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BackgroundAs the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals’ risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services. ObjectiveThe aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. MethodsWe conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm’s allocation based on a multidimensional self-report questionnaire. ResultsOf the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts’ ratings, with a substantial interrater agreement (κ=0.67; P

BackgroundGlobally, there are fundamental shortcomings in mental health care systems, including restricted access, siloed services, interventions that are poorly matched to service users’ needs, underuse of personal outcome monitoring to track progress, exclusion of family and carers, and suboptimal experiences of care. Health information technologies (HITs) hold great potential to improve these aspects that underpin the enhanced quality of mental health care. ObjectiveProject Synergy aimed to co-design, implement, and evaluate novel HITs, as exemplified by the InnoWell Platform, to work with standard health care organizations. The goals were to deliver improved outcomes for specific populations under focus and support organizations to enact significant system-level reforms. MethodsParticipating health care organizations included the following: Open Arms–Veterans & Families Counselling (in Sydney and Lismore, New South Wales [NSW]); NSW North Coast headspace centers for youth (Port Macquarie, Coffs Harbour, Grafton, Lismore, and Tweed Heads); the Butterfly Foundation’s National Helpline for eating disorders; Kildare Road Medical Centre for enhanced primary care; and Connect to Wellbeing North Coast NSW (administered by Neami National), for population-based intake and assessment. Service users, families and carers, health professionals, and administrators of services across Australia were actively engaged in the configuration of the InnoWell Platform to meet service needs, identify barriers to and facilitators of quality mental health care, and highlight potentially the best points in the service pathway to integrate the InnoWell Platform. The locally configured InnoWell Platform was then implemented within the respective services. A mixed methods approach, including surveys, semistructured interviews, and workshops, was used to evaluate the impact of the InnoWell Platform. A participatory systems modeling approach involving co-design with local stakeholders was also undertaken to simulate the likely impact of the platform in combination with other services being considered for implementation within the North Coast Primary Health Network to explore resulting impacts on mental health outcomes, including suicide prevention. ResultsDespite overwhelming support for integrating digital health solutions into mental health service settings and promising impacts of the platform simulated under idealized implementation conditions, our results emphasized that successful implementation is dependent on health professional and service readiness for change, leadership at the local service level, the appropriateness and responsiveness of the technology for the target end users, and, critically, funding models being available to support implementation. The key places of interoperability of digital solutions and a willingness to use technology to coordinate health care system use were also highlighted. ConclusionsAlthough the COVID-19 pandemic has resulted in the widespread acceptance of very basic digital health solutions, Project Synergy highlights the critical need to support equity of access to HITs, provide funding for digital infrastructure and digital mental health care, and actively promote the use of technology-enabled, coordinated systems of care.

BackgroundAlong with the proliferation of health information technologies (HITs), there is a growing need to understand the potential privacy risks associated with using such tools. Although privacy policies are designed to inform consumers, such policies have consistently been found to be confusing and lack transparency. ObjectiveThis study aims to present consumer preferences for accessing privacy information; develop and apply a privacy policy risk assessment tool to assess whether existing HITs meet the recommended privacy policy standards; and propose guidelines to assist health professionals and service providers with understanding the privacy risks associated with HITs, so that they can confidently promote their safe use as a part of care. MethodsIn phase 1, participatory design workshops were conducted with young people who were attending a participating headspace center, their supportive others, and health professionals and service providers from the centers. The findings were knowledge translated to determine participant preferences for the presentation and availability of privacy information and the functionality required to support its delivery. Phase 2 included the development of the 23-item privacy policy risk assessment tool, which incorporated material from international privacy literature and standards. This tool was then used to assess the privacy policies of 34 apps and e-tools. In phase 3, privacy guidelines, which were derived from learnings from a collaborative consultation process with key stakeholders, were developed to assist health professionals and service providers with understanding the privacy risks associated with incorporating HITs as a part of clinical care. ResultsWhen considering the use of HITs, the participatory design workshop participants indicated that they wanted privacy information to be easily accessible, transparent, and user-friendly to enable them to clearly understand what personal and health information will be collected and how these data will be shared and stored. The privacy policy review revealed consistently poor readability and transparency, which limited the utility of these documents as a source of information. Therefore, to enable informed consent, the privacy guidelines provided ensure that health professionals and consumers are fully aware of the potential for privacy risks in using HITs to support health and well-being. ConclusionsA lack of transparency in privacy policies has the potential to undermine consumers’ ability to trust that the necessary measures are in place to secure and protect the privacy of their personal and health information, thus precluding their willingness to engage with HITs. The application of the privacy guidelines will improve the confidence of health professionals and service providers in the privacy of consumer data, thus enabling them to recommend HITs to provide or support care.

