RECONSTRUCTION AI APPLICATION DEVELOPMENT DIAGNOSIS CODEFICATION AND HOSPITAL OBSTETRICS CASE ACTION

Abstract

Introduction The electronic medical record (RME) has become the backbone of the modernization of Health Services. However, at this time there are still many found in the process of diagnosis and medical treatment codefication in obstetric cases in hospitals is done manually, which resulted in delays, inaccuracies in the code, as well as the difficulty of BPJS claims. Artificial Intelligence (AI) is one of the solutions in improving the accuracy, efficiency, and consistency of codefication. Objective: This study aims to accelerate the adoption of RME technology, ensure the security of health data, and support interoperability between health facilities, thus speeding up processes, minimizing errors, and supporting the quality of Health Services. Method :  This study was conducted with the concept of Research and Development (RnD). The stages include problem identification, data collection, application design, design validation, revision, limited trial, re-revision, interpretation of results, and socialization. The study was conducted at Rsia Mutiara Bunda Padang in July 2025 involving 6 respondents (code officers, casemix, and management). Data were collected through FGDs, interviews, and observations. Validation is performed by medical records and IT experts, while application trials are judged on speed and accuracy.  Result : The AI application developed can accelerate the process of diagnosis and action codefication on cases with 100% accuracy and coding time efficiency increased by 40% compared to manual methods. Discussion : Reconstruction the development of this AI application improves the accuracy and speeds up the process of codefication of diagnoses and ac apa kabartions in obstetric cases, minimizes errors, and can support the digital transformation of hospitals.

Reconstruction, Application, AI, Codefication, Obstetric.