Pooling Mental Health Data with Chatbots

Abstract

For an invisible condition, clinical anxiety casts a long shadow. In the United States, about 40 million adults -a full 18.1 percent of the population -suffer from an anxiety disorder, making it the most common type of mental health problem in the country [1]. Anxiety is insidious; it can crop up like a weed at any point in life, coloring one's days and nights with unaccountable feelings of dread. Anxiety is expensive. A recent study estimated that in the United States, the total costs of treatment, lost productivity, and lost wages are about $42 billion every year (Du Pont et al., 1996) [2]. This amount exceeds the most recent annual revenues of Facebook, Coca-Cola, Nike, and Morgan Stanley. Anxiety is thriving. There is a shortage of mental health workers (clinicians) in the United States, and experts believe that this problem of supply and demand will worsen substantially by 2025. Even when help is available, high prices and perceptions of social stigma prevent many people from seeking help [1].This chapter explores a new type of technology that relies in part upon large sets of patient data to relieve anxiety symptoms (Dobbs, 2017). The technology is software that can simulate certain kinds of conversations with human therapists. Referred to as "conversational agents" or "therapy chatbots," the idea might sound like science fiction, but it is a commercial reality. Recent studies indicate that conversational agents are useful in helping to reduce the symptoms of anxiety and depression. A recent peerreviewed study showed that the program highlighted in this chapter, "Woebot," can significantly reduce depressive symptoms in two weeks of regular use [3]. Some therapy chatbots have also been able to predict the onset of panic attacks or depressive episodes based on patterns in user behavior (Mathotaarachchi, et al., 2017). A key feature of therapy chatbots is their ability to improve over time by drawing insights from the ever-