Beyond the Therapist's Office: Merging Measurement-Based Care and Digital Medicine in the Real World

The Pain Intervention & Digital Research Program: an operational report on combining digital research with outpatient chronic disease management

Chronic pain affects up to 28% of U.S. adults, costing ∼$560 billion each year. Chronic pain is an instantiation of the perennial complexity of how to best assess and treat chronic diseases over time, especially in populations where age, medical comorbidities, and socioeconomic barriers may limit access to care. Chronic disease management poses a particular challenge for the healthcare system's transition from fee-for-service to value and risk-based reimbursement models. Remote, passive real-time data from smartphones could enable more timely interventions and simultaneously manage risk and promote better patient outcomes through predicting and preventing costly adverse outcomes; however, there is limited evidence whether remote monitoring is feasible, especially in the case of older patients with chronic pain. Here, we introduce the Pain Intervention and Digital Research (Pain-IDR) Program as a pilot initiative launched in 2022 that combines outpatient clinical care and digital health research. The Pain-IDR seeks to test whether functional status can be assessed passively, through a smartphone application, in older patients with chronic pain. We discuss two perspectives-a narrative approach that describes the clinical settings and rationale behind changes to the operational design, and a quantitative approach that measures patient recruitment, patient experience, and HERMES data characteristics. Since launch, we have had 77 participants with a mean age of 55.52, of which n = 38 have fully completed the 6 months of data collection necessitated to be considered in the study, with an active data collection rate of 51% and passive data rate of 78%. We further present preliminary operational strategies that we have adopted as we have learned to adapt the Pain-IDR to a productive clinical service. Overall, the Pain-IDR has successfully engaged older patients with chronic pain and presents useful insights for others seeking to implement digital phenotyping in other chronic disease settings.

A framework for precision "dosing" of mental healthcare services: algorithm development and clinical pilot

BACKGROUND

One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients.

METHODS

Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as "session dosing": 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients.

RESULTS

The developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified.

CONCLUSIONS

It is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.

Barriers and facilitators to technology-enhanced measurement based care for depression among Canadian clinicians and patients: Results of an online survey

BACKGROUND

Measurement-based care (MBC) is an evidence-based approach that can improve management of depression, but its use in clinical settings remains low. The use of digital technology, or technology-enhanced MBC (eMBC), may address some of the barriers to implementation of MBC by enabling patients to easily complete outcome assessments. This study aims to identify the perceived facilitators and barriers to MBC and eMBC from both patient and provider perspectives in Canada.

METHODS

The study recruited 108 clinicians and 131 patients with a self-reported diagnosis of depression to participate in an online survey about the acceptability of both MBC and eMBC.

RESULTS

Most clinicians (90/108, 83.3 %) agreed that MBC is important for clinical judgement, and felt trained to interpret their scores; however, only 43/108 (39.8 %) use MBC routinely. Clinicians (95/108, 88.0 %) felt they would be more likely to use MBC if it was automated and available to use electronically. Most patients (117/131, 89.3 %) felt MBC would be helpful for their treatment and were willing to use a mobile app to track their symptoms.

LIMITATIONS

Recruitment was done online and, hence, this study may not capture the perspective of patients who are not already familiar with using online programs to complete questionnaires.

CONCLUSIONS

Clinicians and patients in Canada would be willing to utilize MBC and eMBC in their psychiatric care, but barriers to implementation include perceptions about efficiency, ease of use, and accessibility. These results highlight the importance of engaging both clinicians and patients to effectively implement MBC and eMBC for depression.

Utilizing digital predictive biomarkers to identify Veteran suicide risk

Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.

To Utility and Beyond! Specifying and Advancing the Utility of Measurement-Based Care for Youth

Mental health organizations that serve youth are under pressure to adopt measurement-based care (MBC), defined as the continuous collection of client-report data used to support clinical decision-making as part of standard care. However, few frameworks exist to help leadership ascertain how to select an MBC approach for a clinical setting. This paper seeks to define how an MBC approach can display clinical utility to provide such a framework. Broadly, we define clinical utility as evidence that an MBC approach assists stakeholders in fulfilling clinical goals related to care quality (i.e., improve client-clinician alliance and clinical outcomes) at the client (i.e., youth and caregiver), clinician, supervisor, and administrator levels. More specifically, our definition of clinical utility is divided into two categories relevant to the usability and usefulness of an MBC approach for a specific setting: (a) implementability (i.e., evidence indicating ease of use in a clinical setting) and (b) usefulness in aiding clinical activities (i.e., evidence indicating the potential to improve communication and make clinical activities related to care quality easier or more effective). These categories provide valuable information about how easy an MBC approach is to use and the potential benefits that the MBC data will confer. To detail how we arrived at this definition, we review prior definitions of clinical utility, discuss how previous definitions inform our definition of clinical utility for MBC, and provide examples of how the concept of clinical utility can be applied to MBC. We finish with a discussion of future research directions.