Creating a 21st Century Helpline for Enhanced Support and Continuity of Care (x3 PhD based at Monash)

Project Description:

Turning Point is a renowned addiction treatment and research centre specialising in the prevention, treatment, and support services for individuals affected by substance use disorders, gambling addiction, and mental health issues. Turning Point operates a network of 26 helplines across the country, ensuring accessible and immediate support for individuals in need. These helplines serve as a vital resource for individuals seeking assistance, information, and guidance related to addiction and mental health concerns.

The helplines at Turning Point are staffed by trained professionals who offer compassionate support, active listening, and evidence-based advice. These dedicated helpline operators play a crucial role in providing essential information, crisis intervention, and referrals to appropriate services based on the individual's needs. They handle a wide range of inquiries, from general inquiries about addiction and mental health to urgent situations requiring immediate intervention.

By partnering with Turning Point's helpline service, students on the Next Generation of Graduates in AI in Mental Health program will have the opportunity to enhance the capabilities of these helplines using data driven and interdisciplinary approaches. The broader goals of the project will be to empower helpline operators with intelligent systems, improve user experience by understanding the help-seeking behaviours of callers, and enhance psychological support techniques as a result, ultimately enabling Turning Point to deliver even more effective and efficient assistance to individuals across the country.

This program  of work will engage three PhD students, each from a different background (technical, psychology, design/HCI), to contribute their expertise towards enhancing the helpline service and improving the overall support for individuals seeking help for addiction. Students will be able to, for example:

• Apply AI, ML, and NLP techniques to develop intelligent systems capable of understanding and responding to helpline calls more effectively.
• Conduct research on the psychological aspects of helpline interactions and help-seeking behaviours to develop strategies to enhance user experience and emotional support.
• Employ co-design and HCI principles to create user-driven insights that will ensure both helpline operators and callers have the best possible experience.
• Collaborate with Turning Point to integrate the developed research into their existing helpline infrastructure.
• Evaluate the impact of the implemented advancements on the helpline service's effectiveness and efficiency.

This program of work represents a unique opportunity to collaborate with Turning Point in revolutionising their helpline service through the integration of AI. By engaging a small team of PhD students from technical, psychology, and design/HCI backgrounds, we aim to create innovative solutions that enhance the effectiveness, efficiency, and user experience of the helpline service, ultimately making a significant impact on mental health support in the community.

PhD/Masters:

3 x PhD

Student Experience required:

We are primarily seeking applicants with interests in supporting addiction research. We will accept applicants from a technical or psychology/ design background. The student should have experience in AI/ machine learning and/or NLP and/or mental health

Host University:

• Monash University

Industry Partner:

• Turning Point

Enhancing Service User Care Pathway Experience through AI-Driven Personalisation (x1 PhD based at Monash)

Project Description:

We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and resource recommendations, ultimately optimising the overall user journey. This project will improve the care pathway experience for young people and families accessing mental health services through the headspace website.

Possible approaches to addressing this challenge might include:

• Developing algorithms to identify patterns and preferences based on service users’ previous content engagement on the headspace website, enabling the generation of tailored service and resource suggestions
• Exploring 1:1 and group chat inputs to identify recurring themes in presenting needs and implement AI-driven tools to automate content suggestions or propose next steps.
• Establishing a system to share identified themes and content suggestions in real time with service providers during live engagements, to facilitate more specific and timely content recommendations by service providers to enhance user engagement.
• Analysing website activity data to identify potential crisis points that might require intervention or escalation to a clinician when critical points are detected, ensuring timely support.
• Utilising data inputs from service user surveys to enhance the generation of personalised service and resource recommendations and implementing strategies to continuously refine recommendations based on evolving user preferences and needs.
• Analysing assessment inputs to determine appropriate next steps which may include appropriate resource or service recommendations or follow-up assessments.

Prospective candidates should possess a strong background in artificial intelligence, machine learning, and data analysis. Additionally, experience in healthcare informatics, user experience research, and a commitment to improving mental health services are highly desirable.

PhD/Masters:

PhD

Host University:

• Monash University

Industry Partner:

• Headspace

Enabling intelligent measurement-based care for clinicians (x1 PhD based at Melbourne)

Project Description:

Orygen is a not-for profit organisation who believes that all young people deserve to grow into adulthood with optimal mental health. Everything they do is focused on that outcome.

