ABSTRACT
This research focuses on the assessment of business intelligence
platforms for medium-to-large hospitals using hierarchical decision modeling
methodology (HDM) and expert judgment. The healthcare data come from different source systems, like EMRs or HR
software, to different departments, like radiology or pharmacy. The data comes
from all over the organization. Healthcare data is complex and complicated,
i.e., the data is structured and unstructured and has various formats like
text, numeric, images, multimedia, paper, etc. Even before the pandemic, the
healthcare data was growing. However, healthcare data has grown exponentially
during the pandemic, resulting in a growing need for storage, retrieval, and
analysis challenges using the traditional approaches. In addition, there is a
growing demand for clinical and administrative information from hospital
executives and services providers. The use of business intelligence (BI) is
seen as a possible solution to this challenge. BI helps direct, high-level
decision-making by evaluating practice performance and predicting patient
outcome trends. BI application/use has the potential to reduce costs and build
healthier revenue streams, and at the same time, deliver the best patient care.
BI can assist decision-makers from clinic to billing, HR, and everything in
between. The
objective of the current research paper is to help healthcare executives in the
assessment of BI alternatives by considering multiple criteria and multiple
stakeholders. The research applied a multi-criteria approach, considered
People, Business Processes, and Technology perspectives, and used Hierarchical
Decision Modeling (HDM) methodology to evaluate solution alternatives. HDM
methodology requires the expert judgment of experts to provide the relative
ranking of the perspectives and criteria. There are four alternative solutions
suggested. Three solutions are cloud-based, and one is legacy, aka traditional
in-house solution. Cloud and legacy systems will co-exist for the long run.