ANTIDEPRESSANT MINING A Z AMAZ DESIGNING A WEB BASED ALGORITHM AND VISUAL LANGUAGE FOR ANTIDEPRESSANT DRUG SELECTION TO EDUCATE PRIMARY CARE PRACTITIONERS

By Amy Zhong

A thesis submitted to Johns Hopkins University in conformity with the requirements for the degree of Master of Arts

Baltimore, Maryland March, 2016

© 2016 Amy Zhong All Rights Reserved ABSTRACT Depression is a common mental disorder that affects approximately 14.8 million American adults each year. In addition to being a debilitating condition, depression often occurs in tandem with other medical conditions such as diabetes, heart disease, and cancer. While psychiatric professionals are essential for the management of mental health, majority of patients seek care from their primary care practitioners. This phenomenon is of great concern because diagnosis of depression within primary care settings has only been accurate 25-50% of the time. The antidepressant drug selection algorithm utilizes a unique formula to integrate patient and family medical histories, patient symptoms, and patient preferences to make optimal treatment selections. The development of a visual language explores the use of graphic elements to improve understanding of major pharmacological mechanisms, knowledge essential to making rational DQWLGHSUHVVDQWGUXJVHOHFWLRQV,QFUHDWLQJWKLVPRELOHZHEEDVHGDSSOLFDWLRQZHKRSHWR¿OOD void in resources available to primary care practitioners, and improve management of mental health within the primary care setting.

By Amy Zhong

Chairpersons of the Supervisory Committee

Adam I. Kaplin, M.D., Ph.D., esis Preceptor Assistant Professor, Departments of Psychiatry and Neurology e Johns Hopkins University School of Medicine

Kristen Rahn Hollinger, Ph.D., esis Preceptor Instructor, Departments of Psychiatry and Neurology e Johns Hopkins University School of Medicine

Jennifer E. Fairman, M.A., C.M.I., F.A.M.I., Departmental Advisor Assistant Professor, Department of Art as Applied to Medicine e Johns Hopkins University School of Medicine

ii ACKNOWLEDGEMENTS I would like to take this opportunity to express how eternally grateful I am to have been invited into the Hopkins family two years ago. I am thankful for the aspiring guidance, invaluable constructive criticism and unyielding support that many have shown me over the past few years, all of whom helped bring this thesis into fruition. To Drs. Adam I. Kaplin and Kristen Rahn Hollinger from the Departments of Psychiatry and Neurology, I owe a deep sense of gratitude to both as my thesis preceptors, my guidance counselors and my friends. It was truly an honor to have had the opportunity to work with you both this year. To my faculty advisor, Jennifer E. Fairman, I would like to express a special appreciation for being my rock. Her optimism and enthusiasm helped me stay focused and motivated through the research and writing of this thesis. I would also like to extend my sincere thanks to the rest of the thesis team: Mark Kandrysawtz and Josiah Heigel from Benjamin & Bond and Mike Wang. A shout out to everyone who helped fund my thesis on Experiment.com (Jason Zhong, Kevin G Chen, Joanne Yuan, Steven Chen, Denny Luan, Adam Kaplin, John Sandford, Andrew Kim, Wendy Yuan, Calvin Chan, Minna Chan, Andrew W. Lee, David Dickerman, Ada Lin, Tiffany Liang, Andrew Wu, Rachel Zhou, John Megahan, Weiling Chou, Chris Chen, Claire Vergara, Peter Yeung, Tim Su, Christine Lin, Pamela Krikorian, Wendy Luo, Harry Zhang, Joe Samson, Jeremy Reppy, Lindaanne Donohoe, Patrick Chen, Louise Giam, Andy Li, Ashley Chung, Ashley Jiang, Jeff Lin, Ray Lian, Clarice Chan, Richard Chen, Carmen Loo, Michael Kim, Robin Muccari, Joseph Lee, Jeff G, Xuesi Li, Jeff Day, Ian Suk, Wenjing Wu, Kimberly Stemp, Justine Mirasol, Joshua Gabriel, George Dong, Victoria Tsay, Robert Paul Malchow, Kate Ray, April Koh, Ashley Lau, Tom Liang, Cindy Wu, Douglas A. Mata, Eric Damon Walters, and Adam Davies) and the individuals who helped endorse my project (Corinne Sandone, Justine Mirasol, Lynn Ngai, Felicia Hardi, and Cheri Dijamco). I want to acknowledge the faculty and staff in the Department of Art as Applied to Medicine who were an invaluable support system: Corinne Sandone, Tim Phelps, David Rini, Gary Lees, Juan Garcia, Lydia Gregg, Dacia Balch, Carol Pfeffer, and Ed Philips. I would like to thank my second family in Chicago, whose words of encouragement remind me every day that I am capable of accomplishing anything I put my mind to. Thank you John Sanford, Pamela Krikorian, Robin Muccari, Lindaanne Donohue, and Phil Martin.

iii ACKNOWLEDGEMENTS Thank you to my class, the Class of 2016, the classes of 2015 and 2017, and our wonderful alumni Hannah Ahn and James Abraham for the weekly goodies, midnight critiques, spontaneous food adventures and overall good times. I want to thank my godparents Xiaoyu Jing and Kevin Chen for always looking out for me and ensuring that I didn’t starve. I would like to thank my dog Jamie for her unconditional love and enforcement of potty breaks and playtime, which helped maintain my sanity throughout this thesis. To Calvin J. Chan, I am so grateful for his love and support, and extraordinary patience.

This thesis is dedicated to my amazing parents Holly Xiong and Pingyu Zhong for whom I owe everything I am today. To my baby brother Arthur Zhong whose creativity and enthusiasm for the arts inspires me to be more fearless. To my grandparents I want to thank for their experiences and words of wisdom. I am so blessed to have such inspiringly hardworking people in my life. You have and will always be my role models. I love you.

iv TABLE OF CONTENTS

Abstract ii Chairpersons of the Supervisory Committee ii Acknowledgements iii Table of Contents v Index of Tables vii Index of Figures vii

Introduction Overview of Mental Healthcare 1 Depression with a capital “D” 2 Psychiatric Consultations 3 Existing Algorithms for Antidepressant Drug Selection 4 Visuals for Teaching Psychopharmacology 5 Potential for a Smartphone Accessible Resource 5 Project Objective 6 Disclaimer 6

Materials & Methods  Development of Antidepressant Charts 8 Development of Application Flowchart 11 Development of Drug Selection Algorithm 13 Development of Visual Language 19 Development of Application 22

Results  Access to Assets resulting from this thesis 28

Discussion 

Conclusion 

v TABLE OF CONTENTS

Appendices  Appendix A: Antidepressant Charts 32 Appendix B: Drug Selection Flowchart 42 Appendix C: Drug Selection Questions 43 Appendix D: Visual Language 48 Appendix E: Web Application Wireframe & Design 50 Appendix F: Test Scenarios & Results 54 Appendix G: IRB Pre/Post Questionnaires 64

References 

Vita 

vi INDEX OF TABLES Table 1 9 Table 2 10 Table 3 14 Table 4 16 Table 5 20 Table 6 21

INDEX OF FIGURES Figure 1 12 Figure 2 17 Figure 3 17 Figure 4 21 Figure 5 22 Figure 6 25 Figure 7 26

vii INTRODUCTION

Overview of Mental Healthcare Depression is a life debilitating mood disorder that affects approximately 14.8 million American adults each year (ADAA 2014). In addition to being a chronic mental illness, studies have shown that depression is also a frequent comorbidity of other conditions such as HIV/ AIDS, diabetes, multiple sclerosis, and cancer. Though depression is a treatable condition, it is frequently underdiagnosed and undertreated, and the disease is responsible for a 5-fold increased risk of mortality of comorbid medical conditions (ESC 2015). In 2010, antidepressants were the

second most commonly prescribed drug in the US (IMS Health 2011). Between 1988-1994 and 2005-2008 their use increased by 400% (Pratt, et al. 2011). Meanwhile, disturbing evidence shows a steady increase in suicide rates since 2005 with the US suffering nearly 43,000 deaths in 2014 (AFSP 2014). This phenomenon has launched awareness of the increasing involvement of primary care practitioners (PCPs) in mental health – 80% of psychotropic drug prescriptions were made by PCPs in 2013 (Barkil-Oteo 2013) – and the evaluation of their abilities to detect depression and implement the necessary treatment. /LNHRWKHUSV\FKLDWULFGLVRUGHUVGHSUHVVLRQLVDGLI¿FXOWFRQGLWLRQWRUHFRJQL]H'HSUHVVLYH symptoms not only include depressed mood and anhedonia (inability to experience pleasure), but can range from insomnia and anorexia to decreased cognition and suicidal behavior. The SUHYDOHQFHRIV\PSWRPVLVSDWLHQWVSHFL¿FDQGFDQEHVXSHULPSRVHGZLWKV\PSWRPVRIRWKHU comorbid diagnoses. Though screening tools such PHQ-9 and PHQ-2 exist to detect characteristics RIGHSUHVVLRQWKH\DUHQRWPHDQWWRVXEVWLWXWHIRUTXDOL¿HGPHQWDOKHDOWKFDUHSUDFWLWLRQHUV&XWRII points in depression screening questionnaires are typically based off data collected from a variety RIFRQFHSWXDOO\VLPLODUVFLHQWL¿FVWXGLHV7KLVJHQHUDWHVDQH[DJJHUDWHGHVWLPDWHRIDFFXUDF\ leaving room for a margin of error. The situation, as Dr. Ramin Mojtabai, Associate Professor in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health describes, “Without enough time to really talk to patients about depression and with some patients increasingly open to the idea of an antidepressant for mild conditions, the primary care setting can be vulnerable to overprescribing” (Katz 2012).

