Enhancing Healthcare Delivery Through Evidence-Based Data Utilization
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Amitava Podder
Abstract
The field of healthcare is being transformed by new technologies and the use of a variety of data available online. During this part, we talk about how using evidence-based data can improve healthcare quality, boost efficiency in care delivery, and positively impact patient outcomes. It starts by providing key concepts, showing how data integration has changed over time in healthcare, and emphasizing the main ideas of evidence-based practice. Numerous types of healthcare data, for instance, clinical data, administrative data, patient-generated data, and data from IoT devices and wearables, are analyzed regarding what they can give to healthcare and what issues they may face. The chapter points out the main issues caused by scattered data, having systems that do not work together, and the need to standardize. Analysis methods, such as descriptive, predictive, prescriptive, machine learning, and artificial intelligence, are covered for helping in clinical and operational decisions. These approaches are used in practice to help with diagnosis, manage resources, look after population health, and apply personalized medicine. Privacy, approval, following rules, and fighting bias are main concerns to produce fair and balanced results. Problems such as organizational and technological barriers, data quality issues, reluctance to change, and the complexity of dealing with unclear data are all considered. Eventually, healthcare businesses will pay attention to real-time analytics, the use of AI, improving data literacy among the workforce, and policy moves that support a sustainable healthcare data system. It provides research, practice, and policy experts, with insights to base decisions on actual evidence.
Abstract
The field of healthcare is being transformed by new technologies and the use of a variety of data available online. During this part, we talk about how using evidence-based data can improve healthcare quality, boost efficiency in care delivery, and positively impact patient outcomes. It starts by providing key concepts, showing how data integration has changed over time in healthcare, and emphasizing the main ideas of evidence-based practice. Numerous types of healthcare data, for instance, clinical data, administrative data, patient-generated data, and data from IoT devices and wearables, are analyzed regarding what they can give to healthcare and what issues they may face. The chapter points out the main issues caused by scattered data, having systems that do not work together, and the need to standardize. Analysis methods, such as descriptive, predictive, prescriptive, machine learning, and artificial intelligence, are covered for helping in clinical and operational decisions. These approaches are used in practice to help with diagnosis, manage resources, look after population health, and apply personalized medicine. Privacy, approval, following rules, and fighting bias are main concerns to produce fair and balanced results. Problems such as organizational and technological barriers, data quality issues, reluctance to change, and the complexity of dealing with unclear data are all considered. Eventually, healthcare businesses will pay attention to real-time analytics, the use of AI, improving data literacy among the workforce, and policy moves that support a sustainable healthcare data system. It provides research, practice, and policy experts, with insights to base decisions on actual evidence.
Chapters in this book
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
Chapters in this book
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
