Revolutionizing Radiological Analysis: The Future of French Language Automatic Speech Recognition in Healthcare.

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in the French language. The methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. This research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with electronic health records (EHRs) and its utility in medical education. This study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.

Documentation of paediatric head injuries in a mixed metropolitan emergency department.

OBJECTIVE: Head injuries are a common presentation of children to Australian EDs. Healthcare documentation is an important tool for enhancing patient care. In our study, we aimed to assess the adequacy of paediatric head injury documentation in a mixed ED. METHODS: A retrospective analysis of presentations to a mixed ED between 2017 and 2018. Children aged <16 years old with a primary diagnosis of head injury were included. Documentation items based on local head injury guidelines were assessed in both medical and nursing documentation. We compared cases aged <1 and ≥1 year. RESULTS: There were 427 presentations that met the case definition. Medical documentation was present in 422 cases and nursing documentation in 310 cases. In combined medical and nursing documentation, items poorly documented include blood pressure (BP; 21.3%) and secondary survey (16.9%). In solely medical documentation, least commonly documented items are high-risk bony injuries (22.5%), high-risk soft tissue injuries (22.3%), seizure (22.0%) and non-accidental injury (3.6%). Glasgow Coma Scale (GCS) was poorly documented in cases aged <1 year (10.9%, P < 0.001). CONCLUSIONS: The largest gaps in the documentation of paediatric head injuries were BP and paediatric GCS in infants. Future audits and educational strategies should focus on targeting clinically relevant items that are predictive of serious outcomes.

Provider Adoption of Speech Recognition and its Impact on Satisfaction, Documentation Quality, Efficiency, and Cost in an Inpatient EHR.

This study utilizes qualitative and quantitative methods to measure the adoption of speech recognition (SR) and its impact ON provider satisfaction, documentation quality, efficiency, and cost when used for clinical documentation within the electronic health record (EHR). Qualitative surveys gauged providers' expectations and experiences regarding documentation before and after SR implementation. A new methodology was developed to measure SR adoption as a proportion of total documentation volume. Quantitative data was collected from the EHR, medical transcription and SR solutions to measure SR adoption and cost savings. Study results revealed significant improvements in satisfaction, documentation quality, and efficiency among providers as a direct result of SR use. An improved provider experience correlated to an 81% reduction in monthly medical transcription costs, an increase from 20% to 77% in electronic clinical documentation adoption, and a 74% SR adoption rate.

Natural Language Processing-Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study.

BACKGROUND: The process of documentation in electronic health records (EHRs) is known to be time consuming, inefficient, and cumbersome. The use of dictation coupled with manual transcription has become an increasingly common practice. In recent years, natural language processing (NLP)-enabled data capture has become a viable alternative for data entry. It enables the clinician to maintain control of the process and potentially reduce the documentation burden. The question remains how this NLP-enabled workflow will impact EHR usability and whether it can meet the structured data and other EHR requirements while enhancing the user's experience. OBJECTIVE: The objective of this study is evaluate the comparative effectiveness of an NLP-enabled data capture method using dictation and data extraction from transcribed documents (NLP Entry) in terms of documentation time, documentation quality, and usability versus standard EHR keyboard-and-mouse data entry. METHODS: This formative study investigated the results of using 4 combinations of NLP Entry and Standard Entry methods ("protocols") of EHR data capture. We compared a novel dictation-based protocol using MediSapien NLP (NLP-NLP) for structured data capture against a standard structured data capture protocol (Standard-Standard) as well as 2 novel hybrid protocols (NLP-Standard and Standard-NLP). The 31 participants included neurologists, cardiologists, and nephrologists. Participants generated 4 consultation or admission notes using 4 documentation protocols. We recorded the time on task, documentation quality (using the Physician Documentation Quality Instrument, PDQI-9), and usability of the documentation processes. RESULTS: A total of 118 notes were documented across the 3 subject areas. The NLP-NLP protocol required a median of 5.2 minutes per cardiology note, 7.3 minutes per nephrology note, and 8.5 minutes per neurology note compared with 16.9, 20.7, and 21.2 minutes, respectively, using the Standard-Standard protocol and 13.8, 21.3, and 18.7 minutes using the Standard-NLP protocol (1 of 2 hybrid methods). Using 8 out of 9 characteristics measured by the PDQI-9 instrument, the NLP-NLP protocol received a median quality score sum of 24.5; the Standard-Standard protocol received a median sum of 29; and the Standard-NLP protocol received a median sum of 29.5. The mean total score of the usability measure was 36.7 when the participants used the NLP-NLP protocol compared with 30.3 when they used the Standard-Standard protocol. CONCLUSIONS: In this study, the feasibility of an approach to EHR data capture involving the application of NLP to transcribed dictation was demonstrated. This novel dictation-based approach has the potential to reduce the time required for documentation and improve usability while maintaining documentation quality. Future research will evaluate the NLP-based EHR data capture approach in a clinical setting. It is reasonable to assert that EHRs will increasingly use NLP-enabled data entry tools such as MediSapien NLP because they hold promise for enhancing the documentation process and end-user experience.

Comparison of abbreviation usage practices in two Philippine tertiary hospitals and perceived effects on medical training

OBJECTIVES:To (1) compare abbreviation usage practices in the Doctors' Orders and History Sheet in two tertiary hospitals in Cebu City, namely, Cebu Velez General Hospital (CVGH) and Vicente Sotto Memorial Medical Center (VSMMC); (2) determine why medical students and physicians use abbreviations and (3) determine the perceived effects on medical student training. METHODS:This is a descriptive cross-sectional study utilizing medical chart reviews as well as group and key informant interviews. Stratified Random Sampling with Proportionate Allocation was used to select 100 charts each from CVGH and VSMMC. Purposive sampling was done for key informants. RESULTS: All patients' clinical history, physical examination and doctors' orders in all departments of both hospitals contained abbreviations. First initialization was the most common form, e.g. BP (blood pressure). Non-universally-accepted abbreviations were common, e.g. HFD (heredofamilial disease). Potentially dangerous abbreviations were noted, e.g. d/c, D/C. Abbreviations were used to maintain patient-doctor confidentiality, save space and time, and for convenience. Perceived effects on medical training included speeding up of task performance. CONCLUSIONS: Use of abbreviations in medical charts among medical students and physicians in both private and public tertiary hospitals in the Philippines is a prevalent practice. While such has its perceived benefits, it also poses potential danger to patients because not all abbreviations are understood and used the same way. Medical schools and their training hospitals must initiate moves to standardize the use of abbreviations in medical education and promote awareness of their potential dangers. The authors suggest that potential dangers/benefits of abbreviations be formally introduced in medical school as a separate topic.