 Depression with a capital “D” The ability to detect clinical depression manifests in the understanding of the condition. What is depression? Everyone experiences sadness, but what makes a patient clinically depressed? +LSSRFUDWHV %& ¿UVWGHVFULEHGWKHVWDWHRIGHSUHVVLRQDVWKH³DYHUVLRQWRIRRG despondency, sleeplessness, irritability, [and] restlessness” (Kaplin 2014). In a lecture made by Dr. Adam Kaplin, Assistant Professor in the Departments of Psychiatry and Neurology at the -RKQV+RSNLQV6FKRRORI0HGLFLQHKHFODUL¿HVWKDWGHSUHVVLRQZLWKDVPDOOµG¶LVDV\PSWRP It is an intermittent, universal experience in response to one’s environment, where the degree DQGGXUDWLRQRIVDGQHVVLVDSSURSULDWHWRWKHVWUHVVRU,QFRQWUDVWGHSUHVVLRQZLWKDFDSLWDOµ'¶ is not merely severe sadness, but a syndrome. “Sadness is to major depression what cough is to pneumonia. A cough can be an indicator of pneumonia, but not every cough is the result of pneumonia” (Kaplin 2014). The use of the word “depression” can be confusing. Not only is the term associated with the general state of unhappiness, but clinically, multiple terms are used interchangeably to describe the condition: major depressive disorder (MDD), major depression, depressive disorder, major depressive disorder, clinical depression, and clinically depressed. All these terms fall under the umbrella term of “mood disorder.” When diagnosing depression, psychiatrists use the mnemonic “SIGEMCAPS.” If the patient has experienced at least 5 of the 9 symptoms for at least 2 weeks, it is likely that the patient is clinically depressed. Those symptoms are: Sleep (lack of/excessive sleep) Interest (loss of interest and pleasure) Guilt (feeling worthless or guilty) Energy (fatigue, loss of energy) Mood (sadness) Concentration (trouble concentrating or making decisions) Appetite (increased/decreased appetite, correlates with weight) Psychomotor Retardation (or agitation) Suicidal Thoughts

 Unlike stimulant drugs, antidepressants do not work unless a patient is suffering from a major depressive disorder. If an individual is not responding to antidepressants, the medication will do one of three things: (1) nothing, (2) cause side effects, or (3) make the patient worse. For instance, an individual with bipolar disorder may experience irritability with an incorrectly prescribed drug. For this reason, the most critical step in treating mood disorder is an accurate diagnosis.

Psychiatric Consultations :KHQDSDWLHQWLVUHIHUUHGWRDSV\FKLDWULVWWKH¿UVWDSSRLQWPHQWLVFDOOHGDFRQVXOWDWLRQ This 3-4 hours initial meeting is designed to collect the necessary background and symptom information to make a diagnosis and provide any medication. Every psychiatrist approaches consultations differently, but standard practice involves providing patients with a preregistration packet consisting of forms for general information, medical history, family history and brief self-report psychometric questionnaires. Before meeting with the patient, the psychiatrist will ¿UVWDVVHVVWKHUHVXOWVRIWKHSUHUHJLVWUDWLRQSDFNHW7KHFRQVXOWDWLRQZLOOWKHQSURFHHGLQWKUHH SDUWV  DRQHRQRQHGLVFXVVLRQZLWKWKHSDWLHQWWRFROOHFWVXI¿FLHQWKLVWRU\DQGPHGLFDOGDWD to formulate the problem (conducting mental status, neurological and physical examinations as necessary); (2) a private conversation with an individual of close relation, typically a spouse, to gain a different perspective; and (3) a group discussion with both the patient and close relation regarding the differential diagnosis and treatment options. While the ability to make a diagnosis through discussion is a method similar to that of a medical doctor, the nature of drug selection can often be an enigma. Prior to attending a consultation with Dr. Kaplin, initial research on antidepressants proved helpful for understanding drug options, but ZDVQRWVXI¿FLHQWHQRXJKWRGLIIHUHQWLDWHWKHDGYDQWDJHVRIVHOHFWLQJRQHGUXJRYHUDQRWKHU7KUHH hours of seemingly unstructured discussion about a patient’s life later, Dr. Kaplin had eliminated DODUJHOLVWRIDQWLGHSUHVVDQWGUXJRSWLRQVDQGLGHQWL¿HGWKUHHGUXJVEHVWWDLORUHGIRUDSDWLHQW¶V condition. As Dr. Kaplin itemized the choices of medication, describing how and why treatments were selected (and occasionally mentioning those that were not), the inspiration for this thesis was born. The mind is a complex network, but no matter how complicated it may be, the results of the consult proved that there exists a formula for antidepressant drug treatment selection.

 Existing Algorithms for Antidepressant Drug Selection The prescription of antidepressant drugs is a dilemma due to perception of drugs being LQÀXHQFHGE\³DGYHUWLVLQJSKDUPDFHXWLFDOUHSUHVHQWDWLYHVH[SHULHQFHVZLWKWKHGUXJVZKHQ WDNHQE\VLPLODUSDWLHQWVLQWKHSDVWIRUPXODU\OLPLWDWLRQVOLWHUDWXUHUHYLHZVDQGWKHLQÀXHQFH of colleagues” (Zetin et al., 2006). For this reason, a proper understanding of how antidepressant drugs work will allow for the ability to identify particular characteristics of antidepressant medications that may make it a better choice for a patient. In 2006, the authors of an article published in the Psychopharmacology Bulletin, titled “Rational Antidepressant Selection,” discusses elements they found often understated. The article goes into detail about the management of comorbidities and drug selection based on the co-effectiveness of the drug to treat symptoms other than depression. Examples were provided describing situations where certain antidepressants would be more effective than others. Though this information was insightful and the tables informative, like other psychopharmacology textbooks, a 45-page paper is not an effective resource in a busy clinical setting. In 2008 the Texas Department of State Health Services produced “Major Depressive Disorder Algorithms,” a procedural manual to facilitate clinical decision making in the management of three conditions commonly encountered in the Texas public mental health system: schizophrenia, bipolar I disorder, and major depressive disorder (Suehs et al., 2008). The treatment algorithms provided sequential drug regimen options, driven by the evaluation of the patient’s response to treatment. After assessment of the patient, “stage 1” treatment options included “SSRIs, BUP, SR/ XL, MRT, and SNRIs.” In translation, clinicians have a choice of 15 drugs for Stage 1: 7 SSRIs, Bupropion - generic or brand, Mirtazapine, and 5 SNRIs. The next stage of action was determined using results of the QIDS (Quick Inventory of Depressive Symptomatology), a test to assess the severity of depressive symptoms. Though an evidence-based algorithm, this method not only fails WRFUHDWHSDWLHQWVSHFL¿FGUXJUHFRPPHQGDWLRQVEXWGRHVQRWGLIIHUHQWLDWHEHWZHHQGUXJVZLWKLQ the same drug class until the medication charts listed in the Appendix. In 2010, the National Health Service in England published prescribing guidelines for the treatment of depression (Berkshire Healthcare 2010). Though the overall document covers the necessary components for making a drug selection, the information was made available

 via tables scattered throughout the packet. The information unfortunately did not translate into the algorithm. While a helpful teaching aid, this packet is not useful for a fast-paced work environment. In 2015, the Eisenberg Center in Rockville, MD, developed a depression treatment decision aid, called Depression Medication Choice (Kroenke 2015). This tool utilized decision aid ÀDVKFDUGVWRKLJKOLJKWWKHPDMRUVLGHHIIHFWFRQFHUQVRIVHFRQGJHQHUDWLRQDQWLGHSUHVVDQWV On average, 3-4 cards were used during a single clinical visit. This generated discussion about WKHVLGHHIIHFWSUR¿OHVRIHDFKGUXJ7KLVGHSUHVVLRQWUHDWPHQWGHFLVLRQDLGGHVSLWHEHLQJDQ educational resource, was designed primarily to improve patient experience, not teach primary care physicians.

Visuals for Teaching Psychopharmacology The availability of visuals for teaching drug side effects is currently limited to the textbook, Stahl’s Essential Psychopharmacology. Nancy Muntner’s use of a circle with protruding polygonal VWUXFWXUHVLVDFOHDURIGHVFULELQJDGUXJ¶VELQGLQJDI¿QLW\WRGLIIHUHQWUHFHSWRUV8QIRUWXQDWHO\ WKHVHGLDJUDPVDUHRYHUVLPSOL¿HGDQGQRWLQIRUPDWLYHWRWKRVHZLWKDEDVLFXQGHUVWDQGLQJRI QHXURELRORJ\)XUWKHUPRUHZLWKRXWWKHXVHRIDVFDOHWKHFRPSDULVRQRIGUXJELQGLQJDI¿QLWLHVWR the same receptor required graphic elements to be placed in juxtaposition. Taken out of context, a single drug’s diagrammatic representation could not be easily deciphered. To communicate to a primary care audience, infographics also need to convey the relationship EHWZHHQPHFKDQLVPVRIDFWLRQZLWKGUXJVLGHHIIHFWSUR¿OHV,OOXVWUDWLRQVZKHUH0XQWQHUDWWHPSWV to represent that relationship, the extensive variability in the use of color, shapes, and style grow EXV\DQGGLI¿FXOWWRFRPSUHKHQGZLWKRXWUHIHUHQFLQJEDFNWRWKHWH[W

Potential for a Smartphone Accessible Resource Originally marketed to executives on-the-go, the convenience of mobile-accessible services has dramatically risen smartphone usage to approximately 84% of the US population in 2016 (Statista 2016). For those who cannot afford home broadband services, low income families have come to rely on this platform for internet access. While the vast majority of smartphone owners use their

 devices for texting and social media, 62% also use their mobile devices to access information about health related conditions (Anderson 2015). We therefore chose to use a mobile-accessible platform not only for its ability to disseminate information, but also for the ease of keeping up to date with the evolution of psychopharmacology.

