- Title Information
- Title
- Piecing the Puzzle Together: Building a Bridge to Discovery Using Health Informatics Approaches for Autism Spectrum Disorder
- Type of Resource (primo)
- dissertations
- Name:
Personal
- Name Part
- Brown, Katie Ann
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Chen, Elizabeth
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Sarkar, Neil
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Elwy, Rani
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Donise, Kathleen
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Center for Computational Molecular Biology
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2024
- Physical Description
- Extent
- 22, 285 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2024
- Genre (aat)
- theses
- Abstract
- Children with high-needs, including those with Autism Spectrum Disorder (ASD), require extensive medical, behavioral, and educational support due to their complex conditions. These challenges necessitate innovative data-driven approaches to inform decision-making and improve care. The Learning Health System (LHS) framework systematically incorporates data-driven insights and evidence-based knowledge into healthcare practice to enhance patient outcomes.
This dissertation focuses on the knowledge discovery aspect of LHSs by using a mixed methods approach, combining qualitative insights and computational findings to inform a tailored healthcare intervention. The three aims are to: (1) Conduct an in-depth qualitative study with caregivers of children with high-needs to examine unmet needs, social challenges, emotional impacts, and essential resources; (2) Use computational methods to study the ASD population and associated comorbidities using statewide clinical data; and, (3) Integrate the qualitative and computational findings to create a comprehensive strategy for a technology-based application that addresses the identified needs. This overall approach strengthens the basis for more informed healthcare technologies and supports the LHS principle of continuous, evidence-based improvement.
The findings highlight the need for enhanced mental health support and personalized care plans, specifically focusing on the increased risk of suicidal thoughts and behaviors in individuals with ASD. The combined research findings further inform a concept and mockup for a digital safety plan application that addresses mental health safety and provides customized resources, aiming to improve access to necessary services for high-needs children and ASD. By integrating diverse research outputs, design and functionality of targeted technological solutions can be improved, leading to more effective and personalized health interventions.
The components of this dissertation introduce a nested framework within the larger LHS paradigm for knowledge discovery in healthcare, emphasizing the bridge of qualitative insights and computational findings. The proposed Bridge to Discovery in Learning Health Systems (BD-LHS) framework showcases an integrated approach, leveraging the combination of stakeholder insights and computational data analysis to drive evidence-based interventions. This holistic approach aims to generate a responsive, adaptive healthcare system that meets community needs and sets a new standard for interdisciplinary collaboration in health informatics, fostering continuous improvement in health outcomes.
- Subject
- Topic
- Machine Learning
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01747518")
- Topic
- Autism spectrum disorders
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01749921")
- Topic
- Mixed methods research
- Subject
- Topic
- data science
- Subject
- Topic
- electronic health record
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/02040333")
- Topic
- Implementation science
- Subject
- Topic
- biomedical informatics
- Subject
- Topic
- adolescent mental health
- Subject
- Topic
- Medical informatics (Health informatics)
- Subject
- Topic
- health data
- Subject
- Topic
- learning health system
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20240513