Manifesto for an international digital mental health network

Maron, Eduard; Baldwin, David S.; Balõtšev, Roman; Fabbri, Chiara; Gaur, Vikas; Hidalgo‐Mazzei, Diego; Hood, Sean; Juhola, Martti; Kampman, Olli; Kasper, S.; Kärkkäinen, Hilkka; Látalová, Klára; Lähteenvuo, Markku; Mastellos, Nikolas; McTigue, Joseph; Metsallik, Janek; Metspalu, Andres; Nutt, David; Nykänen, Pirkko; Olisa, Nigel; Pogarell, Oliver; Popović, Dina; Ramos‐Quiroga, Josep Antoni; Ross, Peeter; Serretti, Alessandro; Spies, Marie; Thibaut, Florence; Tiainen, Kimmo; Tiihonen, Jari; Vieta, Eduard; Võhma, Ülle; Westrhenen, Roos van; Zubanovic, Belmin; Eberhard, Jonas

doi:10.1080/2575517x.2019.1617575

Cited by 16 publications

(12 citation statements)

References 11 publications

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“…Multiple recent reviews for delivering mental health care via digital technologies have shown that these are safe, feasible and effective among groups of people with mental health disorders [7][8][9][10][11][12][13]. Furthermore, digital technologies have the potential to improve mental health care systems, increase system capacity and transparency [14], enable data collection for monitoring and measure health outcomes, facilitate data sharing among health services [15,16], enhance access in rural and remote areas [17][18][19][20], and improve rationing of costs and human resources [14,16]. The integration and reimbursement of digital technology tools at different levels of the health systems depends on the performance and capacity to meet health system objectives such as quality, accessibility, efficiency and equity [21].…”

Section: Introductionmentioning

confidence: 99%

Barriers and facilitators to the integration of digital technologies in mental health systems: A protocol for a qualitative systematic review

et al. 2021

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Introduction Digital technology has the potential to improve health outcomes and health system performance in fragmented and under-funded mental health systems. Despite this potential, the integration of digital technology tools into mental health systems has been relatively poor. This is a protocol for a synthesis of qualitative evidence that will aim to determine the barriers and facilitators to integrating digital technologies in mental health systems and classify them in contextual domains at individual, organisational and system levels. Methods and analysis The methodological framework for systematic review of qualitative evidence described in Lockwood et al. will be applied to this review. A draft search strategy was developed in collaboration with an experienced senior health research librarian. A systematic search of Medline, Embase, Scopus, PsycInfo, Web of Science and Google Scholar, as well as hand searching of reference lists and reviews will identify relevant studies for inclusion. Study selection will be carried out independently by two authors, with discrepancies resolved by consensus. The quality of selected studies will be assessed using JBI Critical Appraisal Checklist for Qualitative Research. Data will be charted using JBI QUARI Data Extraction Tool for Qualitative Research. Findings will be defined and classified both deductively in a priori conceptual framework and inductively by a thematic analysis. Results will be reported based on the Enhancing transparency in reporting the synthesis of qualitative research. The level of confidence of the findings will be assessed using GRADE-CERQual. Ethics and dissemination This study does not require ethics approval. The systematic review will inform policy and practices around improving the integration of digital technologies into mental health care systems.

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Section: Introductionmentioning

confidence: 99%

Barriers and facilitators to the integration of digital technologies in mental health systems: A protocol for a qualitative systematic review

et al. 2021

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“…Mental health disorders today account for more years lived with disability (YLDs) than any other health condition, with close to 30% of all people worldwide having a mental health disorder at one point during their lifetimes [ 1 , 2 ]. Children and adolescents are particularly vulnerable.…”

Section: Introductionmentioning

confidence: 99%

Testing an individualized digital decision assist system for the diagnosis and management of mental and behavior disorders in children and adolescents

