Abbreviation use remains a significant cause of miscommunication among healthcare practitioners worldwide, creating uncertainty in interpretation and leading to poorer patient outcomes. This study aimed to assess the effectiveness of implementing auto-expansion prompts to reduce abbreviation use in electronic discharge letters (eDLs).

METHODS

Observational pre- and post-intervention study conducted in 2019 at a tertiary referral hospital in Western Sydney.

PARTICIPANTS

Junior medical officers (JMOs) in postgraduate years 1 and 2.

INTERVENTION

The intervention consisted of an email invitation to JMOs, outlining the risks of abbreviation use in eDLs, and providing instructions on how to use auto-expand prompts for 11 commonly used abbreviations in Cerner Powerchart.

PRIMARY OUTCOME MEASURE

Reduction in the frequency of use of 11 commonly used abbreviations selected for auto-expansion, measured by a 200 eDL audit pre- and post-intervention.

SECONDARY OUTCOME MEASURES

Reduction in the total number of abbreviations used and the mean number of abbreviations per eDL in the post-intervention audit compared to pre-intervention.

RESULTS

The baseline audit identified 1668 abbreviation uses in 200 eDLs, consisting of 350 different abbreviations. In the post-intervention audit, use of the 11 auto-expand abbreviations decreased by 43.6%, with decreased frequency of use for 9 of the 11 abbreviations. Post-intervention there was a 34.4% reduction in the total number of abbreviations used, with 1093 abbreviations identified in 200 eDLs.

CONCLUSIONS

Advising JMOs to implement auto-expansion prompts for specific abbreviations, in combination with education on the risks of abbreviation use, is a cheap and effective solution to reducing abbreviation use in eDLs. This approach could significantly improve clarity of communication between hospital doctors and community healthcare professionals during patient care transition, potentially reducing medical errors.

How to write a good discharge summary: a primer for junior physicians

A discharge summary is an important clinical document that summarizes a patient's clinical information and relevant events that occurred during hospitalization. It serves as a detailed handover of the patient's most recent and updated medical case records to general practitioners, who continue longitudinal follow-up with patients in the community and future medical care providers. A copy of the redacted/abbreviated form of the discharge summary is also usually given to patients and their caregivers so that important information, such as diagnoses, medication changes, return advice, and follow-up plans, is clearly documented. However, in reality, as discharge summaries are often written by junior physicians who may be inexperienced or have lacked medical training in this area, clinical audits often reveal poorly written discharge summaries that are unclear, inaccurate, or lack important details. Therefore, in this article, we sought to develop a simple "DISCHARGED" framework that outlines the important components of the discharge summary that we derived from a systematic search of relevant literature and further discuss several pedagogical strategies for training and assessing discharge summary writing.

Transforming healthcare documentation: harnessing the potential of AI to generate discharge summaries

BACKGROUND

Hospital discharge summaries play an essential role in informing GPs of recent admissions to ensure excellent continuity of care and prevent adverse events; however, they are notoriously poorly written, time-consuming, and can result in delayed discharge.

AIM

To evaluate the potential of artificial intelligence (AI) to produce high-quality discharge summaries equivalent to the level of a doctor who has completed the UK Foundation Programme.

DESIGN & SETTING

Feasibility study using 25 mock patient vignettes.

METHOD

Twenty-five mock patient vignettes were written by the authors. Five junior doctors wrote discharge summaries from the case vignettes (five each). The same case vignettes were input into ChatGPT. In total, 50 discharge summaries were generated; 25 by Al and 25 by junior doctors. Quality and suitability were determined through both independent GP evaluators and adherence to a minimum dataset.

RESULTS

Of the 25 AI-written discharge summaries 100% were deemed by GPs to be of an acceptable quality compared with 92% of the junior doctor summaries. They both showed a mean compliance of 97% with the minimum dataset. In addition, the ability of GPs to determine if the summary was written by ChatGPT was poor, with only a 60% accuracy of detection. Similarly, when run through an AI-detection tool all were recognised as being very unlikely to be written by AI.

CONCLUSION

AI has proven to produce discharge summaries of equivalent quality to a junior doctor who has completed the UK Foundation Programme; however, larger studies with real-world patient data with NHS-approved AI tools will need to be conducted.

Leveraging Large Language Models for Clinical Abbreviation Disambiguation

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods.

Communication in Sudden-Onset Major Incidents: Patterns and Challenges-Scoping Review

OBJECTIVE

To identify and describe patterns and challenges in communication in sudden-onset major incidents.

METHODS

Systematic scoping review according to Joanna Briggs Institute and PRISMA-ScR guidelines. Data sources included Cochrane Library, EMBASE, PubMed/MEDLINE, Scopus, SweMed+, Web of Science, and Google Scholar. Non-indexed literature was searched as well. The included literature went through data extraction and quality appraisal as per pre-registered protocol.

RESULTS

The scoping review comprised 32 papers from different sources. Communication breakdown was reported in 25 (78.1%) of the included papers. Inter-authority communication challenges were reported in 18 (56.3%) of the papers. System overload and incompatibility was described in 9 papers (28.1%). Study design was clearly described in 30 papers (93.8%).

CONCLUSIONS

The pattern in major incident communication is reflected by frequent breakdowns with potential and actual consequences for patient survival and outcome. The challenges in communication are predominantly inter-authority communication, system overload and incompatibility, and insufficient pre-incident planning and guidelines.