Digital Technologies in Psychiatry: Present and Future
Abstract
This article highlights gaps in mental health services that emerging technologies may fill; summarizes what digital health can offer for assessment, treatment, and care integration; and describes some of the challenges and some new directions for innovations in this field.
Abstract
The digital revolution has reached the world of mental health. Prominent examples include the rapidly growing use of mobile health apps, the integration of sophisticated machine learning or artificial intelligence for clinical decision support and automated therapy, and the incorporation of virtual reality-based treatments. These diverse technologies hold the promise of addressing several important problems in mental health care, including lack of measurement, uneven access to clinicians, delay in receiving care, fragmentation of care, and negative attitudes toward psychiatry. Here, the authors summarize the current and swiftly changing state of digital mental health. Specifically, they highlight the current unmet needs that emerging technologies may be able to address; summarize what digital health can offer for assessment, treatment, and care integration; and describe some of the challenges and some new directions for innovations in this field. The review concludes with guidance for clinicians to integrate digital technologies into their work and to provide responsible and useful advice to their patients.
Over the past two decades, technology has transformed many aspects of everyday life, from commerce to entertainment. Consumers seek information via Google Search, navigate with smart maps informed by real-time feedback from millions of drivers, and share ideas with others via social media. None of these technologies were available two decades ago. Today, there are over two billion monthly users of Facebook (1), 2.4 billion smartphones in circulation (2), and over two trillion searches every year on Google (3). This technology revolution has spawned not only new tools but also a new “do-it-yourself” culture in which intermediaries have been replaced by direct connections between markets and consumers. Amazon, with no brick-and-mortar retail stores, has become one of the largest sellers of consumer goods; Airbnb, with no real estate, has become one of the largest hotel chains; and Uber and Lyft, without owning fleets of cars, have replaced taxis in many markets.
What does this technology revolution have in store for psychiatry? Will brick-and-mortar clinics be replaced by teleclinicians? Will smartphones become the new clinical interface for diagnosis and treatment? Will psychiatrists be replaced by artificial intelligence (AI)-engineered conversational bots trained on millions of clinical interviews and treatment sessions? These questions, which seemed premature a decade ago, have become relevant, and even urgent today. In this review, we describe some of the recent experiences with the use of technology in psychiatry, recognizing that this is a rapidly changing landscape with new products emerging monthly. As just one reflection of the growth of health technology, a recent report identified over 1,000 companies launched in the past five years, with a total investment of over $20 billion (4). A 2017 IQVIA Institute for Human Data Science report found 318,000 health apps and 340 consumer wearable devices available worldwide; apps that addressed mental health and behavioral disorders constituted 28% of all disease-specific apps (5). Furthermore, 45% of the respondents to an anonymous, online depression screening tool offered by Mental Health America (MHA) in 2016 stated that they would be interested in receiving online- or mobile-based support from MHA for mental health issues (only 18% would accept referral for a face-to-face interview) (6).
Problems That Digital Health May Address
With smartphones, virtual reality (VR), AI, and machine learning algorithms, there is no shortage of digital tools for transforming psychiatry, but how will these be used to make a difference for patients and providers? In this context, it may be useful to identify five broad problems that digital mental health may address.
First is the absence of objective and standardized measurement. Although other medical fields, such as oncology (7), have integrated structured, standardized, and validated tools based on biological markers and objective signs to diagnose and guide treatment, analogous tools in psychiatry are either absent or, when available (e.g., semistructured interviews for diagnosis), rarely used outside of research settings. Objective, continuous, ecological measurements of emotion, cognition, and behavior are not available either for diagnosis or for assessing outcomes. Recently, measurement-based care has been suggested as a foundation for improving the quality of mental health care (8).
The second issue is related to access to mental health services. In many parts of the developed world and most parts of the developing world, evidence-based mental health care is not available. The Substance Abuse and Mental Health Services Administration reports that 55% of U.S. counties lack a mental health professional (9). A recent interagency report on the state of treatment for serious mental illness reported that about 2% of patients receive evidence-based treatments such as assertive community treatment, supported employment, and family psychoeducation (10).
