Skip to main content

A Blockchain Patient-Centric Records Framework for Older Adult Healthcare

  • Conference paper
  • First Online:
Future Access Enablers for Ubiquitous and Intelligent Infrastructures (FABULOUS 2023)

Abstract

Patient-centric medical record systems provide patients control over their health data versus electronic health record (EHR) systems that are health provider based and typically geared around bill presentment and payment. There are several limitations in current EHR systems, such as details of healthcare not making it into the system, the loss of out of network healthcare and potential for malicious cyber exploitations. This research effort posits the potential of utilizing blockchain to support a patient-centered personal health record (PHR) system focused on the healthcare needs of older adults. Such a system expands the data collected to include every source of healthcare provider from optometrists to chiropractors to oncologists. Blockchain technologies would provide architecture and security for such a system.

Specifically, we present the framework geared to track older adult health records including modules that provide early disease detection and drug-drug interaction for the top chronic diseases experienced by older adults using various machine learning classification algorithms. The algorithms evaluate the entirety of diagnoses and symptoms to find co-morbidities that may be an indicator of latent disease such as early signs of dementia and Alzheimer’s diseases. The patient’s health information is interpreted by a nurse practitioner or hospitalist who can determine if a specialist needs to be involved to evaluate the predicted disease. The proposed approach will provide a secure way to have a comprehensive view of the patient’s health data and arm the patient with the most inclusive set of information for doctors to provide the best health care.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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. Alder, S.: Largest healthcare data breaches of 2021. HIPAA J. (2021). https://www.hipaajournal.com/largest-healthcare-data-breaches-of-2021/. Accessed 26 May 2023

  2. Bhuiyan, J.: Cyberattack disrupts hospital computer systems across US, hindering services. The Guardian (2023). https://www.theguardian.com/us-news/2023/aug/04/cyberattack-us-hospitals-california. Accessed 30 Aug 2023

  3. Erol, I., Oztel, A., Searcy, C., Medeni, IT.: Selecting the most suitable blockchain platform: a case study on the healthcare industry using a novel rough MCDM framework. Technol. Forecast. Soc. Chang. 186, 122132 (2023). https://doi.org/10.1016/j.techfore.2022.122132

    Article  Google Scholar 

  4. Subramaniam, H.: Co-morbidities in dementia: time to focus more on assessing and managing co-morbidities. Age Ageing 48(3), 314–315 (2019). https://doi.org/10.1093/ageing/afz007

    Article  Google Scholar 

  5. Poulos, J., Zhu, L., Shah, A.D.: Data gaps in electronic health record (EHR) systems: an audit of problem list completeness during the COVID-19 pandemic. Int. J. Med. Inform. 150, 104452 (2021). https://doi.org/10.1016/j.ijmedinf.2021.104452

    Article  Google Scholar 

  6. Philips, S., Willett, D., Das, S., Sara, E., Kannan, V., Zaha, V.: Use of an electronic health records registry to identify care gaps in cardiovascular care of cancer patients. J. Am. College Cardiol. 71(11_Supplement), A698–A698 (2018). https://doi.org/10.1016/S0735-1097(18)31239-7

  7. Nurse Journal Staff. Nurse Practitioner (NP) Career Overview. American Association of Nurse Practitioners (2023). https://www.aanp.org/about/all-about-nps/whats-a-nurse-practitioner. Accessed 3 April 2023

  8. Wang, Y., Kung, L., Byrd, T.A.: Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang. 126, 3–13 (2018). https://doi.org/10.1016/j.techfore.2015.12.019

    Article  Google Scholar 

  9. Polge, J., Robert, J., Le Traon, Y.: Permissioned blockchain frameworks in the industry: a comparison. ICT Express 7(2), 229–233 (2021). https://doi.org/10.1016/j.icte.2020.09.002

    Article  Google Scholar 

  10. Martin, C.B., Ogden, C.L., Hales, C.M., Gu, Q.: Prescription drug use in the United States, 2015–2016, NCHS Data Brief, no. 334, p. 8 (2019)