Clausen

Leventhal

Nytrø

et al. 2020

BMC Med Inform Decis Mak

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Background Nearly half of all mental health disorders develop prior to the age of 15. Early assessments, diagnosis, and treatment are critical to shortening single episodes of care, reducing possible comorbidity and long-term disability. In Norway, approximately 20% of all children and adolescents are experiencing mental health problems. To address this, health officials in Norway have called for the integration of innovative approaches. A clinical decision support system (CDSS) is an innovative, computer-based program that provides health professionals with clinical decision support as they care for patients. CDSS use standardized clinical guidelines and big data to provide guidance and recommendations to clinicians in real-time. IDDEAS (Individualised Digital DEcision Assist System) is a CDSS for diagnosis and treatment of child and adolescent mental health disorders. The aim of IDDEAS is to enhance quality, competency, and efficiency in child and adolescent mental health services (CAMHS). Methods/design IDDEAS is a mixed-methods innovation and research project, which consists of four stages: 1) Assessment of Needs and Preparation of IDDEAS; 2) The Development of IDDEAS CDSS Model; 3) The Evaluation of the IDDEAS CDSS; and, 4) Implementation & Dissemination. Both qualitative and quantitative methods will be used for the evaluation of IDDEAS CDSS model. Child and adolescent psychologists and psychiatrists (n = 30) will evaluate the IDDEAS` usability, acceptability and relevance for diagnosis and treatment of attention-deficit/hyperactivity disorder. Discussion The IDDEAS CDSS model is the first guidelines and data-driven CDSS to improve efficiency of diagnosis and treatment of child and adolescent mental health disorders in Norway. Ultimately, IDDEAS will help to improve patient health outcomes and prevent long-term adverse outcomes by providing each patient with evidence-based, customized clinical care. Trial registration ISRCTN, ISRCTN12094788. Ongoing study, registered prospectively 8 April 2020 10.1186/ISRCTN12094788

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“…Other future developments in the field include the evaluation of the role of rare variants and more robust estimations of pharmacogenetic testing cost-effectiveness in clinical practice. A comprehensive predictive model including clinical predictors may the then be hypothesized as a valuable support tool for clinicians [44], ideally integrated in electronic health records [45]. Routine clinical implementation, particularly when more extensive genotyping will be performed, will however have to face a number of management and ethical issues.…”

Section: Discussionmentioning

confidence: 99%

Genetics of Treatment Outcomes in Major Depressive Disorder: Present and Future

Fabbri

Serretti

2020

Clin Psychopharmacol Neurosci

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Pharmacogenetic testing is a useful and increasingly widespread tool to assist in antidepressant prescription. More than ten antidepressants (including tricyclics, selective serotonin reuptake inhibitors and venlafaxine) have already genetic biomarkers of response/side effects in clinical guidelines and drug labels. These are represented by functional genetic variants in genes coding for cytochrome enzymes (CYP2D6 and CYP2C19). Depending on the predicted metabolic activity, guidelines provide recommendations on drug choice and dosing. Despite not conclusive, the current evidence suggests that testing can be useful in patients who did not respond or tolerate at least one previous pharmacotherapy. However, the current recommendations are based on pharmacokinetic genes only (CYP450 enzymes), while pharmacodynamic genes (modulating antidepressant mechanisms of action in the brain) are still being studied because of their greater complexity. This may be captured by polygenic risk scores, which reflect the cumulative contribution of many genetic variants to a trait, and they may provide future clinical applications of pharmacogenetics. A more extensive use of genotyping in clinical practice may lead to improvement in treatment outcomes thanks to personalized treatments, but possible ethical issues and disparities should be taken into account and prevented.

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Manifesto for an international digital mental health network

Cited by 16 publications

References 11 publications

Barriers and facilitators to the integration of digital technologies in mental health systems: A protocol for a qualitative systematic review

Barriers and facilitators to the integration of digital technologies in mental health systems: A protocol for a qualitative systematic review

Testing an individualized digital decision assist system for the diagnosis and management of mental and behavior disorders in children and adolescents

Genetics of Treatment Outcomes in Major Depressive Disorder: Present and Future

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