Third, for those who find treatment, there is usually an extensive delay that is due not only to lack of access but also to reluctance to seek care. Among individuals seeking treatment for first-episode psychosis in community clinics in the United States, the median duration of untreated psychosis was 74 weeks (11). This delay has serious consequences for outcomes, as patients often first receive treatment after a long period of psychosocial disability, impairing their ability to recover.
Fourth, mental health care is highly fragmented, with different professionals, different treatments, and even different perspectives creating a maze that patients are challenged to navigate. Despite significant comorbidity among psychiatric disorders (including substance use disorders) and physical medical diseases, in practice, their care is siloed, with different medical records and different reimbursement plans (12). Even mental health care itself frequently lacks continuity. As just one example, only 52% of patients who were discharged from an emergency room following an incident of self-harm were seen within 30 days for an outpatient mental health follow-up visit (13).
Finally, and connected to the other four problems, are the attitudinal barriers, often referred to as stigma and consisting of a range of negative attitudes—especially negative views of psychiatrists, diagnostic labels, and treatments—that limit some people from seeking or adhering to care. For instance, results of the National Comorbidity Survey Replication revealed that 55% of people who were diagnosed as having a mental illness are not in care (14). Although many assumed that this low rate of care was due to lack of access, a revealing follow-up to this report found that the reason was more often “attitudinal barriers” rather than “structural barriers” (14).
Can digital tools solve the problems of measurement, access, delay, fragmentation, and negative attitudes? There is no app that will fix mental health care. However, there are interesting innovations in assessment, treatment, and care integration that are already beginning to change how patients and providers engage. We do not see an Amazon or an Airbnb for psychiatry yet, but the field may be disrupted much sooner than most of us realize. In the following sections, we summarize some of the innovations and some of the challenges in these three areas: assessment, treatment, and integration of care.
Assessment: Digital Phenotyping Addressing Lack of Measurement
The lack of biomarkers (objective signs of medical state that can be accurately and reproducibly measured; 15) has been a challenge for both diagnosis and measuring outcomes in psychiatry (16). Even when validated assessment tools are used, clinicians are limited to episodic self-report measures collected in a clinic, which are subject to recall bias. In addition, many of these clinically oriented tools are necessarily brief and therefore capture only a narrow spectrum of a patient’s overall state (e.g., general depression symptoms) and are administered infrequently, leading to a collection of one-time, or “snapshot,” impressions of a patient’s mental health. Ideally, clinicians would have access to continuous, precise, objective, and passively collected data about their patients, which would be synthesized to yield clinically meaningful information.
The ubiquity and intense use of smartphones and wearable devices may change how we assess mood, cognition, and behavior. Digital phenotyping is the term used to describe this new form of assessment (17). Digital phenotyping is based on sensor data, such as activity and geolocation; human–computer interaction reflected in keyboard and scrolling patterns; and voice and speech patterns. In addition, “digital exhaust,” like the wake behind a boat, including social media posts and search histories, can be part of the digital phenotype. Although smartphones can be used to collect rating scales and momentary assessments throughout the day, the appeal of digital phenotyping is that the assessment is passive, not requiring patients to do anything other than charge and use their phones. Wearable sensors can provide some of the same kind of information, such as sleep and activity, to augment self-reports with quantitative, daily accounts.
It may not be surprising that severe depression or mania would be reflected in activity or sleep data or that psychosis would influence human–computer interaction. How would these data be used in practice? Current studies are exploring several applications. In clinical trials, digital phenotyping is being used to detect early changes in depression, schizophrenia, and posttraumatic stress disorder (PTSD), validated by gold-standard clinical ratings. The hope is that signals from the phone or a wearable will ultimately augment, or even replace, clinical ratings, potentially providing earlier signals of relapse or recovery. Other trials are using the rich data from smartphones to search for cognitive or behavioral subtypes that might predict treatment response. Still others are using digital phenotyping to monitor patients with serious mental illness, creating a “smoke alarm” of early relapse for care managers. The concept behind all of these applications is that monitoring behavior ecologically by using a device that has already been adopted by patients will improve diagnosis and treatment, specifically targeting the lack of measurement and the delay in care noted previously. This approach requires that the phone delivers valid, actionable signals and that those signals be transmitted to a clinician who can respond.
