About This Project
Understanding pharmacological agents is key to treatment selection. For the increasing involvement of primary care physicians (PCPs) in mental health, we are creating a mobile web application resource to help PCPs determine whether patient symptoms warrant clinical treatment of depression and if indicated, drug selection. We predict this will greatly decrease cases of misdiagnosis and lack of patient compliance due to ineffective prescriptions or intolerable side effects.
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What is the context of this research?
Antidepressants have become the second-most commonly prescribed drug in the United States. Yet rates of suicide are continually increasing, standing at 41,000 suicides in the US as of 2013. Approximately 80% of psychotropic drug prescriptions are written by primary care physicians (PCPs) with limited training in mental health. Unwanted side effects, including insomnia, diarrhea and sexual dysfunction, are more likely to occur when antidepressants are inappropriately prescribed. Educating PCPs on the relationship between pharmacological agents and drug side effects is not only vital to patient remission, but will also improve communication between patients and PCPs, keeping the patient well informed of why he/she is taking the drug(s) prescribed.
What is the significance of this project?
Existing antidepressant prescribing resources provide general prescribing instruction. While helpful benchmarks, these tools do not teach PCPs how to integrate preexisting conditions to determine the best possible treatment. To integrate evidence-based treatment, it is essential that PCPs understand pharmacodynamics in order to tailor medication. The mobile web application we are creating uses a visual language to enhance understanding of molecular factors that contribute to varying degrees of depressive symptoms. This visual language is designed to communicate complex concepts, and bridge the gap between mental health physicians and PCPs. This channel of communication will make side effect profiles easier to compare and ensure knowledge retention.
What are the goals of the project?
1. To develop a unique resource to help integrate primary care physicians with their increasing role in the field of mental health.
2. To explore the use of a visual learning style to demonstrate the relationship between pharmacodynamics and side effect profiles of antidepressants.
3. To incorporate the results of the research study to improve on how we teach psychopharmacology in the future.
Given the short time-frame of this project (three months), we plan to hire a programmer to create a deliverable antidepressant-prescribing algorithm platform capable of being tested in a clinical settings. This will not only help determine the effectiveness of the visual language, but will also maximize the amount of time spent on application content. Funding this project will advance our research and help to establish a working platform applicable when teaching psychopharmacology in the future.
Meet the Team
Adam Kaplin, MD, PhD and Kristen Rahn, PhD are neuropsychiatric clinicians and researchers at the Johns Hopkins Hospital, aiming to enhance psychiatric care and decrease stigma surrounding mental illness. Jennifer Fairman, CMI, FAMI is an award-winning medical illustrator with an eye for visual communication. Benjamin & Bond is a firm that develops digital products for industries in healthcare with the mission to improve human experience and reinterpreting information.
Amy Zhong is a graduate student in the Johns Hopkins School of Medicine, department of Art as Applied to Medicine. Prior to her graduate studies, she received a BFA from the University of Michigan and worked three years in graphic design and animation at the Cricket Magazine Group (CMG). It was during her time working on CMG's collection of children's storybook applications that inspired her to adapt the mobile application platform as a vessel to teach.
Disclaimer: This device is intended as an information resource for health providers, not a replacement for a mental health physician.
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