Project Objective The objective of this thesis is to develop a decision support tool to educate and assist practitioners with modest formal training in Psychiatry to make more informed decisions when prescribing DQWLGHSUHVVDQWV7KHSURGXFWRIWKLVWKHVLVDPRELOHZHEEDVHGDSSOLFDWLRQZLOO¿OODUHVRXUFH void, permitting easy and immediate access to clinically relevant information about antidepressant WUHDWPHQW,QXVLQJWKLVWRROZHSUHGLFWWKDWSUDFWLWLRQHUVZLOOIHHOPRUHFRQ¿GHQWFRQYH\LQJWRWKHLU patient the reasoning behind a diagnosis and drug treatment selection. We predict that improved patient-practitioner communication will strengthen patient understanding and improve patient compliance to antidepressant drug treatment, and, to a larger extent, improve management of mental health within the primary care setting. The web-based application is designed with a user-friendly interface to navigate a unique algorithm constructed to take an evidence-based approach to drug selection. With education as the primary intent for this creation, the application explores the use of a visual learning style to HQKDQFHXQGHUVWDQGLQJRIWKHUHFHSWRUELQGLQJDI¿QLWLHVWKDWFRQWULEXWHWRGHSUHVVLYHV\PSWRPV The visual language is designed to communicate complex concepts, and bridge the gap between mental health practitioners and PCPs. Not only will this channel of communication make side HIIHFWSUR¿OHVHDVLHUWRFRPSDUHEXWZLOODVVLVWLQNQRZOHGJHUHWHQWLRQ

Disclaimer This resource does not yet cover depression in children or adolescents, postpartum depression, or depression in patients with coexisting psychiatric disorders. The medications included in this algorithm are FDA approved for depression and found to be clinically relevant to a practitioner working in primary care. Psychotherapy, light therapy, and exercise were not considered in the making of this application.

 The drug selection tool is not intended to replace a practitioner’s clinical judgment, nor is it LQWHQGHGWRGH¿QHDQH[FOXVLYHFRXUVHRIWUHDWPHQW7KHDOJRULWKPDLPVWRSURYLGHLQIRUPDWLRQ and assist practitioners in decisions regarding mental healthcare. Primary care practitioners using this resource are responsible for considering their patient’s unique condition to evaluate the appropriateness of the algorithm’s results.

 MATERIALS & METHODS

Development of Antidepressant Charts Initial research of antidepressant content was obtained by reading Stephen M. Stahl’s Essential Psychopharmacology. A table was created created Microsoft Excel to organize facts on 35 antidepressants. Data such as generic name, brand name, drug class, half life, side effect SUR¿OHDQGNH\QHXURWUDQVPLWWHUV\VWHPVZHUHUHFRUGHG&RPSOLFDWLRQVZHUHPHWGXHWRWKH availability of multiple drug databases, research papers and textbooks concerning the effects of antidepressants. Ultimately, Medscape was chosen as the primary reference not only for

its reliability, but side effect frequency data, which was extracted from double-blind studies

published in The Physicians’ Desk Reference (PDR). The PDSP K1 Database supplied most GHWDLOVRQQHXURWUDQVPLWWHULQKLELWLRQSRWHQF\OHYHOVUHFHSWRUDI¿QLWLHVIRU9LOD]RGRQH :DQJ et al 2015) and Vortioxetine (Connolly, et al 2016) were found elsewhere. 'XHWRWKHFRPSOH[LW\RIGDWDHVVHQWLDOWRWKLVVWXG\VLGHHIIHFWSUR¿OHVGUXJGUXJLQWHUDFWLRQV FRQWUDLQGLFDWHGSDWLHQWSUR¿OHVDQGPHFKDQLVPVRIDFWLRQZHUHGLYLGHGLQWRIRXUWDEOHV)XUWKHU efforts to simplify the antidepressant charts as much as possible were made by combining side effects with similar implications. For example, drowsiness, sedation and hypnotic now fall under the umbrella term of “somnolence.” Low energy, weakness and fatigue fall under “asthenia” (Table 1). With the guidance of Dr. Kaplin, the list of antidepressants was also reduced to 20 to include only drugs most clinically relevant (Table 2). For instance, the class of monoamine oxidase inhibitors (MAOIs) was removed due to dangerous drug interactions and serious dietary restrictions, ultimately making MAOIs unfavorable among modern-day clinicians. Similarly, the drawback of older tricyclic antidepressants (TCA) were removed due to their propensity to cause serious side effects, many of which the mechanisms of action are not well understood.

 Table . 1 Side Effect Category Umbrella Term Used Terms Listed on Medscape Akathisia/Tremor Akathisia Agitation Akathisia Feeling Jittery Inner Restlessness Irritability Jittery Sensation Restlessness/Akathisia Appetite Decreased Appetite Anorexia Decreased Appetite Blood Pressure Orthostatic Hypotension Hypotension Orthostatic Hypotension Vasodilation Cognition Confusion Confusion Disorientation Lack of Concentration Decreased Concentration Disturbance in Thinking Energy Activating Activating Anxiety Nervousness Asthenia Asthenia Fatigue Low Energy Weakness Gastrointestinal Distress Dry Mouth Dry Mouth Xerostomia Nausea Nausea Nausea/Vomiting Heart Rhythm Tachycardia Increased Heart Rate Tachycardia Sensory Disturbances Blurred Vision Abnormal Vision Blurred Vision Vision Disturbances Dizziness Dizziness Lightheadedness Vertigo

 Side Effect Category Umbrella Term Used Terms Listed on Medscape Sexual Dysfunction Ejaculation Failure Ejaculation Disorder Delayed Ejaculation Decreased Libido Decreased Libido Libido Decrease Male Sexual Dysfunction Skin Edema of Lower Extremity Edema Swelling Peripheral Edema Sweating Diaphoresis Hyperhidrosis Increased Sweating Sweating Sleep Disturbance Nightmares Abnormal Dreams Dream Disorder Somnolence Drowsiness Hypnotic Sedation

Table . 2 Original List Reason for Elimination Isocarboxazid MAOI Phenelzine MAOI Selegiline, transdermal MAOI Tranylcypromine MAOI Mirtazapine Bupropion Agomelatine foreign drug, not FDA approved Nefazodone sale of brand drug discontinued due to hepatotoxicity risk Trazodone Desvenlafaxine Duloxetine Milnacipran FDA approved to treat Fibromyalgia, used off-label for Depression Levomilnacipran Venlafaxine Vilazodone Citalopram

 Original List Reason for Elimination Escitalopram Fluoxetine Fluvoxamine FDA approved to treat OCD, used off-label for Depression Paroxetine Sertraline Vortioxetine Amitriptyline Amoxapine old TCA Clomipramine Desipramine Dothiepin foreign drug, not FDA approved Doxepin Imipramine Lofepramine foreign drug, not FDA approved Maprotiline old TCA Nortriptyline Protriptyline old TCA Tianeptine foreign drug, not FDA approved Trimipramine old TCA

Development of Application Flowchart Following the model of general psychiatric consultation practices, the major themes relevant WRGUXJVHOHFWLRQZHUHLGHQWL¿HGWREHJHQHUDOLQIRUPDWLRQSDWLHQWPHGLFDOKLVWRU\IDPLO\ PHGLFDOKLVWRU\SDWLHQWV\PSWRPVDQGSDWLHQWSUHIHUHQFHV$ÀRZFKDUWZDVFUHDWHGRQ$GREH Illustrator CC to illustrate where these major decision points of the algorithm would appear in the application (Figure 1). Considerations were made for the possibility of saving patient records as reference for future medical follow-ups.

 Figure . 1 Flowchart identifying the major decision points in the algorithm.

 Development of Drug Selection Algorithm

Algorithm .

Patient Medical History 7KH¿UVWUHQGLWLRQRIWKHDOJRULWKPZDVGHWHUPLQLQJWKHEHVWVWUDWHJ\IRUUHGXFLQJ WKHQXPEHURIGUXJRSWLRQV,WHPVRIHOLPLQDWLRQZHUHLGHQWL¿HGDVFRQWUDLQGLFDWHGKHDOWK conditions, life circumstances (pediatric, pregnancy, breast feeding), and drug interactions. Contraindications were obtained via Medscape’s interactions, warnings and pregnancy sections. ,I0HGVFDSHLGHQWL¿HGWKHGUXJ¶VSUHJQDQF\FDWHJRU\DV³;'RQRWXVHLQSUHJQDQF\´WKHQWKH drug would be eliminated from further algorithm calculations for a pregnant patient. Similarly, if DSDWLHQWLGHQWL¿HGZLWK476\QGURPHWKHFODVVRIWULF\FOLFDQWLGHSUHVVDQWVZRXOGEHHOLPLQDWHG Drug selection questions were developed to elicit drug elimination therefore only conditions, circumstances and drugs marked “contraindicated” on Medscape were listed as possible answer choices (Appendix B).

Patient Symptoms 3DWLHQWV\PSWRPVZHUHLGHQWL¿HGXVLQJDOLVWRIGUXJVHOHFWLRQTXHVWLRQV5HVSRQVHVZHUH employed to weigh drug options using a point system. Drugs begin with a value of zero. One point per side effect deductions were made for drugs yielding side effects patients were already experiencing. Drugs yielding side effects opposite that of patient symptoms would increase by one point. If a drug had no effect on patient symptoms, no points were deducted or added. For example, if a patient suffered from insomnia, a drug such as Mirtazapine (which causes somnolence) would increase by one point. A drug such as Bupropion (which also causes insomnia) would be deducted by one point. For a drug like Vortioxetine (which causes neither insomnia nor somnolence) no points were deducted or added. Side effects such as blurred vision or vomiting, where there is no “opposite” side effect (Table 3), points can only be deducted.

 Table . 3 Side Effect Opposite Side Effect Increased Appetite Decreased Appetite Weight Gain Weight Loss Asthenia Activating Lethargy N/A Insomnia Somnolence Nightmares N/A Decreased Libido N/A Anorgasmia N/A Ejaculation Failure N/A Impotence N/A Lack of Concentration N/A Confusion N/A Constipation Diarrhea Diarrhea Constipation Dyspepsia N/A Xerostomia N/A Nausea N/A Vomiting N/A Abdominal Cramping N/A Headache N/A Musculoskeletal Pain N/A Palpitaions N/A Myalgia N/A Tremor N/A Akathisia N/A Blurred Vision N/A Mydriasis N/A Tinnitus N/A Dizziness N/A Frequent Urination Urine Retention Proteinuria N/A Psoriasis N/A Edema of Lower Extremity N/A Sweating N/A Hypertension Orthostatic Hypotension Bradycardia Tachycardia

 Drug Weight Calculations The number of added points (positive side effects) and deducted points (negative side effects) from patient symptoms were used to arrange drugs into a list of ideal drug options, with the most ideal drugs (higher drug weight) listed at the top.

