Role Play versus Small Group Discussion in Teaching Prescription Communication Skills: A Comparative Study on students of Phase Two of the Bachelor of Medicine and Bachelor of Surgery (MBBS) Course.

Introduction: Inadequate prescription communication skills of the medical graduates lead to poor therapeutic outcome and increased burden on the healthcare system. This gap has to be addressed through effective methods for teaching prescription communication skills to medical students. This study compared the effectiveness of Role play and Small Group Discussion (SGD) in teaching prescription communication skills to students of Phase Two of the Bachelor of Medicine and Bachelor of Surgery (MBBS) course. Methods: This was a prospective interventional study done in the Department of Pharmacology affiliated to the Department of Surgery at a Tertiary Care Centre in North Kerala for a period of 3 months from March 2021 to May 2021. After obtaining ethical clearance and informed consent, students of Phase Two of the MBBS course students (n=60) were selected by convenience sampling and divided into 2 groups by simple randomisation. The groups were taught prescription communication skills by Role play and SGD, respectively. Each group received six independent interventions on different topics. An Objective Structured Clinical Examination (OSCE) was conducted 1 week after each session for both groups. At the end of six sessions, feedback on the sessions was collected through a perception questionnaire. Quantitative data were compared using independent t-test. Ordinal data were expressed as percentages. Statistical analysis was done using online statistical calculators. P<0.05 was considered statistically significant. Results: Mean OSCE scores for each session was significantly higher in the Role play group than the SGD group. Mean total OSCE score of the Role play group was significantly higher than the SGD group (60.39±6.33, 47.79±4.27, P <0.001). Conclusion: Role play is more effective than SGD in teaching prescription communication skills to MBBS students. Students have shown more favourable perception towards Role play than SGD for teaching prescription communication skills.

COVID-19 infection, and reinfection, and vaccine effectiveness against symptomatic infection among health care workers in the setting of omicron variant transmission in New Delhi, India.

Background: Surge of SARS CoV-2 infections ascribed to omicron variant began in December 2021 in New Delhi. We determined the infection and reinfection density in a cohort of health care workers (HCWs) along with vaccine effectiveness (VE) against symptomatic infection within omicron transmission period (considered from December 01, 2021 to February 25, 2022. Methods: This is an observational study from the All India Institute of Medical Sciences, New Delhi. Data were collected telephonically. Person-time at risk was counted from November 30, 2021 till date of infection/ reinfection, or date of interview. Comparison of clinical features and severity was done with previous pandemic periods. VE was estimated using test-negative case-control design [matched pairs (for age and sex)]. Vaccination status was compared and adjusted odds ratios (OR) were computed by conditional logistic regression. VE was estimated as (1-adjusted OR)X100-. Findings: 11474 HCWs participated in this study. The mean age was 36⋅2 (±10⋅7) years. Complete vaccination with two doses were reported by 9522 (83%) HCWs [8394 (88%) Covaxin and 1072 Covishield (11%)]. The incidence density of all infections and reinfection during the omicron transmission period was 34⋅8 [95% Confidence Interval (CI): 33⋅5-36⋅2] and 45⋅6 [95% CI: 42⋅9-48⋅5] per 10000 person days respectively. The infection was milder as compared to previous periods. VE was 52⋅5% (95% CI: 3⋅9-76⋅5, p = 0⋅036) for those who were tested within 14-60 days of receiving second dose and beyond this period (61-180 days), modest effect was observed. Interpretation: Almost one-fifth of HCWs were infected with SARS CoV-2 during omicron transmission period, with predominant mild spectrum of COVID-19 disease. Waning effects of vaccine protection were noted with increase in time intervals since vaccination. Funding: None.

Healthcare performance management using integrated FUCOM-MARCOS approach: The case of India.

