Design of Electronic-Based Medical Records to Support The Quality Of Reporting in The Internal Disease Polyclinic at Al Ihsan Hospital

Soleah

Soleah Politeknik Piksi Ganesha

Keywords: Electronic Medical Records, Python Tkinter, ICD-10.

Abstract

Electronic medical records (RME) are a digital transformation solution to overcome the inefficiencies of conventional paper-based systems. This study aims to design a GUI-based RME system using Python Tkinter to improve the quality of reporting at the Internal Medicine Poly of Al Ihsan Hospital. The research methods include observation, interviews, and literature study, with the waterfall approach including needs analysis, design (flowmap, ERD, DFD, context diagram), implementation, and black box testing. As a result, the system is integrated with key features: ICD-10-based patient management and medical records, automated reporting (daily/monthly) in Excel format, visualization of diagnostic statistics, and role-based access control. Testing showed improved data search efficiency (from 15 minutes to <1 minute) and reporting accuracy (95%). The system meets user-centered design standards and is ready for widespread implementation, with recommendations for the development of BPJS API integration and follow-up notifications. For further development updates, it can be done by integrating hospital databases and done efficiently by adding AI automation features for more efficient and optimal data classification by relying on AI, which is expected to help the work process to be shorter and more optimal with a simple program.

Author Biography

Soleah, Politeknik Piksi Ganesha

Electronic medical records (RME) are a digital transformation solution to overcome the inefficiencies of conventional paper-based systems. This study aims to design a GUI-based RME system using Python Tkinter to improve the quality of reporting at the Internal Medicine Poly of Al Ihsan Hospital. The research methods include observation, interviews, and literature study, with the waterfall approach including needs analysis, design (flowmap, ERD, DFD, context diagram), implementation, and black box testing. As a result, the system is integrated with key features: ICD-10-based patient management and medical records, automated reporting (daily/monthly) in Excel format, visualization of diagnostic statistics, and role-based access control. Testing showed improved data search efficiency (from 15 minutes to <1 minute) and reporting accuracy (95%). The system meets user-centered design standards and is ready for widespread implementation, with recommendations for the development of BPJS API integration and follow-up notifications. For further development updates, it can be done by integrating hospital databases and done efficiently by adding AI automation features for more efficient and optimal data classification by relying on AI, which is expected to help the work process to be shorter and more optimal with a simple program.