Mixed-Initiative Approach to Extract Data from Pictures of Medical Invoice

Abstract

Extracting data from pictures of medical records is a common task in the insurance industry as the patients often send their medical invoices taken by smartphone cameras. However, the overall process is still challenging to be fully automated because of low image quality and variation of templates that exist in the status quo. In this paper, we propose a mixed-initiative pipeline for extracting data from pictures of medical invoices, where deep-learning-based automatic prediction models and task-specific heuristics work together under the mediation of a user. In the user study with 12 participants, we confirmed our mixed-initiative approach can supplement the drawbacks of a fully automated approach within an acceptable completion time. We further discuss the findings, limitations, and future works for designing a mixed-initiative system to extract data from pictures of a complicated table.