Interprofessional George Mason researchers awarded more than $1 million to improve outcomes for patients with depression

Jennifer Pocock — Tue, 10 Dec 2024 18:25:35 +0000

Interprofessional George Mason researchers awarded more than $1 million to improve outcomes for patients with depression Jennifer Pocock Tue, 12/10/2024 - 13:25

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Farrokh Alemi, PhD

Kevin Lybarger

Alison Evans Cuellar, PhD, MBA

Özlem Uzuner

Researchers Farrokh Alemi and Kevin Lybarger receive ��AV’s first Patient-Centered Outcomes Research Institute (PCORI) award to develop innovative Artificial Intelligence (AI) technology, including large language models, for improving antidepressant recommendations.

Farrokh Alemi and Kevin Lybarger

AI will soon receive a dose of empathy with the goal of helping to match people with depression to their best-fit medication. A team led by Farrokh Alemi, a professor in the College of Public Health (CPH), and Kevin Lybarger, an assistant professor in the College of Engineering and Computing (CEC), received $1,049,998 in research funding from the Patient-Centered Outcomes Research Institute (PCORI) to continue their work on developing an AI system that helps patients find the right depression medications.

With this funding support, Co-PIs Alemi and Lybarger will hone large language models (LLMs) to address known challenges in AI, including mitigating biases, reducing the potential for inaccurate information, and incorporating an empathetic tone, according to Alemi.

The new study will introduce an innovative way for AI to help patients make medication decisions. The AI system will engage patients in natural-language conversations to collect information about their medical history. The system will draw upon more than 10 million patient experiences with 15 different oral antidepressants and a National Institutes of Health All of Us database, which includes records from more than 80,000 participants with major depressive disorders, to help create a plan that is statistically likely to succeed. Alemi and Lybarger believe this will help alleviate the trial and error that can lead to negative patient outcomes.

The researchers will also introduce a first-of-its-kind patient simulator capable of mimicking various medical, linguistic, and behavioral characteristics. This simulator will be used to test and refine the AI system by simulating diverse patient scenarios, including infrequent but critical events such as suicidal ideation, to ensure the system’s recommendations are safe, culturally sensitive, and empathetic.

“This study was selected for its potential to address a high-priority methodological gap in patient-centered comparative clinical effectiveness research,” said PCORI Executive Director Nakela L. Cook. “We look forward to following the study’s progress and working with George Mason to share the results.”

This is the first PCORI-funded study that George Mason has received. “Depression is a major public health problem and we are excited to see the development of new AI-based decision tools, leveraging the multidisciplinary talents of our college to help tackle it,” said CPH Associate Dean of Research Alison Cuellar.

"This innovative study promises to generate methodologies for using AI for medical decision-support and for empowering patients to make critical health decisions beyond mental health,” says Özlem Uzuner, chair of CEC’s Department of Information Sciences and Technology.

This study is one of the latest funded by PCORI to examine which medical treatments work best, where and when treatment falls flat, and how to address the gaps. These studies deliver results that guide researchers in planning future studies and provide patients, their caregivers, and clinicians with the evidence-based information needed to make better-informed health and health care decisions.

Topics

grants

Depression

antidepressants

natural language processing

Artificial Intelligence

Online learning expert suggests best practices for promoting academic honesty

Jennifer Pocock — Tue, 13 Aug 2024 17:44:33 +0000

Online learning expert suggests best practices for promoting academic honesty Jennifer Pocock Tue, 08/13/2024 - 13:44

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John Cantiello, PhD

The growing popularity of easy-to-use artificial intelligence (AI) interfaces has reignited discussions about cheating in academics. Academic dishonesty, however, is a long-standing concern for faculty and a burgeoning area of research for online coursework. Whether cheating is more or less prevalent in remote coursework has been the topic of debate since online coursework went mainstream in the early 2000s. While online learning provides access to education for nontraditional students and in nontraditional circumstances (such as the COVID-19 pandemic), it’s important to ensure that students are authentically learning the material.

John Cantiello. Photo provided

To understand the prevalence of cheating and help teachers address it, John Cantiello, a professor of the Department of Health Administration and Policy, reviewed the existing literature on the topic. In the Journal of Health Administration Education, Cantiello, who was honored last year as an online teacher of distinction by the Stearns Center of Teaching and Learning, reports that many methods for preventing academic dishonesty are effective.

Not everyone agrees on what constitutes “dishonesty,” he points out. While some forms of cheating are clear-cut—having others do assignments or take tests in the place of the student, plagiarism, or copying answers, others are not. Up for debate are the use of tools like artificial intelligence, or consulting publications that, in a professional setting, would be available to them while solving problems.

“This paper illustrates varying results on the frequency of cheating in online, college-level courses,” Cantiello said. “Tools and strategies for preventing academic dishonesty vary in effectiveness, but some show significant success.”

Cantiello examined many different interventions to prevent online academic dishonesty, including frequent discussions on what constitutes cheating and plagiarism (e.g., can they use AI tools in this course?), technology (e.g., biometric hardware and software such as Turnitin or Examity), and varied testing strategies (e.g., limiting testing times, multiple versions of tests, and leaking fake answers online).

He suggests that a combination of strategies is the best way to limit cheating. “A holistic approach to course design that incorporates these elements and includes a variety of learning activities fosters an effective learning environment where academic integrity can be upheld,” Cantiello said. However, he says, more research is needed on how often and in what capacity cheating happens.

“Preventing Academic Dishonesty in Online Courses: Best Practices to Discourage Cheating” was published online in March 2024 in the Journal of Health Administration Education. Renee Hotchkiss Geschke is co-author on the paper.

Topics

College of Public Health

Artificial Intelligence

Jenn Pocock

Interprofessional George Mason researchers awarded more than $1 million to improve outcomes for patients with depression

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Online learning expert suggests best practices for promoting academic honesty

In This Story

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