We are witnessing a paradigm shift from a reactive healthcare model to Predictive, Preventive, Personalized, and Participatory medicine for a holistic and proactive management of health across the entire lifespan in the 21st century. Innovative strategies are needed for this to be implemented and affordable across diverse populations and in resource constraint settings. Embracing the tenets of traditional medicine practices that have evolved in different parts of the world to address local and global needs has found many proponents in recent times.
Modern technology such as big data analytics can help to make traditional knowledge-based medical approaches “evidence based” by unveiling previously “invisible” information about their efficacy, interactions, and effects.
Ayurveda is one of the oldest known medical systems in human history, an ancient Indian approach of holistic medicine that is still contemporary and encompasses all aspects of Predictive, Preventive, Personalized, and Participatory medicine for dealing with healthy and diseased individuals with an additional component of promotion of health.[1–3] Although, 75% of Indians avail Ayurveda, a major fraction map to rural India where it is the preferred primary healthcare system. In Ayurveda, individuals are classified on the basis of their constitution types Prakriti that allows the practitioners to ascertain their baseline homeostatic states, assess perturbations during disease states (Vikriti), and recommend customized therapy for restoring homeostasis.[2,3] A multipronged approach using genomics (Ayurgenomics) has provided the scientific basis of Prakriti principles of Ayurveda for understanding human genetic individuality and its potential in stratified medicine.[4–11]
The exhaustive documentation of Ayurveda provides a generic framework that allow practitioners to diversify, evolve, and customize a variety of approaches in disease management keeping the local biodiversity, demographics, and spatiotemporal aspects in context. A trained Ayurveda practitioner can treat a multitude of disease even though they may specialize with respect to treatment modalities and therapies. This, however, to a modern audience conveys the impression that the system does not conform to set protocols. Because many preventive aspects of Ayurveda are enmeshed in Indian traditional living, most times these are also mistaken as grandmother’s recipes and concoctions. Even if registered Ayurveda practitioners prescribe interventions and treatments, individuals who avail both conventional and ayurvedic medicine for chronic conditions are hesitant to reveal this to a treating contemporary physician. Thus, one cannot really gauge the efficacy of Ayurveda during a combination therapy and only during rare cases of side effects it gets highlighted. Besides, evidence for well-controlled clinical trials and systematic research reviews are sparsely available in digital form.
To start objectivizing the beneficial aspects of Ayurveda for its integration with mainstream medicine, one needs to start collecting, structuring, and organizing its information without compromising the aspects of heterogeneity in disease management and individuality aspects. This should also be amenable to big data analytics framework and portability for sharing and dissemination in a language that is contemporary. The publication on “Big data analysis of traditional knowledge-based Ayurveda medicine” by the Chauhan and Brahmachari group published in the journal is first of its kind in this direction, which provides a snapshot of its practice through digital records. Three hundred Ayurveda doctors took part in the study which analyzed 353,000 patients’ data digitally captured records over teleconsultation and in-person consultations in the lead author’s telemedicine JIVA enterprise (Jiva Ayurveda, Faridabad, India). This study addressed a few aspects, such as the general demographics of population, kinds of diseases where individuals approach an Ayurveda practitioner, what practitioners cure, and the perceived side effects due to drug–herbal interactions, etc. The study has first curated and collated descriptive stats from records of individuals’ profiles with respect to age, sex, region, chronicity, nature of disease perturbation described by Vikriti, disease morbidity and comorbid conditions, and reported effectiveness of the treatment. It reveals that a bulk of the fraction comprise individuals who have chronic noncommunicable diseases for more than 5 years and comprise 5 main organ system, namely, digestive (30.6%), endocrine (14.6%), skeleton (13.5%), skin (11.2%), nervous (7.6%), and respiratory (7.4%). Furthermore, they have used state-of-the-art big data analytics techniques to not only understand the underlying structure of data but also the comorbidity pattern and differences across age and gender.
For the first time, this study not only provides a framework for big data analytics on Ayurveda health records but also provides insights into diseases for which it could be a preferred choice for alternative treatment and the target population. For example, the digestive system gets highlighted as a major ailment for which individuals avail Ayurveda medicine across gender and different age groups. Through their analysis of patterns of prevalence in early and later ages for a disease of a particular organ system, the authors highlight the need for longitudinal trials on a controlled population to see if further progression in these diseases can be controlled through Ayurveda. One such example is of the endocrine system wherein obesity is observed between 20 and 40 years and diabetes in 40–60 years, respectively. Although biases in the kind of people who approach Ayurveda through telemedicine cannot be ruled out, the authors provide evidence of similar trends in telemedicine versus hospital settings.
