In the Technology Acceptance Model, an individual’s acceptance of a new technology is determined by its perceived usefulness and perceived ease of use [18]. Therefore, perceived usefulness and perceived ease of use govern individuals’ attitudes toward the new technology and, in turn, their use of it [18]. Therefore, the successful implementation of an IMHP-facing technology in inpatient psychiatric care must consider the perceived use of the IMHP (“will they trust this innovation?”), the perceived ease of use of the IMHP (“is this innovation technologically unambiguous?”), and the attitude of the IMHP (“will this innovation positively contribute to their treatment of patients?”).

Technology acceptance was tested significantly during the global COVID-19 pandemic [19]. Quarantine meant an immediate and massive digital migration, where digital immigrants found themselves thrust into unavoidable technology and unprecedented connectivity [20,21]. By the same token, the demographic landscape of the mental health care system comprises predominantly digital immigrants—generally considered those who are older than 40 years and were born before the “proliferation of digital technologies.” In 2022, the average age of a mental health care professional in the United States is 44 years, with 60% of the professionals being older than 40 years [22]. Thus, designers of digital interventions for mental health must carefully consider usability, inclusivity, and accessibility [23,24].

User-centered design is an approach to development wherein the intended users of the system (end users) are actively and intentionally involved in its development. There are three major tenets of this framework, defined by Gould and Lewis [25] as follows:

Despite its proven effectiveness in developing digital systems, there is still a widely held belief that applying user-centered design is superfluous to the development life cycle [26]. It is common for developer-driven needs to take priority over those of the user [26,27]. Additionally, there is a stigma that user-centered design is time-consuming and costly to the developer; on the contrary, by involving the intended user throughout the design process, usability problems are identified and resolved before the system is ever deployed, saving time and money [26-28]. By emphasizing empathy research, empirical measures of usability and iterative design based on user-centered design principles increase the likelihood for successful implementation and acceptability. Digital transformation in inpatient psychiatric care will require a shift to the user-driven development of novel technologies and interventions.

Internet and smartphone availability are ubiquitous in the general population. However, this ubiquity is curtailed by the following two primary factors: socioeconomic disparities and mental illness symptoms. A closer look reveals that the first gap is in smartphone ownership and internet access. Of patients discharged from inpatient psychiatric units in the United States, 82% are estimated to have smartphones and internet access [29]; however, in high-income households, that number is close to 100% [30]. This digital divide correlates with socioeconomic and racial disparities. For example, low-income Hispanic and Black youth are less likely to have home or smartphone internet access [31]. So, while digital technologies promise to bring individualized high-tech monitoring, their effect could be limited by a return to underresourced environments upon discharge from an inpatient setting.

The second gap relates to the severity of mental illness symptoms that affect trust in and use of technology. In a survey of individuals with severe mental illness (SMI) on internet and smartphone use during the global COVID-19 pandemic, 61.6% of them were limited users or nonusers of the internet, mostly using phones to communicate with their families and friends [32]. A quarter of the surveyed sample—markedly among older individuals with schizophrenia—reported being weary of internet use for “security concerns.” Thus, while most mobile health interventions report favorable feasibility outcomes, they typically do not have the same success rate in including patients with SMI. Studies that document significant web-based participation among individuals with SMI demonstrate a correlation with positive recovery and positive emotions toward technology [33]. These findings suggest that attitudes of people with SMI meaningfully affect successful digital transformation. There are gaps in our understanding of how delusions, paranoia, and other severe symptoms may impact the scalability of the TIME framework as well, and these are intriguing areas for continued research and development.

When we have more technology available than ever before, the entire onus of patient safety should not fall on the IMHP. An important feature of the TIME framework is its balance of the “high-tech” prototype with the “high-touch” protocol. Most psychiatric hospitals rely heavily on “high-touch” interventions, that is, interventions that require in-person, face-to-face interaction with the patient [13]. Those that are exploring digital mental health care interventions, particularly for outpatient care, often copiously commit to the “high-tech” model, losing the crucial human element [34-37]. Solving the complex challenges faced by mental health care providers demands that new mental health technology tools are developed in concert with a use protocol by and for the end user (mental health care provider), allowing the “high-touch” to inform the “high-tech” and vice versa.

High-tech interventions and their corresponding high-touch protocols must be developed alongside each other to ensure usability and end user acceptability. Thus, we have defined a model—the TIME framework—for developing novel IMHP-facing tools and their clinical workflows alongside each other (Figure 1). This model is grounded in established frameworks for user-centered design and digital technology development (Figure 3).

To clarify the phases of our TIME model, we apply it as a framework for developing a nurse station monitoring dashboard to visualize wearable-based data in real time.

In an inpatient hospital setting, there is an average of 56.7 patient suicides a year [43]. Further, 74% of those occur while the patient is undergoing psychiatric treatment. The issue of suicide is pressing. The Assess, Intervene, and Monitor for Suicide Prevention model proposes a clinical management framework to assess suicide risk, intervene with coping strategies, and monitor high-risk patients [44] (Figure 4A). This is a high-touch solution. We propose that by leveraging the continuous availability and predictive power of technology through digital transformation, we may be able to rearrange the Assess, Intervene, and Monitor model as follows: (1) assess suicide risk, (2) monitor patient safety status by using wearable devices, and (3) intervene and prevent suicide every time (Figure 4B). This is a high-tech and high-touch solution. The capitalization on the continuous, noninvasive availability of technology has been described as “digital phenotyping” [13]. We can draw upon wearable technology to provide insight and predictive power for a patient’s status without having to be present in front of them. Not only will this monitoring dashboard display a patient’s current state (awake, asleep, walking, sitting, etc) as measured by the wearable device, but also it will serve as a “digital smoke alarm” for a critical change in the patient’s status [13]. Feedback provided by IMHP end users will inform both the development of the dashboard’s user interface and the crisis intervention protocol when a dashboard alert is issued.

Below, we outline how the TIME framework can be implemented to develop the high-tech remote patient monitoring dashboard in parallel with the high-touch dashboard alert protocol: