Machine learning methods have emerged as powerful tools for enhancing healthcare quality by leveraging large datasets and identifying patterns that can inform decision-making and improve patient outcomes. This review explores the application of machine learning in healthcare quality, focusing on areas such as disease diagnosis and prognosis, personalized treatment planning, predictive analytics, fraud detection, and remote care. Machine learning algorithms have demonstrated promising results in disease detection, enabling early and accurate diagnoses. By analyzing patient data, including medical images, electronic health records, and genetic information, these algorithms can provide insights into disease progression and prognosis. Moreover, medical history, and treatment response data, leading to improved treatment effectiveness and reduced adverse events. Predictive analytics techniques facilitate the identification of patients at risk of adverse events, enabling proactive interventions to prevent complications and improve patient safety.
Top1. Introduction
Machine learning (ML) is a rapidly evolving field that holds immense potential to revolutionize healthcare. With the exponential growth of data and advancements in computational power, ML techniques have gained prominence in various industries, including healthcare. In the healthcare domain, ML can analyze vast amounts of patient data, identify patterns, and generate insights to improve diagnostics, treatment strategies, patient monitoring, and overall quality of care. This section aims to explore the transformative impact of ML in healthcare and its potential for revolutionizing the industry (Khang & Rana et al., 2023).
1.1 Advancements in Diagnostics
ML techniques have shown remarkable success in improving diagnostic accuracy. By analyzing medical images, such as X-rays, MRIs, and CT scans, ML algorithms can detect and classify diseases with high precision. For instance, deep learning algorithms have demonstrated the ability to detect skin cancer, identify breast abnormalities, and diagnose diabetic retinopathy. ML models can also integrate patient data, including medical records, lab results, and genetic information, to provide accurate and timely diagnoses (Pokkuluri et al., 2020).
1.2 Personalized Treatment and Precision Medicine
ML algorithms can predict treatment response, identify optimal drug regimens, and guide dosage adjustments. Additionally, ML models can analyze genomic data to uncover genetic markers associated with drug response, facilitating precision medicine approaches tailored to an individual's unique genetic profile (Sree et al., 2014).
1.3 Predictive Analytics and Proactive Care
ML techniques can leverage historical patient data to predict future health outcomes and identify individuals at risk of developing certain conditions. By analyzing a multitude of variables, such as demographics, medical history, lifestyle factors, and environmental data, ML algorithms can identify early warning signs and enable proactive interventions. ML models can also support predictive analytics for hospital readmissions, patient deterioration, and disease progression, leading to more efficient resource allocation and targeted interventions (Sree et al., 2014) as shown in Figure 24.1.
Figure 1.
Machine learning applications in various fields
1.4 Efficient Healthcare Resource Management
ML techniques can optimize healthcare resource allocation and management. ML models can analyze historical patient data, population health trends, and resource utilization patterns to forecast demand, optimize bed occupancy, and streamline healthcare workflows. By predicting patient flow and resource needs, ML can help hospitals and healthcare systems allocate resources efficiently, reduce waiting times, and improve overall operational efficiency (Sharmila et al., 2023).