LAUSR

In October 2020, Dr Harsh Vardhan, the Union Minister for Health & Family Welfare, Government of India, released the ‘National Clinical Management Protocol based on Ayurveda and Yoga for Management of COVID-19’. This protocol advocated the integration of Ayurveda and Yoga along with modern medical treatment. This was a historical moment for Integrative Medicine. It is important to note that the inclusion of AYUSH management in the protocol was based on critical analysis of available scientific literature, empirical research and learning from practice. The Journal of Ayurveda and Integrative Medicine (J-AIM) advocating rational integrative medicine for a decade, sees this as a much-awaited step towards ‘Swastha and Atma Nirbhar Bharat’ (healthy and self-reliant India). The Minister affirmed this saying “this protocol dealing with preventive and prophylactic measures is a significant step, not only in the management of COVID-19, but also in making traditional knowledge relevant to solving problems of the modern time". This decision is extremely important as it has policy implications towards addressing the neglect of, and dual standards towards, AYUSH contributions for the current pandemic's health crisis. While it is justified, and sometimes necessary, that Indian health system may borrow the best practices from biomedicine, it is serious that we neglect the health practices available in the AYUSH systems. The prejudice against AYUSH rests on the fallacious evidence model that values repetitive observations in controlled settings as the sole basis for evidence and ignores large-scale real-life human experiences. Several of the preventive practices based on Ayurveda and Yoga are embedded in the Indian culture, often making it difficult to isolate and evaluate these as stand-alone medical interventions. Their assessment needs advanced statistics, because analysis of such data is not as straight forward as in randomized controlled trials (RCTs). Today, statistical approaches to appraise real life evidence, should be oriented towards studying evidence and efficacy of AYUSH systems that do not always fit conventional reductionist analytical approaches. Evidence generation, particularly for health interventions is a complex exercise. Competent statisticians are aware of the limitations of Evidence-based Medicine (EBM) mainly due to designs based on inadequate capture of variability and meta-analysis based on samples sizes that are both small and insufficiently representative. Hence, extrapolation from such data sets has limitations. In addition to the RCTs, there is an urgent need to develop protocols for evidence-based practice pathways. Moving to practice-based