AIMS: The goal of this research is to propose a simpler and more efficient model for evaluating healthcare establishments (HCEs). With this motivation, this study aims to discover key performance indicators (KPIs) that affect HCE performance, present a ranking model for KPIs in Indian HCEs, and evaluate Indian HCEs using the identified and prioritised KPIs. MATERIAL AND METHODS: Through extensive literature review and expert opinions, this research identifies the various KPIs in HCEs, classifies them into six main categories, and prioritises them using the full consistency method (FUCOM). Further, well-known HCEs across northern India were evaluated and ranked using Measurement Alternatives and Ranking according to Compromise Solution. RESULTS: The 'technology adoption related indicators' is found as the most important main KPIs, whereas 'adequate number of hospital beds and bathrooms (IE5)' as the most dominating sub-category KPIs. Also, amongst the 20 evaluated Indian HCEs 'healthcare establishment-1 (HCE1)' was found to be the best performing HCE while 'healthcare establishment-12 (HCE12)' was found to be the worst-performing HCE. The stability and consistency of the results are ascertained by performing sensitivity analysis and comparing the results with other existing methodologies. CONCLUSION: The findings of this study are quite important for HCEs management to fully comprehend the key areas to improve upon so that managers can improve medical standards in a targeted manner. The developed prioritisation model and methodology shown in this paper will help and motivate managers and intellectuals of HCEs to evaluate and improve the HCE's performance.

SARS-CoV-2 Reinfection Rate and Estimated Effectiveness of the Inactivated Whole Virion Vaccine BBV152 Against Reinfection Among Health Care Workers in New Delhi, India.

Importance: A surge of COVID-19 occurred from March to June 2021, in New Delhi, India, linked to the B.1.617.2 (Delta) variant of SARS-CoV-2. COVID-19 vaccines were rolled out for health care workers (HCWs) starting in January 2021. Objective: To assess the incidence density of reinfection among a cohort of HCWs and estimate the effectiveness of the inactivated whole virion vaccine BBV152 against reinfection. Design, Setting, and Participants: This was a retrospective cohort study among HCWs working at a tertiary care center in New Delhi, India. Exposures: Vaccination with 0, 1, or 2 doses of BBV152. Main Outcomes and Measures: The HCWs were categorized as fully vaccinated (with 2 doses and ≥15 days after the second dose), partially vaccinated (with 1 dose or 2 doses with <15 days after the second dose), or unvaccinated. The incidence density of COVID-19 reinfection per 100 person-years was computed, and events from March 3, 2020, to June 18, 2021, were included for analysis. Unadjusted and adjusted hazard ratios (HRs) were estimated using a Cox proportional hazards model. Estimated vaccine effectiveness (1 - adjusted HR) was reported. Results: Among 15 244 HCWs who participated in the study, 4978 (32.7%) were diagnosed with COVID-19. The mean (SD) age was 36.6 (10.3) years, and 55.0% were male. The reinfection incidence density was 7.26 (95% CI: 6.09-8.66) per 100 person-years (124 HCWs [2.5%], total person follow-up period of 1696 person-years as time at risk). Fully vaccinated HCWs had lower risk of reinfection (HR, 0.14 [95% CI, 0.08-0.23]), symptomatic reinfection (HR, 0.13 [95% CI, 0.07-0.24]), and asymptomatic reinfection (HR, 0.16 [95% CI, 0.05-0.53]) compared with unvaccinated HCWs. Accordingly, among the 3 vaccine categories, reinfection was observed in 60 of 472 (12.7%) of unvaccinated (incidence density, 18.05 per 100 person-years; 95% CI, 14.02-23.25), 39 of 356 (11.0%) of partially vaccinated (incidence density 15.62 per 100 person-years; 95% CI, 11.42-21.38), and 17 of 1089 (1.6%) fully vaccinated (incidence density 2.18 per 100 person-years; 95% CI, 1.35-3.51) HCWs. The estimated effectiveness of BBV152 against reinfection was 86% (95% CI, 77%-92%); symptomatic reinfection, 87% (95% CI, 76%-93%); and asymptomatic reinfection, 84% (95% CI, 47%-95%) among fully vaccinated HCWs. Partial vaccination was not associated with reduced risk of reinfection. Conclusions and Relevance: These findings suggest that BBV152 was associated with protection against both symptomatic and asymptomatic reinfection in HCWs after a complete vaccination schedule, when the predominant circulating variant was B.1.617.2.

Artificial Intelligence against COVID-19 Pandemic: A Comprehensive Insight.

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.