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Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

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SAGE awardees

Monique Cano, PhD

Clinical Assistant Professor

Department of Psychological Science, The University of Texas Rio Grande Valley

Dr. Monique T. Cano is a Licensed Clinical Psychologist and received her Ph.D. in clinical psychology from Palo Alto University in 2020. She completed her NIH T32 Postdoctoral Fellowship in Substance Use Disorders Treatment and Services Research at the University of California, San Francisco (UCSF) in the Department of Psychiatry and Weil Institute for Neurosciences in 2022. Dr. Cano is currently a Clinical Assistant Professor at The University of Texas Rio Grande Valley.

Dr. Cano’s research interests are focused on decreasing the cost and suffering associated with problematic substance use, addressing barriers to accessing mental health care for underserved populations, and enhancing health-related outcomes through work in behavioral health settings. She is dedicated to developing health-related interventions, which incorporate methods to cultivate self-efficacy in patients with co-occurring disorders. She is also interested in the integration of various recruitment methodologies utilized in social science research (i.e., psychology, anthropology, sociology) with the specific aim to inform and increase the successful recruitment of underserved populations with co-occurring disorders who participate and engage in mental health treatment as well as clinical research. This work will correspondingly inform future research efforts in developing quality improvement initiatives in public hospital systems that primarily serve underserved populations to minimize healthcare gaps.

SAGE Project: Factors associated with smoking in low-income persons with and without chronic disease

Tobacco disparities persist among low-income smokers who seek care from safety-net clinics. Many of these patients suffer from chronic illnesses that are associated with and exacerbated by smoking. The objective of this study was to examine the differences between safety-net patients with and without chronic illness in terms of nicotine dependence and related factors (such as depression, and anxiety) and self-efficacy regarding the ability to abstain from smoking. Results showed that patients suffering from chronic illness had significantly higher anxiety and nicotine dependence and may have less confidence in their ability to quit smoking, suggesting that interventions that increase the sense of self-efficacy may be necessary to help low-income smokers quit smoking. This work was published in Tobacco Induced Diseases (PMCID: PMC8280622)

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    Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

    Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

    Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).