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Current mental health services across the world remain expert-centric and are based on traditional workflows, mostly using impractical and ineffective electronic record systems or even paper-based documentation. The international network for digital mental health (IDMHN) is comprised of top-level clinicians, regulatory and ICT experts, genetic scientists, and support organizations. The IDMHN has been formed to enable the implementation of digital innovations in clinical practice, hereby facilitating the transformation of current mental health services to be more personalized and more responsive to patients and healthcare needs. This consensus statement summarizes the consortium’s vision and strategy for further development of digital mental health.

Aim This study sought to identify clinical, demographic and service-related factors associated with psychological distress amongst outpatient chemotherapy patients. Background Distress in cancer patients leads to increased risk of psychological comorbidity, contributing to sub-optimal treatment adherence and potentially leading to poorer health outcomes. Screening and recognition of distress and risk factors is an important aspect of holistic care within a multidisciplinary team environment. Methods Data were obtained via survey and chart review of ambulatory chemotherapy patients at three public tertiary referral hospitals in Perth, Western Australia. The DASS-21 was used to screen for psychological distress. Regression analyses were used to assess the relationship between distress and a range of cancer, socioeconomic and treatment factors. Results Patients with a Karnofsky Performance Score ≤ 80 ( OR 3.8, 95% CI [1.7, 78.7]) and average waiting time (between oncology outpatient appointment and commencement of chemotherapy infusion) > 60 min ( OR 2.4, 95% CI [1.04, 5.5]) were at increased risk of moderate-severe distress. Patients with a household income between $AU 50–75,000 p.a. had a lower risk of distress compared to OR 0.05, 95% CI [0.01, 0.52]). On sub-scale analysis, depression and anxiety contributed more to overall distress than the stress subscales. Conclusions Performance status, waiting times and household income were key predictors of distress. Findings could assist clinicians to identify higher-risk population subsets that could benefit from targeted screening and additional psychological and social work support. Findings could also assist administrators to consider the contribution of modifiable factors such as waiting times to patient distress.