Skip to main content

The Internet of Medical Things in the Patient-Centered Digital Clinic’s Ecosystem

  • Conference paper
  • First Online:
Information Technology for Education, Science, and Technics (ITEST 2022)

Abstract

The article analyzes the prospects of introducing a patient-oriented digital clinic with IoMT support. The synthesis of data-generating personal medical components and AI, ML, DL, VR, AR, blockchain technologies in the environment of digital clinic based on IoMT to improve the quality and availability of health care is described. The object of research is the processes of providing medical care to patients and perspective medical services provided by updated outpatient clinics. It is determined that the introducing of patient-oriented digital clinic with IoMT technology and voice virtual medical assistants has significant benefits for outpatient clinics functionality, contributes to the evolution of digital clinics, opens new opportunities for diagnosis, treatment and diseases prevention of patients, so the demand for this component of modern eHealth ecosystem is growing and will grow in the future. It is established that the strengthening and promising component of a modern digital clinic is the creation of voice virtual medical assistants for the patient with support for AI, ML, AR, VR. It is justified that the proposed solution expands the functionality of the digital clinic and makes it a powerful tool in the management and care of patient health.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dwivedi, R., Mehrotra, D., Chandra, S.: Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: a systematic review. J. Oral Biol. Craniofac. Res. 12(2), 302–318 (2022). https://doi.org/10.1016/j.jobcr.2021.11.010

    Article  Google Scholar 

  2. Murphy, E.P., et al.: Are virtual fracture clinics during the COVID-19 pandemic a potential alternative for delivering fracture care? A systematic review. Clin. Orthop. Relat. Res. 478(11), 2610–2621 (2020). https://doi.org/10.1097/CORR.0000000000001388

    Article  Google Scholar 

  3. Nerpin, E., Toft, E., Fischier, J., Lindholm-Olinder, A., Leksell, J.: A virtual clinic for the management of diabetes-type 1: study protocol for a randomised wait-list controlled clinical trial. BMC Endocr. Disord. 20(1), 137 (2020). https://doi.org/10.1186/s12902-020-00615-3

    Article  Google Scholar 

  4. Healy, P., et al.: Virtual outpatient clinic as an alternative to an actual clinic visit after surgical discharge: a randomised controlled trial. BMJ Qual. Saf. 28(1), 24–31 (2019). https://doi.org/10.1136/bmjqs-2018-008171

    Article  Google Scholar 

  5. Boyd, C., et al.: Machine learning quantitation of cardiovascular and cerebrovascular disease: a systematic review of clinical applications. Diagnostics 11(3), 551 (2021). https://doi.org/10.3390/diagnostics11030551

    Article  Google Scholar 

  6. Murthy, N.S., Bethala, C.: Review paper on research direction towards cancer prediction and prognosis using machine learning and deep learning models. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03147-3

  7. Tan, K.R., et al.: Evaluation of machine learning methods developed for prediction of diabetes complications: a systematic review. J. Diabetes Sci. Technol. 3, 1–16 (2021). https://doi.org/10.1177/19322968211056917

    Article  Google Scholar 

  8. Gautam, R., Sharma, M.: Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. J. Med. Syst. 44(2), 1–24 (2020). https://doi.org/10.1007/s10916-019-1519-7

    Article  Google Scholar 

  9. Rutkowski, S., et al.: Use of virtual reality-based training in different fields of rehabilitation: a systematic review and meta-analysis. J. Rehabil. Med. 52(11), 1–16 (2020). https://doi.org/10.2340/16501977-2755

    Article  Google Scholar 

  10. Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., Vento, M.: Trends in IoT based solutions for health care: moving AI to the edge. Pattern Recogn. Lett. 135, 346–353 (2020). https://doi.org/10.1016/j.patrec.2020.05.016

    Article  Google Scholar 

  11. Poongodi, M., Sharma, A., Hamdi, M., Maode, M., Chilamkurti, N.: Smart healthcare in smart cities: wireless patient monitoring system using IoT. J. Supercomput. 77(11), 12230–12255 (2021). https://doi.org/10.1007/s11227-021-03765-w

    Article  Google Scholar 

  12. Coulby, G., Clear, A., Jones, O., Young, F., Stuart, S., Godfrey, A.: Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design. Biomed. Eng. Online 19(1), 80 (2020). https://doi.org/10.1186/s12938-020-00825-9

