An exciting era is emerging in psychiatry. With the increasing availability of technology to support the delivery of mental health care, it is anticipated that we will see a transformation in our ability to deliver efficient and optimized care for our patients. Currently, suboptimal outcomes are ubiquitous in psychiatry. Even with the availability of advanced treatment regimens, our patients suffer from residual symptoms, as acute exacerbations of symptoms and illness recurrence. The increasing availability of digital technologies to support diagnosis, treatment and health care system management offers us the potential to develop innovative diagnostics and psychosocial interventions, along with clinical decision-making algorithms, advanced data analytic insights, and context-aware programs to deliver more effective care and improve clinical outcomes.
In most areas of medicine, quantifiable variables are used to evaluate treatment progress—for example, blood pressure, cholesterol, and blood glucose levels, cardiac ejection fraction, and white blood cell counts. Psychiatry, however, has been the exception. Objective, measurement-based approaches are not routinely used in the management of serious mental illness, and yet such measures would help to reduce patient and clinical bias in reliably evaluating clinical state and treatment efficacy. There is a need for pragmatic, meaningful, objective measurement tools that can be used by busy practitioners.
Psychiatry has also relied on retrospective and intermittent patient report, with no means to monitor patient symptoms or functional status on an ongoing basis. There is a need to move to moment-to-moment measures of clinical state that can be accessed remotely (ie, do not require a patient to visit an office). Ecological Momentary Assessment (EMA), for example, is a longitudinal assessment methodology in which an electronic signaling device such as a smartphone or personal digital assistant is used to prompt patients to complete self-report questionnaires in real time, and in real-world environments, multiple times each day. Such a method for assessment has advantages in that it minimizes the potential for memory biases in self-reporting symptoms and functioning. There is a tendency, for instance, for schizophrenic patients to overestimate the frequency or intensity of their symptoms.1 Fluctuations in symptoms can also be captured by this momentary assessment method, as well as other convergent measures that may relate to symptoms such as stress reactivity and cognitions. Momentary assessment also holds important promise for monitoring a patient’s clinical state on an ongoing basis and to alert a physician to treatment progress, emergent symptoms or side effects after a treatment decision (eg, a medication switch) has been made.
There has been an increasing focus on the use of passive sensing technologies in multiple clinical conditions. Passive technologies are able to provide quantitative and continuous data that is independent of the patient’s input. Wearable sensors can gather physiological data to enable identification and quantification of a patient’s clinical state. For example, physical activity and posture sensors such as accelerometers, actigraphy, step-counters, and gyroscopes are of interest in Parkinson’s disease, Multiple Sclerosis and Epilepsy. In psychiatric conditions, sensor technologies can provide objective biometric data about patient clinical state such as residual symptoms (eg, sleep disturbance) and medication side effects (eg, sedation). Further work is needed to better understand the clinical usefulness of data derived from passive sensing technologies (eg, activity, rest data) and to provide actionable clinical guidance.
Functional impairments in psychiatric patients can also be investigated via passive sensing technologies. Currently, social functioning is assessed via patient self-report or clinician rating scales. However, studies have begun to examine social functioning using interaction data obtained from mobile phones such as call data records and SMS logs.2 Further, monitoring the Bluetooth environment can provide data on social encounters.3 Wearable device or sensors with global positioning systems (GPS) allow the location and extent of activity to be determined. If combined with basic user-entered input such as the location of a patient’s workplace or college, this passive data collection can provide clinically meaningful information about patient work or social functioning. A critical advantage of sensors being built into multifunction devices such as smartphones is that patients are not required to wear or carry additional devices.
Psychiatry currently employs a relatively narrow base of assessment that focuses on manifest symptoms as assessed by clinician observer or patient self-report. This narrow assessment needs to be expanded. A recent example of the effort to move beyond the DSM diagnostic system is the NIMH Research Domain Criteria project (RDoC). RDoC offers a novel neurobiological approach to diagnosis and treatment.4 RDoC outlines ways of classifying psychopathology based on 5 broad domains of function: negative valence, positive valence, neurocognition, social cognition and arousal/modulation. These domains broadly define areas in which brain function can be examined through a number of methods including genetic, molecular, cellular, circuitry, physiological, observed behavior and self-report. Albeit that RDoC is an early and preliminary approach, it illustrates the conceptual shift, based on the progress in understanding the neurobiology of psychiatric illness, from a narrow symptom-based assessment and treatment model to a broader vulnerability-based model.
Advances in our knowledge about the neurobiology of psychiatric illness has changed our understanding of the nature of mental illness and opened new opportunities for more effective treatments. This new knowledge provides the basis for technological interventions that represent a fundamental advance in treatment; technologies can help us move from the current state of symptom- focused treatment toward addressing the persisting, functional vulnerabilities underlying a heightened risk of becoming psychiatrically ill. These same vulnerabilities continue to impair the day-to-day function and adaptation of the afflicted individual even when the symptoms have been treated.
As an example, computerized cognitive training programs aimed at treating specific deficits in cognition have shown promise for treating underlying neurocognitive deficits associated with clinical disorders, and thus have the potential to improve brain functioning and yield a better overall outcome for the patient. Meta-analyses have reported effect size between 0.41—0.78 for social and neurocognitive remediation in schizophrenia.5,6
In schizophrenia, bipolar disorder and MDD, evidence-based psychosocial treatments have been demonstrated to be useful in decreasing symptom severity, relapse rates, hospitalization and increased functioning. However, barriers include a critical shortage of trained professionals, uneven geographic distribution of those skilled clinicians that do exist, and high variability in the conduct of evidence-based psychotherapies in practice.
