Abstract

Objective is to identify the patients at high risk for the future emergency or unplanned hospital admission. Unplanned hospital affirmation and re-confirmation are considered as markers of expensive and unacceptable medicinal services and their evasion is principle issue of strategy creators for some nations. In the three years period, patient’s data like released from a hospital and re-confessed to hospital expense contain more than a billion every year. Thus, our point is to decreasing unplanned admission rates, the proof for their productivity and lessen the expense with the specific aim of reduce the future admission or re-admission of patients we build a model to use for distinguish the patients at high hazard for unplanned admission or re-admission in next 12 months. Our target is to utilize an approved calculation to case-find Medicaid patients at a high danger of hospitalization in one year from now and distinguish obstruction and responsive attributes to lessen hospitalization cost.

Authors and Affiliations