    Google Scholar 

  11. Vigliotti, M.G.: What do we mean by smart contracts? open challenges in smart contracts. Front. Blockchain 3 (2021). https://www.frontiersin.org/articles/https://doi.org/10.3389/fbloc.2020.553671. Accessed 7 May 2023

  12. Antwi, M., Adnane, A., Ahmad, F., Hussain, R., Habib ur Rehman, M., Kerrache, C.A.: The case of HyperLedger Fabric as a blockchain solution for healthcare applications. Blockchain: Res. Appl. 2(1), 100012 (2021). https://doi.org/10.1016/j.bcra.2021.100012

  13. Ekblaw, A., Azaria, A., Halamka, J.D., Lippman, A.: MedRec: using blockchain for medical data access and permission management. In: 2016 2nd International Conference on Open and Big Data (OBD), pp. 25–30. IEEE , Vienna, Austria (2016). https://doi.org/10.1109/OBD.2016.11

  14. Zhang, P., White, J., Schmidt, D.C., Lenz, G., Rosenbloom, S.T.: FHIRChain: applying Blockchain to securely and scalably share clinical data. Comput. Struct. Biotechnol. J. 16, 267–278 (2018). https://doi.org/10.1016/j.csbj.2018.07.004

    Article  Google Scholar 

  15. Chen, M., et al.: Blockchain-Enabled healthcare system for detection of diabetes. J. Inform. Secur. Appl. 58, 102771 (2021). https://doi.org/10.1016/j.jisa.2021.102771

    Article  Google Scholar 

  16. Lo, Y.-S., Yang, C.-Y., Chien, H.-F., Chang, S.-S., Lu, C.-Y., Chen, R.-J.: Blockchain-enabled iwellchain framework integration with the national medical referral system: development and usability study. J. Med. Internet Res. 21(12), e13563 (2019). https://doi.org/10.2196/13563

    Article  Google Scholar 

  17. Hassan, J., et al.: A lightweight proxy re-encryption approach with certificate-based and incremental cryptography for fog-enabled e-healthcare. Secur. Commun. Networks 2021, e9363824 (2021). https://doi.org/10.1155/2021/9363824

    Article  Google Scholar 

  18. Ismail, L., Zeadally, S.: Healthcare insurance frauds: taxonomy and blockchain-based detection framework (Block-HI). IEEE Commun. Mag. 23(4), 36–43 (2021). https://doi.org/10.1109/MITP.2021.3071534

    Article  Google Scholar 

  19. Aich,S., et al.: Protecting personal healthcare record using blockchain & federated learning technologies. In: 2022 24th International Conference on Advanced Communication Technology (ICACT), pp. 109–112 (2022). https://doi.org/10.23919/ICACT53585.2022.9728772

  20. Moon, S., et al.: Identifying information gaps in electronic health records by using natural language processing: gynecologic surgery history identification. J. Med. Internet Res. 24(1), e29015 (2022). https://doi.org/10.2196/29015

    Article  Google Scholar 

  21. Yan, X., et al.: Persistent cardiometabolic health gaps: can therapeutic care gaps be precisely identified from electronic health records. Healthcare 10(1), Art. no. 1 (2022). https://doi.org/10.3390/healthcare10010070

  22. Kuo, T.-T., Ohno-Machado, L. : ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks (2018). https://doi.org/10.48550/arXiv.1802.01746

  23. Mei, S., Zhang, K.: A machine learning framework for predicting drug–drug interactions. Sci. Rep. 11(1), Art. no. 1 (2021). https://doi.org/10.1038/s41598-021-97193-8

  24. Han, K., et al.: A review of approaches for predicting drug–drug interactions based on machine learning. Front. Pharmacol. 12 (2022). https://www.frontiersin.org/articles/https://doi.org/10.3389/fphar.2021.814858. Accessed 30 April 2023

  25. Mackey, T.K., et al.: ‘Fit-for-purpose?’ - challenges and opportunities for applications of blockchain technology in the future of healthcare. BMC Med. 17(1), 68 (2019). https://doi.org/10.1186/s12916-019-1296-7

    Article  Google Scholar 

  26. Chowdhury, M.J.M., et al.: A comparative analysis of distributed ledger technology platforms. IEEE Access 7, 167930–167943 (2019). https://doi.org/10.1109/ACCESS.2019.2953729

