Integrated Analytical Framework for Optimizing Healthcare Decision Making: a Software Development Approach
Lemon, Ahsan (2024)
Lemon, Ahsan
2024
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024060621619
https://urn.fi/URN:NBN:fi:amk-2024060621619
Tiivistelmä
This thesis presents an Integrated Analytical Framework designed to enhance healthcare decision-making
through advanced data analytics. The framework consolidates diverse healthcare data sources and employs
sophisticated analytical methods to produce actionable insights that support evidence-based decisions. This
study evaluates the framework's effectiveness in addressing key healthcare challenges, such as resource allo-
cation, patient management, and treatment optimization, through literature review and experimental analysis.
The results indicate that the framework can accurately predict patient outcomes, optimize resource use, and
improve healthcare processes. However, issues such as data integration, scalability, and user adoption have
been highlighted. The study recommends techniques for improving the framework's usability, interoperability,
and practical use. Future research directions include combining developing technologies and investigating novel
analytical methodologies. In general, this work emphasizes the importance of ongoing refinement to adapt to
changing healthcare needs and maximize the framework's impact on healthcare delivery, ultimately improving
patient outcomes on a global scale.
through advanced data analytics. The framework consolidates diverse healthcare data sources and employs
sophisticated analytical methods to produce actionable insights that support evidence-based decisions. This
study evaluates the framework's effectiveness in addressing key healthcare challenges, such as resource allo-
cation, patient management, and treatment optimization, through literature review and experimental analysis.
The results indicate that the framework can accurately predict patient outcomes, optimize resource use, and
improve healthcare processes. However, issues such as data integration, scalability, and user adoption have
been highlighted. The study recommends techniques for improving the framework's usability, interoperability,
and practical use. Future research directions include combining developing technologies and investigating novel
analytical methodologies. In general, this work emphasizes the importance of ongoing refinement to adapt to
changing healthcare needs and maximize the framework's impact on healthcare delivery, ultimately improving
patient outcomes on a global scale.