Technology Implementation for Mental Health End Users: A Model to Guide Digital Transformation for Inpatient Mental Health Professionals

Westheimer, Jessa Lin; Moukaddam, Nidal; Lindsay, Jan A.; Sabharwal, Ashutosh; Najafi, Bijan; Iacobelli, Peter; Boland, Robert; Patriquin, Michelle A.

doi:10.2196/40429

Cited by 4 publications

References 39 publications

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The Current Status of Global Digital Mental Health Implementation: Results and Implications of a Web-Based Survey from All WPA Regions

Volpe,

Orsolini,

Ramalho

et al. 2024

J. technol. behav. sci.

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The aim of this study was to compare the availability and use of digital mental health (DMH) across all World Psychiatric Association (WPA) regions (WR) and to guide future regional-tailored initiatives to upscale DMH contingent on introducing international policies, regulations, guidelines, education, and training, the WPA Working Group on Digital Psychiatry developed and disseminated a web-based survey among all 145 WPA National Psychiatric Association (NPA) members, according to official WR including (1) The Americas (WR1), (2) Europe (WR2), (3) Africa, Middle East, Central/Western Asia (WR3), and (4) Asia/Australasia (WR4). Collected data were analyzed using the Qualtrics analytic dashboard. The availability of digital tools/programs in DMH largely varies among WPA regions. In Europe and Asia/Australasia, mobile apps were the most available digital tools (respectively, 76.9% and 90.9%), followed by telemental health (respectively, 65.4% and 81.8%). Wearables, serious games, virtual/augmented reality, and chatbots represented the least commonly used tools/programs across all WR. National policies were mainly reported by Asia/Australasia (81.8%), followed by Europe (38.5%) and the Americas (27.3%). In all WR, less than 40% of NPAs reported the provision of education and training in the use of digital tools and programs in their countries. WPA regional analysis of digital needs promotes designing a roadmap to develop targeted actions to implement DMH and guide global digital upscaling of psychiatric services. Improving digital literacy and digital capacity building of the psychiatric workforce are key priorities for future digital initiatives led by the WPA across all WR.

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The Current Status of Global Digital Mental Health Implementation: Results and Implications of a Web-Based Survey from All WPA Regions

Volpe,

Orsolini,

Ramalho

et al. 2024

J. technol. behav. sci.

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A qualitative analysis of healthcare professionals’ experiences with an internet-based emotion regulation intervention added to acute psychiatric inpatient care

Bielinski,

Wälchli,

Lange

et al. 2024

BMC Psychiatry

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Background Healthcare professionals play an important role in successfully implementing digital interventions in routine mental healthcare settings. While a larger body of research has focused on the experiences of mental healthcare professionals with the combination of digital interventions and face-to-face outpatient treatment, comparatively little is known about their experiences with digital interventions combined with inpatient treatment. This is especially true for acute psychiatric inpatient care, where studies on the implementation of digital interventions are more rare. The current study aimed to investigate healthcare professionals’ experiences with an internet-based emotion regulation intervention added to acute psychiatric inpatient treatment. Methods Physicians, nurses, psychologists, social workers, and occupational therapists from three acute inpatient wards (n = 20) were interviewed regarding their experiences. A thematic analysis approach was used to analyze the interview data. Results The following themes and corresponding subthemes were identified: lack of experience (few or no previous experiences, no expectations, few points of contact), the intervention as a contemporary complement (positive expectations, necessary and contemporary, positive effects on therapeutic work and patients, characteristics of the internet-based program), concerns about fit for acute psychiatric inpatient care (fit for acute psychiatric inpatients, doubts about implementation), the human factor as essential for implementation (the team makes or breaks it, guidance is key, patient characteristics), and requirements for implementation beyond the human factor (integration into existing treatment structure, resources, changes to the internet-based program, timing). Conclusions While healthcare professionals reported few points of contact with the intervention, they saw it as a contemporary complement to acute psychiatric inpatient care with benefits for therapeutic work and patients. The findings further suggest that specific concerns regarding the fit for acute psychiatric inpatient care remain and that human factors such as support from the ward team, human guidance during the intervention and being mindful of specific patient characteristics are considered important for implementation. Moreover, factors such as integration of the intervention into the ward program, resource availability and the timing of the intervention during a patient’s individual stay should be considered for successful implementation. Trial registration Clinicaltrials.gov, NCT04990674, 04/08/2021.

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Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study

Hong,

Kang,

Yang

et al. 2024

J Med Internet Res

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Background Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnout, yet research on the introduction of digital technologies in this field remains limited. The combination of continuous and objective wearable sensor data acquired from patients with deep learning techniques holds the potential to overcome the limitations of traditional psychiatric assessments and support clinical decision-making. Objective This study aimed to develop and validate wearable-based deep learning models to comprehensively predict patient symptoms across various acute psychiatric wards in South Korea. Methods Participants diagnosed with schizophrenia and mood disorders were recruited from 4 wards across 3 hospitals and prospectively observed using wrist-worn wearable devices during their admission period. Trained raters conducted periodic clinical assessments using the Brief Psychiatric Rating Scale, Hamilton Anxiety Rating Scale, Montgomery-Asberg Depression Rating Scale, and Young Mania Rating Scale. Wearable devices collected patients’ heart rate, accelerometer, and location data. Deep learning models were developed to predict psychiatric symptoms using 2 distinct approaches: single symptoms individually (Single) and multiple symptoms simultaneously via multitask learning (Multi). These models further addressed 2 problems: within-subject relative changes (Deterioration) and between-subject absolute severity (Score). Four configurations were consequently developed for each scale: Single-Deterioration, Single-Score, Multi-Deterioration, and Multi-Score. Data of participants recruited before May 1, 2024, underwent cross-validation, and the resulting fine-tuned models were then externally validated using data from the remaining participants. Results Of the 244 enrolled participants, 191 (78.3%; 3954 person-days) were included in the final analysis after applying the exclusion criteria. The demographic and clinical characteristics of participants, as well as the distribution of sensor data, showed considerable variations across wards and hospitals. Data of 139 participants were used for cross-validation, while data of 52 participants were used for external validation. The Single-Deterioration and Multi-Deterioration models achieved similar overall accuracy values of 0.75 in cross-validation and 0.73 in external validation. The Single-Score and Multi-Score models attained overall R² values of 0.78 and 0.83 in cross-validation and 0.66 and 0.74 in external validation, respectively, with the Multi-Score model demonstrating superior performance. Conclusions Deep learning models based on wearable sensor data effectively classified symptom deterioration and predicted symptom severity in participants in acute psychiatric wards. Despite lower computational costs, Multi models demonstrated equivalent or superior performance than Single models, suggesting that multitask learning is a promising approach for comprehensive symptom prediction. However, significant variations were observed across wards, which presents a key challenge for developing clinical decision support systems in acute psychiatric wards. Future studies may benefit from recurring local validation or federated learning to address generalizability issues.

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Technology Implementation for Mental Health End Users: A Model to Guide Digital Transformation for Inpatient Mental Health Professionals

Cited by 4 publications

References 39 publications

The Current Status of Global Digital Mental Health Implementation: Results and Implications of a Web-Based Survey from All WPA Regions

The Current Status of Global Digital Mental Health Implementation: Results and Implications of a Web-Based Survey from All WPA Regions

A qualitative analysis of healthcare professionals’ experiences with an internet-based emotion regulation intervention added to acute psychiatric inpatient care

Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study

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