Revolutionizing clinic waste management: government intervention, clinic registration, investment strategies using predictive machine learning models

Abstract

Contemporary healthcare organizations, especially smaller-scale clinics, are up against a rather significant waste disposal problem. Identifying that balance between bringing advanced healthcare services and being responsible with resources has never been more critical. Hence, this article seeks to discuss the topic with a particular focus on how government involvement, clinic mandatory registration, suitable clinic investment towards waste management, and inclusion of advanced prediction machine learning models can immensely change the current position. This work establishes the state of practice in minor healthcare institutions' waste disposal and highlights their challenges. Government legislative and regulatory measures are required to make clinics formally register and adhere to policies on waste disposal. In fact, the city of Hyderabad, which is nestled in the Sindh province of Pakistan, also suffers acute Healthcare waste management issues. Hence, this project aims to enhance healthcare institutions' waste management operations based on clinic registration with garbage management and the latest big data. Grievous results involve a 23% reduction in waste output that results from the implementation of strict controls as well as compliance with WHO specifications. A comprehensive result showed that appropriating an SDSS raised garbage collection efficiency by thirty-seven percent. The future success of the clinic might be predicted using linear regression (LR) and random forest (RF) algorithms. However, the prediction accuracy for RF was significantly higher at 87% than the above computed LR of 69.2%. The obtained outcomes shed light on the conceivable opportunities for enhancing healthcare waste management by applying new technologies that may help enhance sustainability and protect the environment.