Towards a Multi-dimensional Health Data Analysis Framework

Abstract

Healthcare processes need to be streamlined to offer better healthcare services. Data analysis can be crucial in reducing costs, optimizing processes, and analyzing treatment effectiveness. However, data analysis in healthcare is complex due to the variety and complexity of patient data. This paper proposes a multi-dimensional comparative analysis method that offers healthcare professionals a lens to delve into healthcare datasets from various perspectives. The paper discusses the importance of comparative analysis in healthcare illustrated by two examples on how we can understand the pattern of comorbidity and how we can analyze the effectiveness of internet delivered psychological interventions. The paper presents a multi-dimensional comparative analysis framework covering various use cases in analysing healthcare data. The framework allows healthcare professionals to compare and contrast healthcare data across multiple dimensions, including clinical dimensions such as diagnosis, outcome measures, time dimension, patient dimensions (engagement, involvement), cost dimension, and other relevant factors. This approach offers a more insightful understanding of healthcare data and facilitates informed decision-making in healthcare practices.