The search for valid, actionable signals is ongoing. Sleep and activity are relatively straightforward, but digital phenotyping also includes digital biomarkers extracted from subtle aspects of smartphone use, such as keystroke and scrolling patterns. For consenting individuals who had their smartphone keystroke and scrolling patterns collected and who also completed extensive gold-standard neuropsychological tasks, machine learning algorithms identified digital features—essentially, biomarkers—associated with processing speed, verbal memory, and measures of executive function with high correlations (approximately 0.8 [18]). Similarly designed, in-progress studies are following adults posttrauma and examining youth at an ultra-high risk for psychosis to search for digital markers that may precede PTSD and psychotic disorders, respectively.
The smartphone may seem the ideal solution to the measurement problem in psychiatry. In fact, rigorous studies of digital phenotyping demonstrate that this is feasible but also challenging. Depressed people stop charging and using their phones; sensor data are noisy; and collecting speech, text, or search data introduces serious privacy issues. Wearable devices provide valid measures of sleep and activity; but adoption and adherence are significant problems, with fewer than 10% still in use after 6 months (19). Additional challenges include problems with reproducibility in diverse samples; it is not yet clear that the digital biomarkers collected in 20-year-olds who use their smartphones more than 4 hr/day will generalize to 70-year-olds who use their phones much less frequently. Furthermore, validation remains a challenge: Unlike the development of digital tools for diabetes or heart disease, the lack of “ground truth” physiologic biomarkers for mental disorders may limit the interpretation of digital data (20). Nevertheless, there is a real opportunity to identify digital features that can serve as early warning signs of recovery or relapse. In particular, the use of passive, content-free data, examining not what but how you type may be a breakthrough for the ecological assessment of mood and cognition (18).We are just at the beginning of analyzing the rich, continuous data now becoming available. Digital phenotyping could alter how we diagnose mental illness, identifying behavioral subgroups for precision medicine and giving us earlier signs that may improve the prediction and detection of psychiatric disorders, as well as facilitating measurement-based care.
Treatments: Digital Interventions Addressing Access and Delay
There is a robust literature that supports digital technologies for mental health treatment, beginning with telepsychiatry and early attempts to move evidence-based therapies online. These approaches were largely developed to address the barriers to access and delays in treatment. In recent years, multiple studies have addressed the efficacy—a measure of how well an intervention leads to the expected results in an ideal or controlled circumstance—of digital mental health interventions. However, fewer studies have directly measured the effectiveness—the degree to which an intervention performs under real-world conditions—of these rapidly evolving technologies. Nevertheless, available evidence suggests the value of digital interventions relative to face-to-face interventions as a means of improving access to evidence-based treatments.
Evidence for Efficacy
A recent meta-analysis of 22 smartphone-based apps for depression among 18 randomized controlled trials (RCTs) and 3,414 participants found a significant decrease in depressive symptoms from baseline (Hedges’ g [a measure of standardized mean difference between study groups]=0.38; 95% confidence interval [CI]: 0.24–0.52; p<0.001), including all 18 studies, and a larger effect when smartphone-based interventions were compared with inactive controls (g=0.56; 95% CI=0.38–0.74, p<.001) than when compared with active controls (g=0.22; 95% CI=0.10–0.33, p<.001; 21).
In a similarly designed meta-analysis of 9 RCTs of smartphone apps that targeted anxiety, the overall effect size for the smartphone interventions was significant (g=0.325, 95% CI=0.17–0.48, p<0.01), though the small number of trials limited the authors’ ability to conduct subgroup analyses (e.g., interventions with and without face-to-face supplementation; 22). Like the depression-oriented apps, study sizes (N=15–248 per arm), mean ages (18–43 years), and outcome measures (including the seven-item Generalized Anxiety Disorder Scale and the Depression Anxiety Stress Scales) varied among the included studies. In both depression and anxiety meta-analyses, the authors found no evidence of publication bias among the included studies.
