Data for Action: Using Predictive Risk Modelling Tools in Social Services Agencies

This free online short course is for directors and senior managers at government social services agencies who would like to understand the potential and practical realities of using analytics tools to support frontline decisions.

Many agencies are waiting for cross-agency integrated data systems or their move to the cloud before they start using predictive analytics.
This course shows how agencies can and are using data they already have in their existing systems to build advanced analytics tools.
Predictive risk modelling (PRM) tools can support clinicians, social workers, case-managers and other frontline workers to make consistent, high quality decisions under time pressure and with limited information.
These tools need care with respect to obtaining and maintaining social licence from the community and embedding ethics and transparency throughout the whole project.
Drawing on case studies of Government agencies using data for prediction and prescription, this course will provide participants the tools to decide whether and how their organisation could benefit from PRM.

What does it take to use data for action in the social services sector?

At this (2x half day) free online course, hosted by the Institute for Social Science Research (The University of Queensland), an international expert in PRM decision support tools will provide a framework for agencies wanting to start a conversation about the use of these data tools at an operational level.

Professor Rhema Vaithianathan will explain the decision making ‘gaps’ that PRM tools can help to fill, describe how these tools are being used in social sectors like child protection, housing and education and provide a starting point for agencies wanting to explore the potential use of these tools.

Topics covered include the challenges of time- and information- constrained decision making, how PRM tools can and have addressed those challenges in social domains, problem identification and feasibility analysis, and the principles that should underpin successful implementation of a PRM tool (including aspects like privacy, transparency, social licence and consent).

The course will be in an online format delivered via Zoom with an emphasis on the presentation of international case studies and group discussion.

After taking this short course, you should be able to:

Understand the challenges posed by time- and information-constrained decision making and how they can affect achievement of agency priorities.
Describe how PRM tools can potentially mitigate the effects of decision-making challenges and help with the achievement of agency priorities.
Reference international case studies where PRM tools for action were implemented and made a measurable difference to the achievement of agency goals.
Identify possible ‘decision points’ within your own agency where data tools could help with frontline decisions.
Describe the principles and requirements that should underpin the successful implementation of a PRM tool.

Who should attend?

This free short course is aimed at directors and senior managers at government social services agencies in Australia and New Zealand. It is designed for participants with an interest in better understanding the potential of PRM tools for frontline decisions.

Course dates:

TBA

Presenter:

Rhema Vaithianathan is a Professor of Social Data Analytics at the Institute for Social Science Research, University of Queensland. She is also a Professor of Economics at Auckland University of Technology (New Zealand) where she is the founder and co-director of the Centre for Social Data Analytics. Rhema is internationally recognised for the implementation of machine learning tools in high stakes government systems, such as the Allegheny Family Screening Tool, a predictive risk model for child welfare call screening in Allegheny County, PA, USA. Her work has been published in top journals and profiled in The New York Times and Nature. Her methods for screening child abuse calls using machine learning tools are being adopted internationally.