Data-driven approaches to tackling mental health
1 University of Derby, Management, College of Business and Law, Derby, England, United Kingdom.
2 Clemson University, Policy Studies, South Carolina, United State of America.
3 John Hopkins School of Medicine, Department of Neurosurgery, School of Medicine, Baltimore, United State of America.
4 Kazimieras Simonavicius University, Organizational Innovation and Management, Vilnius, Lithuania.
5 Emporia State University, School of Business, College of Liberal Arts, Emporia, Kansas, United State of America.
6 Georgia Southern University, School of Earth, Environment and Sustainability, Georgia, United State of America.
7 University of Texas, Department of Geography and the Environment. Austin, Texas, United State of America.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 001–016
Publication history:
Received on 19 July 2024; revised on 28 August 2024; accepted on 30 August 2024
Abstract:
Background: Over the past few years there has been immense evolution in various areas particularly in the areas of digital technologies wherein the pace of change is very high. Industrial areas such as operations and supply chain management together with advanced technologies such as machine learning, big data analytics, artificial intelligence, as well as the Internet of Things, create completely different forms of operational models for various industries. In the area of healthcare too, these emerging computational sophistication is introduced to revolutionise the approaches to prevent, diagnose and treat diverse diseases and illnesses.
Objective: The objective of this study is to provide an extensive review of the contemporary approaches utilizing data to cope with significant mental disorders. From over 60 relevant scholarly articles published between 2011 and 2023, it discusses how tools such as predictive modelling, social media analysis, data from smartphones, and chatbots help with issues such as early detection, telemonitoring, provision of psychological support, and individualised prevention.
Method: An initial literature review to analyse over 60 research articles, which include empirical studies that were conducted between 2011 and 2023. The research assessed implemented novel digital approaches to mental health interventions including big data analytics for predicting condition status, machine learning for examining social media content, behaviour monitoring through smartphone sensors, and using conversational agents or chatbots. The following is an overview of general conclusions from experimental and descriptive secondary research studies published in professional outlets concerning possible advantages and disadvantages of data science applied to important concerns in mental health.
Results: Research reveals that integrating subtle e-health tools in tandem with typical treatment approaches holds the potential to expand mental health services to more or less integrate them into clients’ day-to-day lives, and practically individualize effective treatments accordingly. Technological solutions for instance allow remote risk assessment, symptom monitoring and determination of treatment compliance. New lines of virtualized paradigm solve social challenges that interfere with the conventional provision and consumption of care. However, questions of privacy and the long-term effects as well as clinical adoption are yet to be solved in a analytically distinct manner.
Discussion: Despite there is a great number of opportunities, certain critical issues need to be solved to unleash the full potential of the data-driven approach in mental health care. Namely, technology integration into the streams of a provider’s work assumes seamless compatibility and observable benefits. Appreciating population effects calls for steady long-term semantics assessments. For people of color to feel respected the designs and language used must be culturally appropriate. Resolving such barriers will strengthen trust and outcomes, thus merit focused efforts from technology designers, clinicians and policymakers.
Keywords:
Mental health; Machine learning; Artificial intelligence; Big data; Predictive modeling; Smartphone sensors; Just-in-time adaptive interventions; Digital biomarkers; Clinical integration; Technology adoption; Data-driven healthcare.
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0