    Article  Google Scholar 

  27. Ateniese, G., Fu, K., Green, M., Hohenberger, S.: Improved proxy re-encryption schemes with applications to secure distributed storage. ACM Trans. Inf. Syst. Secur. 9(1), 1–30 (2006). https://doi.org/10.1145/1127345.1127346

    Article  Google Scholar 

  28. Yusof, M.M., Kuljis, J., Papazafeiropoulou, A., Stergioulas, L.K.: An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit). Int. J. Med. Inform. 77(6), 386–398 (2008). https://doi.org/10.1016/j.ijmedinf.2007.08.011

    Article  Google Scholar 

  29. Miyachi, K., Mackey, T.K.: HOCBS: a privacy-preserving blockchain framework for healthcare data leveraging an on-chain and off-chain system design. Inf. Process. Manage. 58(3), 102535 (2021). https://doi.org/10.1016/j.ipm.2021.102535

    Article  Google Scholar 

  30. Peng, L., Feng, W., Yan, Z., Li, Y., Zhou, X., Shimizu, S.: Privacy preservation in permissionless blockchain: a survey. Digital Commun. Networks 7(3), 295–307 (2021). https://doi.org/10.1016/j.dcan.2020.05.008

    Article  Google Scholar 

  31. Hylock, R.H., Zeng, X.: A Blockchain framework for patient-centered health records and exchange (HealthChain): evaluation and proof-of-concept study. J. Med. Internet Res. 21(8), e13592 (2019). https://doi.org/10.2196/13592

    Article  Google Scholar 

  32. Agbo, C.C., Mahmoud, Q.H., Eklund, J.M.: Blockchain technology in healthcare: a systematic review. Healthcare (Basel) 7(2), 56 (2019). https://doi.org/10.3390/healthcare7020056

    Article  Google Scholar 

  33. Jacob, L., Breuer, J., Kostev, K.: Prevalence of chronic diseases among older patients in German general practices. Ger. Med. Sci. 14, Doc03 (2016). https://doi.org/10.3205/000230

  34. Wei, P., Zhu, S.: An improved secure unidirectional proxy re-encryption scheme. In: 2013 5th International Conference on Intelligent Networking and Collaborative Systems, pp. 681–684 (2013). https://doi.org/10.1109/INCoS.2013.130

  35. Morris, J.M.: User interface design for older adults. Interact. Comput. 6(4), 373–393 (1994). https://doi.org/10.1016/0953-5438(94)90009-4

    Article  Google Scholar 

  36. Randeree, E., Whetstone, M.: Personal Health Records: Patients in Control. In: Health Information Systems : Concepts, Methodologies, Tools, and Applications, in “Contemporary Research in Information Science and Technology, vol. 4, , pp. 2111–2124. Medical Information Science Reference (an imprint of IGI Global), Hershey, PA (2010). https://www-igi-global-com.libweb.lib.utsa.edu/chapter/personal-health-records/49984. Accessed 13 May 2023

  37. Suggs, L.S.: A 10-year retrospective of research in new technologies for health communication. J. Health Commun. 11(1), 61–74 (2006). https://doi.org/10.1080/10810730500461083

    Article  Google Scholar 

  38. Coley, N., et al.: Factors predicting engagement of older adults with a coach-supported ehealth intervention promoting lifestyle change and associations between engagement and changes in cardiovascular and dementia risk: secondary analysis of an 18-month multinational randomized controlled trial. J. Med. Internet Res. e32006 (2022). https://doi.org/10.2196/32006

  39. Centers for Disease Control and Prevention: Promoting Health for Older Adults [Chronic Disease Fact Sheets]. Promoting Health for Older Adults, 8 September 2022. https://www.cdc.gov/chronicdisease/resources/publications/factsheets/promoting-health-for-older-adults.htm

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sheri Osborn .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Osborn, S., Choo, KK.R. (2024). A Blockchain Patient-Centric Records Framework for Older Adult Healthcare. In: Perakovic, D., Knapcikova, L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-50051-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50051-0_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50050-3

  • Online ISBN: 978-3-031-50051-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics