Extracting Value from Healthcare Data: An Analysis of Industry Leading Data Models

Data Models serve as blueprints to identify the structures necessary to design an operational data store, data ware house, or data marts. Choosing the right data model can help healthcare organizations obtain deeper strategic and operational insights to realize data driven healthcare improvements and further their cost optimization efforts.

With increasing emphasis on providing integrated and personalized care, healthcare data models have become key to driving efficient data exchange and interoperability across the healthcare community and government healthcare programs.

The paper discusses three industry-leading data models that enable the seamless flow of information between stakeholders in the healthcare ecosystem to deliver patient-centric and accountable care.

The IBM Healthcare Provider Data Model: It is a part of the IBM InfoSphere software portfolio. It integrates clinical, administrative, and financial data to support real time analytical needs. The solution helps correlate clinical, financial, operational, and payer data in a cohesive and flexible manner.

The Teradata Healthcare Logical Data Model (HC-LDM) offers cross-functional coverage and a single view of data across the enterprise. The solution integrates data from electronic medical records, clinical departmental systems, patient accounting, back office, research, and various other source systems and supports the creation of an ideal framework for a wide range of knowledge applications as well as new payment models.

The Oracle Healthcare Data Model provides an integrated view of enterprise-wide clinical and operational data for better decision making. It supports common entities such as party and care site, core clinical activities such as observation, intervention and order, and financial and billing activities for accounting, equipment, HR, and payroll.

All of these models offer predictable implementation, reduced deployment risks and faster time to value, while enabling a cost efficient and higher quality healthcare delivery system. However, the chosen data model must be robust enough to support the current needs while being scalable to address future requirements. Healthcare organizations that identify the right data model for their unique needs will gain a truly comprehensive approach to healthcare intelligence, resulting in competitive advantage.

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