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Securing E-health Networks from Counterfeit Medicine Penetration Using Blockchain

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Abstract

This paper attempts to explain a solution to tackle the problem of counterfeit medicines in India by proposing a resilient electronic health networks using blockchain. The distribution and consumption of fake medicines take thousands of lives every year. There are no effective measures to combat the network of the fake medicine syndicate in the country, and the stakeholders in the healthcare ecosystem have to work under trust-deficit relationships amongst them. Blockchain is a decentralized system of computer nodes, where each node stores the same data, and coexist with other nodes without having to trust them. The proposed solution is based on recording the medicine logistics requirements from medicine manufacturing to the patient on the blockchain network. If, at any stage, counterfeit medicine is introduced into the system, it will be detected immediately, and its further penetration will be stopped. The system is simulated using a hyper ledger fabric platform, and its performance is also compared with other existing methods. Results show that the system thus formed is computationally intensive but offers a reliable solution to the menace of fake medicines.

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References

  1. Federico, F. (2019). The five rights of medication administration. Retrieved January 15, 2019, from http://www.ihi.org/resources/Pages/ImprovementStories/FiveRightsofMedicationAdministration.aspx.

  2. Indian Pharmaceutical Industry, India brand equity foundation. (2019). Retrieved Accessed July 30, 2019, from https://www.ibef.org/industry/pharmaceutical-india.aspx.

  3. Singh, J. (2015). Fake drugs constitute 25% of domestic medicines market in India: ASSOCHAM. Retrieved January 15, 2019, from https://www.downtoearth.org.in/news/fake-drugs-constitute-25-of-domestic-medicines-market-in-india-assocham-45393.

  4. Verma, S., Kumar, R., & Philip, P. J. (2014). The business of counterfeit drugs in India: A critical evaluation. International Journal of Management and International Business Studies, 4(2), 141–148.

    Google Scholar 

  5. Nandan, D. (2018). @Blockchain: The next frontier for pharmaceutical supply chains. Retrieved January 15, 2019, from https://www.pharmalogisticsiq.com/logistics/articles/blockchain-the-next-frontier-for-pharmaceutical-supply-chains.

  6. Rajput, S. (2019). Mumbai: Police bust medicine theft racket at Tata Memorial Hospital. Retrieved December 28, 2019, from https://www.mid-day.com/articles/mumbai-police-bust-medicine-theft-racket-at-tata-memorial-hospital/15759168.

  7. Kuo, T.-T., Kim, H., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and health care applications. Journal of the American Medical Informatics Association, 24, 1211–1220.

    Article  Google Scholar 

  8. Swan, M. (2015). Blockchain thinking: The brain as a decentralized autonomous corporation. IEEE Technology and Society Magazine, 34, 41–52.

    Article  Google Scholar 

  9. https://www.ibm.com/blogs/blockchain/2017/05/thedifference-between-public-and-private-blockchain/. Last visited January 16, 2019.

  10. Nakamoto, S. (2009). Bitcoin: A peer-to-peer electronic cash system. Retrieved January 15, 2019, from https://bitcoin.org/bitcoin.pdf.

  11. Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the Internet of Things. IEEE Access, 4, 2292–2303.

    Article  Google Scholar 

  12. Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An overview of blockchain technology: Architecture, consensus, and future trends. In Proceedings of the 2017 IEEE international congress on Big Data (BigData Congress), Boston, MA, USA, pp. 557–564.

  13. Yin, S., Bao, J., Zhang, Y., & Huang, X. (2017). M2M security technology of CPS based on blockchains. Symmetry, 9, 193.

    Article  Google Scholar 

  14. Litoriya, R., & Kothari, A. (2013). An efficient approach for agile web based project estimation: AgileMOW. Journal of Software Engineering and Applications (Scientific Research USA), 6(6), 297–303.

    Article  Google Scholar 

  15. Rajput, G. S., & Litoriya, R. (2014). Corad agile method for Agile software cost estimation. Open Access Library Journal, 1, e579. https://doi.org/10.4236/oalib.1100579.

    Article  Google Scholar 

  16. Litoriya, R., Sharma, N., & Kothari, A. (2012). Incorporating cost driver substitution to improve the effort using Agile COCOMO II. In CSI sixth international conference on software engineering (CONSEG). https://doi.org/10.1109/CONSEG.2012.6349494.

  17. Litoriya, R., & Kothari, A. (2013). Cost estimation of web projects in context with Agile paradigm: Improvements and validation. International Journal of Software Engineering (A Publication of Software Engineering Competence Center - Egypt), 6(2), 91–114.

    Google Scholar 

  18. Weinberg, B. (2019). 10 major real use cases of blockchain in healthcare. Retrieved July 15, 2019, from https://openledger.info/insights/blockchain-healthcare-use-cases/.

  19. WHO [World Health Organisation]. (2010). Monitoring the building blocks of health systems. Geneva: World Health Organization. Retrieved January 15, 2019, from http://www.who.int/healthinfo/systems/WHO_MBHSS_2010_full_web.pdf.