Although it might be in authors’ interest to highlight the effectiveness of their treatment as a stakeholder with a declared commercial interest, digitization of records ensures testability of claims and also highlights aspects where there is a further scope for validation and development. For instance, it provides a framework that can be integrated and implemented in diverse settings, including modern clinics for assessing outcomes of alternative medicines. This is relevant in diseases where either the costs of the treatment become extremely prohibitive or the quality of life is majorly compromised due to long-term medications, dietary restrictions, or side effects. These would be important in autoimmune conditions and complex diseases such as rheumatoid arthritis, Celiac’s, Parkinson’s, Alzheimer’s, multiple sclerosis, ulcerative colitis, and inflammatory bowel diseases to name a few. Also under cancerous conditions, where comorbid conditions primarily arise due to side effects of intensive therapies, patients are increasingly seeking alternative therapies. Comprehending this enormous maze of information on clinical phenotypes, objective clinical measures from advanced imaging instruments multiomic read outs and other state-of-art intervention technologies, however, need a Big Data framework. This also provides an opportunity for participation of other stakeholders for the development of an innovative framework that could enable aggregation of information from a heterogeneous set of practitioners and from individuals’ experience who avail dual system of medicine and in diverse settings. An immense opportunity now exists for (1) increasing Ayurveda’s acceptability through building scientific credence, (2) providing affordable healthcare through its stratified approach and integration in preventive and management aspects, and (3) its global adoption through a generic framework. Although telemedicine has the power of outreach and digitization, it might be worthwhile to consider that an increase in objectivity does not lead to fragmentation of the system’s essence of holistic and personalized approach. Long overlooked by Western science, traditional treatments can yield useful interventions for prevention and treatment. However, alternative medicine often provides patients with too many unscientific options.
At the same time, most physicians and patients are unaware about the relative benefits of ancient systems like Ayurvedic medicine. An informed choice based on scientific evidence can guide individuals to the most effective and appropriate health interventions. A closer scientific look at Ayurvedic medicine using modern technologies should be in the interest of the patients, physicians, and other stakeholders to improve quality of life across the health span.
. Lemonnier N, Zhou GB, Prasher B, et al. Traditional knowledge-based medicine: a review of history, principles, and relevance in the present context of P4 systems medicine. Prog Prev Med. 2017;7:e0011. doi. 10.1097/pp9.0000000000000011.
. Bhavana P, Greg G, Mitali M. Genomic insights into ayurvedic and western approaches to personalized medicine. J Genet. 2016; 95:209–228.
. Mukerji M, Prasher B. Kumar D, Chadwick R. Genomics and traditional Indian ayurvedic medicine. In: Genomics and Society: Ethical, Legal, Cultural and Socioeconomic Implications. 2016:Cambridge, MA: Academic Press; 271–292.
. Prasher B, Negi S, Aggarwal S, et al.; Indian Genome Variation Consortium. Whole genome expression and biochemical correlates of extreme constitutional types defined in Ayurveda. J Transl Med. 2008;6:48.
. Aggarwal S, Negi S, Jha P, et al.; Indian Genome Variation Consortium. EGLN1 involvement in high-altitude adaptation revealed through genetic analysis of extreme constitution types defined in Ayurveda. Proc Natl Acad Sci U S A. 2010;107:18961–18966.
. Sethi TP, Prasher B, Mukerji M. Ayurgenomics: a new way of threading molecular variability for stratified medicine. ACS Chem Biol. 2011;6:875–880.
. Juyal RC, Negi S, Wakhode P, et al. Potential of ayurgenomics approach in complex trait research: leads from a pilot study on rheumatoid arthritis. PLoS One. 2012;7: e45752.
. Govindaraj P, Nizamuddin S, Sharath A, et al. Genome-wide analysis correlates Ayurveda Prakriti. Sci Rep. 2015;5:15786.
. Rotti H, Mallya S, Kabekkodu SP, et al. DNA methylation analysis of phenotype specific stratified Indian population. J Transl Med. 2015;13:151.
. Chauhan NS, Pandey R, Mondal AK, et al. Western Indian rural gut microbial diversity in extreme Prakriti
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. Prasher B, Varma B, Kumar A, et al. Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations. J Ethnopharmacol. 2017;197:274–293.
. Singh H, Bhargava S, Ganeshan S, et al. Big Data Analysis of Traditional Knowledge-based Ayurveda Medicine. PROGREVMED 2018;3:e0020.