DHA-7012 v2: Data-Driven Decision Making

Ozcan, Y. A. (2017).Analytics and decision support in health care operations management. San Francisco, CA: Jossey-Bass.

Read Chapter 2, Predictive Analytics

Read Chapter 3, Decision Making in Health Care

Cleemput, I., Devriese, S., Kohn, L., & Westhovens, R. (2018). A multi-criteria decision approach for ranking unmet needs in healthcare. Health Policy, 122 (8), 878-884. 

Harris, S. L., May, J. H., & Vargas, L. G. (2016). Predictive analytics model for healthcare planning and scheduling. European Journal of Operational Research, 253(1), 121–131.

Siegel, E. (2016). Predictive analytics: The power to predict who will click, buy, lie, or die.

Read Table 4, Predictive Analytics in Healthcare on pages 276-278.

Thornton, D., Mueller, R. M., Schoutsen, P., & Hillegersberg, J. (2013). Predicting healthcare fraud in Medicaid: A multidimensional data model and analysis techniques for fraud detection.

Waterman, R. (Academic). (2014). Use of regression models [Streaming video]. SAGE Research Methods

Watch video 15 minutes in length.

Ganguly, A., & Nandi, S. (2016). Using Statistical Forecasting to Optimize Staff Scheduling in Healthcare Organizations. Journal of Health Management, 18(1), 172.

Quraishi, J. A., & Jordan, L. M. (2017). Anesthesia Medicare Trend Analysis Shows Increased Utilization of CRNA Services. AANA Journal, 85(5), 375–383.

Revels, S., Kumar, S. A. P., & Ben-Assuli, O. (2017). Predicting obesity rate and obesity-related healthcare costs using data analytics. Health Policy & Technology, 6(2), 198.

Spoelman, W. A., Bonten, T. N., de Waal, M. W. M., Drenthen, T., Smeele, I. J. M., Nielen, M. M. J., & Chavannes, N. H. (2016). Effect of an evidence-based website on healthcare usage: an interrupted time-series study. BMJ OPEN, 6(11).

Van, T., Annis, A. M., Yosef, M., Robinson, C. H., Duffy, S. A., Li, Y.-F., Taylor, B. A., Krein, S., Sullivan, S. C., & Sales, A. (2020). Nurse staffing and healthcare-associated infections in a national healthcare system that implemented a nurse staffing directive: Multi-level interrupted time-series analyses. International Journal of Nursing Studies, 104.

Worni, M., Guller, U., Maciejewski, M. L., Curtis, L. H., Gandhi, M., Pietrobon, R., Jacobs, D. O., & Østbye, T. (2013). Racial differences among patients undergoing laparoscopic gastric bypass surgery: a population-based trend analysis from 2002 to 2008. Obesity Surgery, 23(2), 226–233.