    Article  Google Scholar 

  13. Aghdam, Z.N., Rahmani, A.M., Hosseinzadeh, M.: The role of the Internet of Things in healthcare: future trends and challenges. Comput. Methods Programs Biomed. 199, 105903 (2021). https://doi.org/10.1016/j.cmpb.2020.105903

    Article  Google Scholar 

  14. de Queiroz, D.A., da Costa, C.A., de Queiroz, E.A.I.F., da Silveira, E.F., da Rosa Righi, R.: Internet of Things in active cancer treatment: a systematic review. J. Biomed. Inform. 118, 103814 (2021). https://doi.org/10.1016/j.jbi.2021.103814

  15. Mamdiwar, S.D., Shakruwala, Z., Chadha, U., Srinivasan, K., Chang, C.Y.: Recent advances on IoT-assisted wearable sensor systems for healthcare monitoring. Biosensors 11(10), 372 (2021). https://doi.org/10.3390/bios11100372

    Article  Google Scholar 

  16. Jagadeeswari, V., Subramaniyaswamy, V., Logesh, R., Vijayakumar, V.: A study on medical Internet of Things and Big Data in personalized healthcare system. Health Inf. Sci. Syst. 6(1), 1–20 (2018). https://doi.org/10.1007/s13755-018-0049-x

    Article  Google Scholar 

  17. Kamruzzaman, M.M., Alrashdi, I., Alqazzaz, A.: New opportunities, challenges, and applications of Edge-AI for connected healthcare in Internet of Medical Things for smart cities. J. Healthcare Eng. 2022, 1–6 (2022). https://doi.org/10.1155/2022/2950699

    Article  Google Scholar 

  18. Muna, A.: Internet of medical things and edge computing for improving healthcare in smart cities. Math. Probl. Eng. 2022, 1–10 (2022). https://doi.org/10.1155/2022/5776954

    Article  Google Scholar 

  19. Tiwari, A., Viney, D., Mohamed, A.M., Haider, A., Abolfazl, M., Mohammad, S.: Patient behavioral analysis with smart healthcare and IoT. Behav. Neurol. 2021, 1–9 (2021). https://doi.org/10.1155/2021/4028761

    Article  Google Scholar 

  20. Amin, S.U., Hossain, M.S.: Edge intelligence and the Internet of Things in healthcare: a survey. IEEE Access 9, 45–59 (2021). https://doi.org/10.1109/ACCESS.2020.3045115

    Article  Google Scholar 

  21. Alshehri, F., Muhammad, G.: A comprehensive survey of the Internet of Things (IoT) and AI-based smart healthcare. IEEE Access 9, 3660–3678 (2021). https://doi.org/10.1109/ACCESS.2020.3047960

    Article  Google Scholar 

  22. Veeramakali, T., Siva, R., Sivakumar, B., Senthil Mahesh, P.C., Krishnaraj, N.: An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model. J. Supercomput. 77(9), 9576–9596 (2021). https://doi.org/10.1007/s11227-021-03637-3

    Article  Google Scholar 

  23. Thomson, C., Beale, R.: Is blockchain ready for orthopedics? A systematic review. J. Clin. Orthop. Trauma 23(1), 101615 (2021). https://doi.org/10.1016/j.jcot.2021.101615

    Article  Google Scholar 

  24. Aujla, G.S., Jindal, A.: A decoupled blockchain approach for edge-envisioned IoT-based healthcare monitoring. IEEE J. Sel. Areas Commun. 39(2), 491–499 (2021). https://doi.org/10.1109/JSAC.2020.3020655

    Article  Google Scholar 

  25. Alkhateeb, A., Catal, C., Kar, G., Mishra, A.: Hybrid blockchain platforms for the Internet of Things (IoT): a systematic literature review. Sensors 22(4), 1304 (2022). https://doi.org/10.3390/s22041304

    Article  Google Scholar 

  26. Kamruzzaman, M.M., Bingxin, Y., Nazirul, I., Alruwaili, O., Min, W., Alrashdi, I.: Blockchain and fog computing in IoT-driven healthcare services for smart cities. J. Healthcare Eng. 2022(9957888), 1–13 (2022). https://doi.org/10.1155/2022/9957888

