6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery
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Songita Sett
Abstract
The adoption of modern technologies such as telemedicine, predictive analytics, and artificial intelligence (AI) has significantly altered the home healthcare (HHC) scene. This chapter examines how new technologies can revolutionize patient care, increase accessibility, and improve health outcomes.
The significance of telemedicine in facilitating timely treatments and reducing the need for hospital visits is highlighted, especially in the context of remote consultations and continuous monitoring for patients with chronic illnesses.
With the help of artificial intelligence (AI) and machine learning, predictive analytics analyzes vast amounts of patient data to forecast the disease progression, improve treatment regimens, and prevent complications. Additionally, sophisticated genomics opens up new possibilities for personalized care by tailoring therapies to an individual’s genetic profile, thereby increasing medication efficacy and reducing risk. The significance of blockchain technology in securing private telemedicine interactions and protecting sensitive health data is also covered in this chapter. Furthermore, by lowering the carbon footprint of healthcare delivery systems, green logistics—which includes the use of electric vehicles (EVs) and optimized routing algorithms—contribute to the sustainability of HHC. Through case studies in emergency response, diabetes management, and cancer treatment, this chapter demonstrates how these technologies are currently applied in practical contexts to improve patient outcomes, reduce healthcare costs, and increase the overall effectiveness of home-based care. As the demand for home healthcare continues to grow, these developments will be crucial in shaping the future of healthcare systems, making them more sustainable, patient-centered, and efficient.
Abstract
The adoption of modern technologies such as telemedicine, predictive analytics, and artificial intelligence (AI) has significantly altered the home healthcare (HHC) scene. This chapter examines how new technologies can revolutionize patient care, increase accessibility, and improve health outcomes.
The significance of telemedicine in facilitating timely treatments and reducing the need for hospital visits is highlighted, especially in the context of remote consultations and continuous monitoring for patients with chronic illnesses.
With the help of artificial intelligence (AI) and machine learning, predictive analytics analyzes vast amounts of patient data to forecast the disease progression, improve treatment regimens, and prevent complications. Additionally, sophisticated genomics opens up new possibilities for personalized care by tailoring therapies to an individual’s genetic profile, thereby increasing medication efficacy and reducing risk. The significance of blockchain technology in securing private telemedicine interactions and protecting sensitive health data is also covered in this chapter. Furthermore, by lowering the carbon footprint of healthcare delivery systems, green logistics—which includes the use of electric vehicles (EVs) and optimized routing algorithms—contribute to the sustainability of HHC. Through case studies in emergency response, diabetes management, and cancer treatment, this chapter demonstrates how these technologies are currently applied in practical contexts to improve patient outcomes, reduce healthcare costs, and increase the overall effectiveness of home-based care. As the demand for home healthcare continues to grow, these developments will be crucial in shaping the future of healthcare systems, making them more sustainable, patient-centered, and efficient.
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
