EXPLORING PRESCRIPTION TRENDS AND DRUG COMPOSITION PATTERNS IN THE INDIAN PHARMACEUTICAL MARKET
Abstract
The study investigates prescription trends and drug composition patterns within the Indian pharmaceutical market using large-scale pharmaceutical data analytics and machine learning approaches. A comprehensive dataset containing 348,211 pharmaceutical records was utilized to examine prescription dependency, medicine pricing behavior, therapeutic composition complexity, and pharmaceutical distribution patterns. After preprocessing and cleaning, exploratory data
analysis, statistical modeling, correlation analysis, clustering techniques, and principal component analysis were employed to identify hidden pharmaceutical patterns and market structures. The findings revealed that prescription medicines dominate the Indian pharmaceutical sector and exhibit significantly higher pricing structures compared to non-prescription medicines. Frequently occurring pharmaceutical ingredients such as paracetamol, metformin, rabeprazole, and pantoprazole indicate strong market demand for medications associated with pain management, diabetes, and gastrointestinal disorders. The study further demonstrated that medicines with higher composition
complexity generally possess higher average prices, highlighting the influence of therapeutic formulation on pharmaceutical economics. Clustering analysis successfully identified distinct pharmaceutical market segments based on pricing behavior, prescription dependency, and ingredient complexity. The results emphasize the growing importance of healthcare informatics, pharmaceutical analytics, and artificial intelligence in improving pharmaceutical transparency, healthcare accessibility, and evidence-based decision-making. This research contributes to pharmaceutical informatics
literature by providing a scalable analytical framework for large-scale medicine data exploration within the Indian healthcare ecosystem.
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References
Arun, R. (2023). Impact of artificial intelligence on healthcare informatics: opportunities and challenges. Healthcare
informatics, 3(2).
Awrahman, B. J., Aziz Fatah, C., & Hamaamin, M. Y. (2022). A review of the role and challenges of big data in
healthcare informatics and analytics. Computational intelligence and neuroscience, 2022(1), 5317760.
Ayush, A. (2024). India medicines and drug info dataset [Data set].
https://www.kaggle.com/datasets/apkaayush/india-medicines-and-drug-info-dataset
Bhabad, S., Lamkhade, D., Koyate, S., Karanjkhele, K., Kale, V., & Doke, R. (2023). Transformative trends: A
comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research. IP Int J
Comprehensive Adv Pharmacol, 8, 210-9.
Chalmers, J., Siska, M., Le, T., & Knoer, S. (2018). Pharmacy informatics in multihospital health systems:
opportunities and challenges. The Bulletin of the American Society of Hospital Pharmacists, 75(7), 457-464.
Devaraji, V., Sivaraman, J., & Prabhu, S. (2024). Large-scale computational screening of Indian medicinal plants
reveals Cassia angustifolia to be a potentially anti-diabetic. Journal of Biomolecular Structure and Dynamics, 42(1),
-210.
Dhiman, S. K., Gummadi, V., & Dureja, H. (2019). Partnership efforts–Their potential to reduce the challenges that
confront regulators and pharmaceutical industry. Applied Clinical Research, Clinical Trials and Regulatory
Affairs, 6(1), 7-17.
Festa, G., Kolte, A., Carli, M. R., & Rossi, M. (2022). Envisioning the challenges of the pharmaceutical sector in the
Indian health-care industry: a scenario analysis. Journal of Business & Industrial Marketing, 37(8), 1662-1674.
Fink, D. S., Schleimer, J. P., Sarvet, A., Grover, K. K., Delcher, C., Castillo-Carniglia, A., ... & Cerdá, M. (2018).
Association between prescription drug monitoring programs and nonfatal and fatal drug overdoses: a systematic
review. Annals of internal medicine, 168(11), 783-790.
Gonzalez Pena, O. I., López Zavala, M. Á., & Cabral Ruelas, H. (2021). Pharmaceuticals market, consumption trends
and disease incidence are not driving the pharmaceutical research on water and wastewater. International journal of
environmental research and public health, 18(5), 2532.
Hasan, M. E., Islam, M. J., Islam, M. R., Chen, D., Sanin, C., & Xu, G. (2023). Applications of artificial intelligence
for health informatics: A systematic review. J Artif Intell Med Sci, 4(2), 19-46.
Horwitz, J. R., Davis, C., McClelland, L., Fordon, R., & Meara, E. (2021). The importance of data source in
prescription drug monitoring program research. Health Services Research, 56(2), 268-274.
Jayatilake, S. M. D. A. C., & Ganegoda, G. U. (2021). Involvement of machine learning tools in healthcare decision
making. Journal of healthcare engineering, 2021(1), 6679512.
Kim, H. J., Yang, Y. M., & Choi, E. J. (2018). Use patterns of over-the-counter (OTC) medications and perspectives
on OTC medications among Korean adult patients with chronic diseases: gender and age differences. Patient
preference and adherence, 1597-1606.
Kleywegt, S., Payne, M., Ng, F., & Fletcher, T. (2019). Environmental loadings of active pharmaceutical ingredients
from manufacturing facilities in Canada. Science of the Total Environment, 646, 257-264.
Kohli, M., & Gill, S. (2020). Impact of family involvement on strategy and CEO compensation: Evidence from the
Indian pharmaceutical industry. Journal of Family Business Management, 10(3), 189-212.
Luo, Y., Kataoka, Y., Ostinelli, E. G., Cipriani, A., & Furukawa, T. A. (2020). National prescription patterns of
antidepressants in the treatment of adults with major depression in the US between 1996 and 2015: a population
representative survey based analysis. Frontiers in psychiatry, 11, 35.
Masic, I. (2022). Medical decision making-an overview. Acta Informatica Medica, 30(3), 230.
Mulinari, S., Martinon, L., Jachiet, P. A., & Ozieranski, P. (2021). Pharmaceutical industry self-regulation and nontransparency: country and company level analysis of payments to healthcare professionals in seven European
countries. Health Policy, 125(7), 915-922.
Nguyen, A., Lamouri, S., Pellerin, R., Tamayo, S., & Lekens, B. (2022). Data analytics in pharmaceutical supply
chains: state of the art, opportunities, and challenges. International Journal of Production Research, 60(22), 6888-
Ozawa, S., Billings, J., Sun, Y., Yu, S., & Penley, B. (2022). COVID-19 treatments sold online without prescription
requirements in the United States: cross-sectional study evaluating availability, safety and marketing of
medications. Journal of Medical Internet Research, 24(2), e27704.
Rani, D. (2020). An Analysis of the trends in Indian Pharmaceutical Trade. JAC: A Journal Of Composition Theory,
XIII, 724-734.
Ridgely, M. S., Buttorff, C., Wolf, L. J., Duffy, E. L., Tom, A. K., Damberg, C. L., ... & Vaiana, M. E. (2020). The
importance of understanding and measuring health system structural, functional, and clinical integration. Health
Services Research, 55, 1049-1061.
Selvaraj, C., Chandra, I., & Singh, S. K. (2022). Artificial intelligence and machine learning approaches for drug
design: challenges and opportunities for the pharmaceutical industries. Molecular diversity, 26(3), 1893-1913.
Sharma, S., & Modgil, S. (2020). TQM, SCM and operational performance: an empirical study of Indian
pharmaceutical industry. Business Process Management Journal, 26(1), 331-370.
Vich Vila, A., Collij, V., Sanna, S., Sinha, T., Imhann, F., Bourgonje, A. R., ... & Weersma, R. K. (2020). Impact of
commonly used drugs on the composition and metabolic function of the gut microbiota. Nature
communications, 11(1), 362.
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