Predictive informatics

Predictive informatics (PI) is the combination of predictive modeling and informatics applied to healthcare, pharmaceutical, life sciences and business industries.

Predictive informatics enables researchers, analysts, physicians and decision-makers to aggregate and analyze disparate types of data, recognize patterns and trends within that data, and make more informed decisions in an effort to preemptively alter future outcomes.

Current uses of PI

Healthcare

The demand for effective predictive informatics in healthcare has increased significantly as the demand for more quality and better outcomes has increased. Clinical researchers, healthcare administrators, and physicians seek to aggregate research and clinical data in order to improve long-range disease management strategies as well as make better real-time decisions. "PI is a double-edged sword and can act like a preventive watchdog or as a lifesaver keeping a constant eye on the patient's health data," writes Srinivas Denduluri, an expert in the healthcare technology industry.

Over the past decade the increased usage of electronic health records has produced vast amounts of clinical data that is now computable. Predictive informatics integrates this data with other datasets (e.g., genotypic, phenotypic) in centralized and standardized data repositories upon which predictive analytics may be conducted.

Pharmaceuticals

The biopharmaceutical industry uses predictive informatics (a superset of chemoinformatics) to integrate information resources to transform data into knowledge in order to make better decisions faster in the area of drug lead identification and optimization.

Systems biology

Scientists involved in systems biology employ predictive informatics to integrate complex data about the interactions in biological systems from diverse experimental sources.

Other uses

Predictive informatics and analytics are also used in financial services, insurance, telecommunications, retail, and travel industries.

See also

• Predictive analytics
• Informatics
• Predictive modeling
• Biomedical informatics
• Chemoinformatics