Prospective analysis of pain and pain management in an emergency department

Transformer encoder with multiscale deep learning for pain classification using physiological signals

Pain, a pervasive global health concern, affects a large segment of population worldwide. Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which often yield inconsistent results and are susceptible to bias. Recognizing this gap, our study introduces PainAttnNet, a novel deep-learning model designed for precise pain intensity classification using physiological signals. We investigate whether PainAttnNet would outperform existing models in capturing temporal dependencies. The model integrates multiscale convolutional networks, squeeze-and-excitation residual networks, and a transformer encoder block. This integration is pivotal for extracting robust features across multiple time windows, emphasizing feature interdependencies, and enhancing temporal dependency analysis. Evaluation of PainAttnNet on the BioVid heat pain dataset confirm the model's superior performance over the existing models. The results establish PainAttnNet as a promising tool for automating and refining pain assessments. Our research not only introduces a novel computational approach but also sets the stage for more individualized and accurate pain assessment and management in the future.

Analgesic Treatment and the Patients' Opinion on the Hospital Emergency Department

Aim: The aim of the study was to analyze whether a patient’s opinion is related to the effect of analgesic treatment. Methods: The study was conducted using a survey questionnaire among adult patients admitted to the hospital emergency department in March 2021. The Numerical Rating Scale (NRS) was used to assess pain. Patients were asked to rate the intensity of pain during their stay in the emergency department in three situations: (1) at admission; (2) during the stay; and (3) upon discharge. The relationships between qualitative variables were assessed by the chi-squared test. Significance was set at p < 0.05. Results: There was no statistical dependence between the patient’s opinion about the medical institution and results of the effectiveness of analgesic treatments (p = 0.056). The highest percentage of patients satisfied with the received treatment were those who did not feel pain during ED discharge (94.12%), and the lowest were those who complained of severe pain during ED discharge (63.91%). The average mark for the functioning and organization of the emergency department was 7.44 (±2.04). Only 54 patients (29.83%) had taken pain medication before deciding to visit ED. Conclusions: No statistical dependency between the effect of the analgesic treatment and the patient’s opinion has been observed. The majority of patients with pain discomfort visit emergency departments without looking for consultation in other locations or without taking analgesics. In the considered institution, patients were satisfied with the analgesic treatment, staff performance, and with the organization of the department.

The effect of the prospective information-based nursing quality management model on the improvement of management quality in emergency medicine nursing management

OBJECTIVE

This study aimed to explore the clinical effectiveness of applying a prospective information-based nursing quality management model in emergency medicine nursing management and its impact on the management quality.

METHODS

170 inpatients treated in the emergency department of our hospital from April 2018 to March 2019 were recruited as the study cohort and randomly divided into a control group and a study group, with 85 patients in each group. In the study, the patients in the control group were treated using the routine nursing mode, and the patients in the study group were treated using the prospective information-based nursing quality management model to compare the nursing management quality, the nursing satisfaction, the occurrence of adverse events, the nursing compliance, the emergency response times, and the survival rates between the two groups.

RESULTS

We found that the overall nursing management quality in the study group was better than it was in the control group. The nursing satisfaction rate in the study group (96.47%) was higher than the nursing satisfaction rate in the control group (83.53%) The rate of adverse events in the study group (5.88%) was lower than it was in the control group (16.47%), the nursing compliance rate in the study group (89.41%) was higher than it was in the control group (63.53%), the emergency response times in the study group were shorter than they were in the control group, and the postoperative survival rate in the study group was higher than it was in the control group, all with statistically significant differences (P<0.05).

CONCLUSION

The prospective information-based nursing quality management model has a significant influence on emergency medicine nursing management. It can significantly improve the nursing management quality, the patients' satisfaction with the nursing process, and the nursing compliance, and it can effectively avoid adverse events, so it is worthy of promotion.