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Data-Driven Approach for Bio-medical and Healthcare pp 1–21Cite as

Personal Health Record Data-Driven Integration of Heterogeneous Data

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Part of the Data-Intensive Research book series (DIR)

Abstract

Evidence-based health care is a desired concept today, especially in these pandemic circumstances. However, the patients’ data are deficient, especially for patients with chronic diseases and their previous medical treatments and diagnosis if they must be treated in other institutions outside of the country of leaving or private institutions. All data for patients are stored in the institutional database as electronic health data (EHR), owned by healthcare institutions, hospitals, and medical practitioners. It is almost impossible to integrate such data because of the different low treatment of personal health data, especially when the patients are from different countries. One solution for providing evidence-based health care and medicine is creating a personal health record (PHR) owned and managed securely by the patient as a central point of the PHR data-driven integration of all data connected with the patient that are mostly heterogeneous. All data types for the patient’s health and medical condition can be integrated into cloud-based PHR owned and managed by the patient to provide their data with them where the patient resides. In addition, the lack of information on genetic disorders of patients of which they are not aware can contribute to an increase in the risk of patient death. This fact also leads to the need to integrate medical and health data with various biological and omics data, especially in pandemic circumstances. Although the urgent need for health care and medical data integration is apparent, personal data protection laws are rigorous. They do not allow much progress in the field without implementing healthcare data security and privacy standards. The proposed solution for this issue is establishing a personal health record as an integrative system for the patient applying HL7 (FHIR) standards. The well-known medical codding systems promise future data integrations. In addition, some attempts are made to associate diseases with data obtained from external environmental sensors that measure disease-related data. Using these data, called exposure data or exposome, one can clarify the increasing symptoms of diseases influenced by external factors. In the paper, we highlight a cloud-based system—a model of PHR-based health care that collects different data sources such as EHR, health information systems, and sensor measurement into the PHR. Medical data, PHR, numerous biological and exposome data, and data obtained from sensors, are considered, stored, and managed on the cloud.

Keywords

  • Personal health records
  • Electronic health records
  • Internet of medical things
  • PHR data-driven integration
  • Translational bioinformatics

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References

  1. Mountford N et al (2016) Connected Health in Europe: where are we today? University College Dublin

    Google Scholar 

  2. Eysenbach G (2001) What is e-health? J Med Internet Res 3(2)

    Google Scholar 

  3. Lu Y et al (2005) A review and a framework of handheld computer adoption in healthcare. Int J Med Informatics 74(5):409–422

    CrossRef  Google Scholar 

  4. Archer N et al (2011) Personal health records: a scoping review. J Am Med Inform Assoc 18(4):515–522

    CrossRef  Google Scholar 

  5. Fisher B, Bhavnani V, Winfield M (2009) How patients use access to their full health records: a qualitative study of patients in general practice. J R Soc Med 102(12):539–544

    CrossRef  Google Scholar 

  6. Ralston JD et al (2009) Group health cooperative’s transformation toward patient-centered access. Med Care Res Rev 66(6):703–724

    CrossRef  Google Scholar 

  7. Weppner WG et al (2013) Use of a shared medical record with secure messaging by older patients with diabetes. Diabetes Care 33(11):2314–2319

    CrossRef  Google Scholar 

  8. Bergmo TS (2015) How to measure costs and benefits of eHealth interventions: an overview of methods and frameworks. J Med Internet Res 17(11):e254

    CrossRef  Google Scholar 

  9. Bashshur RL et al (2013) Sustaining and realizing the promise of telemedicine. Telemed J E Health 19(5):339–345

    CrossRef  Google Scholar 

  10. ISO/DTR 20514:2005 (2005) Health informatics-electronic health record-definition, scope, and context. [Online]. Available at: http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=39525

  11. Metzger MH et al (2012) The use of regional platforms for managing electronic health records for the production of regional public health indicators in France. BMC Med Inform Decis Mak 12:28

    CrossRef  Google Scholar 

  12. Fernández-Cardeñosa G et al (2012) Analysis of cloud-based solutions on EHRs systems in different scenarios. J Med Syst 36(6):3777–3782

