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AI In-Hospital Claim Prediction

Health is one of the most important aspects of our life, without the enrichment, maintenance, and protection of good health life could be terrible hard for us to manage. What is present in the body is equally present in the mind, so with good care we could live a stress free life. But, when afflicted by an illness or attempting to improve health certain staunch financial decisions have to be made. To provide support for this many healthcare services and insurances do exist; but filing a claim, or even knowing which claims are efficient is a problem faced by the hospital, patients, and the healthcare service providers.

United Arab Emirates


Medical and Healthcare Industry



Our client is a major multi-speciality group of hospitals based in the United Arab Emirates with 15+ years of experience providing treatments and consultations in every field of medicine. Their multinational and multi-ethnic group of doctors and technicians provide impeccable service to society and not just domestic patients but also international ones. They provide support for nearly every major medical insurance companies, committing to their goal of quality and unproblematic healthcare.

We were tasked by the clients to provide a module that could predict insurance claims for the benefit of the client and their patients. The model created needs to focus on every care episode (i.e., the services provided to every patient for the duration of healthcare undertaken in the hospital) which then had to be meticulously analyzed to provide a predictive tool that will resolve the issues of wrongful and missing claims. By utilizing state-of-the-art artificial intelligence technologies and tools, we tackled this problem in a holistic, multi-faceted manner.



We set out to identify challenges that had to be tackled, they are listed below :

  • Policy Differences - Each and every healthcare insurance providers other than a general set of procedures have their own unique rules and regulations. Sometimes these can vary between different policies with the same service provider.

  • Amplification of Charges and Expenses - Sometimes the expenses can exceed what is predicted by subsequent diagnoses. We need to provide support to take care of this volatile amplification.

  • Third-Party Services - Implants and certain other medical devices/drugs can sometimes come from third party sources, this could cause some discrepancies in billing.



We have arrived at a solution with the project basis and our own research regarding the challenges in mind. They are listed below:

  • Claims are collected for every individual care episode with their specific rules and regulations. The collected data is then ”coded”, streamlined and made procedural.

  • By natural language processing operations, the integral elements of these claims are identified. The predictions are then pushed to the billers for further accreditation. By analyzing the rate and objectives of approval of claims, our model can systematically develop itself to fit the criteria even further.

  • The predicted results and their reports are then perused by the Revenue Integrity Officer for finding areas of improvement and ideas to prevent revenue leakage without endangering the hospital's popularity.



Our model would support various elements of healthcare that was once considered impossible to predict. The hospital, patient, and the service providers can pinpoint the types of services needed, the duration of care undertaken, the conditions of claims and other information. This model can bring claim processes to a new level of transparency and efficiency. Agent training and newer technological developments can be implemented via further analysis derived from our tool.

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