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Vinod Subramanian

Bio: Vinod Subramanian is an academic researcher. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 3, co-authored 3 publications receiving 186 citations.

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Journal ArticleDOI
TL;DR: A preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression shows promise.
Abstract: Background: A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. Objective: This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. Methods: In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. Results: The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. Conclusions: The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.

327 citations

Journal ArticleDOI
TL;DR: This work raises five calls to action to ensure the safety, availability, and long-term sustainability of digital technologies during the coronavirus disease (COVID-19) crisis.
Abstract: During the coronavirus disease (COVID-19) crisis, digital technologies have become a major route for accessing remote care. Therefore, the need to ensure that these tools are safe and effective has never been greater. We raise five calls to action to ensure the safety, availability, and long-term sustainability of these technologies: (1) due diligence: remove harmful health apps from app stores; (2) data insights: use relevant health data insights from high-quality digital tools to inform the greater response to COVID-19; (3) freely available resources: make high-quality digital health tools available without charge, where possible, and for as long as possible, especially to those who are most vulnerable; (4) digital transitioning: transform conventional offline mental health services to make them digitally available; and (5) population self-management: encourage governments and insurers to work with developers to look at how digital health management could be subsidized or funded. We believe this should be carried out at the population level, rather than at a prescription level.

48 citations

Journal ArticleDOI
TL;DR: This study shows promise in the use of predictive modeling for early detection of symptoms of depression in people with diabetes using smartphone-sensing information.
Abstract: Background: Research studies are establishing the use of smartphone sensing to measure mental well-being. Smartphone sensor information captures behavioral patterns, and its analysis helps reveal well-being changes. Depression in diabetes goes highly underdiagnosed and underreported. The comorbidity has been associated with increased mortality and worse clinical outcomes, including poor glycemic control and self-management. Clinical-only intervention has been found to have a very modest effect on diabetes management among people with depression. Smartphone technologies could play a significant role in complementing comorbid care. Objective: This study aimed to analyze the association between smartphone-sensing parameters and symptoms of depression and to explore an approach to risk-stratify people with diabetes. Methods: A cross-sectional observational study (Project SHADO—Analyzing Social and Health Attributes through Daily Digital Observation) was conducted on 47 participants with diabetes. The study’s smartphone-sensing app passively collected data regarding activity, mobility, sleep, and communication from each participant. Self-reported symptoms of depression using a validated Patient Health Questionnaire-9 (PHQ-9) were collected once every 2 weeks from all participants. A descriptive analysis was performed to understand the representation of the participants. A univariate analysis was performed on each derived sensing variable to compare behavioral changes between depression states—those with self-reported major depression (PHQ-9>9) and those with none (PHQ-9≤9). A classification predictive modeling, using supervised machine-learning methods, was explored using derived sensing variables as input to construct and compare classifiers that could risk-stratify people with diabetes based on symptoms of depression. Results: A noticeably high prevalence of self-reported depression (30 out of 47 participants, 63%) was found among the participants. Between depression states, a significant difference was found for average activity rates (daytime) between participant-day instances with symptoms of major depression (mean 16.06 [SD 14.90]) and those with none (mean 18.79 [SD 16.72]), P=.005. For average number of people called (calls made and received), a significant difference was found between participant-day instances with symptoms of major depression (mean 5.08 [SD 3.83]) and those with none (mean 8.59 [SD 7.05]), P<.001. These results suggest that participants with diabetes and symptoms of major depression exhibited lower activity through the day and maintained contact with fewer people. Using all the derived sensing variables, the extreme gradient boosting machine-learning classifier provided the best performance with an average cross-validation accuracy of 79.07% (95% CI 74%-84%) and test accuracy of 81.05% to classify symptoms of depression. Conclusions: Participants with diabetes and self-reported symptoms of major depression were observed to show lower levels of social contact and lower activity levels during the day. Although findings must be reproduced in a broader randomized controlled study, this study shows promise in the use of predictive modeling for early detection of symptoms of depression in people with diabetes using smartphone-sensing information.