    CrossRef  Google Scholar 

  13. Fernández-Cardeñosa G (2012) Analysis of the cloud computing paradigm on mobile health records systems. In: Proceedings of the sixth international conference on innovative mobile and internet services in ubiquitous computing, Palermo, Italy. IEEE Computer Society, Washington, DC, USA, pp 927–932

    Google Scholar 

  14. Savoska S et al (2019) Design of cross border healthcare integrated system and its privacy and security issues. In: Computer and communications engineering, vol 13, No. 2/2019 first workshop on information security 2019, 9th Balkan conference in informatics

    Google Scholar 

  15. Rodrigues JJPC, de la Torre I, Fernández G et al (2013) Analysis of the security and privacy requirements of cloud-based electronic health records systems. J Med Internet Res 15(8):186–195

    CrossRef  Google Scholar 

  16. Mowafa H, Kushniruk AW, Borycki EM (eds) (2019) Big data, big challenges: a healthcare perspective: background, issues, solutions and research directions. Springer, Cham

    Google Scholar 

  17. Weber GM, Kenneth DM, Isaac SK (2014) Finding the missing link for big biomedical data. JAMA 311(24):2479–2480

    Google Scholar 

  18. Ekmekci PE (2017) Patients’ rights in cross-border healthcare (Directive 2011/24/EU) and how it applies to Turkey as a negotiating candidate country. Eur J Health Law 24(4):432–444

    Google Scholar 

  19. Fernandez-Luque L et al (2019) Health lifestyle data-driven applications using pervasive computing. Big data, big challenges: a healthcare perspective. Springer, Cham, pp 115–126

    CrossRef  Google Scholar 

  20. Barrett MP (2018) Framework for improving critical infrastructure cybersecurity. National Institute of Standards and Technology, Gaithersburg, MD, USA, Technical Report

    Google Scholar 

  21. Wan K, Vangalur A (2016) Characteristics and classification of big data in health care sector. In: 12th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD). IEEE, pp 1439–1446

    Google Scholar 

  22. Shrestha NM et al (2016) Enhanced e-health framework for security and privacy in healthcare system. In: Sixth international conference on digital information processing and communications (ICDIPC). IEEE, pp 75–79

    Google Scholar 

  23. Shin MS et al (2015) Constructing RBAC based security model in u-healthcare service platform. Sci World J

    Google Scholar 

  24. Gajanayake R et al (2014) Privacy oriented access control for electronic health records. E-J Health Inform 175–186

    Google Scholar 

  25. Bhartiya S et al (2017) Proposing hierarchy-similarity based access control framework: a multilevel Electronic Health Record data sharing approach for interoperable environment. J King Saud Univ-Comput Inf Sci 29(4):505–519

    Google Scholar 

  26. Kahani N et al (2016) Authentication and access control in e-health systems in the cloud. In: IEEE 2nd international conference on big data security on cloud (BigDataSecurity), IEEE international conference on high performance and smart computing (HPSC), and IEEE international conference on intelligent data and security (IDS). IEEE, pp 13–23

    Google Scholar 

  27. Azeez NA, Vyver CV (2018) Security and privacy issues in e-health cloud-based system: a comprehensive content analysis. Egypt Inform J

    Google Scholar 

  28. Patil SD et al (2018) Attribute based access control in personal health records using cloud computing

    Google Scholar 

  29. Lobach D et al (2012) Enabling health care decision-making through clinical decision support and knowledge management. Evid Rep Technol Assess (Full Rep) 203(203):1–784

    Google Scholar 

  30. El Morr C, Subercaze J (2010) Knowledge management in healthcare. In: Handbook of research on developments in E-health and telemedicine: technological and social perspectives. IGI Global, pp 490–510

    Google Scholar 

  31. Abidi SSR (2008) Healthcare knowledge management: the art of the possible. In: Knowledge management for health care procedures: from knowledge to global care, pp 1–20

    Google Scholar 

  32. Evans JM et al (2017) Organizational knowledge and capabilities in healthcare: deconstructing and integrating diverse perspectives. SAGE Open Med 5:2050312117712655

