Kismo is a venture, started at Nagoya University, that promotes research and development to reduce the burden on medical staff. At present, hospitals and clinics are expected to experience a significant drop in revenue due to the increased medical burden caused by the coronavirus, which has led to a drop in the number of other treatments and surgeries, as well as a significant decrease in patients, making it important to smoothen their operations by receiving reimbursement without any omissions.
MEBAIS uses big data AI to learn from cases across the country to analyze obscure reimbursement rules with high accuracy, and provides 3 checking functions to obtain reimbursements without any omissions.
The itemized statements of medical expenses check function analyzes both the AI, which is based upon vast amounts of historical case data, as well as antimony and inclusion rule bases, and alerts and corrects items that may be assessed or returned to the company to prevent declines in compensation. With the add-on check function, the system recommends only those add-on items that have no risk of assessment by enumerating add-on items that are often forgotten and not known to have been accurately collected, thereby allowing for the maximizing of medical fees. The order-checking function, in addition to being a countermeasure for preventing omissions in order entry based on the data of other hospitals at clinics, also allows for the acquiring of knowledge about clinical expertise at other hospitals.