Technology is offering unparalleled opportunity for clinicians to prescribe treatment to patients outside of the clinical setting. Behavioral intervention technologies (BITs) is the term used to refer to the application of behavioral and psychological intervention strategies through the use of technology.7 Digital technologies falling under this classification include telephone and videoconferencing, as well as web-based communication. Although telephone, instant messaging and e-mail have been used to effectively connect providers and patients across geographic locations, these methods continue a reliance on humans to deliver the content, requiring professional time and associated costs.7
Computer-based and mobile interventions may offer a superior solution (Figure 1). There is a progressive development of an evidence base supporting computerized treatments with over 100 randomized, controlled trials identified in 20078 and this number is continuing to increase. Efficacy data for Web-based interventions exists for many disease states including depression, anxiety, alcohol, substance abuse, insomnia, bipolar and schizophrenia.7 Importantly, meta-analytic data for internet based cognitive-behavioral therapy (CBT) has demonstrated comparable effectiveness to therapist-delivered CBT.9
Figure 1. Advantages of Computerized and Mobile-based Intervention Technologies
Improved accessibility to better patient care
Location-free, portable, available at any time, and can be used across a broad range of settings and during patient transitions
Potential cost savings
Versatility in the clinical usefulness of computerized and mobile-based technologies to free up clinician time. Some examples include:
Technology can be individualized and tailored to the patients’ needs and preferences to enhance engagement (eg, personalized feedback and motivational support)
Multimedia format can convey information and concepts in a helpful and engaging manner
Interactive features can link users to a wide range of resources and supports
At the frontier of this work is the incorporation of passive sensing of clinical state into the treatment realm. Patient-report data in combination with sensors can be linked to create context-aware computerized psychotherapeutic treatment programs. Initial feasibility data has been published for a mobile phone intervention for depression which used machine learning models to predict mental health-related states based on concurrent phone sensors variables such as GPS, ambient light, and recent calls and then cues relevant and timely intervention content.10 Mobile Assessment and Treatment for Schizophrenia (MATS) employs ambulatory monitoring methods and cognitive behavioral therapy interventions through mobile phone text messaging.11 This program employs text messages and branching logic to deliver CBT strategies based on patient momentary responding. Significant improvement has been found after 12 weeks of such treatment, including improved mediation adherence (for individuals who were living independently), an increase in the number of social interactions and a reduction in the severity of hallucinations.
The mental health care system in the United States is fragmented, discontinuous, and inconsistent. Many high risk individuals with serious mental illness will “fall through the gaps” in the mental health and social services system, leading to adverse outcomes such as homelessness, hospitalization, and incarceration. Evidence of such gaps in care is reflected in the fact that an estimated 15%–25% of the adult prison population suffers from serious mental illness,12 and 15%—24% of State and Federal inmates report symptoms meeting criteria for psychotic disorder.13
Lack of continuous care of these individuals may be explained, at least in part, by the lack of integration of the data systems across mental health and social services agencies, as well as the criminal justice system. There is a critical need for such data integration of these critically interactive and interdependent systems. This need is exemplified in recent work that has integrated data from a criminal justice and behavioral health system and has revealed that the delivery of case management or medical services after a crisis stabilization event to an individual with serious mental illness who had a previous incarceration is associated with a 50% reduction of risk for subsequent re-arrest.14
Technology and analytics to support care coordination for the mentally ill are becoming increasingly available to providers and systems of care. These include technologies to track referrals and care transitions, and technology that is able to embed calculation and reporting of National Committee for Quality Assurance (NCQA) and other quality evaluation standards for key health care processes within a system of care. Also, technology is becoming available to support behavioral health risk analytics. Such technologies are being implemented in the USA, with the aim to improve the efficiency, effectiveness and accountability of the systems that support patient care.
The notion of a “singularity” has been used in several ways. One is to describe the closing of the gap between the development of new knowledge and its immediate deployment into practice. Digital Technology is also making it possible to integrate and analyze data from many disparate sources, such as that held within databases used by scientific publications, payers, hospitals, providers, industry, and governmental agencies. Digital technology is also enabling us to collate research outcomes across studies. This ability to synthesize all of this information, analyze, and apply it to the care of an individual patient is foreseeable, if not yet accomplishable.
The notion of a “singularity” has also been used to describe a theoretical moment in which machine intelligence reaches a tipping point—there is such an exponential growth and synthesis of technology and information that a critical mass is reached, resulting in a “superintelligence” which achieves insights far beyond what can be expected from human intelligence. On a more practical basis, it is possible that with the accelerated use of digital technology, the advance of data science and an increasing supply of integrated health care data and analytics, we might begin to see salient new insights emerge to help advance the care of mental illness. Taken together, the advances noted may lead to a real and much needed transformation in mental health care.
Institutional Affiliation: Dr Docherty is an employee of ODH, Inc.
Table 1. Technological Innovation: Preparing the Field for Transformation
|Current State||Future State|
|Nosology and Treatment||Symptom based diagnosis and treatment||Vulnerability-Based Diagnosis and Treatment|
|Secondary prevention||Primary Prevention|
|Moving Towards Incorporation of Technology into Practice|
|Measurement and Diagnosis||Qualitative||Quantitative|
|Narrow base||Broad base|
|Restricted access||Expanded access|
|Knowledge Transfer||Knowledge gap||Singularity|