The Fee For Service model for medical payments is the predominant payment scheme in the United States. In the Fee for Service model, physicians are incentivized to provide more treatments regardless of effectiveness. There is no link between the recipient of services and payment for those services. The recipient of the medical services, the patient, does not directly participate in the fee transaction. They are incentivized to accept any health service which may provide some benefit.
The capitation-based model pays a fixed amount for each patient enrolled with the provider. This discourages the medical facility from providing unnecessary care. Providers will be paid the same amount regardless if the patient seeks care or not. Remuneration is based on the average healthcare utilization of the patient. Any individual patient may cost more (or less) than the expected utilization. However, under-utilization will balance out over-utilization. The ability to provide deep analysis of entire populations of medical records is critical to determining the average risk of an institutions users.
Historically, this analysis would take weeks and require many people reviewing millions of documents. Big Data, a mature set of technologies, provides a solution for this problem. Using modern data analysis techniques, coupled with Big Data systems, we are able to determine risk severity of millions of patients rapidly. We reduce the cost of data analysis while increasing the accuracy resulting in an objective measure of risk from subjective medical records.