Digital psychiatry is a field that has come a long way over the last five years. It has witnessed an explosion of research and commercial interest, accompanied by a lot of excitement and hope. The FDA and many other organizations have published guidance and developed programs to foster development of responsible and safe mental health related technologies.
Despite the enthusiasm, the impact on day-to-day clinical practice has been limited. Back in 2013, I had my own realization that psychiatry would be one of the most fertile fields to benefit from emerging technologies, prompting my transition from traditional psychiatry to working on artificial intelligence and augmented reality.
With that said, digital psychiatry has considerable potential to change future clinical practice and to augment the work of clinicians and researchers. One area of impact will be the typical outpatient office visit. We continue to rely on subjective patient reporting, often relying on recollection of symptoms and functioning over many weeks. This reporting is combined with our ability only to assess the patient during that appointment in the clinic, a snapshot moment in time. But now we have technologies, like smartphones, whose sensors can help us to monitor our patient’s mood and behaviors during the 99% of the time that they are not in our clinics. This longitudinal assessment can provide for more objective and quantitative patient data.
Digital psychiatry has considerable potential to change future clinical practice and to augment the work of clinicians and researchers.
Research has already made headway into “digital phenotyping,” a process where a smartphone (or smart device) can collect data about how a user interacts with it, and how the user engages with the world around them. For example, background data is collected as individuals go about their normal lives (passive data), such as call/message logs, the GPS movements of the user, or their typing characteristics on the screen keyboard. Data can also be collected when the user is explicitly asked to perform a task, like filling out a mood diary or a memory test (active data).
There is also a digital means of measuring almost every component of the mental state exam (MSE). This cornerstone of clinical practice remains subjective and non-quantitative, and technology may allow for a more quantitative and objective way of assessing an MSE. Consider speech as a component of any good MSE. Research has shown that conditions such as psychosis or depression can be evaluated through digital analysis of speech production that looks at components such as phonation, resonance, pitch, and language patterns.1,2
The task of taking enormous amounts of data and making it meaningful is no easy feat, and arguably one of the most difficult. Yet, we may find that through technologies like machine learning, we may uncover digital signatures that could help subtyping of heterogenous clinical conditions or allow for models to help predict relapse or treatment response. These advances could help us to understand our patients in a data-driven way that would hold advantages through the expert-driven frame works of psychiatric illness (DSM-5) and brain functioning (Research Domain Criteria, or RDoC). Benefits include a more reliable and accurate assessment compared with DSM-5, and the ability to incorporate it into clinical practice (unlike NIMH RDoC).
Broadly, I think that we are seeing digital psychiatry at work in three key areas:
1. Technologies that can help improve current services and treatment (eg, access, adherence)
2. Technologies can become the treatment (eg, digital therapies)
3. Technologies that can have far reaching effects in terms of prevention and research (eg, phenotyping, big data approaches, combination with other research modalities)
Telepsychiatry is an example of a technology whose clinical use will mature over the short term. The growth in telepsychiatry has been fueled by increased legislative support, broader reimbursement, decreased equipment cost, increased patient and provider acceptance, and to address a psychiatrist shortage in many communities. Many clinicians, like myself, already run a series of telepsychiatry clinics every month. While connecting patients to psychiatrists via technology is not a new development, a range of “chatbot” digital therapists have also been created to provide CBT-based therapy. One commercially available chatbot has been shown to reduce depression symptoms when used as the intervention in a controlled research trial.
1. Rezaii, N, Walker E, Wolff P. A machine learning approach to predicting psychosis using semantic density and latent content analysis. NPJ Schizophr. 2019;5:9.
2. McGinnis EW, Anderau SP, Hruschak J, et al. Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood. IEEE J Biomed Health Inform. 2019 Apr 26 [Epub ahead of print].