Impacts of business intelligence on population health: a systematic literature review

ABSTRACT

"Business Intelligence" is an area of Information Technology (IT) that involves the collection, analysis and presentation of large amounts of data. BI has been successfully applied to promote good decision making in a variety of environments, and has high potential to make a significant impact in the domain of population health. The promotion of population health is a key concern of government authorities and various health institutions and officials making decisions about interventions that may impact on population health would benefit from the use of information on population health. BI could clearly be a facilitator in this regard, but evidence of its current application and impact in this field is not easily accessible to policy makers. This systematic literature review explored the literature and provided a synthesis of information available on the current use of BI in this area, and evidence of the impact of its use on population health. An array of applications of BI for population health were found, including data warehouses, analytics, reports, data warehouse browsers, OLAP, GIS, Dashboards and Alerts. Evidence of the impact of these applications on population health was mainly anecdotal, with only one empirical study found. Issues and challenges encountered in the development and use of BI are Privacy and Security, Data Quality and Development and Maintenance of BI infrastructure

References

1. Asokan, G.V. and Asokan, V., 2015. Leveraging "big data" to enhance the effectiveness of "one health" in an era of health informatics. Journal of Epidemiology and Global Health 5, 4 (12//), 311--314. DOI= http://dx.doi.org/
2. Batini, C., Cappiello, C., Francalanci, C., and Maurino, A., 2009. Methodologies for Data Quality Assessment and Improvement. ACM Computing Surveys 41, 3.
3. Ben Said, M., Le Mignot, L., Mugnier, C., Richard, J.B., Le Bihan-Benjamin, C., Jais, J.P., Guillon, D., Simonet, A., Simonet, M., and Landais, P., 2005. A Multi-Source Information System via the Internet for End-Stage Renal Disease: Scalability and Data Quality. Stud Health Technol Inform 116, 994--999.
4. Berndt, D.J., Hevner, A.R., and Studnicki, J., 2000. Hospital discharge transactions: a data warehouse component. In System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on, 10 pp. vol.11.
5. Berndt, D.J., Hevner, A.R., and Studnicki, J., 2003. The Catch data warehouse: support for community health care decision-making. Decision Support Systems 35, 3 (6//), 367--384. DOI= http://dx.doi.org/
6. Catalyst, H., 2016. Interview with Stanford CIO, Carolyn Byerly: Launching A Clinical Data Warehouse in Months. In Health Catalyst.
7. Devoe, J.E. and Sears, A., 2013. The OCHIN community information network: bringing together community health centers, information technology, and data to support a patient-centered medical village. J Am Board Fam Med 26, 3 (May-Jun), 271--278.
8. Garay, J., Cartagena, R., Esensoy, A.V., Handa, K., Kane, E., Kaw, N., and Sadat, S., 2015. Strategic analytics: towards fully embedding evidence in healthcare decision-making. Healthc Q 17 Spec No, 23--27.
9. Iden, J. and Eikebrokk, T.R., 2013. Implementing IT Service Management: A systematic literature review. International Journal of Information Management 33, 3 (2013/06/01/), 512--523. DOI= http://dx.doi.org/
10. Inmon, W.H., 2005. Building the Data Warehouse. Wiley, Indianapolis.
11. Jean-Baptiste, R., Toubiana, L., Le Mignot, L., Ben Said, M., Mugnier, C., Le Bihan-Benjamin, C., Jais, J.P., and Landais, P., 2005. A Web-based GIS for health care decision-support. AMIA Annu Symp Proc, 365--369.
12. Kimball, R., Reeves, L., Ross, M., and Thornthwaite, W., 1998. The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing and Deploying Data Warehouses. Wiley, USA.
13. Kindig, D., 2007. Understanding Population Health Terminology. The Milbank Quarterly 85, 1, 139--161.
14. Kindig, D. and Stoddard, G., 2003. "What is Population Health?". Am J Public Health 93, 3, 380--383.
15. Krumholz, H.M., 2014. Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff (Millwood) 33, 7 (Jul), 1163--1170.
16. Lee, Y.W., Strong, D.M., Kahn, B.K., and Wang, R.Y., 2002. AIMQ: a methodology for information quality assessment. Information & Management 40 (2002) 133--146 40, 133--146.
17. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L.A., and Group, P.-P., 2015. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews 4, 1.
18. Mooney, S.J., Westreich, D.J., and El-Sayed, A.M., 2015. Commentary: Epidemiology in the era of big data. Epidemiology 26, 3 (May), 390--394.
19. Musa, G.J., Chiang, P.H., Sylk, T., Bavley, R., Keating, W., Lakew, B., Tsou, H.C., and Hoven, C.W., 2013. Use of GIS Mapping as a Public Health Tool-From Cholera to Cancer. Health Serv Insights 6, 111--116.
20. News@Orchard, 2015. Crystal Run Healthcare Using Data Analytics as an Integral Part of Improving Patient Care. In news@Orchard Orchard Software Corporation, Indiana, USA.
21. Oates, B.J., 2011. Evidence-based Information Systems: A Decade Later. In European Conference on Information Systems (ECIS).
22. Olszak, C.M. and Batko, K., 2012. The use of business intelligence systems in healthcare organizations in Poland. In Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on, 969--976.
23. Paré, G., Tate, M., Johnstone, D., and Kitsiou, S., 2016. Contextualizing the twin concepts of systematicity and transparency in information systems literature reviews. European Journal of Information Systems: An Official Journal of the Operational Research Society 25, 6, 493--508.
24. Pfeiffer, D.U. and Stevens, K.B., 2015. Spatial and temporal epidemiological analysis in the Big Data era. Prev Vet Med(Jun 6).
25. Richard, J.B., Toubiana, L., LE Mignot, L., Ben Said, M., Mugnier, C., Le Bihan-Benjamin, C., Jais, J.P., and Landais, P., 2005. SIGNe: A Geographic Information System on the Web for End-Stage Renal Disease. Stud Health Technol Inform 116, 713--718.
26. Rizi, S.A. and Roudsari, A., 2013. Development of a public health reporting data warehouse: lessons learned. Stud Health Technol Inform 192, 861--865.
27. Shani, M., 2000. The impact of information on medical thinking and health care policy. International Journal of Medical Informatics 58--59(9/1/), 3--10. DOI= http://dx.doi.org/
28. Silva, J.C., Shah, S.C., Rumoro, D.P., Bayram, J.D., Hallock, M.M., Gibbs, G.S., and Waddell, M.J., 2013. Comparing the accuracy of syndrome surveillance systems in detecting influenza-like illness: GUARDIAN vs. RODS vs. electronic medical record reports. Artificial Intelligence in Medicine 59, 3 (11//), 169--174. DOI= http://dx.doi.org/
29. Tremblay, M.C., Fuller, R., Berndt, D., and Studnicki, J., 2007. Doing more with more information: Changing healthcare planning with OLAP tools. Decision Support Systems 43, 4 (8//), 1305--1320. DOI= http://dx.doi.org/
30. Tsui, F.C., Espino, J.U., Dato, V.M., Gesteland, P.H., Hutman, J., and Wagner, M.M., 2003. Technical description of RODS: a real-time public health surveillance system. J Am Med Inform Assoc 10, 5 (Sep-Oct), 399--408.
31. Wagner, M.M., Robinson, J.M., Tsui, F.-C., Espino, J.U., and Hogan, W.R., 2003. Design of a national retail data monitor for public health surveillance. Journal of the American Medical Informatics Association 10, 5 (9//), 409--418. DOI= http://dx.doi.org/
32. Walji, M.F., Kalenderian, E., Stark, P.C., White, J.M., Kookal, K.K., Phan, D., Tran, D., Bernstam, E.V., and Ramoni, R., 2014. BigMouth: a multi-institutional dental data repository. J Am Med Inform Assoc 21, 6 (Nov-Dec), 1136--1140.
33. Wand, Y. and Wang, R.Y., 1996. Anchoring Data Quality Dimensions in Ontological Foundations. Communications of the ACM 39, 11.
34. Wisniewski, M.F., Kieszkowski, P., Zagorski, B.M., Trick, W.E., Sommers, M. and Weinstein, R.A., 2003. Development of a clinical data warehouse for hospital infection control. Journal of the American Medical Informatics Association 10, 5 (9//), 454--462. DOI= http://dx.doi.org/
35. Wixom, B.H. and Watson, H.J., 2001. An Empirical Investigation of the Factors Affecting Data Warehousing Success. MIS Quarterly.
36. Xiaohui, Z., Fiedler, R., and Popovich, M., 2004. A biointelligence system for identifying potential disease outbreaks. Engineering in Medicine and Biology Magazine, IEEE 23, 1, 58--64.
37. Yeoh, W. and Koronios, A., 2010. Critical Success Factors for Business Intelligence Systems. Journal of computer information systems 50, 3, 23--32.

Index Terms

• Information systems

  • Data management systems

    • Information integration

      • Data warehouses

  • Information systems applications

    • Decision support systems