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

Public Involvement and Engagement in Big Data Research: Scoping Review

BACKGROUND

The success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research.

OBJECTIVE

This review aims to synthesize the evidence on public involvement and engagement in big data research.

METHODS

This scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review.

RESULTS

A total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research.

CONCLUSIONS

This review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-050167.

Views on sharing mental health data for research purposes: qualitative analysis of interviews with people with mental illness

BACKGROUND

Improving the ways in which routinely-collected mental health data are shared could facilitate substantial advances in research and treatment. However, this process should only be undertaken in partnership with those who provide such data. Despite relatively widespread investigation of public perspectives on health data sharing more generally, there is a lack of research on the views of people with mental illness.

METHODS

Twelve people with lived experience of mental illness took part in semi-structured interviews via online video software. Participants had experience of a broad range of mental health conditions including anxiety, depression, schizophrenia, eating disorders and addiction. Interview questions sought to establish how participants felt about the use of routinely-collected health data for research purposes, covering different types of health data, what health data should be used for, and any concerns around its use.

RESULTS

Thematic analysis identified four overarching themes: benefits of sharing mental health data, concerns about sharing mental health data, safeguards, and data types. Participants were clear that health data sharing should facilitate improved scientific knowledge and better treatments for mental illness. There were concerns that data misuse could become another way in which individuals and society discriminate against people with mental illness, for example through insurance premiums or employment decisions. Despite this there was a generally positive attitude to sharing mental health data as long as appropriate safeguards were in place.

CONCLUSIONS

There was notable strength of feeling across participants that more should be done to reduce the suffering caused by mental illness, and that this could be partly facilitated by well-managed sharing of health data. The mental health research community could build on this generally positive attitude to mental health data sharing by following rigorous best practice tailored to the specific concerns of people with mental illness.

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

BACKGROUND AND OBJECTIVE

Due to the constraints of the COVID-19 pandemic, healthcare workers have reported acting in ways that are contrary to their moral values, and this may result in moral distress. This paper proposes the novel digital phenotype profile (DPP) tool, developed specifically to evaluate stress experiences within participants. The DPP tool was evaluated using the COVID-19 VR Healthcare Simulation of Stress Experience (HSSE) dataset (NCT05001542), which is composed of passive physiological signals and active mental health questionnaires. The DPP tool focuses on correlating electrocardiogram, respiration, photoplethysmography, and galvanic skin response with moral injury outcome scale (Brief MIOS).

METHODS

Data-driven techniques are encompassed to develop a tool for robust evaluation of distress among participants. To accomplish this, we applied pre-processing techniques which involved normalization, data sanitation, segmentation, and windowing. During feature analysis, we extracted domain-specific features, followed by feature selection techniques to rank the importance of the feature set. Prior to classification, we employed k-means clustering to group the Brief MIOS scores to low, moderate, and high moral distress as the Brief MIOS lacks established severity cut-off scores. Support vector machine and decision tree models were used to create machine learning models to predict moral distress severities.

RESULTS

Weighted support vector machine with leave-one-subject-out-cross-validation evaluated the separation of the Brief MIOS scores and achieved an average accuracy, precision, sensitivity, and F1 of 98.67%, 98.83%, 99.44%, and 99.13%, respectively. Various machine learning ablation tests were performed to support our results and further enhance the understanding of the predictive model.

CONCLUSION

Our findings demonstrate the feasibility to develop a DPP tool to predict distress experiences using a combination of mental health questionnaires and passive signals. The DPP tool is the first of its kind developed from the analysis of the HSSE dataset. Additional validation is needed for the DPP tool through replication in larger sample sizes.

Participatory evaluation of the process of co-producing resources for the public on data science and artificial intelligence

BACKGROUND

The growth of data science and artificial intelligence offers novel healthcare applications and research possibilities. Patients should be able to make informed choices about using healthcare. Therefore, they must be provided with lay information about new technology. A team consisting of academic researchers, health professionals, and public contributors collaboratively co-designed and co-developed the new resource offering that information. In this paper, we evaluate this novel approach to co-production.

