By Gabriel Maeck, LCSW
There are two factors currently driving the urgency for technological innovation in the mental health field: a rising need for telemental health services due to COVID-19 and value-based care models being applied to mental health care. Further, I predict a rising trend towards the incorporation of objective device data into psychiatric remote patient monitoring (RPM) programs.
COVID-19 has caused a widespread mental health crisis and this impact has only been intensified for Black Indigenous People of Color (BIPOC). This increased need for mental health support comes at a time when traditional sources of this care have been shut off due to physical distancing requirements and budget cuts for brick and mortar clinics.
To meet this need there has been significant investment in the past few months in online video and chat therapy and psychiatric programs that remotely monitor objective device data to increase engagement and real-world insight. On the other hand, psychiatric data has traditionally been subjective, meaning it is primarily derived from patient self-reporting, clinician judgment, and observations from third parties like family, friends, or other providers.
As value-based models are now being applied to mental health care, I forecast a growing need for remote monitoring of objective device data and for the technology to share this data across interdisciplinary providers easily. As subjective psychiatric data by itself has led to difficulties in measuring the quality of care, objective and subjective data combined could help establish more reliable standards for mental health care in value-based models.
As a licensed mental health professional and as someone who works in the personal health data IT field, I will present recommendations on what kind of data to incorporate as well as explore the potential benefits of psychiatric RPM on symptom monitoring and health outcomes.
Using Device Data to Improve Symptom Monitoring
Most symptoms are best assessed for by subjective patient self-report, such as those regarding feelings and cognitions. Objective data can be introduced through tracking symptoms with physiological or functional manifestations. These symptoms have traditionally been evaluated subjectively but can also be tracked objectively.
Many of these metrics are already passively tracked by sensors on smartphones and consumer wearable devices. Significant weight loss/change in appetite, sleep disturbances, psychomotor agitation/retardation, and fatigue/loss of energy are all symptoms of a major depressive episode. By augmenting the existing subjective means, all of these symptoms could be tracked through objective measures like monitoring activity (steps), weight, nutrition, and sleep data. Additionally, multiple conditions, including generalized anxiety disorder, posttraumatic stress disorder, recurrent panic attacks, and others assess for physiological reactivity that could be captured by objectively monitoring heart rate, temperature, and heart rate variability.
Additionally, these same objective data points could be used to examine symptoms of other psychiatric conditions. For example, clinicians could remotely track the decreased need for sleep along with the increased activity level for someone undergoing a manic episode or remotely monitor weight and nutrition of someone with an eating disorder.
It is also essential to accurately determine the duration, frequency, and severity of these symptoms. While subjective recall of symptoms can be prone to error, devices can provide months of historical data to show trends of active symptoms as well as periods of remission – providing a more accurate diagnostic picture.
Using Device Data to Improve Outcome Measures
While it won’t apply to all psychiatric patients and disorders, remote monitoring can help keep patients engaged while physical distancing measures are in place in combination with teletherapy and establish objective data as a larger part of mental health care.
Through combining subjective and objective data in a psychiatric RPM program, clinicians can improve diagnostic consistency and quality of care, especially in value-based models. The objective data would challenge subjective biases and offer more concrete evidence of the patient’s real-world functioning. Providers would then be incentivized to improve care coordination as they would have greater awareness of the patient’s daily life and have objective data to craft more personalized treatments. The deeper insight derived from this data would also empower patients to better advocate for themselves and take positive action.
Symptom reduction is fundamental to clinical practice but it shouldn’t be the only outcome by which clinicians are held accountable (i.e., the patient went from having six to four major depressive symptoms over three months). This outcome shows progress in the remission of symptoms during that time but it doesn’t provide context of the patient’s actual quality of life.
I envision objective device data establishing quantifiable criteria for a strengths-based model in which outcomes for a whole team of interdisciplinary providers are measured by promoting healthy activity, sleep, nutrition, and biometric levels along with subjective patient self-reported outcomes. In short, device data could help transform the goal of clinical practice from solely reducing a negative in the short-term – human suffering – to promoting a positive in the long-term – human wellness.
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