    Article  Google Scholar 

  27. Gunasekeran, D.V., Tseng, R.M., Tham, Y.C., Wong, T.Y.: Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. Digit. Med. 4(1), 40 (2021). https://doi.org/10.1038/s41746-021-00412-9

    Article  Google Scholar 

  28. Shamsabadi, A., et al.: Internet of things in the management of chronic diseases during the COVID-19 pandemic: a systematic review. Health Sci. Rep. 5(2), e557 (2022). https://doi.org/10.1002/hsr2.557

    Article  Google Scholar 

  29. Hrynzovskyi, A.M., Bielai, S.V., Kernickyi, A.M., Pasichnik, V.I., Vasischev, V.S., Minko, A.V.: Medical social and psychological aspects of assisting the families of the military personnel of Ukraine who performed combat tasks in extreme conditions. Wiadomosci Lekarskie 75(2), 310–317 (2022). https://pubmed.ncbi.nlm.nih.gov/35182141/

  30. Ruggiano, N., et al.: Chatbots to support people with dementia and their caregivers: systematic review of functions and quality. J. Med. Internet Res. 23(6), e25006 (2021). https://doi.org/10.2196/25006

    Article  Google Scholar 

  31. Oh, Y.J., Zhang, J., Fang, M.L.: A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. Int. J. Behav. Nutr. Phys. Activity 18(160) (2021). https://doi.org/10.1186/s12966-021-01224-6

  32. Azure IoT Platform: Azure IoT Hub. https://azure.microsoft.com/en-us/services/iot-hub/#overview

  33. Google Cloud IoT Platform: Google Cloud IoT solutions. https://cloud.google.com/solutions/iot

  34. AWS IoT Platform: AWS IoT services. https://aws.amazon.com/iot/

  35. IBM Watson™ IoT Platform: IoT solutions. https://www.ibm.com/cloud/internet-of-things

  36. Microsoft Cloud for Healthcare: Transform the Healthcare Journey. https://www.microsoft.com/en-us/industry/health/microsoft-cloud-for-healthcare

  37. ThingSpeak Platform: ThingSpeak for IoT Projects. https://thingspeak.com/

  38. ScienceSoft Platform: IoT Solutions for Healthcare. https://www.scnsoft.com/services/iot/medical

  39. Rahman, M.A., Hossain, M.S.: An Internet-of-medical-things-enabled edge computing framework for tackling COVID-19. IEEE Internet Things J. 8(21), 15847–15854 (2021). https://doi.org/10.1109/JIOT.2021.3051080

    Article  Google Scholar 

  40. Chalyi, A.V., Kryvenko, I.P., Chalyy, K.O.: Synergetic Integration of Traditional and AR-Content during Medical Informatics Studies. https://lib.iitta.gov.ua/id/eprint/727353

  41. Kryvenko, I.P., Chalyy, K.O.: Providing Authentic Learning in Online Courses by Tools of Augmented and Virtual Reality. https://lib.iitta.gov.ua/id/eprint/730975

  42. Kryvenko, I.P., Chalyy, K.O.: Modern eHealth Technologies and Patient-Centered Applications Usability. https://wiadlek.pl/05-2022

  43. Kalashchenko, S.I., Hrynzovskyi, A.M.: Immersion technologies influence on students’ psychophysiological status of the National guard military academy of Ukraine. Ukrayins’kyy zhurnal viys’kovoyi medytsyny 3(1), 60–66 (2022). https://doi.org/10.46847/ujmm.2022.1(3)-060

  44. Health Bot Overview: A managed service purpose-built for development of virtual healthcare assistants. https://docs.microsoft.com/en-us/azure/health-bot/overview

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Inna Kryvenko .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kryvenko, I., Hrynzovskyi, A., Chalyy, K. (2023). The Internet of Medical Things in the Patient-Centered Digital Clinic’s Ecosystem. In: Faure, E., Danchenko, O., Bondarenko, M., Tryus, Y., Bazilo, C., Zaspa, G. (eds) Information Technology for Education, Science, and Technics. ITEST 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-031-35467-0_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35467-0_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35466-3

  • Online ISBN: 978-3-031-35467-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics