Health Information Technology Resources to Support Measurement-Based Care

Examining time-varying dynamics of co-occurring depressed mood and anxiety

BACKGROUND

Dimensional frameworks of psychopathology call for multivariate approaches to map co-occurring disorders to index what symptoms emerge when and for whom. Ecological momentary assessment (EMA) offers a method for assessing and differentiating the dynamics of co-occurring symptoms with greater temporal granularity and naturalistic context. The present study used multivariate mixed effects location-scale modeling to characterize the time-varying dynamics of depressed mood and anxiety for women diagnosed with social anxiety disorder (SAD) and major depression (MDD).

METHODS

Women completed five daily EMA surveys over 30 days (150 EMA surveys/woman, T ≈ 5250 total observations) and two clinical diagnostic and retrospective self-report measures administered approximately two months apart.

RESULTS

There was evidence of same-symptom lagged effects (bs = 0.08-0.09), but not cross-symptom lagged effects (bs < 0.01) during EMA. Symptoms co-varied such that momentary spikes from one's typical level of anxiety were associated with increases in momentary depressed mood (b = 0.19) and greater variability of depressed mood (b = 0.06). Similarly, spikes from one's typical levels of depressed mood were associated with increases in momentary anxiety (b = 0.19). Furthermore, the presence and magnitude of effects demonstrated person-specific heterogeneity.

LIMITATIONS

Our findings are constrained to the dynamics of depressed and anxious mood among cisgender women with primary SAD and current or past MDD.

CONCLUSIONS

Findings from this work help to characterize how daily experiences of co-occurring mood and anxiety fluctuate and offer insight to aid the development of momentary, person-specific interventions designed to regulate symptom fluctuations.

Advancing Pharmacy Practice: The Role of Intelligence-Driven Pharmacy Practice and the Emergence of Pharmacointelligence

The field of healthcare is experiencing a significant transformation driven by technological advancements, scientific breakthroughs, and a focus on personalized patient care. At the forefront of this evolution is artificial intelligence-driven pharmacy practice (IDPP), which integrates data science and technology to enhance pharmacists' capabilities. This prospective article introduces the concept of "pharmacointelligence", a paradigm shift that synergizes artificial intelligence (AI), data integration, clinical decision support systems (CDSS), and pharmacy informatics to optimize medication-related processes. Through a comprehensive literature review and analysis, this research highlights the potential of pharmacointelligence to revolutionize pharmacy practice by addressing the complexity of pharmaceutical data, changing healthcare demands, and technological advancements. This article identifies the critical need for integrating these technologies to enhance medication management, improve patient outcomes, and streamline pharmacy operations. It also underscores the importance of regulatory and ethical considerations in implementing pharmacointelligence, ensuring patient privacy, data security, and equitable healthcare delivery.

Veteran Experiences With an mHealth App to Support Measurement-Based Mental Health Care: Results From a Mixed Methods Evaluation

BACKGROUND

Mental health conditions are highly prevalent among US veterans. The Veterans Health Administration (VHA) is committed to enhancing mental health care through the integration of measurement-based care (MBC) practices, guided by its Collect-Share-Act model. Incorporating the use of remote mobile apps may further support the implementation of MBC for mental health care.

OBJECTIVE

This study aims to evaluate veteran experiences with Mental Health Checkup (MHC), a VHA mobile app to support remote MBC for mental health.

METHODS

Our mixed methods sequential explanatory evaluation encompassed mailed surveys with veterans who used MHC and follow-up semistructured interviews with a subset of survey respondents. We analyzed survey data using descriptive statistics. We then compared responses between veterans who indicated having used MHC for ≥3 versus <3 months using χ2 tests. We analyzed interview data using thematic analysis.

RESULTS

We received 533 surveys (533/2631, for a 20% response rate) and completed 20 interviews. Findings from these data supported one another and highlighted 4 key themes. (1) The MHC app had positive impacts on care processes for veterans: a majority of MHC users overall, and a greater proportion who had used MHC for ≥3 months (versus <3 months), agreed or strongly agreed that using MHC helped them be more engaged in their health and health care (169/262, 65%), make decisions about their treatment (157/262, 60%), and set goals related to their health and health care (156/262, 60%). Similarly, interviewees described that visualizing progress through graphs of their assessment data over time motivated them to continue therapy and increased self-awareness. (2) A majority of respondents overall, and a greater proportion who had used MHC for ≥3 months (versus <3 months), agreed/strongly agreed that using MHC enhanced their communication (112/164, 68% versus 51/98, 52%; P=.009) and rapport (95/164, 58% versus 42/98, 43%; P=.02) with their VHA providers. Likewise, interviewees described how MHC helped focus therapy time and facilitated trust. (3) However, veterans also endorsed some challenges using MHC. Among respondents overall, these included difficulty understanding graphs of their assessment data (102/245, 42%), not receiving enough training on the app (73/259, 28%), and not being able to change responses to assessment questions (72/256, 28%). (4) Interviewees offered suggestions for improving the app (eg, facilitating ease of log-in, offering additional reminder features) and for increasing adoption (eg, marketing the app and its potential advantages for veterans receiving mental health care).

