SwasthyaBuddy: A Comprehensive System for Assessing Chronic Obstructive Pulmonary Disorder Patients

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a multifaceted respiratory disorder that
has a significant global impact on healthcare systems and patient well-being. This paper
describes an innovative approach to COPD management that employs advanced technologies
to detect acid-base disorders, provide real-time statistics to healthcare providers and patients,
enable online consultations, predict disease severity using AI-ML models, and seamlessly
integrate medical reports with the National Digital Health Mission (NDHM). This paper
introduces a method for characterizing acid-base disorders in COPD patients using arterial
blood gas (ABG) reports to address the critical issue of acid-base disorders. This paper can
precisely identify respiratory acidosis and alkalosis by analyzing ABG data, providing
clinicians with valuable insights into the patient's condition and guiding appropriate
interventions. Furthermore, this system provides both healthcare providers and patients with
real-time statistics, providing them with up-to-date information on COPD trends, treatment
options, and outcomes. Patients can access their health data through an easy-to-use interface,
fostering a collaborative approach to COPD management. The system incorporates machine
learning models to predict COPD severity based on clinical data such as ABG reports,
spirometry results, and patient history across the healthcare ecosystem. The system
seamlessly integrates medical reports, including ABG results, with the NDHM to promote
interoperability and efficient healthcare delivery. This integration ensures the secure
exchange of health information while also allowing for a comprehensive view of the patient's
medical history across the healthcare ecosystem. In conclusion, this proposes an integrated
approach to COPD management that uses technology to detect acid-base disorders, provide
real-time statistics, provide online consultations, predict disease severity, and seamlessly
integrate medical reports with the NDHM. This paper hopes to revolutionize COPD care and
improve outcomes in this difficult chronic disease by combining these features