METHODS

We used participatory evaluation to understand the co-production process. This consisted of creative approaches and reflexivity over three stages. Firstly, everyone had an opportunity to participate in three online training sessions. The first one focused on the aims of evaluation, the second on photovoice (that included practical training on using photos as metaphors), and the third on being reflective (recognising one's biases and perspectives during analysis). During the second stage, using photovoice, everyone took photos that symbolised their experiences of being involved in the project. This included a session with a professional photographer. At the last stage, we met in person and, using data collected from photovoice, built the mandala as a representation of a joint experience of the project. This stage was supported by professional artists who summarised the mandala in the illustration.

RESULTS

The mandala is the artistic presentation of the findings from the evaluation. It is a shared journey between everyone involved. We divided it into six related layers. Starting from inside layers present the following experiences (1) public contributors had space to build confidence in a new topic, (2) relationships between individuals and within the project, (3) working remotely during the COVID-19 pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) requirements that co-production needs to be inclusive and accessible to everyone, (6) expectations towards data science and artificial intelligence that researchers should follow to establish public support.

CONCLUSIONS

The participatory evaluation suggests that co-production around data science and artificial intelligence can be a meaningful process that is co-owned by everyone involved.

Exploring how to improve the involvement of Polish and South Asian communities around big data research. A qualitative study using COM-B model

Introduction

Involving public contributors helps researchers to ensure that public views are taken into consideration when designing and planning research, so that it is person-centred and relevant to the public. This paper will consider public involvement in big data research. Inclusion of different communities is needed to ensure everyone's voice is heard. However, there remains limited evidence on how to improve the involvement of seldom-heard communities in big data research.

Objectives

This study aims to understand how South Asians and Polish communities in the UK can be encouraged to participate in public involvement initiatives in big data research.

Methods

Forty interviews were conducted with Polish (n=20) and South Asian (n=20) participants on Zoom. The participants were living in the United Kingdom and had not previously been involved as public contributors. Transcribed interviews were analysed using reflexive thematic analysis.

Results

We identified eight themes. The 'happy to reuse data' theme sets the scene by exploring our participants' views towards big data research and under what circumstances they thought that data could be used. The remaining themes were mapped under the capability-opportunity-motivation-behaviour (COM-B) model, as developed by Michie and colleagues. This allowed us to discuss multiple factors that could influence people's willingness to become public contributors.

Conclusions

Our study is the first to explore how to improve the involvement and engagement of seldom-heard communities in big data research using the COM-B model. The results have the potential to support researchers who want to identify what can influence members of the public to be involved. By using the COM-B model, it is possible to determine what measures could be implemented to better engage these communities.

Mental Health Needs Assessment During the COVID-19 Pandemic: Consensus Based on Delphi Study

The COVID-19 pandemic has revealed significant gaps in mental health in terms of unrecognized and unmet needs. The goal was to accurately assess the needs and identify gaps in this area during the epidemiological crisis. A Delphi study to identify the needs was conducted with a group of decision-makers, experts, and users of mental health services. A starting point of the Delphi study was prepared in two working groups, based on recognizable international recommendations and experiences of the practitioners from the field situation. This initial set of emergency measures was supplemented through the first Delphi round, and consensus about the importance was reached in the second round. A total of 41 activities were derived, the vast majority of which were rated with a score of 4 or more. Mental health activities, which should be addressed in terms of needs, can be divided into systemic measures and service measures. This study recognizes a need to reorganize services in the direction of improving local accessibility and strengthening the network of services for immediate responses to the psychological, health, and social needs of individuals, including those arising from crisis situations, such as COVID-19 pandemic. The results of this study are in line with the international recommendations and also influenced the formulation of the Action Plan of the National Mental Health Program, while some of the measures were already implemented during the publication of the research results.