Cross-Disciplinary Innovations in Healthcare Delivery Systems

  • Authors

    • Delphina Sherly Indra Ganesan College of Physiotheraphy, Tiruchirappalli, Tamil Nadu, India. Author

    Published 2026-01-08

  • Cross-Disciplinary Healthcare, Iom, Telemedicine, Artificial Intelligence In Health, Digital Health, Smart Hospitals, Robotics In Surgery, Healthcare Operations Optimization

    Issue

    Vol. 1 No. 1 (2026)

    Section

    Articles

    How to Cite

    [1]
    D. Sherly, “Cross-Disciplinary Innovations in Healthcare Delivery Systems”, IJETMR, vol. 1, no. 1, pp. 46–61, Jan. 2026, Accessed: Mar. 02, 2026. [Online]. Available: https://worldcometresearchgroup.com/index.php/ijetmr/article/view/89

  • Abstract

    Healthcare delivery systems are undergoing rapid transformation driven by technological advancements, rising chronic diseases, workforce shortages, cost pressures, and the demand for equitable care. Traditional siloed healthcare models are increasingly inadequate to address these complex challenges, necessitating cross-disciplinary innovation that integrates medicine with engineering, data analytics, management science, and behavioral sciences. This paper examines interdisciplinary healthcare delivery models incorporating telemedicine, artificial intelligence, robotics, Internet of Medical Things, big data analytics, and digital therapeutics within the P4 medicine framework. Using systematic literature review, design-science research, and case-based analysis, the study evaluates innovations across technological integration, system redesign, clinical process enhancement, and socio-economic transformation. Findings indicate that digitally enabled hybrid systems outperform conventional models in treatment accuracy, turnaround time, accessibility, and operational sustainability. However, challenges such as interoperability, cybersecurity, ethical governance, and digital literacy remain barriers to large-scale adoption. The study concludes that cross-disciplinary collaboration is essential for building resilient, patient-centered, and future-ready healthcare systems.

  • References

    [1] K. Sood and S. Bhatia, “Telemedicine and e-health: Transforming healthcare delivery in remote regions,” Telemedicine Journal and e-Health, vol. 26, no. 9, pp. 1052–1060, 2020.

    [2] World Health Organization, Global Strategy on Digital Health 2020–2025, WHO Press, Geneva, Switzerland, 2021.

    [3] K. M. Hersh et al., “Impact of telehealth on patient engagement and clinical outcomes: A systematic review,” J. Med. Internet Res., vol. 24, no. 5, pp. 1–15, 2022.

    [4] G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.

    [5] S. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, pp. 24–29, 2019.

    [6] T. Davenport and R. Kalakota, “The potential for AI in healthcare,” Future Healthcare Journal, vol. 6, no. 2, pp. 94–98, 2019.

    [7] A. M. Okamura, “Haptics in robot-assisted surgery: Challenges and opportunities,” IEEE Trans. Biomed. Eng., vol. 61, no. 6, pp. 243–252, 2014.

    [8] Rane and G. M. Mohan, “Evolution of surgical robotics: Past, present, and future,” Int. J. Med. Robotics & Computer Assisted Surgery, vol. 14, no. 6, pp. 1–10, 2018.

    [9] Wu and Y. Mu, “Automation and lean methodologies in healthcare: Enhancing operational efficiency,” Healthcare Management Review, vol. 47, no. 3, pp. 167–177, 2022.

    [10] J. Gubbi et al., “Internet of Things (IoT): A vision, architecture elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.

    [11] P. Gope and B. Sikdar, “Secure and privacy-preserving data aggregation in IoMT,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6078–6092, 2020.

    [12] K. Islam et al., “Wearable medical sensors and devices: A review of challenges and opportunities,” Sensors, vol. 20, no. 23, pp. 1–29, 2020.

    [13] S. R. Morton et al., “Big data and analytics in healthcare: Promise and potential,” Health Information Science and Systems, vol. 8, no. 1, pp. 1–9, 2020.

    [14] P. K. Singh and D. R. Patel, “Predictive analytics in healthcare using machine learning,” IEEE Rev. Biomed. Eng., vol. 14, pp. 19–34, 2021.

    [15] G. Demiris and B. K. Hensel, “Technologies for an aging society: A human-centered design perspective,” Gerontechnology, vol. 17, no. 1, pp. 1–12, 2018.

  • Downloads

    • ga