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Insights and Advancing Mental Health Care: The Utility of Administrative Health Records
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- Author / Creator
- Pierce, Derek V
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AHRs' efficacy in mental health research. These papers are meant to showcase the application of machine learning (ML) to predict opioid overdose risks, the examination of developmental disorder utilization shifts within the Alberta healthcare system, and the impact of the pandemic on neurocognitive disorder trends and healthcare demands. These examples underline the diverse applications and insights AHRs can provide into mental health issues.
Looking to the future, the dissertation advocates for broadening AHRs' applications, including their potential in post-disaster mental health outcome predictions. It proposes an integration of AHRs with wearable device data, aiming to transform mental health care from a traditionally reactive approach to a proactive and preventive strategy. This forward-thinking perspective envisions a system where real-time data from wearables enriches AHRs, offering nuanced, immediate insights into individual mental health statuses.
Overall, the dissertation aims to comprehensively dissect the capacity of AHRs to revolutionize mental health care research and practice. It not only addresses the challenges of privacy and the necessity for cross-sector collaboration but also demonstrates the practical applications of AHRs in current mental health scenarios and anticipates their future role in advancing mental health care, especially in contexts affected by disasters.
This work not only highlights the present state of mental health care research but also recommends new directions for future innovations in the field. -
- Subjects / Keywords
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- Mental Health Care Analysis
- Machine Learning
- Opioid Overdose Prediction
- Health Data Privacy
- Synthetic Health Data
- Interdisciplinary Collaboration
- Predictive Modeling
- Post-Disaster Mental Health
- Wearable Device Data Integration
- Proactive Mental Health Care
- Predictive Analytics
- Data Privacy
- Real-Time Mental Health Monitoring
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- Graduation date
- Fall 2024
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.