Predictive Analytics in Health Care
Healthcare in the world has slowly been progressing in data collection, data sharing and data analytics. A number of use cases in healthcare are well suited for a big data solution. The value for big data in healthcare today is largely limited to research because using big data requires a very specialized skill set. Using big data for predictive analytics is one major improvement in health care industry.
- Predicts or forecast variety of things and events.
- Predicts potential fraudulent claims & providers.
- Data preparation capabilities
- Effective and Efficient usage of Algorithms
- Automation and iterative processes
- Ensemble modeling
- Data Cleaning
- Data Integration
- Data Selection & Transformation
- Data Mining
- Pattern Evaluation
- Knowledge Representation
Healthcare fraud is majority first party that is committed by providers to whom most of the payments are made. It is raising costs for patients and cutting sharply into margins for insurance payers. Hence Predictive Analytics is used to fight all frauds happening in Healthcare industry.
Apatics healthcare fraud detection solution helps in identifying frauds at the Provider & Payer Side –
- Providers billing for services not provided
- Providers administering (more) tests and treatments or providing equipments that are not medically necessary
- Providers administering more expensive tests and equipments (up-coding)
- Providers multiple-billing for services rendered
- Providers unbundling or billing separately for laboratory tests performed together to get higher reimbursements
- Providers charging more than peers for the same services
- Providers conducting medically unrelated procedures and services
- Policy holders traveling long distance for treatment which may be available nearby. (Possibly scams by bogus providers)
- Policy holders letting others use their healthcare cards
Our Data Scientists employ multiple approaches & strategies to identify Medicaid and Medicare frauds in healthcare industry –
- Predictive models, which compare charges with a fraud profile and raise suspicion
- Rules-based models, which automatically flag certain charges