Automating sedation state assessments using natural language processing.

INTRODUCTION: 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. DESIGN: A prospective observational study was conducted. METHODS: 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. RESULTS: 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. CONCLUSION: 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. CLINICAL RELEVANCE: 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).

Emotional intelligence of nurses caring for COVID-19 patients: A cross-sectional study.

PURPOSE: The aim of this study was to assess the emotional intelligence of nurses caring for COVID-19 patients. METHODS: This was a descriptive cross-sectional study that was conducted from May to July 2020 in Tehran, Iran. Nurses caring for patients with COVID-19 were requested to fill in Bradbury and Graves's questionnaire online using a questionnaire in electronic format. RESULTS: Finally 211 nurses completed the questionnaires. Most of the nurses were working in critical care wards and caring for critical patients (61.6). Nurses' emotional intelligence was reported to be 63.19 (8.22). In general, the nurses' emotional intelligence was moderate. Between the dimensions, self-awareness and self-management had the highest scores. Also, the lowest score was related to self-management. The ward type and complexity of care had no effect on the scores of emotional intelligence. Nurses caring for patients with moderate disease severity had a higher relationship management score than nurses caring for critically ill patients (P < 0.05). CONCLUSION: The total score of emotional intelligence was moderate. Due to the continuation of the COVID-19 pandemic and the possibility of mental and physical fatigue of health care workers, improving emotional intelligence can be effective in resilience and stability of the psychological status of employees.

Effectiveness of self-care program on the quality of life in patients with coronary artery disease undergoing cardiac rehabilitation: A Randomized clinical trial.

BACKGROUND: Coronary artery disease (CAD) is a life-threatening condition that causes physical and psychological disorders and decreases patients' quality of life (QoL). Performing proper educational self-care program may lead to higher QoL in these patients. This study was performed to investigate the effectiveness of a self-care educational program on QoL in patients with CAD. MATERIALS AND METHODS: This semi-experimental study was performed on 60 patients with CAD referred to the cardiac rehabilitation (CR) center of Vali Asr hospital in Qom, Iran, in 2018-2019. Patients were divided into control and intervention groups by randomized sampling. The self-care educational program was provided through lectures and booklet. Data collection was done using the "demographic and clinical data questionnaire," and "Seattle Angina questionnaire." Questionnaires were completed in both groups, before and at least 1 month after education. Analysis of the obtained data was performed using SPSS software (version 25), central indexes, Mann-Whitney test, and Wilcoxon test. RESULTS: No significant differences were observed between the two groups for demographics characteristics and quality of life before the intervention. Before the self-care program, the mean score of the QoL in the intervention and control group were 56.14 ± 9.75 and 58.46 ± 11.71, respectively. After that, the mean score of the QoL in the intervention and control group were 59.25 ± 10.56 and 59.7 ± 13.33, respectively. The statistical analysis showed significant differences in the mean scores of QoL in the intervention group before and after the intervention (P < 0.05). However, no statistically significant differences were seen in the control group before and after the study (P > 0.05). CONCLUSIONS: The self-care educational program improved the QoL in patients with CAD. Therefore, lectures and educational booklets should be considered by CR nurses.