Data-Driven Decision-Making Models for Public Service Optimization: A Comprehensive Analysis of Water, Mental Health and Homelessness Sectors in Arizona and the United States
Thunderbird School of Global Management, Arizona State University, Phoenix, Arizona, United States of America.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 3274-3305
Publication history:
Received on 08 June 2024; revised on 15 July 2024; accepted on 18 July 2024
Abstract:
Background: The system of service delivery in the United States is experiencing unprecedented levels in management of resources and delivery of the service required, especially in the state of Arizona that has undergone a population boom, climatic strains, and the demand of divergent requirements of the population. The potential transformative role of data-driven decision-making models based on predictive analytics, machine learning, and analysis of performance measures to help improve the work of the industry of public services in such vital areas as water management, mental health services, and homelessness are part and parcel. The combination of Electronic Health Records, administrative databases, and real-time monitoring systems will bring the possibility of evidence-based service optimization unprecedented opportunities to serve needs, efficiently allocate resources and positively affect the population overall.
Materials and Methods: This integrated analysis uses mixed methodology integrating systematic review of the literature, case study analysis and prediction modeling frameworks in a review of data-driven decision-making model across three of the most crucial areas of state public services in Arizona. Systematic research databases such as academic journals, government and industry reports were searched using keywords advanced in predictive analytics, machine learning, public service optimization, and the name of a particular sector to which predictive analytics may be applied. Analysis was done using quantitative measures of performance, the qualitative evaluation of implementation success, and reflections of comparison case studies to present the best practices and successful implementation plans in various geographical settings and the context of service delivery.
Results: All the three sectors under study reflected positive changes in the improvement of the delivery of public services in their data-driven decision-making model implementation in their respective areas. Predictive analytics and smart metering-based water management systems showed savings of water usage (average) of 20-30% and increased efficiency of conserving programs. The predictive models integrated with mental health services using electronic health records have demonstrated the capacity to improve efficiency in the resource allocation and patient outcome measurement by 25-35%. Programs. Some special implementations in Arizona featured especially good results, as the Phoenix-based water department managed to cut their quantity of lost water by 28% and the Tucson-based mental health services achieved a 32% improvement in patient access time via predictive scheduling systems.
Discussion: Effective realization of data-driven decision-making models need end-to-end technological environment, capacity building within the organizations and strategic policy framework to accommodate innovation and change management. The experience of Arizona shows the relevance of the multi-stakeholder approach, community involvement, and adaptive implementation plans focused on the availability of solutions to local contexts and issues of service delivery. The use of artificial intelligence technologies, machine learning algorithms, and predictive analytics platforms means the possibility of fundamental optimization of the sphere of public services but also increases concerns about data privacy, bias in algorithms, and accessibility to receiving them.
Conclusion: The use of data-driven decision models is a paradigm shift in optimizing services to the public with the potential of transforming resource utilization, service delivery, and community outcomes in the sectors of water management services, mental health services, and homelessness prevention. The implementation experiences in Arizona are a good source of concepts to be used to implement predictive analytics, machine learning, and performance measurement systems to reach the objective of making public service more effective in supporting population needs. These facts show that a systematic introduction of evidence-based decisions leads to considerable advances in the efficiency and effectiveness of services delivery, their resource consumption, and community performance and underpins sustainable evidence-based governance processes serving the interests of all the locations in the public service landscape.
Keywords:
Data-driven decision-making; Public service optimization; Predictive analytics; Machine learning; Water resource management; Mental health services; Homelessness prevention; Resource allocation
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0