Co-development of a Best Practice Checklist for Mental Health Data Science: A Delphi Study

Abstract

Background: Mental health research is commonly affected by difficulties in recruiting and retaining participants, resulting in findings which are based on a sub-sample of those actually living with mental illness. Increasing the use of Big Data for mental health research, especially routinely-collected data, could improve this situation. However, steps to facilitate this must be enacted in collaboration with those who would provide the data - people with mental health conditions.Methods: We used the Delphi method to create a best practice checklist for mental health data science. Twenty participants with both expertise in data science and personal experience of mental illness worked together over three phases. In Phase 1, participants rated a list of 63 statements and added any statements or topics that were missing. Statements receiving a mean score of 5 or more (out of 7) were retained. These were then combined with the results of a rapid thematic analysis of participants' comments to produce a 14-item draft checklist, with each item split into two components: best practice now and best practice in the future. In Phase 2, participants indicated whether or not each item should remain in the checklist, and items that scored more than 50% endorsement were retained. In Phase 3 participants rated their satisfaction with the final checklist.Results: The final checklist was made up of 14 “best practice” items, with each item covering best practice now and best practice in the future. At the end of the three phases, 85% of participants were (very) satisfied with the two best practice checklists, with no participants expressing dissatisfaction.Conclusions: Increased stakeholder involvement is essential at every stage of mental health data science. The checklist produced through this work represents the views of people with experience of mental illness, and it is hoped that it will be used to facilitate trustworthy and innovative research which is inclusive of a wider range of individuals.

Views on sharing mental health data for research purposes: A qualitative study with people with mental illness

“…The findings broadly support researchers' calls for more streamlined access to mental health data under appropriate conditions (Ford et al, 2021). Going forward, the research community should seek to ensure that policy and infrastructure functions to facilitate mental health data science in a manner that is supported by people living with mental illness (Kirkham et al, 2021).…”

“…This finding is supported by previous research citing that the public is less willing to share data with researchers than with healthcare professionals (Fylan & Fylan, 2021). Future interventions focused on improving trust in research institutions may be beneficial, for example ensuring researchers follow best practice guidance when working with mental health data (Kirkham et al, 2021;Kirkham et al, 2020). Though healthcare professionals may be more trusted with mental health data overall, there were nevertheless concerns about stigma from these individuals as well.…”

“…Further, the concept of researcher accountability and institutional oversight appeared important, with one individual stating they would like any issues to be "tracked back" to the researcher. Accountability and oversight were also key elements identified in a recent Delphi study which resulted in a checklist for stakeholder-informed best practice in mental health data science (Kirkham et al 2021)…”

“…Responsible sharing of health data can overcome some of the limitations of traditional participant-based research models by supporting the collection of larger and more representative population samples (Furimsky et al, 2008;Kirkham et al, 2021;Woodall et al, 2010). This is particularly relevant to mental health research, as traditional research approaches necessarily depend upon recruitment of people who face multiple barriers to participation relating to their time, health, and finances (Kaminsky et al, 2003;Wang et al, 2011).…”

Development of a data-driven digital phenotype profile of distress experience of healthcare workers during COVID-19 pandemic

“…Data science techniques were adopted for a novel pipeline to evaluate user distress experience. Data science in mental health is a set of techniques that include data privacy, data quality, literature support, data-driven analysis, and visualizations [46] . As for other medical fields, the use of data science techniques in mental health could benefit all dimensions of clinical practice, including assessment, screening, diagnosis, and treatment selection.…”