Skip to main content

The Role of Big Data and Analytics in Health Payer Transformation to Consumer-Centricity

  • Chapter
Healthcare Information Management Systems

Part of the book series: Health Informatics ((HI))

Abstract

Historically, information management for payers has been focused on enabling efficient operations, such as claims administration and group management.Recently, with the rise of individual insurance and increasing pressure to reduce cost through better health management, healthcare payers are transforming its business to be increasingly consumer-centric. In the healthcare payer setting, consumer-centricity means to put individual consumer at the focus of payer operations. It is to understand and engage individual consumer throughout the insurance lifecycle, from assisting prospects to choose the most suitable product, engaging new enrollees in wellness, to assisting members navigate the healthcare system. In this chapter, we discuss the implications of consumer-centricity and external data explosion on payer information management, ranging from data management, analytics applications and use cases, to the analytics delivery platform. We also discuss how consumer data and open data support this transformation, shedding insights into range of business processes, and expanding the role of informatics in the payer organization.

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 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alexe B. Surfacing time-critical insights from social media, SIGMOD. New York: ACM; 2012.

    Book  Google Scholar 

  2. Andrews M. Insurers open stores to peddle health plans. 2012. Retrieved 2 Feb 2015, from http://kaiserhealthnews.org/news/health-insurance-stores-022812-michelle-andrews/.

  3. Bank of America, M. Consumer-centric health: treasury impacts on payers and providers. 2014. Retrieved 2 Feb 2015, from http://corp.bankofamerica.com/documents/10157/67594/ConsumerCentricHealthcare.pdf.

  4. Bruell A. Health-insurance providers seek direct-to-consumer connections. 2012. Retrieved 2 Feb 2015, from advertising age: http://adage.com/article/news/health-insurance-providers-seek-direct-consumer-connections/232697/.

  5. Deloitte Center for Health Solutions. Health insurance exchanges: a strategic perspective. 2011. Retrieved 2 Feb 2015, from http://www.amcp.org/WorkArea/DownloadAsset.aspx?id=10594.

  6. Department of Health and Human Services Office. Health insurance marketplace: summary enrollment report for the initial annual open enrollment period. 2014. Retrieved 2 Feb 2015, from http://aspe.hhs.gov/health/reports/2014/MarketPlaceEnrollment/Apr2014/ib_2014apr_enrollment.pdf.

  7. Dixon-Fyle S, Gandhi S, Pellathy T, Spatharou A. Changing patient behavior: the next frontier in healthcare value. Health Int. 2012;12:65–73.

    Google Scholar 

  8. Draper D. Commercial health plans’ care management activities and the impact on costs, quality and outcomes. Center for Studying Health System Change. Washington, DC: Congressional Testimony; 2007.

    Google Scholar 

  9. Garla S, Hopping A, Monaco R, Rittman S. What do your consumer habits say about your health? Using third-party data to predict individual health risk and costs. SAS global forum. San Francisco: SAS; 2013.

    Google Scholar 

  10. Georgetown University. Disease management programs: improving health while reducing costs? Health policy institute. Washington, DC: Georgetown University; 2004.

    Google Scholar 

  11. Herman B. Insurers build retail centers to enhance brand image. 2014. Retrieved 2 Feb 2015, from http://www.modernhealthcare.com/article/20141101/MAGAZINE/311019962

  12. HIMSS Analytics. Electronic Medical Record Adoption Model (EMRAM). 2014. Retrieved 2 Feb 2015, from http://www.himssanalytics.org/emram/emram.aspx

  13. InfoSys Public Services. Healthcare insights – consumer engagement. 2014. Retrieved 5 Feb 2015, from http://www.infosyspublicservices.com/insights/Documents/consumer-engagement.pdf

  14. Insurers leveraging Patients’ personal data for predictive analytics. 2014. Retrieved 2 Feb 2015, from iHealthBeat: http://www.ihealthbeat.org/articles/2014/6/30/insurers-leveraging-patients-personal-data-for-predictive-analytics.

  15. Fox P, Kongstved P. A history of managed health care and health insurance in the United States. In: Peter Kongstvedt, editor. Essentials of managed health care. Burlington, Sixth Edition: Jones & Bartlett Learning; 2013.

    Google Scholar 

  16. Mccarthy D, Cohen A, Johnson MB. Gaining ground: care management programs to reduce hospital admissions and readmissions among chronically Ill and vulnerable patients. The Commonwealth Fund Publication. 2013;5:1658.

    Google Scholar 

  17. Furukawa M, Patel V. Hospital electronic health information exchange grew substantially in 2008–12. Health Aff. 2013;32(8):1346–54.

    Article  Google Scholar 

  18. Parks Associates. Nearly 30% of U.S. broadband households own and use a connected health device. 2014. Retrieved 2 Feb 2015, from ParksAssociate: http://www.parksassociates.com/events/connected-health/media/chs-2014-pr17

  19. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2(3):2–10.

    Google Scholar 

  20. Rao A. Health insurers tune in to twitter for customer service. 2013. Retrieved 2 Feb 2015, from http://kaiserhealthnews.org/news/health-insurers-take-to-twitter-for-customer-service/

  21. Singer N. When a health plan knows how you shop. 2014. Retrieved 2 Feb 2015, from NY Times. http://www.nytimes.com/2014/06/29/technology/when-a-health-plan-knows-how-you-shop.html.

  22. Stehno C, Johns C. You are what you eat: using consumer data to predict health risk. Contingencies. 2006.

    Google Scholar 

  23. Tang L, Liu H. Community detection and mining in social media. Morgan & Claypool Publishers, Synthesis Lectures on Data Mining and Knowledge Discovery; 2010.

    Google Scholar 

  24. The Henry J. Kaiser Family Foundation. Health insurance coverage of the total population. 2014. Retrieved 2 Feb 2015, from http://kff.org/other/state-indicator/total-population/.

  25. Winkelman R, Mehmud S. A comparative analysis of claims-based tools for health risk assessment. Schaumburg: Society of Actuaries; 2007.

    Google Scholar 

  26. Shahar Y, Goren-Bar D. Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions. Artif Intell Med. 2006;38(2):115–35.

    Article  PubMed  Google Scholar 

  27. Zhou J, Wang F, Hu J, Ye J. From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records. 2014. KDD. Association of Computing Machinery.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gigi Yuen-Reed PhD .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Yuen-Reed, G., Mojsilović, A. (2016). The Role of Big Data and Analytics in Health Payer Transformation to Consumer-Centricity. In: Weaver, C., Ball, M., Kim, G., Kiel, J. (eds) Healthcare Information Management Systems. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-20765-0_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20765-0_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20764-3

  • Online ISBN: 978-3-319-20765-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics