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This study was aimed at exploring the new management mode of medical information processing and emergency first aid nursing management under the new artificial intelligence technology. This study will use the artificial intelligence algorithm to optimize medical information processing and emergency first aid nursing management process, in order to improve the efficiency of emergency department and first aid efficiency. The successful rescue rates of hemorrhagic shock, coma, dyspnea, and more than three organs injury were 96.7%, 92.5%, 93.7%, and 87.2%, respectively, after the emergency first aid nursing mode was used in the hospital emergency center. The success rates of first aid within three years were compared, which were 91.8%, 93.4%, and 94.2%, respectively, showing an increasing trend year by year. 255 emergency patients in five batches in June and five batches in July were selected as the research objects by convenience sampling method. Among them, 116 cases in June were taken as the experimental group, and 139 cases in July were taken as the control group, which was used to verify the efficiency of the design model in this study. The results showed that the triage time of the two groups was 8.16 ± 2.07 min and 19.21 ± 6.36 min, respectively, and the difference was statistically significant ( P < 0.01 ). The triage coincidence rates were 96.35% and 90.04%, respectively, and the difference was statistically significant ( P < 0.05 ). The research proved that the design of intelligent medical information processing and emergency first aid nursing management research model can effectively improve the triage efficiency of the wounded, assist the efficiency of emergency nursing of medical staff, and improve the survival rate of emergency patients, which is worthy of clinical promotion.
This study was aimed at exploring the new management mode of medical information processing and emergency first aid nursing management under the new artificial intelligence technology. This study will use the artificial intelligence algorithm to optimize medical information processing and emergency first aid nursing management process, in order to improve the efficiency of emergency department and first aid efficiency. The successful rescue rates of hemorrhagic shock, coma, dyspnea, and more than three organs injury were 96.7%, 92.5%, 93.7%, and 87.2%, respectively, after the emergency first aid nursing mode was used in the hospital emergency center. The success rates of first aid within three years were compared, which were 91.8%, 93.4%, and 94.2%, respectively, showing an increasing trend year by year. 255 emergency patients in five batches in June and five batches in July were selected as the research objects by convenience sampling method. Among them, 116 cases in June were taken as the experimental group, and 139 cases in July were taken as the control group, which was used to verify the efficiency of the design model in this study. The results showed that the triage time of the two groups was 8.16 ± 2.07 min and 19.21 ± 6.36 min, respectively, and the difference was statistically significant ( P < 0.01 ). The triage coincidence rates were 96.35% and 90.04%, respectively, and the difference was statistically significant ( P < 0.05 ). The research proved that the design of intelligent medical information processing and emergency first aid nursing management research model can effectively improve the triage efficiency of the wounded, assist the efficiency of emergency nursing of medical staff, and improve the survival rate of emergency patients, which is worthy of clinical promotion.
Background: A conspicuous consequence of gatekeeping arrangements in universal, tax-funded, single-payer health care systems is the long waiting times. Besides limiting equal access to care, long waiting times can have a negative impact on health outcomes. Long waiting times can create obstacles in a patient’s care pathway. Organization for Economic Co-operation and Development (OECD) countries have implemented various strategies to tackle this issue, but there is little evidence for which approach is the most effective. This literature review examined waiting times for ambulatory care. Objective: The aim was to identify the main policies or combinations of policies universal, tax-funded, and single-payer healthcare systems have implemented to improve the governance of outpatient waiting times. Methods: Starting from 1040 potentially eligible articles, a total of 41 studies were identified via a 2-step selection process. Findings: Despite the relevance of the issue, the literature is limited. A set of 15 policies for the governance of ambulatory waiting time was identified and categorized by the type of intervention: generation of supply capacity, control of demand, and mixed interventions. Even if a primary intervention was always identifiable, rarely a policy was implemented solo. The most frequent primary strategies were: guidelines implementation and/or clinical pathways, including triage, guidelines for referral and maxim waiting times (14 studies), task shifting (9 studies), and telemedicine (6 studies). Most studies were observational, with no data on costs of intervention and impact on clinical outcomes.
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