The healthcare system is currently facing challenges such as an ageing population, rising costs, and a shortage of healthcare workers. To address these issues and ensure sustainability, innovative strategies like telemedicine, lean organization, and process mining must be implemented. These strategic levers can improve efficiency, effectiveness, and patient outcomes by employing machine learning elements to predict outcomes and recommend personalized treatment plans based on individual patient data. Risk predictive ML algorithms are crucial in estimating the risk of chronic diseases, moving towards a global assessment to provide personalized recommendations for chronic care processes. By integrating risk stratification methodologies and predictive algorithms, healthcare organizations can improve health outcomes and reduce avoidable costs in managing high-risk populations while considering social data for fragile populations.In the context of chronic care processes, a modern care model based on efficient processes and the support of healthcare companies could offer assistance to vulnerable patients with complex needs. By utilizing digital tools like telemedicine, healthcare organizations can enhance the management of patients and healthcare professionals, ultimately promoting the well-being of all stakeholders. The COVID-19 pandemic has accelerated the adoption of telemedicine and digital tools, showcasing their feasibility and effectiveness on a large scale. Telemedicine plays a crucial role in reorganizing healthcare services, improving accessibility, and quality while offering prevention, education, and patient coaching programs to monitor health indicators and reduce complications.Telemedicine, connected platforms, and ML algorithms are essential in modern healthcare systems to enhance patient-centered care processes, improve coordination between stakeholders, and integrate social and health services for better continuity and quality of care. By leveraging telemedicine, healthcare organizations can optimize resources, provide services close to patients, and implement preventive measures to reduce the risk of complications. The proposed methodology for designing prevention-focused PDTA leverages telemedicine and ML algorithms to predict chronic risks, optimize care processes, and improve patient outcomes. The BPMN-PMO workflow model illustrates the prevention process based on monitoring, data processing, and decision support for general practitioners to ensure the sustainability of the model through technology readiness and organizational capabilities. The integration of telemedicine, ML algorithms, and PMO models is crucial for improving the prevention and management of chronic diseases, enhancing patient care, reducing costs, and improving health outcomes. By adopting innovative technologies and optimizing organizational processes, healthcare organizations can provide personalized and efficient healthcare services for all individuals in need, paving the way for a future of collaborative care and digital healthcare solutions.
The Need for Change for Sustainable Healthcare: A Process Mining Organization (PMO) Trial Applied to Telemedicine
Rosa A;
2024-01-01
Abstract
The healthcare system is currently facing challenges such as an ageing population, rising costs, and a shortage of healthcare workers. To address these issues and ensure sustainability, innovative strategies like telemedicine, lean organization, and process mining must be implemented. These strategic levers can improve efficiency, effectiveness, and patient outcomes by employing machine learning elements to predict outcomes and recommend personalized treatment plans based on individual patient data. Risk predictive ML algorithms are crucial in estimating the risk of chronic diseases, moving towards a global assessment to provide personalized recommendations for chronic care processes. By integrating risk stratification methodologies and predictive algorithms, healthcare organizations can improve health outcomes and reduce avoidable costs in managing high-risk populations while considering social data for fragile populations.In the context of chronic care processes, a modern care model based on efficient processes and the support of healthcare companies could offer assistance to vulnerable patients with complex needs. By utilizing digital tools like telemedicine, healthcare organizations can enhance the management of patients and healthcare professionals, ultimately promoting the well-being of all stakeholders. The COVID-19 pandemic has accelerated the adoption of telemedicine and digital tools, showcasing their feasibility and effectiveness on a large scale. Telemedicine plays a crucial role in reorganizing healthcare services, improving accessibility, and quality while offering prevention, education, and patient coaching programs to monitor health indicators and reduce complications.Telemedicine, connected platforms, and ML algorithms are essential in modern healthcare systems to enhance patient-centered care processes, improve coordination between stakeholders, and integrate social and health services for better continuity and quality of care. By leveraging telemedicine, healthcare organizations can optimize resources, provide services close to patients, and implement preventive measures to reduce the risk of complications. The proposed methodology for designing prevention-focused PDTA leverages telemedicine and ML algorithms to predict chronic risks, optimize care processes, and improve patient outcomes. The BPMN-PMO workflow model illustrates the prevention process based on monitoring, data processing, and decision support for general practitioners to ensure the sustainability of the model through technology readiness and organizational capabilities. The integration of telemedicine, ML algorithms, and PMO models is crucial for improving the prevention and management of chronic diseases, enhancing patient care, reducing costs, and improving health outcomes. By adopting innovative technologies and optimizing organizational processes, healthcare organizations can provide personalized and efficient healthcare services for all individuals in need, paving the way for a future of collaborative care and digital healthcare solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
