Technology Acceptance for Machine Learning Decision Model in the Health Insurance System

Abstract

This research investigates technology acceptance for a machine learning (ML) decision model in the health insurance system. Focusing on claim adjusters' perceptions, the study examines how perceived usefulness and ease of use of the ML model influence their attitudes and ultimately their adoption of the technology. Data collected through a Likert scale questionnaire reveals high overall satisfaction with perceived ease of use. Perceived usefulness achieved moderate to high satisfaction, and the attitude towards use indicated strong interest. These findings suggest that an ML-based health claim consideration system may be a valuable tool to support claim adjusters.