There are also data to support the use of digital interventions for insomnia, PTSD, psychosis, and substance use disorders, but work in each of these areas is still emerging. This promising field remains challenged by the relatively few published RCTs across all of psychiatry (and even fewer that include youths), significant heterogeneity among studies (e.g., inclusion criteria, use of in-person supplementation of digital interventions, control conditions, and outcome measures), and the relatively short study durations (usually one to three months). If the target is access, then the litmus test will be the shift from efficacy to effectiveness, which will require adoption of digital interventions at scale and sustained engagement.
New Trends in Digital Interventions
Three promising trends in the area of interventions should be highlighted. First is the use of digital interventions for patients with serious mental illness (23–25). Apps for peer support, medication adherence, and cognitive remediation are showing high rates of adoption among this heretofore hard-to-reach population (26). Second is the potential for combining digital and traditional approaches. Following a pivotal multisite RCT (24), the U.S. Food and Drug Administration (FDA) approved the first mobile app for substance use disorders among adults as “a prescription digital therapeutic” (essentially, digital cognitive–behavioral therapy [CBT]) to accompany standard outpatient treatment (27). This approval now allows clinicians to prescribe the app, called reSET (Pear Pharmaceutics), to patients as part of a comprehensive care plan that includes conventional, in-person substance use treatment.
A third, more innovative trend is the use of “chatbots” supported by sophisticated AI. A recent RCT studying the effects of a fully automated conversational agent that reinforced CBT techniques in a text-message format, compared with an information-only control, found a significantly greater decrease in depressive symptoms among individuals in the intervention group (28). Though promising as an approach to the problems of access and delay, interventions such as chatbots with sophisticated AI have not yet undergone rigorous testing at scale. While we might assume that our patients would prefer face-to-face interaction to interaction with a bot, that assumption may not hold for the majority of individuals with a mental illness who are not in treatment—either because of lack of access or negative attitudes about psychiatry.
The development of chatbots is only one sign of how recent developments in technology may alter interventions. Video games to train attention or executive function have demonstrated efficacy (29, 30) and are being considered by the FDA as a “medical device” for potential use in the treatment for attention-deficit hyperactivity disorder (ADHD). VR technology has become increasingly sophisticated over the past decade, creating truly immersive digital experiences, mostly for games. In parallel with these VR gaming developments, VR-based treatments for various psychiatric conditions have proliferated. For instance, building on the empirical support for exposure therapy in PTSD (31) and specific phobias (32), multiple companies have released VR programs that simulate fear-evoking situations. Instead of replacing in-person therapy (and improving access), VR technology can augment in vitro therapy, especially when in vivo exposure is impossible or intolerable. In addition, many VR environments can be personalized to closely match the specific situations and stimuli that evoke the patient’s most distressing symptoms.
Challenges in Digital Interventions
The gap between efficacy and effectiveness is nontrivial. In fact, efforts to move digital interventions from research into practice have generally failed (33). Outside of research protocols, patients and providers either do not adopt or do not adhere to online treatments. Mohr et al. (34) remind us that, going forward, we need to consider digital interventions as technology-enabled services rather than stand-alone products, integrating them into the fabric of a patient’s life and the workflow of a provider’s practice. To improve adoption and adherence, the next generation of digital interventions will likely look less like Internet-based manualized treatment and more like the video games for ADHD or immersive VR.
Although here we have stressed the evidence for efficacy, in the world of software development, success is measured by engagement and scale rather than by efficacy and effectiveness. It is important to recognize that digital interventions may not fit the pharmaceutical development model with RCTs and FDA-approved phases of development, because software is constantly changing, improving iteratively to increase adoption and engagement. In practice, this means that the software tested in an RCT and published last year (or last week) is almost certainly obsolete and unlikely to be the same software that patients are accessing today. In this area of psychiatry, adoption and engagement will be as important as efficacy if digital interventions are to solve the problems of access and delay.