CONCLUSIONS

Although experiences with the MHC app varied, veterans were positive overall about its use. Veterans described associations between the use of MHC and engagement in their own care, self-management, and interactions with their VHA mental health providers. Findings support the potential of MHC as a technology capable of supporting the VHA's Collect-Share-Act model of MBC.

The Implementation of Measurement-Based Care in the Context of Telemedicine: Qualitative Study

BACKGROUND

The Measurement Based Care in Mental Health Initiative launched by the Department of Veterans Affairs in 2016 is an example of an evidence-based practice that uses patient-reported outcome measures (PROMs) to improve patient outcomes. The acceptance of measurement-based care (MBC) among Veterans Affairs providers is relatively high. However, there are barriers to MBC for telehealth providers. Health information technologies might afford opportunities to address some of the barriers related to the uptake of MBC.

OBJECTIVE

This paper reports on an implementation effort to integrate MBC into mental health care telehealth practice using eHealth solutions.

METHODS

Qualitative data were generated from 22 semistructured interviews with psychiatrists (n=4), psychologists (n=3), social workers (n=3), nurses (n=6), a pharmacist (n=1), and administrative staff (n=5) who provide telemental health care through a community-based outpatient clinic in the rural Midwestern United States. The interviews were conducted during the pilot phase of an implementation initiative to increase the adoption of MBC by revising clinic workflows to integrate the use of eHealth technologies. Data were analyzed using thematic analysis.

RESULTS

Time burden and workflow issues were the most common barrier to provider adoption of MBC; sharing and reviewing pencil-and-paper measures and results in the same room was no longer possible in novel telehealth workflows necessitated by the COVID-19 pandemic. Providers voiced concerns about how long it would take to collect, adequately score, interpret, share, and document the PROMs during the telehealth visit. Concerns about time might also correspond to a gap in providers' familiarity with these assessments, greater comfort in assessing symptoms through clinical interviews, and being accustomed to using the assessments as screening tools more so than longitudinal outcome measures. Capacities associated with eHealth technologies may address workflow concerns and promote providers' understanding and use of the measures as tracking tools.

CONCLUSIONS

The need to use limited appointment time well was a top priority for telemental health providers. eHealth technologies provided operative supports that protect time in appointments by shifting when and how PROMs are collected. Bolstering providers' familiarity with how to use PROMs in the course of treatment may impact providers' buy-in by encouraging them to reconsider how sharing and acting on PROMs could be time well spent.

Analyzing the Impact of Mobile App Engagement on Mental Health Outcomes: Secondary Analysis of the Unwinding Anxiety Program

BACKGROUND

App-based interventions provide a promising avenue for mitigating the burden on mental health services by complimenting therapist-led treatments for anxiety. However, it remains unclear how specific systems' use of app features may be associated with changes in mental health outcomes (eg, anxiety and worry).

OBJECTIVE

This study was a secondary analysis of engagement data from a stage 1 randomized controlled trial testing the impact of the Unwinding Anxiety mobile app among adults with generalized anxiety disorder. The aims of this study were 2-fold: to investigate whether higher microengagement with the primary intervention feature (ie, educational modules) is associated with positive changes in mental health outcomes at 2 months (ie, anxiety, worry, interoceptive awareness, and emotional reactivity) and to investigate whether the use of adjunctive app features is also associated with changes in mental health outcomes.

METHODS

We analyzed the intervention group during the stage 1 trial of the Unwinding Anxiety mobile app. The total use of specific mobile app features and the use specific to each feature were calculated. We used multivariate linear models with a priori significance of α=.05 to investigate the impact of cumulative app use on anxiety, worry, interoceptive awareness, and emotional regulation at 2 months, controlling for baseline scores, age, and education level in all models. Significant relationships between system use metrics and baseline participant characteristics were assessed for differences in use groupings using between-group testing (ie, 2-tailed t tests for continuous data and chi-square analyses for categorical data).

RESULTS

The sample was primarily female (25/27, 93%), and the average age was 42.9 (SD 15.6) years. Educational module completion, the central intervention component, averaged 20.2 (SD 11.4) modules out of 32 for the total sample. Multivariate models revealed that completing >75% of the program was associated with an average 22.6-point increase in interoceptive awareness (b=22.6; SE 8.32; P=.01; 95% CI 5.3-39.8) and an 11.6-point decrease in worry (b=-11.6; SE 4.12; P=.01; 95% CI -20.2 to -3.1). In addition, a single log unit change in the total number of meditations was associated with a 0.62-point reduction in the Generalized Anxiety Disorder-7 scale scores (b=0.62; SE 0.27; P=.005; 95% CI -1.2 to -0.6), whereas a single log unit use of the stress meter was associated with an average of a 0.5-point increase in emotional regulation scores (Five Facet Mindfulness Questionnaire; b=0.5; SE 0.21; P=.03; 95% CI 0.1-0.9).

CONCLUSIONS

This study offers a clearer understanding of the impact of engagement with app features on broader engagement with the health outcomes of interest. This study highlights the importance of comprehensive investigations of engagement during the development of evidence-based mobile apps.