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Predicting Days in Hospital Using Health Insurance Claims

ABSTRACT

Healthcare administrators worldwide are striving to lower the cost of care whilst improving the quality of care given. Hospitalization is the largest component of health expenditure. In this paper, a method was developed, using large-scale health insurance

Claims data, to predict the number of hospitalization days in a population. The proposed method performs well in the general population as well as in sub-populations. Results indicate that the proposed model significantly improves

Predictions over two established baseline methods.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:-  Health insurers are mostly concerned with insurance risk, because they agree to reimburse health-related services in Exchange  for a fixed monthly premium.       Poor risk measure could result in exceeding a financial budget. Therefore, one of the most obvious goals for health insurers is to identify high risk customers by predicting their health care expenditures.	PROPOSED CONCEPT: The aim of this paper is to develop a model that predicts the total number of days spent in hospital during a calendar Year for individuals from a general population, using large scale health insurance claims data. Since insurance claims have strong socioeconomic characteristics, their power in predicting clinical targets, such as hospitalizations, are seldom investigated.
EXISTING ALGORITHM:- Log-linear analysis algorithm	PROPOSED ALGORITHM:- Decision tree algorithm.
TECHNIQUE DEFINITION:- When aggregating the numeric features extracted from the admission and the procedure claim levels into the customer level, such as prior cost information	TECHNIQUE DEFINITION:- Decision trees are powerful and popular tools for classification and prediction
DRAWBACKS:- claim data, which could reduce predictive power Earlier identification of those at risk would also help reduce unnecessary hospitalizations and potentially save taxpayers billions of dollars every year	ADVANTAGES:- To improve health quality. It also contained information on the cost of each procedure. Each procedure claim was related to a hospital admission record, and a hospital admission record could be associated with multiple procedure claims..