Care Integration: Addressing Fragmentation
Mental health care is fragmented, often divorced from medical care, and frequently lacks continuity. Service innovations such as the collaborative care model address this fragmentation, expanding the impact of a single psychiatrist through integration with care coordinators in primary care settings. Collaborative care has been shown repeatedly to improve outcomes for people with depression (35). Several companies have created software to facilitate collaborative care, with innovations like an AI nurse to extend the care manager’s reach; dashboards for data flow to help triage patient care; and designing matching algorithms to link primary care, specialty care, and patients. In contrast to the most of the intervention software that has been built for patients, digital tools for integration have been built for providers with the goal of improving the efficiency as well as the quality of care.
Because digital technology excels at communication and data flow, it is not surprising that care integration is the most mature innovation for mental health, with self-insured employers and large-payer groups already adopting this approach. The fragmentation problem is, of course, not unique to mental health, but the problem is more complex here because the data streams are often nonexistent or not accessible, and quality metrics, familiar to most of medicine, have not been widely adopted in mental health practice.
One additional challenge for mental health integration, relative to collaborative care for diabetes or cardiovascular disease, is the relative lack of standardized data. Electronic health records have not been widely adopted by outpatient mental health providers (36), and in truth are not especially popular with patients and providers in general medicine (37). Mental health data are both more sensitive and less portable than general medical data. Can digital tools overcome this challenge? One approach, borrowed from the do-it-yourself culture of the digital era, builds on efforts to empower patients and families with their own data by giving patients agency and ensuring transparency in the clinical process. The OpenNotes project, which provides this transparency for medical records, is associated with improved treatment adherence and greater patient satisfaction with their provider, suggesting that this kind of an approach might address not only the fragmentation of care but also the negative attitudes toward mental health conditions (38). Unexpectedly, sharing medical records with patients did not generate a new burden for clinicians. To our knowledge, no one has integrated this kind of transparency with mobile diagnostics or other digital interventions.
Will digital care lower the quality of mental health care, as the patient is not being observed directly by a clinician? There are certainly opportunities for unintended consequences, from patients with substance use disorders misrepresenting their abstinence to nonprofessionals misrepresenting their credentials. The current system—in which most therapy sessions occur behind closed doors without structured and empirically supported outcome metrics—can hardly be the best standard of care. In contrast to the current, predominant system of outpatient mental health care, many digital health platforms collect data on all interactions, allowing continuous, objective assessments of the quality of treatment and the magnitude of outcomes.
Emerging Tools for Mental Health
In addition to innovations for assessment, treatment, and care integration, there are a few emerging tools that may be game changers for mental health. Of course, the smartphone, which is nearly ubiquitous, serves as the most important technology for disrupting diagnosis and treatment. Although smartphone use, especially among youths, may be described more often as part of the problem rather than part of the solution (39), the intense and broad use of these powerful computers gives psychiatry an unprecedented opportunity to influence public health. In the following text, we describe a few other approaches in specific areas that may prove equally disruptive.
Using Digital Technologies to Augment Clinical Capacity
One solution to the global problem of access to mental health providers is task sharing, an approach that uses nonprofessionals with targeted training to extend what an expert clinician can provide. The challenge with task shifting is ensuring that village health workers or suicide hotline volunteers have the skills to provide high-quality care. Is there a technology solution? In the world of managing traffic, many drivers rely on smartphone-based navigation systems to reach their destinations in the most time-efficient manner. Apps like Waze rely on both passive and active data from other users to generate the best route and provide continuously updated, turn-by-turn guidance. In this way, such apps augment a driver’s ability to navigate the roads safely and efficiently. An analogous app for nonprofessional mental health providers could offer patient-specific guidance based on passively and actively imputed data (for instance, information gathered from the electronic health record, or patient-reported symptom-severity scale results) to generate more accurate diagnostic and prognostic data and more effective treatment recommendations. In the context of global mental health or crisis intervention, where we have to rely on task shifting, these tools can give volunteers the ability to provide higher quality care than might be available today. As just one example, data scientists are combining text and social context data to help a volunteer on a suicide text line or phone line assess risk and recommend local interventions (40). The fastest growing smartphone market is in Asia, where the development of software to empower community health workers could become the foundation for a new mental health system.
