Combining clinical notes with structured electronic health records enhances the prediction of mental health crises.

An automatic prediction of mental health crises can improve caseload prioritization and enable preventative interventions, improving patient outcomes and reducing costs. We combine structured electronic health records (EHRs) with clinical notes from 59,750 de-identified patients to predict the risk of mental health crisis relapse within the next 28 days. The results suggest that an ensemble machine learning model that relies on structured EHRs and clinical notes when available, and relying solely on structured data when the notes are unavailable, offers superior performance over models trained with either of the two data streams alone. Furthermore, the study provides key takeaways related to the required amount of clinical notes to add value in predictive analytics. This study sheds light on the untapped potential of clinical notes in the prediction of mental health crises and highlights the importance of choosing an appropriate machine learning method to combine structured and unstructured EHRs.

Two edges of the screen: Unpacking positive and negative associations between phone use in everyday contexts and subjective well-being.

A plethora of past studies have highlighted a negative association between phone use and well-being. Recent studies claimed that there is a lack of strong evidence on the deleterious effects of smartphones on our health, and that previous systematic reviews overestimated the negative link between phone use and well-being. In a three-week long in-the-wild study with 352 participants, we captured 15,607 instances of smartphone use in tandem with rich contextual information (activity, location, company) as well as self-reported well-being measures. We conducted an additional study to gather users' perception of the impact of phone use on their well-being in different daily contexts. Our findings show that context and personal characteristics greatly impact the association between screen time and subjective well-being. This study highlights the complexity of the relationship between phone use and well-being and it deepens our understanding of this problem.

Foundations for fairness in digital health apps.

Digital mental health applications promise scalable and cost-effective solutions to mitigate the gap between the demand and supply of mental healthcare services. However, very little attention is paid on differential impact and potential discrimination in digital mental health services with respect to different sensitive user groups (e.g., race, age, gender, ethnicity, socio-economic status) as the extant literature as well as the market lack the corresponding evidence. In this paper, we outline a 7-step model to assess algorithmic discrimination in digital mental health services, focusing on algorithmic bias assessment and differential impact. We conduct a pilot analysis with 610 users of the model applied on a digital wellbeing service called Foundations that incorporates a rich set of 150 proposed activities designed to increase wellbeing and reduce stress. We further apply the 7-step model on the evaluation of two algorithms that could extend the current service: monitoring step-up model, and a popularity-based activities recommender system. This study applies an algorithmic fairness analysis framework for digital mental health and explores differences in the outcome metrics for the interventions, monitoring model, and recommender engine for the users of different age, gender, type of work, country of residence, employment status and monthly income. Systematic Review Registration: The study with main hypotheses is registered at: https://osf.io/hvtf8.