Development and Validation of an AI-Based MWO Digitization and Performance Evaluation Software for a Peruvian National Hospital

Abstract

The Cayetano Heredia National Hospital (CHNH) is a peruvian national hospital that faces a significant challenge in effectively managing its Maintenance Work Orders (MWO) for biomedical equipment. The current paper-based process increases the risk of delays, transcription errors and difficult access to information. A local Python based software is proposed to optimize MWO management, automate reports with AI algorithms and visualize key performance indicators (KPI). The implementation of the software is expected to reduce report generation time and error rate in MWO registration, enhance productivity, minimize equipment downtime, and facilitate data-driven decision making. This proposal is potentially adaptable to the hospital's dynamic needs and aims to optimize MWO management while increasing service quality at CHNH. For this purpose, a usability validation of the software will be carried out through staff satisfaction surveys. To this end, a visit will be made to validate the use of the software through satisfaction surveys. Clinical Relevance-The clinical relevance of this project lies in its ability to optimize the management of Maintenance Work Orders (MWO) at the Cayetano Heredia National Hospital (CHNH). Implementing a locally-based software will significantly reduce report generation time and error rates in MWO registration. This is crucial, as errors and delays in maintenance management can lead to prolonged equipment downtime, adversely affecting patient care quality.

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