Speaker Bio & Presentations

Tammy Malloy
Jason Shumake

Special Speaker

Dr. Tammy Malloy

Dr. Tammy Malloy received her PhD in Social Work, from Barry University, and is a Licensed Clinical Social Worker as well as a Certified Sex Addiction Therapist. Dr. Malloy has facilitated research for over ten year and has published in academic journals on Addiction, Depression and Spirituality as well as clinical personality pathology and syndromes. Dr. Malloy’s most recent research is in the area of internet pornography use among young adult women. Dr. Malloy has worked in the field of behavioral health for over fifteen years and brings a wealth of knowledge in trauma informed care, family systems, and high-risk behaviors that encompass all addictive behaviors. Dr. Malloy has spoken at conferences both Nationally and Internationally on substance use disorders, co-occurring mental health disorders, and high-risk sexual behaviors. Dr. Malloy’s passion is for teaching others while empowering staff and patients to take ownership of improving their overall quality of life.


Dr. Jason Shumake

Dr. Shumake has 20+ years of scientific research experience and has co-authored more than 60 journal articles in the fields of behavioral neuroscience, experimental psychology, statistics, and machine learning. As the former chief data scientist at the Institute for Mental Health Research, he advanced research in precision psychological medicine and digital mental health. Dr. Shumake brings a valuable set of hybrid skills at the intersection of experimental design, psychometrics, statistical analysis, machine learning, and behavioral science theory/methods.

Presentations & Workshops

Chatbots as Mental Health Assistants:
The Promise and Perils of Digital Phenotyping

Digital health technologies—such as smartphone apps and chatbots—have the potential to augment mental healthcare through more ecological and scalable assessments and interventions. In this presentation, we will review the current state of technology in digital mental health, with a case study of how an artificial-intelligence (AI) tool has been integrated into a recovery-center setting to monitor treatment progress and outcomes. First, we will provide an update on recent technological advances in “digital phenotyping”—using smartphone and computer devices to understand mental health through multimodal assessment (e.g., using screen, microphone, and camera inputs to analyze language, voice, and facial expressions) in a more ecological context (i.e., as part of someone’s lived experience and not just in the clinic). Second, we will discuss the most promising use cases for this technology, from self-screening and early detection of mental health problems to long-term monitoring of chronic psychiatric conditions, with a focus on how this technology can improve upon measurements collected from Likert-scale questionnaires like the PHQ-9. Third, we discuss the challenges and opportunities of implementing technological innovations into mental health care systems, using our firsthand experience of developing and deploying a digital tool for monitoring depression and suicide risk in the context of a recovery program. We have found that some people feel more comfortable conversing with a chatbot, which provides an opportunity to improve detection of distress for individuals who feel uncomfortable with face-to-face contact. We conclude that the potential benefits of these tools outweigh their risks, but their use should ideally be supervised by a mental health professional in the context of a broader treatment plan—both as a guard rail against “edge cases” (atypical inputs that cause an AI system to produce erroneous output) and to encourage user engagement and accountability.

Learning Objectives

  • Describe current research and industry trends in applying machine learning and artificial intelligence to mental health assessment and interventions.
  • Compare mental health assessment using multimodal digital phenotyping vs. Likert-scale questionnaires.
  • Discuss potential risks and benefits of incorporating digital mental health tools into clinical practice.
  • Outline a strategy for critically evaluating claims of efficacy and privacy of digital mental health tools.

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