BackgroundA priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making regarding a service response in terms of more detailed assessments and/or intervention. The aim of this study was to predict self-harm within six-months after initial presentation.MethodThe study included 1962 young people (12-30 years) presenting to youth mental health services in Australia. Six machine learning algorithms were trained and tested with ten repeats of ten-fold cross-validation. The net benefit of these models were evaluated using decision curve analysis.ResultsOut of 1962 young people, 320 (16%) engaged in self-harm in the six months after first assessment and 1642 (84%) did not. The top 25% of young people as ranked by mean predicted probability accounted for 51.6% - 56.2% of all who engaged in self-harm. By the top 50%, this increased to 82.1%-84.4%. Models demonstrated fair overall prediction (AUROCs; 0.744-0.755) and calibration which indicates that predicted probabilities were close to the true probabilities (brier scores; 0.185-0.196). The net benefit of these models were positive and superior to the 'treat everyone' strategy. The strongest predictors were (in ranked order); a history of self-harm, age, social and occupational functioning, sex, bipolar disorder, psychosis-like experiences, treatment with antipsychotics, and a history of suicide ideation.ConclusionPrediction models for self-harm may have utility to identify a large sub population who would benefit from further assessment and targeted (low intensity) interventions. Such models could enhance health service approaches to identify and reduce self-harm, a considerable source of distress, morbidity, ongoing health care utilisation and mortality.

Abstract Background The Somatic and Psychological HEalth REport (SPHERE) is a 34-item self-report questionnaire that assesses symptoms of mental distress and persistent fatigue. As it was developed as a screening instrument for use mainly in primary care-based clinical settings, its validity and psychometric properties have not been studied extensively in population-based samples. Methods We used non-parametric Item Response Theory to assess scale validity and item properties of the SPHERE-34 scales, collected through four waves of the Brisbane Longitudinal Twin Study (N = 1707, mean age = 12, 51% females; N = 1273, mean age = 14, 50% females; N = 1513, mean age = 16, 54% females, N = 1263, mean age = 18, 56% females). We estimated the heritability of the new scores, their genetic correlation, and their predictive ability in a sub-sample (N = 1993) who completed the Composite International Diagnostic Interview. Results After excluding items most responsible for noise, sex or wave bias, the SPHERE-34 questionnaire was reduced to 21 items (SPHERE-21), comprising a 14-item scale for anxiety-depression and a 10-item scale for chronic fatigue (3 items overlapping). These new scores showed high internal consistency (alpha > 0.78), moderate three months reliability (ICC = 0.47–0.58) and item scalability (Hi > 0.23), and were positively correlated (phenotypic correlations r = 0.57–0.70; rG = 0.77–1.00). Heritability estimates ranged from 0.27 to 0.51. In addition, both scores were associated with later DSM-IV diagnoses of MDD, social anxiety and alcohol dependence (OR in 1.23–1.47). Finally, a post-hoc comparison showed that several psychometric properties of the SPHERE-21 were similar to those of the Beck Depression Inventory. Conclusions The scales of SPHERE-21 measure valid and comparable constructs across sex and age groups (from 9 to 28 years). SPHERE-21 scores are heritable, genetically correlated and show good predictive ability of mental health in an Australian-based population sample of young people.

Objectives: There is an ongoing necessity to match clinical interventions with the multidimensional needs of young people. A key step toward better service planning and the design of optimal models of care is to use multidimensional assessment to understand the clinical needs of those presenting to primary mental health care. Methods: 1284 people aged 12–25 years presenting to primary youth mental health services completed an online assessment at service entry. Latent class analysis was conducted for seven scales assessing anxiety, depression, psychosis, mania, functioning (indexed by Work and Social Adjustment Scale), and suicidality. Results: A three-class solution was identified as the optimal solution. Class 1 (n = 305, 23.75%), an early illness stage group, had low and mixed symptomatology with limited functional impairment, class 2 (n = 353, 27.49%) was made up of older persons with established depression and functional impairment, and class 3 (n = 626, 48.75%) had very high and complex needs, with functional impairment, suicidality, and at-risk mental states (psychosis or mania). Additional differentiating characteristics included psychological distress, circadian disturbances, social support, mental health history, eating disorder behaviours, and symptoms of post-traumatic stress disorder. Conclusions: A large proportion of help-seeking young people present with symptoms and functional impairment that may exceed the levels of care available from basic primary care or brief intervention services. These subgroups highlight the importance of multidimensional assessments to determine appropriate service pathways and care options.

Abstract Background The World Economic Forum has recently highlighted substantial problems in mental health service provision and called for the rapid deployment of smarter, digitally-enhanced health services as a means to facilitate effective care coordination and address issues of demand. In mental health, the biggest enabler of digital solutions is the implementation of an effective model of care that is facilitated by integrated health information technologies (HITs); the latter ensuring the solution is easily accessible, scalable and sustainable. The University of Sydney’s Brain and Mind Centre (BMC) has developed an innovative digital health solution – delivered through the Youth Mental Health and Technology Program – which incorporates two components: 1) a highly personalised and measurement-based (data-driven) model of youth mental health care; and 2) an industrial grade HIT registered on the Australian Register of Therapeutic Goods. This paper describes a research protocol to evaluate the impact of implementing the BMC’s digital health solution into youth mental health services (i.e. headspace - a highly accessible, youth-friendly integrated service that responds to the mental health, physical health, alcohol or other substance use, and vocational concerns of young people aged 12 to 25 years) within urban and regional areas of Australia. Methods The digital health solution will be implemented into participating headspace centres using a naturalistic research design. Quantitative and qualitative data will be collected from headspace health professionals, service managers and administrators, as well as from lead agency and local Primary Health Network (PHN) staff, via service audits, Implementation Officer logs, online surveys, and semi-structured interviews, at baseline and then three-monthly intervals over the course of 12 months. Discussion At the time of publication, six headspace centres had been recruited to this study and had commenced implementation and impact evaluation. The first results are expected to be submitted for publication in 2021. This study will focus on the impact of implementing a digital health solution at both a service and staff level, and will evaluate digital readiness of service and staff adoption; quality, usability and acceptability of the solution by staff; staff self-reported clinical competency; overall impact on headspace centres as well as their lead agencies and local PHNs; and social return on investment.