Algorithm . Additional approaches to drug elimination and point adjustments were added to Algorithm 1.0.

General Information Bupropion is the only drug FDA approved for smoking cessation. For patients indicated with smoking cigarettes, one point was added for Bupropion. In addition, if an individual selected “Yes” for regular alcohol use, the CAGE questionnaire was used to screen for alcoholism. If the SDWLHQWLGHQWL¿HGDVEHLQJDQDOFRKROLFDQWLGHSUHVVDQWVFRQWUDLQGLFDWHGZLWKDOFRKROXVHZHUH eliminated.

Patient Medical History “The best predictor of future response is past response (Kaplin 2016).” If a patient had previously taken an antidepressant, the drug will likely continue to yield successful or unsuccessful results. Treatments yielding unsuccessful results in the past were eliminated from further drug selection. Treatments yielding positive results were given one point.

Family Medical History Patterns of heredity imply that having parents or family members with clinical depression can increase the likelihood of an individual being diagnosed. Similar patterns have been observed in patient response to antidepressant medication. If a blood relative had good results with an antidepressant, the drug’s likelihood of working for the patient is high. In this algorithm, these drugs received one point.

 Patient Preferences On Medscape, drug side effects are organized by frequency levels. Among a variety of drug GDWDEDVHVVLGHHIIHFWIUHTXHQF\FODVVL¿FDWLRQWKHPHVZHUH³FRPPRQ´IRUVLGHHIIHFWVZLWKDWOHDVW 10% frequency, “less common” for side effects with 1-10% frequency, and “uncommon” for side effects less than 1%. With the guidance of Dr. Kaplin, this scale was adjusted to emphasize side HIIHFWVPRUHVWDWLVWLFDOO\VLJQL¿FDQW0HGVFDSHKDGFDWHJRUL]HGDVLGHHIIHFWZLWKDIUHTXHQF\ LGHQWLFDOWRWKDWRIDIUHTXHQF\:LWKWKHDGMXVWHGVFDOHDVLGHHIIHFWRIZRXOGEHFODVVL¿HG as “common” and regarded with higher concern than that of 10%. Side effects only as low as 4% frequency were recorded to accommodate for hypersensitive patients (Table 4). Using a scale IURPWRGUXJVLGHHIIHFWVZHUHDVVLJQHGUDQNLQJVIRUVLGHHIIHFWVFODVVL¿HGDV³FRPPRQ´ IRUVLGHHIIHFWVFODVVL¿HGDV³OHVVFRPPRQ´IRUVLGHHIIHFWVFODVVL¿HGDV³XQFRPPRQ´DQGIRU side effects unlisted.

Table . 4 Medscape Scale Algorithm Scale Frequency Side Effect Ranking > 10% 31 - 45+% Common 3 1 - 10% 16 - 30% Less Common 2 < 1% 4 - 15% Uncommon 1 - < 4% unlisted 0

3DWLHQWFKLHIFRQFHUQVZHUHLGHQWL¿HGE\'U.DSOLQZHUHVOHHSDSSHWLWHHQHUJ\VH[XDO dysfunction, gastrointestinal distress, and urinary function. Using the same scale (0-3), patient preferences were ranked: 3 for high preference, 1 for little preference, and 0 for lack of preference. These preferences were built into the algorithm by multiplying the patient preference by the drug VLGHHIIHFWUDQNLQJ$QHJDWLYHZDVXVHGWRGHQRWHGUXJVZLWKVLGHHIIHFWVFRQÀLFWLQJZLWKWKRVH preferred by the patient, and a positive was used to denote drugs with side effects compatible to those preferred by the patient. In the case of sexual dysfunction, patient preference denotes which of the sexual side effects a patient would prefer if one was had (Appendix C). As each of the chief FRQFHUQVDUHFRQVLGHUHGWKHVXPRIDOOVL[QXPEHUVZLOOGH¿QHWKH³'UXJ6FRUH´IRUHDFKGUXJ

 Example #

A patient with insomnia expresses high preference for “increased sleep,” ranking it a 3.

Figure . 2 Sample patient preferences (sleep)

Drug Option #: Vortioxetine

Vortioxetine has no effect on sleep, therefore it’s side effect ranking is 0. Patient Preference (3) x Side Effect Ranking (0) = 0

Drug Option #: Mirtazapine

Mirtazapine has a 54% frequency of somnolence giving it a side effect ranking of +3. Patient Preference (3) x Side Effect Ranking (+3) = +9

Drug Option #: Bupropion

Bupropion has a 15.50% frequency of insomnia, therefore it’s side effect ranking is -2. Patient Preference (3) x Side Effect Ranking (-2) = -6

Figure . 3 Sample patient preferences

 Example #

Drug Option #: Mirtazapine

Appetite: Patient Preference (2) x Side Effect Ranking (-2) = -4 Energy: Patient Preference (1) x Side Effect Ranking (-1) = 0 Sleep: Patient Preference (3) x Side Effect Ranking (+3) = +9 Sexual Dysfunction: Patient Preference (1) x Side Effect Ranking (0) = 0 Gastrointestinal Distress: Patient Preference (2) x Side Effect Ranking (+1) = +2 Urinary Function: Patient Preference (1) x Side Effect Ranking (0) = 0 Drug Score = (-4) + 0 +9 + 0 + 2 + 0 = 7

Patient Symptoms

Similar to algorithm 1.0, drugs will start with the arbitrary number of 0. Successful drugs taken by family members with depression were given one point. Side effects that exacerbate patient symptoms will deduct one point, s ide effects that alleviate patient symptoms will gain one point, and side effects with no effect on existing symptoms will neither gain nor lose points.

Drug Weight Calculations The total number of deducted points (negative side effects) subtracted from the sum of the added points (positive side effects, smoking cessation, and successful blood relative medications) will generate a list of drugs from the most ideal drug selection to the least ideal drug selection. Patient preferences is not integrated into any calculations.

 Development of Visual Language Side effects caused by antidepressant medication on key neurotransmitter systems were LGHQWL¿HGXVLQJDFRPELQDWLRQRIStahl’s Essential Psychopharmacology, the article “Interactions of Antidepressants with Neurotransmitter Transporters and Receptors and their Clinical Relevance” by Dr. Elliot Richelson (Richelson, 2003), and discussion with Dr. Kaplin (Table 5).

Side Eect Icons Icons were created using Adobe Illustrator CC. These icons correlate with the primary pharmacologic action of the key mechanisms listed in Table 5. They do not encompass all cataloged drug side effects in Appendix A, because many mechanisms are not yet well understood. The objective in the creation of these graphic elements were to ensure easy recognition and stylistic cohesiveness (Appendix D).

Receptor Anity Levels

KiYDOXHV $SSHQGL[$ DUHUHFHSWRUELQGLQJDI¿QLWLHVJHQHUDWHGE\WKHGUXJGRVDJHQHHGHGWR block 50% of human brain receptors. The lower the number, the higher the potency. To make this relationship more intuitive, the inverse of each value was calculated. (DFKVHWRIDI¿QLWLHVLVUHFHSWRUVSHFL¿F7RJHQHUDWHDXQLYHUVDOVFDOHRYHUDOOWKHUHFHSWRUV DUHFHSWRU¶VPD[LPXPUHFRUGHGDI¿QLW\ZDVGLYLGHGE\HDFKGUXJ¶VDI¿QLW\WRWKDWUHFHSWRU

This calculation was made with all receptors, with the exception of the H1 Histamine Receptor Antagonist. Due to the extremely high potency levels of Mirtazapine and Doxepin for the

antihistamine receptor, normalizing H1DI¿QLW\YDOXHVWR0LUWD]DSLQHFDXVHGGUXJVZLWKFOLQLFDOO\

relevant antihistamine effects to be insubstantial. Therefore, H1DI¿QLWLHVZHUHQRUPDOL]HGWR

Amitriptyline’s H1DI¿QLW\YDOXH

 Table . 5 Mechanism Primary Pharmacologic Action Side Effect Serotonin Transporter Increase Serotonin (5HT) Levels Anxiety (SERT) Inhibition Nausea Sexual Dysfunction Norepinephrine Increase Norepinephrine (NE) Levels Hypertension Transporter (NET) Stimulating Inhibition Sweating Tachycardia Tremors Dopamine Transporter Increase Dopamine (DA) Levels Enhanced Motivation (DAT) Inhibition Mitigation against Prolactin Elevation Stimulating Mitigate Craving for Tobacco Combination of SERT Increase 5HT Levels Pain Control and NET Inhibiton Increase NE Levels Alpha-1 Adrenergic Orthostatic Hypotension Receptor Antagonist H1 Histamine Receptor Sedation Antagonist Weight Gain Treat Allergies M1 Muscarinic Blurred Vision Acetylcholine Receptor Cognitive Problems Antagonist Constipation Dry Mouth Increase Appetite Sedation Tachycardia Urinary Retention Alpha-2 Adrenergic Increase 5HT Levels (only Mirtazapine) Receptor Antagonist Increase NE Levels Na+ Channel Blocker Impairs Heart Conduction (only TCA)

 %\WDNLQJWKHORJRIHDFKDI¿QLW\YDOXHDPRUHGLJHVWLEOHVFDOHZDVJHQHUDWHG$IHZ adjustments were made by Dr. Kaplin to titrate certain values up and down to match what is REVHUYHGFOLQLFDOO\/RJYDOXHVOHVVWKDQPHDQWWKHGUXJKDGQRDI¿QLW\WRWKHUHFHSWRU/RJ YDOXHVRIGHQRWHGWKHGUXJKDGPD[LPXPDI¿QLW\WRWKHUHFHSWRU 7DEOH 

Table . 6 Log Value Receptor Affinity Visual Scale < 0 None 0 0 Little 1 1 Substantial 2 2 More Substantial 3 3 Maximum 4

Visual Language Each mechanism was assigned a color. Side effect logos were originally colorized with their respective mechanisms. When considering layout design, the constant repetition of graphic images if multiple mechanisms were to cause the same side effect, grew busy and hard to comprehend. By allowing side effect logos to remain colorless, colored bars were used to represent neurotransmitter inhibition potency levels that contribute to its respective side effect. This not only reduced the amount of extraneous visuals, but enhanced readability (Figure 4). 1 1 1 1

Drug #1 Drug #1

1 1 1 1 2 2 2 3 3

Figure . 4 Side effect logo remains colorless to reduce extraneous visuals.

