Modelling the factors in the adoption of artificial intelligence in Indian management institutes

PurposeThis study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship.Design/methodology/approachTo determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India.FindingsThis study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d' Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors.Practical implicationsThis study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes.Originality/valueAccording to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.

Long-term health consequences of COVID-19 in survivors hospitalised at a tertiary care hospital and their correlation with acute COVID-19 severity and associated risk factors.

OBJECTIVES: To determine post-COVID syndromes in the Indian population, correlating a wide spectrum of post-COVID manifestations with acute disease severity and associated risk factors. BACKGROUND: Post-COVID Syndrome (PCS) is defined as signs and symptoms that develop during or after acute COVID-19 infection. DESIGN OF STUDY: This is a prospective observational cohort with repetitive measurements. METHODS: The study followed RT-PCR confirmed COVID-19-positive survivors discharged from HAHC Hospital, New Delhi, for a period of 12 weeks. The patients were interviewed over the phone at 4 weeks and 12 weeks from the onset of symptoms for evaluation of clinical symptoms and health-related quality of life parameters. RESULTS: A total of 200 patients completed the study. At the baseline, 50% of the patients were categorised as severe based on their acute infection assessment. At 12 weeks after symptom onset, fatigue (23.5%), hair loss (12.5%) and dyspnea (9%) were the main persistent symptoms. The incidence of hair loss (12.5%), memory loss (4.5%) and brain fog (5%) were found to be increased as compared to the acute infection period. Severity of the acute COVID infection behaved as an independent predictor for the development of PCS, with high odds of experiencing persistent cough (OR = 13.1), memory loss (OR = 5.2) and fatigue (OR = 3.3). Further, 30% of subjects in the severe group experienced statistically significant fatigue at 12 weeks (p < .05). CONCLUSION: From the results of our study, it can be concluded that there is a huge disease burden of post-COVID Syndrome (PCS). The PCS comprised multisystem symptoms ranging from serious complaints of dyspnea, memory loss and brain fog to non-serious complaints of fatigue and hair loss. Severity of the acute COVID infection behaved as an independent predictor for the development of PCS. Our findings strongly recommend vaccination against COVID-19, for protection from disease severity as well as prevention of PCS. RELEVANCE TO CLINICAL PRACTICE: The findings of our study support the multidisciplinary approach required for the management of PCS with a team comprising of physicians, nurses, physiotherapists and psychiatrists working in close coordination for the rehabilitation of these patients. As nurses are considered the most trusted professionals in the community and the class of health workers associated with rehabilitation, focus should be given to educating them on PCS, which would prove to be an important strategy for efficient monitoring and long-term management of COVID-19 survivors.

Assessment of potential risk factors for COVID-19 among health care workers in a health care setting in Delhi, India -a cohort study.

INTRODUCTION: Healthcare workers (HCW) are most vulnerable to contracting COVID-19 infection. Understanding the extent of human-to-human transmission of the COVID-19 infection among HCWs is critical in managing this infection and for policy making. We did this study to estimate new infection by seroconversion among HCWs in recent contact with COVID-19 and predict the risk factors for infection. METHODS: A cohort study was conducted at a tertiary care COVID-19 hospital in New Delhi during the first and second waves of the COVID-19 pandemic. All HCWs working in the hospital during the study period who came in recent contact with the patients were our study population. The data was collected by a detailed face-to-face interview, serological assessment for anti- COVID-19 antibodies at baseline and end line, and daily symptoms. Potential risk factors for seroprevalence and seroconversion were analyzed by logistic regression keeping the significance at p<0.05. RESULTS: A total of 192 HCWs were recruited in this study, out of which 119 (62.0%) were seropositive. Almost all were wearing Personal protective equipment (PPE) and following Infection prevention and control (IPC) measures during their recent contact with a COVID-19 patient. Seroconversion was observed among 36.7% of HCWs, while 64.0% had a serial rise in the titer of antibodies during the follow-up period. Seropositivity was negatively associated with being a doctor (odds ratio [OR] 0.35, 95% Confidence Interval [CI] 0.18-0.71), having COVID-19 symptoms (OR 0.21, 95% CI 0.05-0.82), having comorbidities (OR 0.14, 95% CI 0.03-0.67), and received IPC training (OR 0.25, 95% CI 0.07-0.86), while positively associated with partial (OR 3.30, 95% CI 1.26-8.69), as well as complete vaccination for COVID-19 (OR 2.43, 95% CI 1.12-5.27). Seroconversion was positively associated with doctor as a profession (OR 13.04, 95% CI 3.39-50.25) and with partially (OR 4.35, 95% CI 1.07-17.65), as well as fully vaccinated for COVID-19 (OR 6.08, 95% CI 1.73-21.4). No significant association was observed between adherence to any IPC measures and PPE adopted by the HCW during the recent contact with COVID-19 patients and seroconversion. CONCLUSION: Almost all the HCW practiced IPC measures in these settings. High seropositivity and seroconversion are most likely due to concurrent vaccination against COVID-19 rather than recent exposure to COVID-19 patients. Further studies using anti-N antibodies serology may help us find the reason for the seropositivity and seroconversion among HCWs.

Effectiveness of BBV152/Covaxin and AZD1222/Covishield vaccines against severe COVID-19 and B.1.617.2/Delta variant in India, 2021: a multi-centric hospital-based case-control study.

OBJECTIVES: India introduced BBV152/Covaxin and AZD1222/Covishield vaccines in January 2021. We estimated the effectiveness of these vaccines against severe COVID-19 among individuals aged ≥45 years. METHODS: We did a multi-centric, hospital-based, case-control study between May and July 2021. Cases were severe COVID-19 patients, and controls were COVID-19 negative individuals from 11 hospitals. Vaccine effectiveness (VE) was estimated for complete (2 doses ≥ 14 days) and partial (1 dose ≥ 21 days) vaccination; interval between two vaccine doses and vaccination against the Delta variant. We used the random effects logistic regression model to calculate the adjusted odds ratios (aOR) with a 95% confidence interval (CI) after adjusting for relevant known confounders. RESULTS: We enrolled 1143 cases and 2541 control patients. The VE of complete vaccination was 85% (95% CI: 79-89%) with AZD1222/Covishield and 71% (95% CI: 57-81%) with BBV152/Covaxin. The VE was highest for 6-8 weeks between two doses of AZD1222/Covishield (94%, 95% CI: 86-97%) and BBV152/Covaxin (93%, 95% CI: 34-99%). The VE estimates were similar against the Delta strain and sub-lineages. CONCLUSION: BBV152/Covaxin and AZD1222/Covishield were effective against severe COVID-19 among the Indian population during the period of dominance of the highly transmissible Delta variant in the second wave of the pandemic. An escalation of two-dose coverage with COVID-19 vaccines is critical to reduce severe COVID-19 and further mitigate the pandemic in the country.