    Google Scholar 

  33. Bertino E et al (2006) Secure knowledge management: confidentiality, trust, and privacy. IEEE Trans Syst, Man, Cybern—Part A: Syst Hum 36(3):429–438

    CrossRef  Google Scholar 

  34. Mundy D, Chadwick DW (2005) Secure knowledge management for healthcare organizations. In: Creating knowledge-based healthcare organizations. IGI Global, pp 321–336

    Google Scholar 

  35. Silvestri S et al (2019) A big data architecture for the extraction and analysis of EHR data. In: IEEE world congress on services (SERVICES) 978-1-7281-3851-0/19/

    Google Scholar 

  36. Jayaratne M et al (2018) A data integration platform for patient-centered e-healthcare and clinical decision support. Future Gener Comput Syst. https://www.researchgate.net/publication/327924847

  37. Goodrum H et al (2020) Automatic classification of scanned electronic health record documents. Int J Med Informatics 144:104302

    CrossRef  Google Scholar 

  38. Serbanati LD (2020) Health digital state and smart EHR systems. Informatics Med Unlocked 21:100494

    CrossRef  Google Scholar 

  39. Sitapati A et al (2018) Integrated precision medicine: the role of electronic health records in delivering personalized treatment. Wiley Interdiscip Rev Syst Biol Med. Author manuscript; available in PMC

    Google Scholar 

  40. Rubí JNS, Gondim PRL (2019) IoMT platform for pervasive healthcare data aggregation, processing, and sharing based on OneM2M and OpenEHR. Sensors 19:4283

    CrossRef  Google Scholar 

  41. Callahan A et al (2019) Medical device surveillance with electronic health records. npj Dig Med 2:94. https://doi.org/10.1038/s41746-019-0168-z

  42. Liang J et al (2020) Privacy-preserving range query over multi-source electronic health records in public clouds. J Parallel Distrib Comput 135:127–139

    CrossRef  Google Scholar 

  43. Prados-Suárez B et al (2020) Providing an integrated access to HER using electronic health records aggregators, digital personalized health and medicine. In: Pape-Haugaard LB et al (eds) European federation for medical informatics (EFMI). IOS Press. https://doi.org/10.3233/SHTI200191

  44. Koren A et al (2019) Requirements and challenges in integration of aggregated personal health data for inclusion into formal electronic health records (EHR). In: 2nd international colloquium on smart grid metrology (SMAGRIMET), Split, Croatia. pp 1–5

    Google Scholar 

  45. Shah SM, Khan RA (2020) Secondary use of electronic health record: opportunities and challenges. arXiv:2001.09479v1 [cs.CY]

  46. Gamal A et al (2020) Standardized electronic health record data modeling and persistence: a comparative review. J Biomed Inform 103670

    Google Scholar 

  47. Bertagnolli MM et al (2020) The electronic health record as a clinical trials tool: opportunities and challenges. Clin Trials 1–6

    Google Scholar 

  48. Wanga Y et al (2020) Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records. J Biomed Inform 102:103364

    CrossRef  Google Scholar 

  49. Vidal ME et al (2019) Transforming heterogeneous data into knowledge for personalized treatments—a use case. Datenbank-Spektrum

    Google Scholar 

  50. Saripalle R et al (2019) Using HL7 FHIR to achieve interoperability in patient health record. J Biomed Inform 94:103188

    CrossRef  Google Scholar 

  51. Warner JL et al (2016) Integrating cancer genomic data into electronic health records. Genome Med 8:113

    Google Scholar 

  52. Savoska S et al (2020) Cloud based personal health records data exchange in the age of IoT: the cross4all project. In: Dimitrova V, Dimitrovski I (eds) ICT innovations 2020. Machine learning and applications, communications in computer and information science, vol 1316. Springer, Cham

    Google Scholar 

  53. Savoska S et al (2019) Design of cross border healthcare integrated system and its privacy and security issues. In: Proceedings of computer and communications engineering, workshop on information security, 9th Balkan conference in informatics, vol 13. pp 58–64