 Development of Application The mobile web application was designed to provide a sequential navigation structure that guides the user through an abridged consultation to identify a list of ideal drug selections. Wireframes were drafted by Benjamin & Bond (B&B) based on initial discussion of mobile web application goals and objectives. Using the wireframes as a foundation, preliminary application designs were produced using Adobe Illustrator CC (Appendix E). With the guidance of B&B, color palette and design adjustments were made to create a more inviting platform. Wording of drug selection questions were PRGL¿HGWRUHPRYHXQQHFHVVDU\UHSHWLWLRQWRGHYLVHDPRUHLQWXLWLYHXVHUH[SHULHQFH )LJXUH 

none option 1 option 1

Y N option 2 option 2 option 3 option 3

option 4 option 4

Figure . 'UXJVHOHFWLRQTXHVWLRQVZHUHPRGL¿HGIRUPRUHLQWXLWLYHXVHUH[SHULHQFH

Example Question: Does the patient express concerns regarding sexual dysfunction? Y/N If Yes, what side effects is the patient expressing concerns about? (Multiple Selection)

• Decreased Libido • Inability to Achieve Orgasm • Failure to Ejaculate • Trouble Getting an Erection

Adjusted Question: Does the patient express concerns regarding sexual dysfunction? (Multiple Selection) • No Sexual Dysfunction Concerns • Decreased Libido • Inability to Achieve Orgasm • Failure to Ejaculate • Trouble Getting an Erection

 RESULTS A prototype was delivered and assessed on March 16, 2016 (Appendix E).

Menu Designed for mobile use, the menu of this web-based application was designated using a three-line menu icon. Menu options include About, Login, Drug Selection Tool, Drug Library, Request Access, Contact, and Logout. Upon each use of the drug selection algorithm, agreement to the terms of the disclaimer was required before proceeding to the diagnostic evaluation. This GLVFODLPHUVSHFL¿HVWKHDSSOLFDWLRQ¶VREMHFWLYHDVDQHGXFDWLRQDOUHVRXUFHDQGQRWDUHSODFHPHQW

for a mental healthcare practitioner.

Diagnostic Evaluation The mobile web application has a diagnostic evaluation function to screen for depression before proceeding to the drug selection questions. Patient Health Questionnaire (PHQ) 2 was used to assess whether patient symptoms warranted a diagnosis of major depression. Patients who screened positive (>2) continued to PHQ-9 for further evaluation. PHQ-2 and PHQ-9 (a total of 9 questions) are standardized assessment tools, but there remains a margin for error. For this reason, the ability to override the results of the evaluation and proceed to the drug selection algorithm were enabled.

Drug Selection Algorithm A 35-year old male is diagnosed with clinical depression. He smokes cigarettes regularly, does not drink alcohol, and has an aunt who was also diagnosed with major depression. The aunt had been on two different antidepressants, Paroxetine (without success) and Bupropion (with success). This man is quite healthy. He has no allergies, no preexisting conditions, no plans to undergo surgery in the next 1-2 months, and has been on one antidepressant, Desvenlafaxine (without success). When asked about symtpoms, the man says his appetite has increased and that KHKDVJDLQHGZHLJKW+HLVFRQVWDQWO\IHHOLQJGURZV\DQGODFNVHQHUJ\+H¿QGVLWYHU\KDUGWR concentrate on work and says that he has a constant urg to urinate, but very little comes out.

 2QFHWKHSDWLHQW¶VLQIRUPDWLRQLVLQSXWWHGLQWRWKHDSSOLFDWLRQWKHDOJRULWKP¿OWHUVSRVVLEOH drug options and generates a list (Figure 6). The representation of algorithm calculations is LQGLFDWHGLQEOXHJUHHQDQGUHG7KLVIHDWXUHZLOOEHUHPRYHGIRUWKH¿QDOSURGXFW7KHWZR6 values correlate with positive and negative symptoms, green and red respectively. The M value are extra points given to medications for successful patient or blood relative use, and smoking cessation (which only applies to Bupropion). W is the weighted number calculated by the subtraction of the red S value by the sum of the green S and M values. Calculation details can be found on Appendix A. The drug score value (DS) indicates level of adherence of each drug to the patient’s preferences. The drug selection algorithm has four sets of questions: General Information, Family Medical History, Patient Medical History and Patient Symptoms. Bupropion ranked highest in the list of drug options due to the following calculations.

General Information (M value) The patient smoked cigarettes so the M value increased by 1.

Family Medical History (M value) The patient’s blood relative had success with Bupropion so the M value increased by 1.

Patient Medical History (M value) There were no details entered in this section that contributed the points accumulated.

Patient Symptoms (S values) The patient mentioned increased appetite, increased weight and somnolence. Bupropion causes decreased appetite (+1), decreased weight (+1) as well as increased weight (-1), and insomnia (+1). All (+1) points were indicated in the green S value, and all (-1) points were indicated in the red S value.

Drug Weight Value (W value) W = 1 + 1 + 1 + 1 – 1 + 1 = 4

 Figure . /LVWRIGUXJRSWLRQV /HIW DQGGUXJSUR¿OHSDJH 5LJKW

Visual Language From the generated list of drug options, medication details can be obtained by clicking WKHOLQNVIRU³0RUH,QIR´7KLVZLOOEULQJ\RXWRWKHGUXJSUR¿OHSDJH3UR¿OHVFDQEHDWWDLQHG directly from the drug selection list or under the “Drug Library” option in the main menu. Drug SUR¿OHVDUHSDWLHQWLQGHSHQGHQWDQGGUXJVSHFL¿F 2QWKHGUXJSUR¿OHSDJHWKHVFDOHEDUVDQGVLGHHIIHFWLFRQVOLVWHGDORQJWKHWRSEHORZWKH GUXJQDPHDFWVDVDFXUVRU\RYHUYLHZRIGUXJVLGHHIIHFWVDQGUHFHSWRUDI¿QLWLHV)RXURIWKHVH side effect icons are chosen to represent the drug in the generated drug option list. %\FOLFNLQJRQDUHFHSWRUDI¿QLW\VFDOHRUVLGHHIIHFWLFRQWKHDSSOLFDWLRQZLWKDXWRPDWLFDOO\ GLUHFW\RXWRWKHUHVSHFWLYHVLGHHIIHFWRUPHFKDQLVPRIDFWLRQZLWKLQWKHGUXJ¶VSUR¿OH)RU H[DPSOHLIWKHXVHUVHOHFWHGWKH6(57UHFHSWRUDI¿QLW\WKHPHGLFDWLRQGHVFULSWLRQSDJHZLOOVFUROO GRZQWRWKHDUHDZKHUHLWGHVFULEHVWKH6(57UHFHSWRUDI¿QLW\DQGWKHOLVWRIVLGHHIIHFWVWKHDI¿QLW\

 contributes to. If the user selected the “sedation” side effect icon, the page will scroll down to where DGHVFULSWLRQRIZKDWPHFKDQLVPVFRQWULEXWHGVSHFL¿FDOO\WRVHGDWLRQ )LJXUH 7KLVDOORZVIRU PXOWLSOHPRGHVRIVHDUFKZLWKLQWKHGUXJSUR¿OH

Figure . 'UXJSUR¿OHZLWKPHGLFDWLRQGHWDLOVEDVHGRQUHFHSWRUDI¿QLWL\VFDOH /HIW DQG medication details based on side effect icon (Right)

The visual language not only allows the user to identify which mechanisms are involved, but also how potent. This information is advantageous when considering antidepressants to avoid possible overdose. For example, if a patient is already taking a drug that increases serotonin levels, antidepressants with high 5HT transporter inhibition should be avoided. In addition, the presence of more than one mechanism per side effect indicates an increased severity of that side effect.

 Algorithm Results Using a series of practice-based scenarios provided by Dr. Kaplin (Appendix F), the algorithm has consistently ranked majority of the ideal drug selections at the top and less ideal drug selections at the bottom. The patient preferences proved helpful in further differentiating between drugs with the same weight value, but condition of information quality needs further investigation. Several drawbacks with this version of the algorithm include the elimination of antidepressant medication options based on drug-drug interactions. Drug contraindications range from chemotherapy to antihistamines so an elimination of an ideal drug choice will be made over the use of an antihistamine. Using drug side effect frequencies to weigh patient symptom calculations may yield more promising results.

 $FFHVVWRWKHZHEVLWH¿OHVHWFUHVXOWLQJIURPWKLVWKHVLVFDQEHYLHZHGE\FRQWDFWLQJ the author at [email protected]. The author may also be reached through the Department of Art as Applied to Medicine via the website www.medicalart.johnshopkins.edu.

 DISCUSSION 7KHJRDORIWKLVWKHVLVZDVWRFUHDWHDQHGXFDWLRQDODQWLGHSUHVVDQWGUXJVHOHFWLRQWRROWR¿OOD void in resources available to primary care practitioners managing mental health. An application prototype was successfully developed, establishing the infrastructure for a novel evidence-based antidepressant prescribing algorithm with patient preference capabilities. Graphic elements designed introduced a unique approach to communicating the effect of antidepressant drugs on NH\QHXURWUDQVPLWWHUV\VWHPVDFRQFHSWYLWDOWRXQGHUVWDQGLQJKRZWRWDUJHWQHXURQVLQVSHFL¿F circuits and make rational antidepressant drug selections.