  20. OECD. (2015). Health at a glance. Paris: OECD Publishing. https://doi.org/10.1787/19991312.

    Book  Google Scholar 

  21. Stegemann, S. (2015). The future of pharmaceutical manufacturing in the context of the scientific, social, technological and economic evolution. European Journal of Pharmaceutical Sciences, 90, 8–13.

    Article  Google Scholar 

  22. Srai, J. S., Harrington, T. S., Alinaghian, L., & Phillips, M. (2015). Evaluating the potential for the continuous processing of pharmaceutical products—A supply network perspective. Chemical Engineering and Processing, 97, 248–258.

    Article  Google Scholar 

  23. Harrington, T. S., Phillips, M. A., & Srai, J. S. (2017). Reconfiguring global pharmaceutical value networks through targeted technology interventions. International Journal of Production Research, 55(5), 1471–1487.

    Article  Google Scholar 

  24. IBM Institute for Business Value. (2016). Healthcare rallies for blockchains: Keeping patients at the center. Retrieved January 15, 2019, from https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=GBE03790USEN.

  25. Krawiec, R., et al. (2016). Blockchain: Opportunities for health care. Retrieved January 15, 2019, from https://www2.deloitte.com/content/dam/Deloitte/us/Documents/public-sector/us-blockchainopportunities-for-health-care.pdf.

  26. Schumacher, A. (2017). Reinventing healthcare: Towards a global, blockchain-based precision medicine ecosystem. Retrieved January 15, 2019, from https://www.researchgate.net/publication/317936859_Blockchain_Healthcare_-_2017_Strategy_Guide.

  27. Nugent, T., Upton, D., & Cimpoesu, M. (2016). Improving data transparency in clinical trials using blockchain smart contracts. F1000 Research, 5, 2541. Retrieved January 15, 2019, from https://f1000research.com/articles/5-2541/v1.

  28. Angraal, S., Krumholz, H. M., & Schulz, W. L. (2017). Blockchain technology: Applications in health care. Circulation: Cardiovascular Quality and Outcomes, 10(9), 1–3.

    Google Scholar 

  29. Alhadhrami, Z., Alghfeli, S., Alghfeli, M., Abedlla, J. A., & Shuaib, K. (2017). Introducing blockchains for healthcare. In Proceedings of the 2017 international conference on electrical and computing technologies and applications (ICECTA), Ras Al Khaimah, UAE, pp. 1–4.

  30. Pandey, M., Litoriya, R., & Pandey, P. (2018). An ISM approach for modeling the issues and factors of mobile app development. International Journal of Software Engineering and Knowledge Engineering, 28(07), 937–953. https://doi.org/10.1142/S0218194018400119.

    Article  Google Scholar 

  31. Pandey, M., Litoriya, R., & Pandey, P. (2019) Identifying causal relationships in mobile app issues: An interval type-2 fuzzy DEMATEL approach. Wireless Personal Communication, 108, 683–710. https://doi.org/10.1007/s11277-019-06424-9.

    Article  Google Scholar 

  32. Pandey, M., Litoriya, R., & Pandey, P. (2019). Application of fuzzy DEMATEL approach in analyzing mobile app issues. Programming and Computer Software, 45(5), 268–287. https://doi.org/10.1134/S0361768819050050.

    Article  Google Scholar 

  33. Pandey, P., & Litoriya, R. (2019). Legal/regulatory issues for MMBD in IoT. In S. Tanwar, S. Tyagi, & N. Kumar (Eds.), Multimedia big data computing for IoT applications concepts, paradigms and solutions (pp. 367–388). Singapore: Springer.

    Google Scholar 

  34. Nilay, S. (2004). Pharmaceutical supply chains: Key issues and strategies for optimisation. Computers & Chemical Engineering, 28(2004), 929–941.

    Google Scholar 

  35. Griggs, K. N., Ossipova, O., Kohlios, C. P., Baccarini, A. N., Howson, E. A., & Hayajneh, T. (2018). Healthcare blockchain system using smart contracts for secure automated remote patient monitoring. Journal of Medical Systems, 42, 130.

    Article  Google Scholar 

  36. Mettler, M. (2016). Blockchain technology in healthcare: The revolution starts here. In Proceedings of the IEEE 18th international conference on e-health networking, applications and services (Healthcom), Munich, Germany, pp. 1–3.

  37. Schöner, M. M., Kourouklis, D., Sandner, P., Gonzalez, E., & Förster, J. (2017). Blockchain technology in the pharmaceutical industry (pp. 1–9). FSBC working paper.

  38. Pandey, M., Litoriya, R., & Pandey, P. (2019). Novel approach for mobile based app development incorporating MAAF. Wireless Personal Communications, 107(4), 1687–1708. https://doi.org/10.1007/s11277-019-06351-9.

