Challenges in Managing Real-Time Data in Health Information System (HIS)

  • Usman Akhtar
  • Asad Masood Khattak
  • Sungyoung LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9677)


In this paper, we have discussed the challenges in handling real-time medical big data collection and storage in health information system (HIS). Based on challenges, we have proposed a model for real-time analysis of medical big data. We exemplify the approach through Spark Streaming and Apache Kafka using the processing of health big data Stream. Apache Kafka works very well in transporting data among different systems such as relational databases, Apache Hadoop and non-relational databases. However, Apache Kafka lacks analyzing the stream, Spark Streaming framework has the capability to perform some operations on the stream. We have identified the challenges in current real-time systems and proposed our solution to cope with the medical big data streams.


Stream processing framework Health-care Information System (HIS) Kafka messaging 


This work was supported by the Industrial Core Technology Development Program (10049079, Develop of mining core technology exploiting personal big data) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) and This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2011-0030079). This research work was also supported by Zayed University Research Initiative Fund R15098.


  1. 1.
    Cattell, R.: Scalable SQL, NoSQL data stores. SIGMOD Rec. 39(4), 12–27 (2010). CrossRefGoogle Scholar
  2. 2.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008). CrossRefGoogle Scholar
  3. 3.
    Peek, N., Holmes, J., Sun, J.: Technical challenges for big data in biomedicine and health: data sources, infrastructure, and analytics. Yearb Med Inform 9(1), 42–7 (2014)CrossRefGoogle Scholar
  4. 4.
    Zaharia, M., Das, T., Li, H., Shenker, S., Stoica, I.: Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters. In: Proceedings of the 4th USENIX Conference on Hot Topics in Cloud Computing, HotCloud 2012, Berkeley, CA, USA, p. 10. USENIX Association (2012).
  5. 5.
    Kaur, K., Rani, R.: Managing data in healthcare information systems: many models, one solution. Computer 3, 52–59 (2015)CrossRefGoogle Scholar
  6. 6.
    Apiletti, D., Baralis, E., Bruno, G., Cerquitelli, T.: Real-time analysis of physiological data to support medical applications. Trans. Info. Tech. Biomed. 13(3), 313–321 (2009). CrossRefGoogle Scholar
  7. 7.
    Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 1–10 (2014). CrossRefGoogle Scholar
  8. 8.
    Hussain, M., Khattak, A., Khan, W., Fatima, I., Amin, M., Pervez, Z., Batool, R., Saleem, M., Afzal, M., Faheem, M., et al.: Cloud-based smart cdss for chronic diseases. Health Technol. 3(2), 153–175 (2013)CrossRefGoogle Scholar
  9. 9.
    Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig latin: a not-so-foreign language for data processing. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, pp. 1099–1110. ACM, New York (2008).
  10. 10.
    Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., Murthy, R.: Hive: a warehousing solution over a map-reduce framework. Proc. VLDB Endow. 2(2), 1626–1629 (2009). CrossRefGoogle Scholar
  11. 11.
    Rabbi, K., Kaosar, M., Islam, M.R., Mamun, Q.: A secure real time data processing framework for personally controlled electronic health record (PCEHR) system. In: Tian, J., Jing, J., Srivatsa, M. (eds.) SecureComm 2014, pp. 141–156. Springer, Heidelberg (2014)Google Scholar
  12. 12.
    Nabi, Z., Wagle, R., Bouillet, E.: The best of two worlds: integrating IBM infosphere streams with apache YARN. In: 2014 IEEE International Conference on in Big Data (Big Data), pp. 47–51. IEEE, (2014)Google Scholar
  13. 13.
    Begum, M., Mamun, Q., Kaosar, M.: A privacy-preserving framework for personally controlled electronic health record (PCEHR) system (2013)Google Scholar
  14. 14.
    Van Gorp, P., Comuzzi, M., Jahnen, A., Kaymak, U., Middleton, B.: An open platform for personal health record apps with platform-level privacy protection. Comput. Biol. Med. 51, 14–23 (2014)CrossRefGoogle Scholar
  15. 15.
    Huang, L.-C., Chu, H.-C., Lien, C.-Y., Hsiao, C.-H., Kao, T.: Privacy preservation and information security protection for patients portable electronic health records. Comput. Biol. Med. 39(9), 743–750 (2009)CrossRefGoogle Scholar
  16. 16.
    Jian, W.-S., Wen, H.-C., Scholl, J., Shabbir, S.A., Lee, P., Hsu, C.-Y., Li, Y.-C.: The taiwanese method for providing patients data from multiple hospital EHR systems. J. Biomed. Inform. 44(2), 326–332 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Usman Akhtar
    • 1
  • Asad Masood Khattak
    • 2
  • Sungyoung Lee
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringKyung Hee UniversityYongin-siSouth Korea
  2. 2.College of Techological InnovationZayed UniversityDubaiUAE

Personalised recommendations