Abstract Background Despite the widely acknowledged potential for health information technologies to improve the accessibility, quality and clinical safety of mental health care, implementation of such technologies in services is frequently unsuccessful due to varying consumer, health professional, and service-level factors. The objective of this co-design study was to use process mapping (i.e. service mapping) to illustrate the current consumer journey through primary mental health services, identify barriers to and facilitators of quality mental health care, and highlight potential points at which to integrate the technology-enabled solution to optimise the provision of care based on key service performance indicators. Methods Interactive, discussion-based workshops of up to six hours were conducted with representative stakeholders from each participating service, including health professionals, service managers and administrators from Open Arms – Veterans & Families Counselling Service (Sydney), a counselling service for veterans and their families, and five headspace centres in the North Coast Primary Health Network, primary youth mental health services. Service maps were drafted and refined in real time during the workshops. Through both group discussion and the use of post-it notes, participants worked together to evaluate performance indicators (e.g. safety) at each point in the consumer journey (e.g. intake) to indicate points of impact for the technology-enabled solution, reviewing and evaluating differing opinions in order to reach consensus. Results Participants (n=84 across participating services) created service maps illustrating the current consumer journey through the respective services and highlighting barriers to and facilitators of quality mental health care. By consensus, the technology-enabled solution as facilitated by the InnoWell Platform was noted to enable the early identification of risk, reduce or eliminate lengthy intake processes, enable routine outcome monitoring to revise treatment plans in relation to consumer response, and serve as a personal data record for consumers, driving person-centred, coordinated care. Conclusions Service mapping was shown to be an effective methodology to understand the consumer’s journey through a service and served to highlight how the co-designed technology-enabled solution can optimise service pathways to improve the accessibility, quality and clinical safety of care relative to key service performance indicators, facilitating the delivery of the right care.

Most mental disorders emerge before the age of 25 years and, if left untreated, have the potential to lead to considerable lifetime burden of disease. Many services struggle to manage high demand and have difficulty matching individuals to timely interventions due to the heterogeneity of disorders. The technological implementation of clinical staging for youth mental health may assist the early detection and treatment of mental disorders. We describe the development of a theory-based automated protocol to facilitate the initial clinical staging process, its intended use, and strategies for protocol validation and refinement. The automated clinical staging protocol leverages the clinical validation and evidence base of the staging model to improve its standardization, scalability, and utility by deploying it using Health Information Technologies (HIT). Its use has the potential to enhance clinical decision-making and transform existing care pathways, but further validation and evaluation of the tool in real-world settings is needed.

Background: Whilst apps and e-tools have tremendous potential as low-cost, scalable mental health intervention and prevention tools, it is essential that consumers and health professionals have a means by which to evaluate their quality and safety. Objective: This study aimed to: 1) adapt the original Mobile App Rating Scale (MARS) in order to be appropriate for the evaluation of both mobile phone applications as well as e-tools; 2) test the reliability of the revised scale; and 3) develop a quality assurance protocol for identifying and rating new apps and e-tools to determine appropriateness for use in clinical practice. Methods: The MARS was adapted to include items specific to health-related apps and e-tools, such as the availability of resources, strategies for self-management, and quality information. The 41 apps and e-tools in the standard youth configuration of the InnoWell Platform, a digital tool designed to support or enhance mental health service delivery, were independently rated by two expert raters using the A-MARS. Cronbach's alpha was used to calculate the internal consistency and interclass correlation coefficients were used to calculate interrater reliability. Results: The A-MARS was shown to be a reliable scale with acceptable to excellent internal consistency and moderate to excellent interrater reliability across the subscales. Given the ever-increasing number of health information technologies on the market, a protocol to identify and rate new apps and e-tools for potential clinical use is presented. Conclusions: Whilst the A-MARS is a useful tool to guide health professionals as they explore available apps and e-tools for potential clinical use, the training, time, and skill required to use it effectively may be prohibitive. As such, health professionals and services are likely to benefit from including a digital navigator as part of the care team to assist in selecting and rating apps and e-tools, increasing the usability of the data, and technology troubleshooting. When selecting, evaluating and/or recommending apps and e-tools to consumers, it is important to consider: 1) the availability of explicit strategies to set, monitor and review SMART goals; 2) the accessibility of credible, user friendly information and resources from reputable sources; 3) evidence of effectiveness; and 4) interoperability with other health information technologies.