37 citations

Journal ArticleDOI
TL;DR: In this article , a retrospective analysis was performed utilizing the Syapse Learning Health Network (SLHN), an electronic medical record (EMR) derived database that collects cancer care data from multiple care settings that make up the US CHSs across 33 states, 450+ hospitals and 1,900+ community employed oncologists.
Journal ArticleDOI
TL;DR: In this paper , the most common gene alteration testing and systemic treatment utilization of patients with Non-Small Cell Lung Cancer (aNSCLC) were analyzed using ICD-10 codes.

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TL;DR: There is compelling evidence to suggest that telehealth may have a significant effect on advancing health care in the future, however, the feasibility and application of telehealth in resource-limited settings and low- and middle-income countries must be established to avail its potential and transform health care for the world's population.
Abstract: Background: With over 37.8 million cases and over 1 million deaths worldwide, the COVID-19 pandemic has created a societal and economic upheaval of unparalleled magnitude. A positive transformation has been brought about by innovative solutions in the health care sector that aim to mitigate the impact of COVID-19 on human health. For instance, the use of telehealth has been on the rise amidst this public health emergency. Objective: Given the unprecedented scale of the pandemic with no definitive endpoint, we aimed to scope the existing telehealth-related literature during a defined period of the ongoing pandemic (ie, January to June 2020). Methods: Our scoping review was guided by the Joanna Briggs Institute Reviewer Manual. We systematically searched PubMed and Embase databases with specific eligibility criteria. Data extracted from the shortlisted articles included first author and affiliation, journal title, publication type, terminologies used to describe telehealth and their accompanying definitions, health discipline or medical specialties and subspecialties wherein telehealth had been applied, the purpose of telehealth use, and the authors’ overall sentiment on telehealth use. We collated the available information and used descriptive statistics to analyze the synthesized data. Results: In all, 543 articles published across 331 different journals were included in this scoping review. The Journal of Medical Internet Research and its sister journals featured the highest number of articles (25/543, 4.6%). Nearly all (533/543, 98.2%) articles were in English. The majority of the articles were opinions, commentaries, and perspectives (333/543, 61.3%). Most authors of the articles reviewed were from high-income countries (470/543, 86.6%), especially from the United States of America (237/543, 43.6%). In all, 39 different definitions were used to describe terms equivalent to telehealth. A small percentage (42/543, 7.7%) of the articles focused on the provision of COVID-19–related care. Moreover, 49.7% (270/543) of the articles primarily focused on the provision of multiple components of clinical care, and 23% (125/543) of the articles focused on various specialties and subspecialties of internal medicine. For a vast majority (461/543, 84.9%) of the articles, the authors expressed a celebratory sentiment about the use of telehealth. Conclusions: This review identified considerable emerging literature on telehealth during the first six months of the COVID-19 pandemic, albeit mostly from high-income countries. There is compelling evidence to suggest that telehealth may have a significant effect on advancing health care in the future. However, the feasibility and application of telehealth in resource-limited settings and low- and middle-income countries must be established to avail its potential and transform health care for the world’s population. Given the rapidity with which telehealth is advancing, a global consensus on definitions, boundaries, protocols, monitoring, evaluation, and data privacy is urgently needed.