    Article  Google Scholar 

  39. Pandey, P., Litoriya, R., & Tiwari, A. (2018). A framework for fuzzy modelling in agricultural diagnostics. Journal Européen Des Systèmes Automatisés, 51, 203–223. https://doi.org/10.3166/jesa.51.203-223.

    Article  Google Scholar 

  40. Pandey, P., Kumar, S., & Shrivastava, S. (2015). A fuzzy decision making approach for analogy detection in new product forecasting. Journal of Intelligent and Fuzzy Systems, 28(5), 2047–2057. https://doi.org/10.3233/IFS-141483.

    Article  MathSciNet  Google Scholar 

  41. Pandey, P., Kumar, S., & Shrivastava, S. (2018). An efficient time series forecasting method exploiting fuzziness and turbulences in data. Intelligent Systems: Concepts, Methodologies, Tools, and Applications. https://doi.org/10.4018/978-1-5225-5643-5.ch078.

    Article  Google Scholar 

  42. Dagher, G. G., Mohler, J., Milojkovic, M., & Marella, P. B. (2018). Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology. Sustainable Cities and Society, 39, 283–297.

    Article  Google Scholar 

  43. Patel, V. (2018). A frame work for secure and decentralized sharing of medical imaging data via block chain consensus. Health Informatics Journal. https://doi.org/10.1177/1460458218769699.

    Article  Google Scholar 

  44. Pandey, P., & Litoriya, R. (2019). A predictive fuzzy expert system for crop disease diagnostic and decision support. In Fuzzy expert systems and applications in agricultural diagnosis (pp. 175–194). https://doi.org/10.4018/978-1-5225-9175-7.ch010.

  45. Pandey, M., Litoriya, R., & Pandey, P. (2018). Mobile App development based on agility function. Ingénierie Des Systèmes d’information RSTI Série ISI, 23(6), 19–44.

    Article  Google Scholar 

  46. Pandey, M., Litoriya, R., & Pandey, P. (2019). Empirical analysis of defects in handheld device applications. In M. Singh, P. K. Gupta, V. Tyagi, J. Flusser, T. Ören, & R. Kashyap (Eds.), Advances in computing and data sciences (pp. 103–113). Singapore: Springer. https://doi.org/10.1007/978-981-13-9942-8_10.

    Chapter  Google Scholar 

  47. Pandey, P., Kumar, S., & Srivastava, S. (2013). A critical evaluation of computational methods of forecasting based on fuzzy time series. International Journal of Decision Support System Technology, 5(1), 24–39. https://doi.org/10.4018/jdsst.2013010102.

    Article  Google Scholar 

  48. Pandey, P., Kumar, S., & Shrivastava, S. (2014). A unified strategy for forecasting of a new product. Decision, 41(4), 411–424. https://doi.org/10.1007/s40622-014-0065-x.

    Article  Google Scholar 

  49. Pandey, P., Kumar, S., & Shrivastava, S. (2017). An efficient time series forecasting method exploiting fuzziness and turbulences in data. International Journal of Fuzzy System Applications. https://doi.org/10.4018/IJFSA.2017100106.

    Article  Google Scholar 

  50. Pandey, M., Litoriya, R., & Pandey, P. (2019). Perception-based classification of mobile apps: A critical review. In A. K. Luhach, K. B. G. Hawari, I. C. Mihai, P.-A. Hsiung, & R. B. Mishra (Eds.), Smart computational strategies: Theoretical and practical aspects (pp. 121–133). Singapore: Springer. https://doi.org/10.1007/978-981-13-6295-8_11.

    Chapter  Google Scholar 

  51. Pandey, M., Litoriya, R., & Pandey, P. (2016). Mobile applications in context of big data: A survey. In Symposium on colossal data analysis and networking (CDAN), pp. 1–5. https://doi.org/10.1109/CDAN.2016.7570942.

  52. Pandey, P., & Litoriya, R. (2019). An activity vigilance system for elderly based on fuzzy probability transformations. Journal of Intelligent and Fuzzy Systems, 36(3), 2481–2494. https://doi.org/10.3233/JIFS-181146.

    Article  Google Scholar 

  53. Vledder, M., Friedman, J., Sjöblom, M., Brown, T., & Yadav, P. (2019). Improving supply chain for essential drugs in low-income countries: Results from a large scale randomized experiment in Zambia. Health Systems & Reform, 5(2), 158–177. https://doi.org/10.1080/23288604.2019.1596050.

    Article  Google Scholar 

  54. Mouaky, M., Berrado, A., & Benabbou, L. (2019). Using a kanban system for multi-echelon inventory management: The case of pharmaceutical supply chains. International Journal of Logistics Systems and Management (IJLSM), 32(3/4), 496–519.

    Article  Google Scholar 

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Pandey, P., Litoriya, R. Securing E-health Networks from Counterfeit Medicine Penetration Using Blockchain. Wireless Pers Commun 117, 7–25 (2021). https://doi.org/10.1007/s11277-020-07041-7

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