Healthcare Data Analytics

The importance of a carefully designed health data structuring and storing cannot be overstated. Such structures are key to optimally querying and reporting on protected health information (PHI). Additionally, the warehouse must contain integrated storage from sources such as electronic health/medical records, enterprise resource programming systems, human resources management systems, public medical databases, research and development outcomes, etc.

Businesses in the medical field can benefit from various analytics pursuits. These include operational analytics like staff scheduling optimization, facility maintenance, and claims management. Furthermore, patient analytics such as patient segmentation, patient journey analysis, and treatment plan optimization may help streamline operations. Finally, financial analytics like fraud detection and risk management can help minimize monetary losses.

The application of large amounts of data in the healthcare sector is a growing trend. Examples include electronic health records, medical devices, patient apps, and portals. Real-time analytics of device information can be used to monitor patients remotely while suggesting tailored care plans. Additionally, warnings can be triggered when there are shifts in a person’s condition requiring attention from a physician.

Predictive analytics can be applied to a variety of healthcare-related scenarios, such as supply chain optimization, disease management, and insurance coverage. Through the leveraging of Machine Learning algorithms, medical professionals can better prepare for potential problems that could arise in terms of patient readmissions, diagnosis validation, or high-value losses. Furthermore, strategies for demand forecasting and supplier risk management can be implemented to facilitate efficient supply chain planning.