...
Objectives
The goal of this project is to develop an automatic claims categorization module for openIMIS based on state-of-the-art Artificial Intelligence (AI) algorithms, standards, and methodologies which will drastically reduce the manpower, resources and time required to review a reimbursement claim. This process is centered on the claim response process. Here are some details about this process.
Current openIMIS claim adjudication process
A claim is what a doctor or health service submit to the patient insurance company so that they are paid for the medical services and/or items (medical prescriptions). Thus, a claim can be composed of one or more medical services and items. After the visit to the Health facility, the claim will be submitted to the insurance by the health facility administrator (called also Claim Administrator). After submission, a claim processor – based on a rule engine that takes in consideration openIMIS configuration (insurance product, medical items and services, insuree, policy, etc.) – will check it for completeness, accuracy and whether the service/item is covered by the patient insurance. If all the services and items contained in a claim are rejected by the Rule Engine, the HF will receive a negative claim response for the respective claim. All the items and services that were statically validated by the Rules engine may be subject to a manual evaluation by a Medical Expert, which can accept, partially accept or reject the selected items/services. As a claim is composed by one or several medical services and/or items, the output of the process can be:
...
Fig. 2 Manual claim adjudication workflow
Future openIMIS AI-based automatic claim adjudication process (vision)
For the AI claim categorization algorithm, the input is represented by a service or item, which has been statically validated by the Rules engine. The output of such a model will be the acceptance or rejection of the item or service. This AI module will allow the Medical Officer to concentrate only on those claims that really need to be reviewed, such as inconsistent or erroneous. Furthermore, in order to detect false positive or false negative cases misclassified by the algorithm, a Quality Assurance module will then select AI-categorized claims to be review by the Medical Officers. This process is illustrated in Fig. 3.
...
Fig. 3 Claim adjudication workflow with AI algorithm
Project realization
The following wiki pages present the project realization:
...