Ubiquitous Health Technology Management (uHTM)

Abstract

The COVID-19 pandemic increased the need for distributed and ubiquitous health technology management. The eminent risk of Sars-CoV-2 contamination when visiting a health care establishment requires an efficient allocation of the technical team. The equipment problems should be quickly identified and fixed to keep the facility working at its full condition. This article presents a solution to perform remote real-time analysis of primary health care technology behavior, detecting and diagnosing the failures to create predictive maintenance plans. The project uses feature engineering to adapt regular machine learning algorithms to multiclass classification of time series data. The methodology was applied to a dental air compressor. It includes data collection, analysis, and exhibition. The model verified the IBM Watson and the Microsoft Azure Machine Learning Studio with the algorithms of neural networks, logistic regression, decision jungle, and decision forest, which was the most suitable one. The transformation performed in the data considered the influence of time in the read values to obtain a more efficient result in the platform. The solution integrated data collected by the sensors with the cloud using an Internet of Things architecture, a web service, and python scripts to exhibit the outcomes on the computer screen. Therefore, the model performs notification and identification of health technology failures, supporting the decision-making process of ubiquitous management in clinical engineering.