{"title":"Leveraging artificial intelligence to promote COVID-19 appropriate behaviour in a healthcare institution from north India: A feasibility study.","authors":"Madhur Verma, Moonis Mirza, Karan Sayal, Sukesh Shenoy, Soumya Swaroop Sahoo, Anil Goel, Rakesh Kakkar","doi":"10.25259/IJMR_337_2024","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13349,"journal":{"name":"Indian Journal of Medical Research","volume":"161 1","pages":"81-90"},"PeriodicalIF":2.7000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878674/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian Journal of Medical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.25259/IJMR_337_2024","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.