Online Peer Support: Will There Be an Airbnb for Mental Health Care?
If one surveys the problems we noted at the outset—lack of measurement, lack of access, delay, fragmentation, and negative attitudes—the most vexing is that most people with a mental illness frequently fail to seek help, in part because of negative attitudes about receiving mental health care. Although there are other explanations (including the helplessness of depression and the denial of illness in psychosis), it is difficult to escape the conclusion that “stigma” contributes to the finding that more than half of people with a mental illness do not seek treatment or seek treatment only after struggling for many years. Although we can improve detection with digital phenotyping and we can reduce the fragmentation of care with digital collaborative care, if people do not want the care we offer, digital tools are not likely to fix the overall problem of morbidity and mortality.
Increasingly, people expect on-demand services that are convenient (usually meaning available at home) and consumer centered. The mental health system is, first and foremost, built for providers, not patients. That is, care is based in clinics or hospitals, with schedules set by clinicians and administrators and treatments delivered through experts. This provider-centered approach has a long tradition and defines our comfort zone but appears less appealing to consumers, especially to millennials, many of whom are accustomed to peer-generated digital content. The future of mental health care might take the shape of various digital mental health services built around peer support within trusted, online communities. These services, some of which are already available, offer free, anonymous, global support within minutes without any brick-and-mortar location. Users can engage with such a service for peer support and, if interested, they can pursue stepped-up options, ranging from text-based therapies to individual psychotherapy with a licensed provider.
These and similar interventions may not be the sole answer for people with serious mental illnesses or for children with developmental disorders, but this general approach offers anonymity, social connections, and skill building for a large population of people in need of mental health care—and it is popular. 7 Cups, for instance, claims over 21 million conversations in 140 languages, as well as 19% month-on-month growth, with two million monthly users (41). As an adjunct to medical care or as a stand-alone support system, this approach fits the on-demand, convenient, consumer-centered, trust-based world that Amazon, Airbnb, and Uber have cultivated. Combining this approach with passive assessments and links to expert providers may usher in a transformed care system.
Limitations and Future Directions
Recent years have seen the exponential growth of digital mental health; however, as reviewed earlier, we are just at the beginning of some of the most innovative and potentially disruptive changes. Digital phenotyping is just beginning to demonstrate its value in care; and digital interventions, such as certain apps for depression and anxiety, currently have supporting scientific evidence but have not been widely adopted in care.
There are several other important limitations of the current digital mental health landscape. First and foremost, protection of mental health information among these technologies is a pressing issue given the complex regulatory structure of the digital landscape. Specifically, at the time of this writing, it seems that data collected on mental health digital platforms are subject to the Health Insurance Portability and Accountability Act Security Rule and Privacy Rule only if they collect personal health information (42). Likewise, there is a risk that developers may apply user-derived information to generate marketing data. Patients and clinicians should carefully evaluate an app’s terms of service before entrusting sensitive, personal information. Specifically, they should review what data will be collected, who will have access to that data, and how privacy will be protected. This is an especially pertinent topic in the context of recent high-profile breaches of personal information from platforms such as Facebook, which have led to calls for improved data-security measures. The use of content-free phone data for phenotyping or anonymized text for peer support suggests potential solutions for privacy issues that are critical for public trust.
Second, although digital technologies hold the potential to reduce disparities in mental health care, evolving technologies may, in fact, widen gaps between those who can access digital care and those who cannot. Cost is becoming less of an issue, with increasing market competition among software developers and near-ubiquitous smartphone ownership in developed countries; however, individuals with lower digital fluency (43) may be intimidated and feel alienated by these digital resources and may find it difficult to engage with them. In the broader digital technology world, user-experience leaders are considering ways to make websites, smartphone interfaces, and mobile apps accessible for individuals with various disabilities ranging from physical (e.g., visual impairment) to cognitive (e.g., dementia) (44). This consideration is especially important in digital interventions for mental health conditions, given high rates of cognitive impairment (e.g., impaired working memory and executive functioning in schizophreniais) and interfering behaviors (e.g., motor tics in Tourette syndrome) in psychiatric disorders that might impede successful use of these technologies. “Digital exclusion” may be decreasing as the Internet and smartphones become an intrinsic part of everyday life (45), but remaining barriers should be proactively addressed to accommodate users of diverse backgrounds and abilities.