    Google Scholar 

  54. Savoska S, Jolevski I (2019) Architectural model of e-health PHR to support the integrated cross-border services. In: proceedings of ISGT conference 2018, Sofia. pp 42–49

    Google Scholar 

  55. Bocevska A et al (2018) Analysis of accessibility of the e-learning platforms according to the WCAG 2.0 standard compliance. In: VIII international conference on applied internet and information technologies, (ICAIIT 2018), Bitola, R. Macedonia. pp 26–31. ISBN 978-9989-870-80-4

    Google Scholar 

  56. Savoska S et al (2019) Towards integration exposome data and personal health records in the age of IoT. In: 11th ICT innovations conference, Ohrid, Republic of Macedonia. pp 237–246

    Google Scholar 

  57. Barouki R et al (2018) Integration of the human exposome with the human genome to advance medicine. Biochimie 152:155–158. https://doi.org/10.1016/j.biochi.2018.06.023.hal-02196327

    CrossRef  Google Scholar 

  58. Schulz S, Stegwee R, Chronaki C (2019) Standards in healthcare data. In: Fundamentals of clinical data science. pp 19–36

    Google Scholar 

  59. Olivero MA et al (2020) Facilitating the design of HL7 domain models through a model-driven solution. BMC Med Inform Decis Mak 20:1–18

    Google Scholar 

  60. Dolin RH et al (2021) vcf2fhir: a utility to convert VCF files into HL7 FHIR format for genomics-EHR integration. BMC Bioinform 22(1):1–11

    Google Scholar 

  61. Bocevska A et al (2021) Cross4all project model of integration of healthcare data using the concepts of EHR and PHR in the Era of IoT. In: Proceedings of the 14th conference on information systems and grid technologies ISGT 2021 Sofia, Bulgaria

    Google Scholar 

  62. Blazeska-Tabakovska N et al (2021) Implementation of cloud-based personal health record integrated with IoMT. In: Proceedings of the 14th conference on information systems and grid technologies ISGT Sofia, Bulgaria

    Google Scholar 

  63. Baig M et al (2015) Mobile healthcare applications: system design review, critical issues and challenges. Australas Phys Eng Sci Med 38(1):23–38

    CrossRef  Google Scholar 

  64. Abouelmehdi K et al (2017) Big data security and privacy in healthcare: a review. Procedia Comput Sci 113:73–80

    Google Scholar 

  65. Kang M, Ko E, Mersha TB (2021) A roadmap for multi-omics data integration using deep learning. Briefings Bioinform

    Google Scholar 

  66. Kim D, Kim JH, Moore JH (2020) Translational bioinformatics: integrating electronic health record and omics data. In: Biocomputing 2021: proceedings of the pacific symposium

    Google Scholar 

  67. López de Maturana E et al (2019) Challenges in the integration of omics and non-omics data. Genes 10(3):238

    Google Scholar 

  68. Ristevski B, Chen M (2018) Big data analytics in medicine and healthcare. J Integr Bioinform 15(3)

    Google Scholar 

  69. RedHat keycloak web site. https://www.keycloak.org

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Authors and Affiliations

  1. Faculty of Information and Communication Technologies, Bitola University “St. Kliment Ohridski”, Bitola, Republic of Macedonia

    Snezana Savoska & Blagoj Ristevski

  2. Faculty of Computer Science and Engineering, “Ss. Cyril and Methodius” University in Skopje, Skopje, Republic of Macedonia

    Vladimir Trajkovik

Authors
  1. Snezana Savoska
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  2. Blagoj Ristevski
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  3. Vladimir Trajkovik
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Corresponding authors

Correspondence to Snezana Savoska or Blagoj Ristevski .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Techno International New Town, Kolkata, West Bengal, India

    Nilanjan Dey

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Savoska, S., Ristevski, B., Trajkovik, V. (2023). Personal Health Record Data-Driven Integration of Heterogeneous Data. In: Dey, N. (eds) Data-Driven Approach for Bio-medical and Healthcare. Data-Intensive Research. Springer, Singapore. https://doi.org/10.1007/978-981-19-5184-8_1

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