Most mental health disorders begin between the early teens to the mid-20s. One in five young people will have experienced a depressive episode by the time they turn 18. Orygen believe in treating early and focusing on recovery. Pioneering reform to deliver real-world practical solutions. Never settling for anything less than what young people need and deserve.

This PhD project will focus on developing intelligent measurement-based care support for clinicians. Measurement Based Care (MBC) involves the collection and feedback of fine-grained data that are contemporaneous with treatment and has repeatedly demonstrated improved outcomes in therapy. However, only 12% of psychologists use progress measures in clinical practice. Furthermore, despite its potential, the implementation and development of MBC is in its infancy. A key area for advancement includes creating algorithms for MBC to guide psychotherapy informed by individual mechanisms of change and treatment response, risk of treatment failure and dropout.

The PhD candidate will develop and evaluate an AI-enabled MBC feedback system capable of predicting individual risk of treatment failure and drop out as well as providing clinicians with real time, adaptive recommendations on what type of therapeutic intervention is likely to work best for each individual.

We are seeking candidates with strong technical backgrounds in machine learning with a passion for mental health.

PhD/Masters:

PhD

Host University:

• Monash University

Industry Partner:

• Orygen

Supporting personalised adaptive intervention strategies for young people (x1 PhD based at Melbourne)

Project Description:

Orygen is a not-for profit organisation who believes that all young people deserve to grow into adulthood with optimal mental health. Everything they do is focused on that outcome.

Most mental health disorders begin between the early teens to the mid-20s. One in five young people will have experienced a depressive episode by the time they turn 18. Orygen believe in treating early and focusing on recovery. Pioneering reform to deliver real-world practical solutions. Never settling for anything less than what young people need and deserve.

This PhD project will focus on developing adaptive intervention strategies that will support personalised care for young people. AI-enabled digital interventions have the ability learn and adapt with continuous use, boosting both engagement and treatment effects. Yet, AI has rarely been used and tested in digital mental health interventions. The candidate will develop and evaluate an AI algorithm capable of personalising the type and sequence of digital interventions recommendations for young people to support personalised mental health provision, optimising long-term engagement and treatment efficacy.

We are seeking candidates with strong technical backgrounds in machine learning and AI with a passion for mental health.

PhD/Masters:

PhD

Host University:

• Monash University

Industry Partner:

• Orygen

Enhancing General Practitioner Decision-Making in Antidepressant Prescriptions: A Data-Driven Approach

Project Description:

The aim of this project is to develop a data-driven decision support system to enhance the prescribing practices of General Practitioners (GPs) when it comes to antidepressant medications. Currently, GPs often rely on their personal preferences and limited information when prescribing antidepressants, leading to suboptimal outcomes for patients. This project seeks to address this issue by leveraging patient data and applying advanced analytics techniques to provide evidence-based recommendations to GPs, taking into account various criteria related to antidepressant use.

The project will begin by gathering a comprehensive database comprising different antidepressant medications, along with information on it’s contraindications and success rates relative to patient demographics (e.g. pregnancy status, gender, age, and potential side effects like weight gain).      Using this rich database, the project team will develop an algorithm that analyses patient-specific information and provides tailored recommendations for antidepressant prescriptions. The algorithm will consider multiple factors, such as the patient's medical history, previous treatment response, potential drug interactions, and individual risk profiles. The system will take into account the latest research and clinical guidelines to ensure evidence-based recommendations.

Throughout the project, the team will engage with GPs, healthcare professionals, and domain experts to ensure the system's usability, reliability, and adherence to ethical guidelines. The system will be designed to facilitate seamless integration into existing electronic health record systems commonly used in primary care settings.      By leveraging data-driven insights, this project aims to empower GPs with evidence-based recommendations for antidepressant prescriptions, ultimately improving patient outcomes and reducing the potential for adverse effects. The decision support system has the potential to revolutionise the way GPs approach antidepressant medication choices, providing a more personalised and effective treatment for patients.

PhD/Masters:

Masters

Student Experience required:

We are seeking a students with strong machine learning skills (necessary). A background in a healthcare related field or industry area would be valuable (preferred).

Host University:

• Monash University

Industry Partner:

• Outcome Health

Supervisors:

• Ingrid Zuckerman (Data Science and AI)
• Chris Pearce (General Practice)