Third, a majority of digital interventions are targeted toward and have evidence for efficacy among adults; there are comparatively fewer resources and data to support digital interventions for youth (46). This age gap can likely be attributed to both market forces (e.g., a more robust user base exists for mobile apps) and regulatory concerns (e.g., human subject protection standards being more stringent for youths compared with noninstitutionalized adults). However, given that as many as one in every three to five individuals will meet criteria for a psychiatric disorder during their childhood or adolescence (47, 48), and psychiatric treatment is most effective when delivered early in development (49), future research and development should focus on the mental health needs of this vulnerable population.
Fourth, to address the current limitations of digital technologies, academics will need to look to industry models for how to make mobile devices attractive to and useful for consumers (50). Likewise, app and device developers will need to integrate insights from carefully controlled, laboratory-based studies to assess the efficacy of these tools. Communication and collaboration between these two disparate fields—one focused on rapid, iterative development, user development, and scalability; and the other focused on scientific rigor and publication of results will continue to be challenging, but the strengths of both are necessary to develop and refine new technologies to help those afflicted by mental illness (51).
Integration of Digital Technologies Into Practice
For the individual clinician, it can be intimidating to choose among the dizzying and rapidly changing array of mobile apps, AI resources, and VR-based therapies currently available. In this context, Chan et al. (52) have presented a set of factors that clinicians should consider when specifically evaluating a mental health app for integration into their practice. The authors identify three major categories: app capabilities and functions (e.g., psychoeducational materials versus symptom tracking, availability of conversational agents, or chatbots), workflow issues (e.g., accommodation of patients’ daily routines, appeal of the app design), and cultural or access issues (e.g., accounting for technological literacy and access issues). To help structure this evaluation, clinicians can use standardized scales such as the publicly available, 23-item Mobile App Rating Scale (53). The American Psychiatric Association has established a five-step “app evaluation model” to help clinicians assess the quality and usability of an app (54).
These rating tools are useful when considering a single app—because either the patient asks about a particular app or the clinician has come across an app through his or her own search—but are less useful when considering a problem (such as depression) and then searching for the “best” app to address the clinical concern. In these, perhaps more common, scenarios, clinicians can consult regularly updated rating guides, such as those maintained by the Anxiety and Depression Association of America (55) and the PsyberGuide (56), part of the nonprofit One Mind Institute.
Ultimately, however, the decision about whether to recommend an app—or any digital health technology—to a patient belongs to the clinician, just as the decision to use online treatments generally is made by the patient, with or without the knowledge and consent of a clinician. As we have reviewed here, there is growing evidence to support the use of tools for better measurement and the efficacy of many mobile apps for treatment, as well as promising digital technologies to coordinate care. However, these are still early days in a fast-moving field. Technology pioneer Bill Gates famously cautioned, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten” (57). Our patients increasingly live in a world of convenient, real-time, consumer-centered services. That does not describe the current world of mental health practice. Perhaps not in two years, but in 10 years, technology may transform how we diagnose and treat mental illness, overcoming the issues of access, delay, negative attitudes, fragmentation, and lack of measurement.
Footnote
NIMH funding had no involvement in the conception, drafting, review, or approval of the manuscript. Dr. Hirschtritt serves as a consultant to Carbon Health. Dr. Insel is an employee of Mindstrong Health and has equity interest in Alphabet, Apple, and Mindstrong Health. He serves on the boards of Mindstrong Health, Inscopix, Compass Pathways, and 7 Cups.
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Published in print: Summer 2018
Published online: 18 July 2018
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This work was funded in part by grant 5R25MH060482-17 from the National Institute of Mental Health (NIMH) to Dr. Hirschtritt.
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