Further Development Research during the production of the algorithm proved there are many aspects of drug VHOHFWLRQZRUWKUHÀHFWLQJXSRQ'HVSLWHNHHSLQJWKHUHVRXUFHVXFFLQFWDQGFRQVLVWHQWZLWKWKH target PCP audience, expanding the algorithm would not only yield more precise drug selections, but allow the algorithm to encompass a broader range of conditions. A sample list for further development include: • Inclusion of all antidepressants (especially those used off-label) • Considerations for pediatric and geriatric depression • Drug dosage and augmentation abilities • Incorporation of drug cost and availability into patient preferences • Consideration for other psychiatric comorbidities • Ability to consider the co-effectiveness of antidepressants to treat other symptoms and conditions besides depression • Include more mechanisms, such as serotonin modulator and stimulators (SMS)

 IRB Approved Study To evaluate the effectiveness of the visual language an IRB approved study would need to be conducted. The application’s ease of use as well as algorithm accuracy and helpfulness would also be assessed. Feedback would provide necessary insight into how to teach PCPs about psychopharmacology and whether a web-based algorithm would improve management of mental health within primary care.

Integration into Johns Hopkins Point of Care Information Technology The Johns Hopkins POC-IT Center is a web, mobile and print accessible collection of evidence-based clinical decision guides developed for healthcare professionals. Integration of the antidepressant drug selection algorithm would bring a unique interactive module to POC-IT, as well as disseminate the contents of this resource to existing POC-IT users.

 CONCLUSION The key to treating depression is to implement the proper management of mental health into primary care. In 2003, Dr. Stephen Bartels, Associate Professor of psychiatry at Dartmouth Medical School, said at least one-third of older adults in primary care presented with depression or other mental illnesses (Rosack 2003). While depression is only accurately diagnosed 25-50% of the time in primary care settings, physicians who do recognize depressive symptoms choose not to enforce depression management (Gallo et al. 2015). On account of the multiple conditions presented by older adult patients, treatment of depression often becomes secondary to the management of “organic” diseases (Gallo et al. 2015). Studies have shown that the implementation of a depression management program actually helps mitigate the combined effects of depression and multimorbidities. In creating this web-based application, we hope to have a number of effects on the treatment of MDD by PCPs, which will be further tested and validated in future studies. Outcomes include but are not limited to the following:

• PCPs will make more optimal treatment selections. They will be able to avoid subtle, but important suboptimal or even dangerous treatments through the utilization of the drug selection algorithm.

• The integration of patient preferences on the evidence-based selection of antidepressant medication will result in treatment that patients will more likely tolerate and adhere to.

• 3&3VZLOOKDYHJUHDWHUFRQ¿GHQFHLQWKHLUDELOLWLHVWRWUHDWGHSUHVVLRQDQGWKXVOHDGWR increased diagnosis and successful outcomes for patients with MDD.

 APPENDIX A: ANTIDEPRESSANT CHARTS

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 Receptor Affinities “. APPENDIX B: DRUG SELECTION FLOWCHART

 APPENDIX C: DRUG SELECTION QUESTIONS “.

PHQ- Over the past 2 weeks, how often have you been bothered by any of the following problems? 1. Little interest or pleasure in doing things 2. Feeling down, depressed or hopeless

Scores < : e patient has screened negative for someone who should be evaluated further for depression. Scores ≥ : e patient has screened positive for someone who should be evaluated further for depression.

 APPENDIX C: DRUG SELECTION QUESTIONS “.

PHQ-
Over the past 2 weeks, how often have you been bothered by any of the following problems? 3. Trouble falling or staying asleep, or sleeping too much 4. Feeling tired or having little energy 5. Poor appetite or overeating 6. Feeling bad about yourself-or that you are a failure or have let yourself or your family down

7. Trouble concentrating on things, such as reading the newspaper or watching television 8. Moving or speaking so slowly that other people could have noticed. Or the opposite EHLQJVR¿GJHW\RUUHVWOHVVWKDW\RXKDYHEHHQPRYLQJDURXQGDORWPRUHWKDQXVXDO 9. Thoughts that you would be better off dead, or of hurting yourself in some way.

Scores ≤ : e patient’s PHQ- score suggests the patient has minimal depression. Scores ­ - ‚: e patient’s PHQ- score suggests the patient has mild depression. Scores ƒ„ - ƒ : e patient’s PHQ- score suggests the patient has moderate depression. Scores ƒ­ - ƒ‚: e patient’s PHQ- score suggests the patient has moderately severe depression. Scores „ - †: e patient’s PHQ- score suggests the patient has severe depression.

 APPENDIX C: DRUG SELECTION QUESTIONS

General Information Questions

. Age ____ (pediatric < , geriatric > )

. Gender (Male, Female, Transgender)

. If Female or Transgender and age „+, is the patient in perimenopause? Y/N If No, ask following (yes or no) questions: a. Is the patient pregnant? b. Does the patient have plans to get pregnant in the next - months? (if yes to either, make pregnancy elimination)

c. Is the patient currently breastfeeding?

(if yes to either, make breastfeeding elimination)

. Does the patient smoke cigarettes? Y/N

(if yes to either, + to Bupropion)

. Does the patient drink alcohol regularly? Y/N If Yes, ask following (yes or no) questions: (if  or more yes’s, make alcohol elimination) a. Has the patient ever felt he/she needed to cut down on his/her drinking? b. Have people annoyed the patient by criticizing his/her drinking? c. Has the patient ever felt guilty about drinking? d. Has the patient ever felt he/she needed a drink rst thing in the morning (eye-opener) to steady his/her nerves or to get rid of a hangover?

Family Medical History Questions

. Does the patient have a family history of major illnesses? (Multiple Selection) • No major illness(es) • History of Suicides (or Suicide Attempts) • Alcoholism • Major Depression • Bipolar Disorder • Neurologic • Drug Abuse • Schizophrenia

 “. . Does the patient have blood relatives with positive experiences with any antidepressant medications? (Multiple Selection) (+ for selected drug/s) • No antidepressant(s) • Imipramine • Amitriptyline HCl • Levomilnacipran • Bupropion HCl • Mirtazapine • Citalopram • Nortriptyline • Clomipramine • Paroxetine HCl • Desipramine HCl • Sertraline HCl • Desvenlafaxine • Trazodone HCl • Doxepin HCl • Venlafaxine HCl • Duloxetine • Vilazodone • Escitalopram • Vortioxetine • Fluoxetine HCl

. Does the patient have blood relatives with negative experiences with any antidepressant medications? (Multiple Selection) • No antidepressant(s) • Amitriptyline HCl • Imipramine • Bupropion HCl • Levomilnacipran • Citalopram • Mirtazapine • Clomipramine • Nortriptyline • Desipramine HCl • Paroxetine HCl • Desvenlafaxine • Sertraline HCl • Doxepin HCl • Trazodone HCl • Duloxetine • Venlafaxine HCl • Escitalopram • Vilazodone • Fluoxetine HCl • Vortioxetine

Patient Medical History Questions

. Has the patient suered a heart attack in the past  weeks? Y/N (if yes, eliminate drugs contraindicated for recovery phase following MI)

. Does the patient plan to undergo surgery in the near future (e.g. - months)? Y/N APPENDIX C: DRUG SELECTION QUESTIONS . Does the patient have any preexisting condition(s)? (eliminate contraindicated) • No preexisting condition(s) • Anorexia/Bulimia • CNS Tumor • Arteriovenous Malformation • Epilepsy • Bipolar Disorder • Glaucoma • Brain Damage • Head Trauma (Severe) • Cardiovascular Disease • QT Syndrome • Chronic Pain Syndrome • Stroke • CNS Infection • Urinary Retention (Severe)

. Is the patient currently taking any medication or receiving any of the following treatment? • No medication(s)/treatment(s) (eliminate contraindicated) • Artemether/Lumefantrine • Pentamidine • Astemizole • Pimozide • Cisapride • Procainamide • Disopyramide • Procarbazine • Disulram • Quinidine • Dronedarone • Rasagiline • Eliglustat • Saquinavir • Flibanserin • Selegiline • Ibutilide • Sotalol • Indapamide • Terfernadine • Linezolid • ioridazine • Lumefantrine

. Has the patient taken any antidepressants with success? • No antidepressant(s) (+ for selected drug/s) • Amitriptyline HCl • Imipramine • Bupropion HCl • Levomilnacipran • Citalopram • Mirtazapine • Clomipramine • Nortriptyline • Desipramine HCl • Paroxetine HCl • Desvenlafaxine • Sertraline HCl • Doxepin HCl • Trazodone HCl • Duloxetine • Venlafaxine HCl • Escitalopram • Vilazodone • Fluoxetine HCl • Vortioxetine

 “.•

. Has the patient taken any antidepressants without success? (eliminate selected drug/s) • No antidepressant(s) • Amitriptyline HCl • Imipramine • Bupropion HCl • Levomilnacipran • Citalopram • Mirtazapine • Clomipramine • Nortriptyline • Desipramine HCl • Paroxetine HCl • Desvenlafaxine • Sertraline HCl • Doxepin HCl • Trazodone HCl • Duloxetine • Venlafaxine HCl • Escitalopram • Vilazodone • Fluoxetine HCl • Vortioxetine

Patient Symptom Questions

. APPETITE: Has the patient’s appetite changed drastically? • No appetite change(s) • Decreased Appetite • Increased Appetite APPENDIX C: DRUG SELECTION QUESTIONS . WEIGHT: Has the patient lost or gained an unhealthy degree of weight? • No weight change(s) • Decreased Weight • Increased Weight Loss

. ENERGY/MOOD: Does the patient feel a change in energy or mood? • No energy/mood change(s) • Activating • Asthenia • Lethargy

. SLEEP: Does the patient have issues regarding sleep (too much or too little)? • No sleep issue(s) • Insomnia • Nightmares • Somnolence

. SEXUAL DYSFUNCTION: Does the patient express concerns regarding sexual dysfunction? • No sexual dysfunction issue(s) • Anorgasmia • Decreased Libido • Ejaculation Failure • Impotence

. COGNITION: Does the patient have trouble with confusion or concentration? • No cognition issue(s) • Confusion • Lack of Concentration

. GASTROINTESTINAL DISTRESS: Does the patient experience any gastrointestinal distress? • No gastrointestinal distress • Constipation • Diarrhea • Indigestion • Dry Mouth • Nausea • Vomiting

 “.

