Conectar
Common goals for procedural sedation are to control pain and ensure the patient is not moving to an extent that is impeding safe progress or completion of the procedure. Clinicians perform regular assessments of the adequacy of procedural sedation in accordance with these goals to inform their decision-making around sedation titration and also for documentation of the care provided. Natural language processing could be applied to real-time transcriptions of audio recordings made during procedures in order to classify sedation states that involve movement and pain, which could then be integrated into clinical documentation systems. The aim of this study was to determine whether natural language processing algorithms will work with sufficient accuracy to detect sedation states during procedural sedation.
A prospective observational study was conducted.
Audio recordings from consenting participants undergoing elective procedures performed in the interventional radiology suite at a large academic hospital were transcribed using an automated speech recognition model. Sentences of transcribed text were used to train and evaluate several different NLP pipelines for a text classification task. The NLP pipelines we evaluated included a simple Bag-of-Words (BOW) model, an ensemble architecture combining a linear BOW model and a “token-to-vector” (Tok2Vec) component, and a transformer-based architecture using the RoBERTa pre-trained model.
A total of 15,936 sentences from transcriptions of 82 procedures was included in the analysis. The RoBERTa model achieved the highest performance among the three models with an area under the ROC curve (AUC-ROC) of 0.97, an F1 score of 0.87, a precision of 0.86, and a recall of 0.89. The Ensemble model showed a similarly high AUC-ROC of 0.96, but lower F1 score of 0.79, precision of 0.83, and recall of 0.77. The BOW approach achieved an AUC-ROC of 0.97 and the F1 score was 0.7, precision was 0.83 and recall was 0.66.
The transformer-based architecture using the RoBERTa pre-trained model achieved the best classification performance. Further research is required to confirm the that this natural language processing pipeline can accurately perform text classifications with real-time audio data to allow for automated sedation state assessments.
Automating sedation state assessments using natural language processing pipelines would allow for more timely documentation of the care received by sedated patients, and, at the same time, decrease documentation burden for clinicians. Downstream applications can also be generated from the classifications, including for example real-time visualizations of sedation state, which may facilitate improved communication of the adequacy of the sedation between clinicians, who may be performing supervision remotely. Also, accumulation of sedation state assessments from multiple procedures may reveal insights into the efficacy of particular sedative medications or identify procedures where the current approach for sedation and analgesia is not optimal (i.e. a significant amount of time spent in “pain” or “movement” sedation states).
Integrated care is seen as an enabling strategy in organising healthcare to improve quality, finances, personnel and sustainability. Developments in the organisation of maternity care follow this trend. The way care is organised should support the general aims and outcomes of healthcare systems. Organisation itself consists of a variety of smaller ‘elements of organisation’. Various elements of organisation are implemented in different organisations and networks. We will examine which elements of integrated maternity care are associated with maternal and neonatal health outcomes, experiences of women and professionals, healthcare spending and care processes.
We will conduct this review using the JBI methodology for scoping reviews and the reporting guideline PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews). We will undertake a systematic search in the databases PubMed, Scopus, Cochrane and PsycINFO. A machine learning tool, ASReview, will be used to select relevant papers. These papers will be analysed and classified thematically using the framework of the Rainbow Model of Integrated Care (RMIC). The Population Concept Context framework for scoping reviews will be used in which ‘Population’ is defined as elements of the organisation of integrated maternity care, ‘Context’ as high-income countries and ‘Concepts’ as outcomes stated in the objective of this review. We will include papers from 2012 onwards, in Dutch or English language, which describe both ‘how the care is organised’ (elements) and ‘outcomes’.
Since this is a scoping review of previously published summary data, ethical approval for this study is not needed. Findings will be published in a peer-reviewed international journal, discussed in a webinar and presented at (inter)national conferences and meetings of professional associations.
The findings of this scoping review will give insight into the nature and effectiveness of elements of integrated care and will generate hypotheses for further research.
The primary objective of this study is to examine the efficiency of various regenerative medicine approaches, such as platelet-rich plasma, cell therapy, stromal vascular fraction, exosomes and stem cell-conditioned medium, in the process of healing hypertrophic and keloid scars. Major databases including PubMed, Scopus and Web of Science were systematically searched, and based on the content of the articles and the inclusion and exclusion criteria, eight articles were selected. Out of these eight articles, there were two non-randomized clinical trial studies (25%), one randomized, single-blinded comparative study (12.5%), one retrospective clinical observational study (12.5%) and four randomized clinical trial studies (50%). We employed EndNote X8 and Google Sheets to conduct article reviews and extract relevant data. Following the review phase, the studies underwent analysis and categorization. In all eight reviewed studies, the effectiveness of regenerative medicine in treating hypertrophic scars and keloids has been proven. Out of these studies, five (62.5%) focused on the effectiveness of platelet-rich plasma, two study (25%) examined the effectiveness of stromal vascular fraction and one study (12.5%) explored the efficacy of stem cell-conditioned medium. In two studies (25%), the treatment methods were added to standard treatment, while in six studies (75%), regenerative medicine was used as the sole treatment method and compared with standard treatment. The use of these treatment methods did not result in any serious side effects for the patients. Regenerative medicine is an effective method with minimal side effects for the treatment of hypertrophic scars and keloids. It can be used as a monotherapy or in combination with other treatment methods. However, further studies are needed to thoroughly evaluate the effectiveness of all sub-branches of this method.
FreshRSS 