The Superintendency of Health is a public agency whose goals include ensuring compliance with the rights of citizens in relation to health care providers and Isapres. Annually, about 45,000 claims of various kinds are received, which take an average of 200 days to be resolved. We identified those claims with the highest volume and which are the most susceptible to being automated. The Base Price Increase constitutes close to 50% of the claims made by citizens. The main goals for the project were:
The great challenge of this project was to create a model that could read the APB letters, recognize on which page the necessary information was found, identify which Isapre the letter corresponds to, read the ID card, verify that the letter and the ID card refer to the same person, and that the person has a valid ID card.
Neural networks were used to analyze the text patterns in each page of the letter presented in order to identify which Isapre it corresponds to, as well as to corroborate that the letter corresponds to the Base Price Increase.
NLP techniques were used to take the claims, clean them and make them ready to be processed by machine learning models in order to achieve an optimal classification.
Subsequently, the claim entered by the user is received by the insurance company in its corresponding portal where he/she can appeal the claim, all within the established legal deadlines.
In addition, a complete survey was made of the processes for the entry of claims and they were redesigned to improve the efficiency of face-to-face and digital attention.
to identify texts and automate text ingestion
to process claim information and subsequent classification
ensuring ad-hoc operation transformation
Reduction of Base Price Increase claim times from 40 days to 15 days
From a process with 107 stages, it was reduced to 93 final stages, of which 34 were automated.
It was estimated that process automation decreased processing times from 202 to 108 days.
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