. PAIN: Does the patient experience pain anywhere in the body? • No pain issue(s) • Abdominal Cramping • Headache • Muscle Pain • Musculoskeletal Pain

. AKATHISIA/TREMOR: Does the patient suer from akathisia or tremor? • No akathisia or tremor • Akathisia • Tremor

. SENSORY DISTURBANCES: Does the patient complain of sensory disturbances? • No sensory disturbance(s) • Blurred Vision • Dizziness • Mydriasis • Tingling • Tinnitus

. URINATION: Does the patient raise concerns regarding urination frequency? • No urinary change(s) • Frequent Urination • Proteinuria • Urine Retention

. SKIN: Has the patient complained of any of the following conditions? • No skin issue(s) • Edema of Lower Extremity • Psoriasis • Sweating

. BLOOD PRESSURE: Is the patient’s blood pressure abnormally increased/decreased? • No blood pressure abnormalities • Hypertension • Orthostatic Hypotension

. HEART RHYTHM: Does the patient have an abnormal heartbeat? • No heartbeat abnormalities • Bradycardia • Palpitations • Tachycardia APPENDIX D: VISUAL LANGUAGE “.

Anxiety Blurred Vision Cognitive Constipation Decreased Problems Appetite

Dry Mouth Enhanced Gastrointestinal Hypertension Impairs Heart Motivation Distress Conduction

Increased Mitigation Mitigate Craving Nausea Orthostatic Appetite Against Prolactin for Tobacco Hypotention Elevation

Pain Control Sedation Sexual Dysfunction Stimulating Sweating

Tachycardia Treat Allergies Tremors Urinary Retention Weight Gain

 APPENDIX D: VISUAL LANGUAGE “.

F47B5B 63C29D

FBAA22 7EA046

F37E8B AA82BB

C49A6C 9B4A4A

 APPENDIX E: WEB APPLICATION WIREFRAMES & DESIGN “. Wireframes

 APPENDIX E: WEB APPLICATION WIREFRAME & DESIGN “.

Design

 APPENDIX E: WEB APPLICATION WIREFRAME & DESIGN “.

 APPENDIX E: WEB APPLICATION WIREFRAME & DESIGN “.•

 APPENDIX F: TEST SCENARIOS & RESULTS SCENARIO 

General Information . Age:  . Gender: Male . Does the patient smoke cigarettes? No . Does the patient drink alcohol regularly? No

Family Medical History . Does the patient have a family history of major illnesses? History of Suicides and Major Depression . Does the patient have blood relatives with positive experiences with any antidepressant medications? No . Does the patient have blood relatives with negative experiences with any antidepressant medications? Paroxetine HCl

Patient Medical History . Has the patient suered a heart attack in the past  weeks? No . Does the patient plan to undergo surgery in the near future (e.g. - months)? No . Does the patient have any preexisting condition(s)? QT Syndrome . Is the patient currently taking any medication or receiving any of the following treatment? No . Has the patient taken any antidepressants with success? No . Has the patient taken any antidepressants without success? Escitalopram .Is the patient hypersensitive to any antidepressants? No

Patient Preferences Increase Sleep  Increase Appetite  Increase Energy  Reverse Hardened Stool 

 Patient Symptoms . Decreased Appetite . Decreased Weight . Lethargy . Insomnia . Decreased Libido . Lack of Concentration . Constipation . No Pain Issues . No Akathisia or Tremor . No Sensory Disturbances . No Urinary Changes . No Skin Issues . Hypertension . No Heartbeat Abnormalities

Dr. Kaplin’s Recommendations Algorithm Recommendations Mirtazapine (good) Mirtazapine Duloxetine (ok) Trazodone Levomilnacipran (ok) Citalopram Sertraline (ok) Fluoxetine Trazodone (ok) Vortioxetine Vilazodone (ok) Sertraline Vortioxetine (ok) Vilazodone Bupropion (less good) Desvenlafaxine Citalopram (less good) Bupropion Desvenlafaxine (less good) Levomilnacipran Escitalopram (less good) Paroxetine Fluoxetine (less good) Duloxetine Paroxetine (less good) Venlafaxine Venlafaxine (less good) Escitalopram Amitriptyline (bad) Amitriptyline Clomipramine (bad) Clomipramine Desipramine (bad) Desipramine Doxepin (bad) Doxepin Imipramine (bad) Imipramine Nortriptyline (bad) Nortriptyline APPENDIX F: TEST SCENARIOS & RESULTS SCENARIO 

1D66$ 1'5, 6$5, 615, 615 0LUWD]DSLQH %XSURSLRQ+&O 7UD]RGRQH+&O 'HVYHQODID[LQH 'XOR[H 7REDFFR8VH $OFRKRO8VH 3UHJQDQW %UHDVWIHHGLQJ )DPLO\+LVWRU\RI6XLFLGHV )DPLO\$'8VH )ROORZLQJ0, 6XUJHU\3ODQV +HDOWK&RQGLWLRQV 3DWLHQW$'8VH 36 $SSHWLWH      36 :HLJKW      36 (QHUJ\0RRG 36 6OHHS     36 6H[XDO'\VIXQFWLRQ 36 &RJQLWLRQ 36 *,'LVWUHVV       36 3DLQ 36 $NDWKLVLD7UHPRU 36 6HQVRU\ 36 8ULQDWLRQ 36 6NLQ 36 %ORRG3UHVVXUH 36 +HDUW5K\WKP 727$/32,176    

 Total Points “.

15, 615, 615, 63$5, 665, 665, 665, 665, 0 HWLQH /HYRPLOQDFLSUDQ 9HQODID[LQH+&O 9LOD]RGRQH &LWDORSUDP (VFLWDORSUDP )OXR[HWLQH+&O3DUR[HWLQH+&O 7REDFFR8VH $OFRKRO8VH 3UHJQDQW %UHDVWIHHGLQJ )DPLO\+LVWRU\RI6XLFLGHV )DPLO\$'8VH )ROORZLQJ0, 6XUJHU\3ODQV +HDOWK&RQGLWLRQV 3DWLHQW$'8VH (/,0,1$7(

     36 :HLJKW      

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           36 3DLQ 36 $NDWKLVLD7UHPRU 36 6HQVRU\ 36 8ULQDWLRQ 36 6NLQ 36 %ORRG3UHVVXUH 36 +HDUW5K\WKP 72         APPENDIX F: TEST SCENARIOS & RESULTS SCENARIO 

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

 Total Points “.

$ 7&$ 7&$ 7&$ 7&$ 7&$ \OLQH &ORPLSUDPLQH 'HVLSUDPLQH+&O 'R[HSLQ ,PLSUDPLQH 1RUWULSW\OLQH

(/,0,1$7( (/,0,1$7( (/,0,1$7( (/,0,1$7( (/,0,1$7( (/,0,1$7(

(/,0,1$7( (/,0,1$7( (/,0,1$7(

   

            

     

      APPENDIX F: TEST SCENARIOS & RESULTS SCENARIO 

1D66$ 1'5, 6$5, 615, 615 0LUWD]DSLQH %XSURSLRQ+&O 7UD]RGRQH+&O 'HVYHQODID[LQH 'XOR[H 33727$/32,176     33 ,QFUHDVH$SSHWLWH     6( ,QFUHDVH$SSHWLWH   6( 'HFUHDVH$SSHWLWH       33 ,QFUHDVH(QHUJ\     6( ,QFUHDVH(QHUJ\    6( 'HFUHDVH(QHUJ\        33 ,QFUHDVH6OHHS     6( ,QFUHDVH6OHHS   6( 'HFUHDVH6OHHS       33 6H[XDO'\VIXQFWLRQ 6( 'HOD\HG(MDFXODWLRQ 6( 'HOD\HG2UJDVP     33 5HYHUVH+DUGHQHG6WRRO     6( 5HYHUVH/RRVH6WRRO     6( 5HYHUVH+DUGHQHG6WRRO       33 8ULQDWLRQ 6( 8ULQDWLRQ 6( 8ULQDWLRQ    

 Patient Preferences “.

15, 615, 615, 63$5, 665, 665, 665, 665, 0 HWLQH /HYRPLOQDFLSUDQ 9HQODID[LQH+&O 9LOD]RGRQH &LWDORSUDP (VFLWDORSUDP )OXR[HWLQH+&O3DUR[HWLQH+&O 3                 6( ,QFUHDVH$SSHWLWH                    33 ,QFUHDVH(QHUJ\             6( ,QFUHDVH(QHUJ\        6( 'HFUHDVH(QHUJ\                       33 ,QFUHDVH6OHHS             6( ,QFUHDVH6OHHS        6( 'HFUHDVH6OHHS                      33 6H[XDO'\VIXQFWLRQ 6( 'HOD\HG(MDFXODWLRQ 6( 'HOD\HG2UJDVP             33 5HYHUVH+DUGHQHG6WRRO             6( 5HYHUVH/RRVH6WRRO         6( 5HYHUVH+DUGHQHG6WRRO                    33 8ULQDWLRQ 6( 8ULQDWLRQ 6( 8ULQDWLRQ             APPENDIX F: TEST SCENARIOS & RESULTS SCENARIO 

