Date of Award

4-2014

Degree Name

Doctor of Philosophy

Department

Interdisciplinary Health Sciences

First Advisor

Dr. Kieran Fogarty

Second Advisor

Dr. Mark Messonnier

Third Advisor

Dr. Robert Wertkin

Keywords

Health science, predictive analytics, health economics, management science

Abstract

Human service organizations need outcome measurement approaches that support project management for efficiency and effectiveness. While, in recent years, human services have increased their capacity to manage data and measure outcomes empirically, several barriers remain. First, current outcome measurement practices are not designed to effectively support the management of human services programs for maximum efficiency and effectiveness. Second, human services organizations need a methodology to manage programs to identified outcomes. This dissertation explored meaningful solutions to both issues. In Paper 1 (Chapter II), this dissertation assessed strengths and limitations of current outcome evaluation approaches and suggested an innovative application of Multi-Objective Value Estimation as a method that may support predictive analytics for project management. Predictive analytics are widely utilized project management methods in government and for-profit businesses. Multi-Objective Value Estimation was field tested for feasibility in Paper 2 (Chapter III). Paper 3 (Chapter IV) took the results of the test conducted in Paper 2 and applied these findings to a project management approach that utilizes predictive analytics. Due to the high level of uncertainty in human services, Monte Carlo simulations were employed to generate baseline, best case, and actual project performance. These performance metrics were graphed in a stochastic S curve. The potential of this approach to provide an elegant and highly intuitive methodology to enhance project management in human services is discussed in the conclusion.

Access Setting

Dissertation-Open Access

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