利用人工智能在印度北部的一家医疗机构促进COVID-19适当行为:可行性研究

IF 2.7 4区 医学 Q3 IMMUNOLOGY
Madhur Verma, Moonis Mirza, Karan Sayal, Sukesh Shenoy, Soumya Swaroop Sahoo, Anil Goel, Rakesh Kakkar
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引用次数: 0

摘要

背景与目的非药物干预措施(NPI)在遏制最初的COVID-19大流行浪潮中至关重要,但依从性很困难。本研究的主要目的是评估使用人工智能(AI)的医疗机构遵守npi的变化,并检查在医疗保健中使用AI系统的障碍和促进因素。方法于2022年4月至7月在印度北部一家医院进行干预前后研究。使用基于YOLO-V5和3D笛卡尔距离算法的AI模块,通过几个参数,如置信度阈值、相交超并阈值、图像大小、距离阈值(6英尺)和3D欧几里得距离估计来确定符合性。通过在标记测试数据集上评估模型的性能来进行验证,准确率为91.3%。干预措施包括对医院工作人员和访客的日常敏化和健康教育,信息、教育和交流(IEC)材料的展示,以及行政监督。与利益相关者进行了深入访谈,以评估可行性问题。三个阶段的标记事件在SPSS中使用单向方差分析检验进行比较。结果与干预前和干预后阶段相比,该模块在干预阶段标记出更高的社会距离依从性事件(P
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging artificial intelligence to promote COVID-19 appropriate behaviour in a healthcare institution from north India: A feasibility study.

Background & Objectives Non-pharmacological interventions (NPI) were crucial in curbing the initial COVID-19 pandemic waves, but compliance was difficult. The primary aim of this study was to assess the changes in compliance with NPIs in healthcare settings using Artificial intelligence (AI) and examine the barriers and facilitators of using AI systems in healthcare. Methods A pre-post-intervention study was conducted in a north-Indian hospital between April and July 2022. YOLO-V5 and 3D Cartesian distance algorithm-based AI modules were used to ascertain compliance through several parameters like confidence threshold, intersection-over-union threshold, image size, distance threshold (6 feet), and 3D Euclidean Distance estimation. Validation was done by evaluating model performance on a labelled test dataset, and accuracy was 91.3 per cent. Interventions included daily sensitization and health education for the hospital staff and visitors, display of information, education and communication (IEC) materials, and administrative surveillance. In-depth interviews were conducted with the stakeholders to assess the feasibility issues. Flagged events during the three phases were compared using One-way ANOVA tests in SPSS. Results Higher social distancing (SD) compliance events were flagged by the module in the intervention phase compared to the pre-intervention and post-intervention phases (P<0.05). Mask non-compliance was significantly lower (P <0.05) in the pre-intervention phase and highest in the post-intervention phase, with varied differences between different intervention phases in the registration hall and medicine out-patient department (OPD). The modules' data safety, transfer, and cost were the most common concerns. Interpretation & conclusions AI can supplement our efforts against the pandemic and offer indispensable help with minimal feasibility issues that can be resolved through adequate sensitization and training.

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来源期刊
CiteScore
5.80
自引率
2.40%
发文量
191
审稿时长
3-8 weeks
期刊介绍: The Indian Journal of Medical Research (IJMR) [ISSN 0971-5916] is one of the oldest medical Journals not only in India, but probably in Asia, as it started in the year 1913. The Journal was started as a quarterly (4 issues/year) in 1913 and made bimonthly (6 issues/year) in 1958. It became monthly (12 issues/year) in the year 1964.
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