15, 63$5,665,665, 1D6666$5, 1'56,65, 6$56,65, 61566,5, 6715& , D[LQH+&O9LOD]RGRQH &LWDORSUDP (VFLWDORSUDP 0)LUOXWRD[]HDWSLQLQHH+&O%XS3URDSURLR[QH+&WLQHO+& 7UO D]6RHGURWUQDHOL+&QH+O'&O HV9YHRQUWODLRI[DH[WLQLQHH '$PXORLWU[LHS   33727$/32,176        33 ,QFUHDVH$SSHWLWH         33 ,QFUHDVH$SSHWLWH                  6( ,QFUHDVH$SSHWLWH    6( ,QFUHDVH$SSHWLWH    6( 'HFUHDVH$SSHWLWH    6( 'HFUHDVH$SSHWLWH                                    33 ,QFUHDVH(QHUJ\         33 ,QFUHDVH(QHUJ\                  6( ,QFUHDVH(QHUJ\     6( ,QFUHDVH(QHUJ\            6( 'HFUHDVH(QHUJ\       6( 'HFUHDVH(QHUJ\                                         33 ,QFUHDVH6OHHS         33 ,QFUHDVH6OHHS                  6( ,QFUHDVH6OHHS     6( ,QFUHDVH6OHHS            6( 'HFUHDVH6OHHS      6( 'HFUHDVH6OHHS                                        33 6H[XDO'\VIXQFWLRQ 33 6H[XDO'\VIXQFWLRQ 6( 'HOD\HG(MDFXODWLRQ 6( 'HOD\HG(MDFXODWLRQ 6( 'HOD\HG2UJDVP 6( 'HOD\HG2UJDVP                          33 5HYHUVH+DUGHQHG6WRRO        33 5HYHUVH+DUGHQHG6WRRO                  6( 5HYHUVH/RRVH6WRRO            6( 5HYHUVH+DUGHQHG6WRRO    6( 5HYHUVH+DUGHQHG6WRRO                                     33 8ULQDWLRQ 33 8ULQDWLRQ 6( 8ULQDWLRQ 6( 8ULQDWLRQ 6( 8ULQDWLRQ 6( 8ULQDWLRQ                         

 Patient Preferences “.

$ 7&$ 7&$ 7&$ 7&$ 7&$ LSW\OLQH &ORPLSUDPLQH 'HVLSUDPLQH+&O 'R[HSLQ ,PLSUDPLQH 1RUWULSW\OLQH            

                                                

                 

     

      APPENDIX F: TEST SCENARIOS & RESULTS SCENARIO 

General Information . Age:  . Gender: Male . Does the patient smoke cigarettes? Yes . Does the patient drink alcohol regularly? Yes a. Has the patient ever felt he/she needed to cut down on his/her drinking? Yes b. Have people annoyed the patient by criticizing his/her drinking? Yes c. Has the patient ever felt guilty about drinking? Yes d. Has the patient ever felt he/she needed a drink rst thing in the morning (eye-opener) to steady his/her nerves or to get rid of a hangover? Yes

Family Medical History . Does the patient have a family history of major illnesses? Alcoholism, Drug Abuse, Major Depression . Does the patient have blood relatives with positive experiences with any antidepressant medications? No . Does the patient have blood relatives with negative experiences with any antidepressant medications? No

Patient Medical History . Has the patient suered a heart attack in the past  weeks? No . Does the patient plan to undergo surgery in the near future (e.g. - months)? No . Does the patient have any preexisting condition(s)? No . Is the patient currently taking any medication or receiving any of the following treatment? No . Has the patient taken any antidepressants with success? No . Has the patient taken any antidepressants without success? Escitalopram .Is the patient hypersensitive to any antidepressants? No

 Patient Preferences Decrase Sleep  Decrease Appetite  Increase Energy 

Patient Symptoms . Increased Appetite . Increased Weight . Lethargy . Somnolence

Dr. Kaplin’s Recommendations Algorithm Recommendations Desvenlafaxine (good) Venlafaxine Fluoxetine (good) Paroxetine Venlafaxine (good) Citalopram Citalopram (ok) Vilazodone Duloxetine (ok) Desvenlafaxine Escitalopram (ok) Fluoxetine Levomilnacipran (ok) Sertraline Sertraline (ok) Duloxetine Vilazodone (ok) Escitalopram Vortioxetine (ok) Vortioxetine Amitriptyline (less good) Desipramine Clomipramine (less good) Doxepin Desipramine (less good) Nortriptyline Doxepin (less good) Clomipramine Imipramine (less good) Imipramine Mirtazapine (less good) Trazodone Nortriptyline (less good) Amitriptyline Paroxetine (less good) Mirtazapine Trazodone (less good) Levomilnacipran Bupropion (bad) Bupropion APPENDIX F: TEST SCENARIOS & RESULTS SCENARIO 

1D66$ 1'5, 6$5, 615, 615 0LUWD]DSLQH %XSURSLRQ+&O 7UD]RGRQH+&O 'HVYHQODID[LQH 'XOR[H 7REDFFR8VH  $OFRKRO8VH (/,0,1$7( 3UHJQDQW %UHDVWIHHGLQJ )DPLO\+LVWRU\RI6XLFLGHV )DPLO\$'8VH )ROORZLQJ0, 6XUJHU\3ODQV +HDOWK&RQGLWLRQV 3DWLHQW$'8VH 36 $SSHWLWH     36 :HLJKW      36 (QHUJ\0RRG 36 6OHHS     36 6H[XDO'\VIXQFWLRQ 36 &RJQLWLRQ 36 *,'LVWUHVV

36 3DLQ 36 $NDWKLVLD7UHPRU 36 6HQVRU\ 36 8ULQDWLRQ 36 6NLQ 36 %ORRG3UHVVXUH 36 +HDUW5K\WKP 727$/32,176    

 Total Points “.

15, 615, 615, 63$5, 665, 665, 665, 665, 0 HWLQH /HYRPLOQDFLSUDQ 9HQODID[LQH+&O 9LOD]RGRQH &LWDORSUDP (VFLWDORSUDP )OXR[HWLQH+&O3DUR[HWLQH+&O 7REDFFR8VH  $OFRKRO8VH (/,0,1$7( (/,0,1$7(

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

36 3DLQ 36 $NDWKLVLD7UHPRU 36 6HQVRU\ 36 8ULQDWLRQ 36 6NLQ 36 %ORRG3UHVVXUH 36 +HDUW5K\WKP   727$/32,176         

 Total Points “.

, 7&$ 7&$ 7&$ 7&$ 7&$ 7&$ LSW\OLQH &ORPLSUDPLQH 'HVLSUDPLQH+&O 'R[HSLQ ,PLSUDPLQH 1RUWULSW\OLQH

   

         

      APPENDIX F: TEST SCENARIOS & RESULTS SCENARIO 

1D66$ 1'5, 6$5, 615, 615 0LUWD]DSLQH %XSURSLRQ+&O 7UD]RGRQH+&O 'HVYHQODID[LQH 'XOR[H 33727$/32,176     33 'HFUHDVH$SSHWLWH     6( ,QFUHDVH$SSHWLWH   6( 'HFUHDVH$SSHWLWH       33 ,QFUHDVH(QHUJ\     6( ,QFUHDVH(QHUJ\    6( 'HFUHDVH(QHUJ\        33 'HFUHDVH6OHHS     6( ,QFUHDVH6OHHS   6( 'HFUHDVH6OHHS       33 6H[XDO'\VIXQFWLRQ 6( 'HOD\HG(MDFXODWLRQ 6( 'HOD\HG2UJDVP     33 5HYHUVH+DUGHQHG6WRRO 6( 5HYHUVH/RRVH6WRRO 6( 5HYHUVH+DUGHQHG6WRRO     33 8ULQDWLRQ 6( 8ULQDWLRQ 6( 8ULQDWLRQ    

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      APPENDIX G: IRB PRE/POST QUESTIONNAIRES

PRE-QUESTIONAIRE . What is your highest level of education? (Categories: MD, Psychiatry resident, Psychiatrist, Other) . How much experience do you have in psychiatry? . What did you nd most dicult when learning about antidepressants? Categories: pharmacodynamics, names of antidepressants, side eect proles, drug drug interactions, keeping up with current events (ie. newly approved medication) Rate -. . How condent are you prescribing antidepressants to patients? . How helpful do you think an application would help prescribe antidepressants? Rate -.

POST-QUESTIONAIRE . How condent are you prescribing antidepressants to patients with the application? Rate -. . Do you agree with the results of the app? Rate -. . Do you trust the application? Rate -. . Do you like using the application? Rate -. . Do you think this application would be helpful for healthcare professionals? Rate -. . How likely would you be to use this application when prescribing antidepressants? Rate -. . Was the application easy to navigate? Rate -. . What improvements would you make with this application? (Categories: Login/Disclaimer Options, Diagnosis, Drug Selection, Overall Design, Visual Language, Descriptions, Other with blank) Based on categories checked, please elucidate. . How well did this application help with the understanding of the following categories: pharmacodynamics, names of antidepressants, side eect proles, drug drug interactions, keeping up with current events (ie. newly approved medication) Rate -.

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6WDKO6WHSKHQ06WDKO¶V(VVHQWLDO3V\FKRSKDUPDFRORJ\1HXURVFLHQWL¿F%DVLVDQG3UDFWLFDO Applications. New York: Cambridge University Press, 2012.

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 VITA Amy Zhong was born on July 22, 1988 in Washington, DC. She spent her childhood and DGROHVFHQFHLQWKH6DQ)UDQFLVFR%D\$UHDGHYHORSLQJDORYHIRUWKH¿QHGHWDLOVRIJUDSKLWHDQG charcoal illustration. In 2010, Amy graduated with a Bachelor of Fine Arts degree for her work in 2D animation from the University of Michigan in Ann Arbor, Michigan. While working the QH[WWZR\HDUVDVD'HVLJQ 2I¿FH$VVLVWDQWDQG$QLPDWRUIRUWKH&ULFNHW0DJD]LQH*URXS (CMG) branch of Carus Publishing in Chicago, Illinois, Amy had the opportunity to illustrate for CMG’s science and arts magazine Muse. It was the challenge of this assignment that led Amy to UHFRQVLGHUDFDUHHULQVFLHQWL¿FLOOXVWUDWLRQZKLFKHYHQWXDOO\UHVXOWHGLQWKHGLVFRYHU\RIPHGLFDO illustration. In the fall of 2014, Amy moved to Baltimore, Maryland to study in the Department of Art as Applied to Medicine at the Johns Hopkins School of Medicine, where she is to be awarded with a Master of Arts degree in Medical and Biological Illustration in May 2016.