A Comparative Evaluation of Measurement-Based Psychiatric Care Delivered via Specialized Telemental Health Platform Versus Treatment As Usual: A Retrospective Analysis

Chokshi, Sara; Senathirajah, Yalini; Yadav, Vandana; Winsberg, Mimi; O’Callaghan, Erin; Sullivan, Scott; Verma, Abhishek; Kachnowski, Stan

doi:10.7759/cureus.21219

Cited by 2 publications

(1 citation statement)

References 26 publications

(24 reference statements)

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“…Suppose you transfer the core idea here—the regular and low-threshold recording of treatment effects and progressions—to implementation in everyday therapy, independent of a telemedicine treatment platform. There should also be exciting improvements in the treatment quality if changes in findings can be applied promptly [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

Tonn¹,

Seule²,

Degani³

et al. 2022

JMIR Form Res

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Background Mood disorders and depression are pervasive and significant problems worldwide. These represent severe health and emotional impairments for individuals and a considerable economic and social burden. Therefore, fast and reliable diagnosis and appropriate treatment are of great importance. Verbal communication can clarify the speaker’s mental state—regardless of the content, via speech melody, intonation, and so on. In both everyday life and clinical conditions, a listener with appropriate previous knowledge or a trained specialist can grasp helpful knowledge about the speaker's psychological state. Using automated speech analysis for the assessment and tracking of patients with mental health issues opens up the possibility of remote, automatic, and ongoing evaluation when used with patients’ smartphones, as part of the current trends toward the increasing use of digital and mobile health tools. Objective The primary aim of this study is to evaluate the measurements of the presence or absence of depressive mood in participants by comparing the analysis of noncontentual speech parameters with the results of the Patient Health Questionnaire-9. Methods This proof-of-concept study included participants in different affective phases (with and without depression). The inclusion criteria included a neurological or psychiatric diagnosis made by a specialist and fluent use of the German language. The measuring instrument was the VoiceSense digital voice analysis tool, which enables the analysis of 200 specific speech parameters based on machine learning and the assessment of the findings using Patient Health Questionnaire-9. Results A total of 292 psychiatric and voice assessments were performed with 163 participants (males: n=47, 28.8%) aged 15 to 82 years. Of the 163 participants, 87 (53.3%) were not depressed at the time of assessment, and 88 (53.9%) participants had clinically mild to moderate depressive phases. Of the 163 participants, 98 (32.5%) showed subsyndromal symptoms, and 19 (11.7%) participants were severely depressed. In the speech analysis, a clear differentiation between the individual depressive levels, as seen in the Patient Health Questionnaire-9, was also shown, especially the clear differentiation between nondepressed and depressed participants. The study showed a Pearson correlation of 0.41 between clinical assessment and noncontentual speech analysis (P<.001). Conclusions The use of speech analysis shows a high level of accuracy, not only in terms of the general recognition of a clinically relevant depressive state in the participants. Instead, there is a high degree of agreement regarding the extent of depressive impairment with the assessment of experienced clinical practitioners. From our point of view, the application of the noncontentual analysis system in everyday clinical practice makes sense, especially with the idea of a quick and unproblematic assessment of the state of mind, which can even be carried out without personal contact. Trial Registration ClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008

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Section: Discussionmentioning

confidence: 99%

Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

Tonn¹,

Seule²,

Degani³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

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Viewpoint: Strategic Insights and a Scoping Review of Telehealth Platforms and SWOT Analysis of Amazon's Clinical Endeavors

Grewal,

Dhillon,

Budhavarapu

et al. 2024

Preprint

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The adoption of telehealth services has surged in the wake of the COVID-19 pandemic, revolutionizing traditional healthcare delivery models. Amazon Clinic's nationwide launch marks a significant milestone in this trend. We aim to offer a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of Amazon Clinic and compare its features with leading virtual healthcare platforms. Amazon Clinic capitalizes on its vast consumer base, transparent pricing, and AI-driven intake for efficient healthcare. The competitive landscape features established healthcare providers and other telehealth platforms like CVS Minute Clinic and Teladoc, each with their unique strengths and services, creating challenges for Amazon Clinic. Strengths include easy access and a transparent pricing model, filling gaps especially for the uninsured and young adults. Weaknesses are the lack of pediatric care and potential fragmented doctor-patient relationships. Opportunities lie in democratizing healthcare and filling system gaps. Threats include navigating complex regulations and competition from well-established providers. Limitations like non-acceptance of insurance could impede broader adoption. The platform excels for minor ailments but is less suitable for complex conditions requiring sustained, personalized care. Future success depends on regulatory navigation and establishing partnerships to broaden its service offerings.

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A Comparative Evaluation of Measurement-Based Psychiatric Care Delivered via Specialized Telemental Health Platform Versus Treatment As Usual: A Retrospective Analysis

Cited by 2 publications

References 26 publications

Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

Viewpoint: Strategic Insights and a Scoping Review of Telehealth Platforms and SWOT Analysis of Amazon's Clinical Endeavors

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