343 citations

Journal ArticleDOI
TL;DR: It is argued that embodied AI is a promising approach across the field of mental health; however, further research is needed to address the broader ethical and societal concerns of these technologies to negotiate best research and medical practices in innovative mental health care.
Abstract: Background: Research in embodied artificial intelligence (AI) has increasing clinical relevance for therapeutic applications in mental health services. With innovations ranging from ‘virtual psychotherapists’ to social robots in dementia care and autism disorder, to robots for sexual disorders, artificially intelligent virtual and robotic agents are increasingly taking on high-level therapeutic interventions that used to be offered exclusively by highly trained, skilled health professionals. In order to enable responsible clinical implementation, ethical and social implications of the increasing use of embodied AI in mental health need to be identified and addressed. Objective: This paper assesses the ethical and social implications of translating embodied AI applications into mental health care across the fields of Psychiatry, Psychology and Psychotherapy. Building on this analysis, it develops a set of preliminary recommendations on how to address ethical and social challenges in current and future applications of embodied AI. Methods: Based on a thematic literature search and established principles of medical ethics, an analysis of the ethical and social aspects of currently embodied AI applications was conducted across the fields of Psychiatry, Psychology, and Psychotherapy. To enable a comprehensive evaluation, the analysis was structured around the following three steps: assessment of potential benefits; analysis of overarching ethical issues and concerns; discussion of specific ethical and social issues of the interventions. Results: From an ethical perspective, important benefits of embodied AI applications in mental health include new modes of treatment, opportunities to engage hard-to-reach populations, better patient response, and freeing up time for physicians. Overarching ethical issues and concerns include: harm prevention and various questions of data ethics; a lack of guidance on development of AI applications, their clinical integration and training of health professionals; ‘gaps’ in ethical and regulatory frameworks; the potential for misuse including using the technologies to replace established services, thereby potentially exacerbating existing health inequalities. Specific challenges identified and discussed in the application of embodied AI include: matters of risk-assessment, referrals, and supervision; the need to respect and protect patient autonomy; the role of non-human therapy; transparency in the use of algorithms; and specific concerns regarding long-term effects of these applications on understandings of illness and the human condition. Conclusions: We argue that embodied AI is a promising approach across the field of mental health; however, further research is needed to address the broader ethical and societal concerns of these technologies to negotiate best research and medical practices in innovative mental health care. We conclude by indicating areas of future research and developing recommendations for high-priority areas in need of concrete ethical guidance.

212 citations

Journal ArticleDOI
TL;DR: There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.
Abstract: Background: Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care. Objective: This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application. Methods: We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis. Results: The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education. Conclusions: The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.

199 citations

Journal ArticleDOI
TL;DR: An overview of the features of chatbots used by individuals for their mental health as reported in the empirical literature is provided to help guide potential users to the most appropriate chatbot to support theirmental health needs.

181 citations

Journal ArticleDOI
TL;DR: Chatbots may have a beneficial role to play in health care to support, motivate, and coach patients as well as for streamlining organizational tasks; in essence, chatbots could become a surrogate for nonmedical caregivers.
Abstract: Background: Many potential benefits for the uses of chatbots within the context of health care have been theorized, such as improved patient education and treatment compliance. However, little is known about the perspectives of practicing medical physicians on the use of chatbots in health care, even though these individuals are the traditional benchmark of proper patient care. Objective: This study aimed to investigate the perceptions of physicians regarding the use of health care chatbots, including their benefits, challenges, and risks to patients. Methods: A total of 100 practicing physicians across the United States completed a Web-based, self-report survey to examine their opinions of chatbot technology in health care. Descriptive statistics and frequencies were used to examine the characteristics of participants. Results: A wide variety of positive and negative perspectives were reported on the use of health care chatbots, including the importance to patients for managing their own health and the benefits on physical, psychological, and behavioral health outcomes. More consistent agreement occurred with regard to administrative benefits associated with chatbots; many physicians believed that chatbots would be most beneficial for scheduling doctor appointments (78%, 78/100), locating health clinics (76%, 76/100), or providing medication information (71%, 71/100). Conversely, many physicians believed that chatbots cannot effectively care for all of the patients’ needs (76%, 76/100), cannot display human emotion (72%, 72/100), and cannot provide detailed diagnosis and treatment because of not knowing all of the personal factors associated with the patient (71%, 71/100). Many physicians also stated that health care chatbots could be a risk to patients if they self-diagnose too often (714%, 74/100) and do not accurately understand the diagnoses (74%, 74/100). Conclusions: Physicians believed in both costs and benefits associated with chatbots, depending on the logistics and specific roles of the technology. Chatbots may have a beneficial role to play in health care to support, motivate, and coach patients as well as for streamlining organizational tasks; in essence, chatbots could become a surrogate for nonmedical caregivers. However, concerns remain on the inability of chatbots to comprehend the emotional state of humans as well as in areas where expert medical knowledge and intelligence is required.

156 citations