Abstract Health Care
MIS 6324
Business Intelligence
Dr. Syam Menon
Term Project
SUBMITTED BY :
Group 6
Swetha, Priya,
Ram, Vishal, Madan
Business Intelligence in Health Care Management
The health care system is more complex than most people know. Its business model is different from most businesses in that its consumers don't usually come willingly. That, plus not getting paid what they bill, hurts cash flow makes health care a shaky business. With uncertain revenue, costs not entirely within their control, and regulatory issues, health care management problems aren't always easy to fix.
Forward-thinking healthcare organizations realize that data and, thus, business intelligence (BI) is at the center of informed and precise decision-making that will improve patient and service outcomes in addition to ensuring their organizations’ future.
This paper gives an insight into which business intelligence tools are currently being used in healthcare and their effectiveness in terms of sustaining the businesses of those organizations that use these tools. The paper also deals with common problems faced while implementing these techniques to patients’ data and ways to overcome problems.
Outdated information management strategies and invalid statistics cause serious problems in investigating health outcomes and negotiating reimbursements. Predictive modeling, however, goes beyond standard regression techniques, expanding advanced analytical options for better, faster decision making. Predictive models use a variety of tools to deliver more accurate, long-range views of treatments and costs.
Also there are many new reporting technologies designed to improve the productivity of business analysts and preserve information consistency throughout an organization. These analytical tools, the advantages vs. problems during implementation and their ability to shape the future of healthcare industry are focus of this
Business Intelligence
Dr. Syam Menon
Term Project
SUBMITTED BY :
Group 6
Swetha, Priya,
Ram, Vishal, Madan
Business Intelligence in Health Care Management
The health care system is more complex than most people know. Its business model is different from most businesses in that its consumers don't usually come willingly. That, plus not getting paid what they bill, hurts cash flow makes health care a shaky business. With uncertain revenue, costs not entirely within their control, and regulatory issues, health care management problems aren't always easy to fix.
Forward-thinking healthcare organizations realize that data and, thus, business intelligence (BI) is at the center of informed and precise decision-making that will improve patient and service outcomes in addition to ensuring their organizations’ future.
This paper gives an insight into which business intelligence tools are currently being used in healthcare and their effectiveness in terms of sustaining the businesses of those organizations that use these tools. The paper also deals with common problems faced while implementing these techniques to patients’ data and ways to overcome problems.
Outdated information management strategies and invalid statistics cause serious problems in investigating health outcomes and negotiating reimbursements. Predictive modeling, however, goes beyond standard regression techniques, expanding advanced analytical options for better, faster decision making. Predictive models use a variety of tools to deliver more accurate, long-range views of treatments and costs.
Also there are many new reporting technologies designed to improve the productivity of business analysts and preserve information consistency throughout an organization. These analytical tools, the advantages vs. problems during implementation and their ability to shape the future of healthcare industry are focus of this