Edge Computing and IoT in a Connected AI Healthcare Ecosystem
Image credit: Depositphotos enhanced by CogWorld
AI in healthcare involves the use of machine learning algorithms and patient data fed in timely ways to these algorithms. IoT is making its way into hospital devices and equipment and is sending this data to computing facilities for AI to work its valuable guidance on it. This consistent data flow streamlines the patient experience by producing inferences from multiple data points, which help to guide and improve healthcare management capabilities with information and data while handling patients, equipment, and procedures.
How can IoT and Edge computing benefit the connected ecosystem?
Image credit: Depositphotos enhanced by CogWorld
The benefits of bringing in IoT to the mainstream in these connected ecosystems stems from decoupling the bulky medical devices for data capture capabilities. IoT devices are more sustainable in the future when left alone to smarten this demanding data capability.
Next in line is edge computing – which can either be near-edge or far-edge, and scales with computing complexities as needed. Edge can deliver the inferences right to the providers and facilities rather than acting as a data accumulation hub. One of the sustainable goals is data minimization for effective and efficient use of data. ML algorithms can provide inferences in real-time to providers and facilities thereby improving the delivery experience as well as reducing costs and gaining efficiency. Edge along with IoT components, when implemented in connected ecosystems, work in a smart cohesive manner.
AI can be used to check symptoms and assist in accessing relevant medical information, identify risks, and provide guidance to patients on convenient devices such as their mobile phones. It can learn from social patterns, diet, nutrition, and the patient’s immediate environment while enhancing the overall healthcare experience. The monitoring and AI guidance of vitals, nutrition, metabolic levels, breathing, sleep and activity patterns are provided to healthcare professionals and doctors in real-time using many different AI techniques in the connected ecosystem.
Image Credit: Utpal Mangla, Dr. Doreen Rosenstrauch and Atul Gupta
These interrelated areas, when connected with the use of data, algorithms, and interoperability, bring together decentralized data in a federated manner to AI in healthcare solutions. This orchestrates the healthcare spectrum with a complete AI driven experience by virtue of connected healthcare AI systems.
This new dimension of healthcare has already been rooted and is now advancing fast enough to become the norm in the coming years. Even though this is gaining momentum, there are still some complex challenges in the delivery of these connected solutions to the industry and providers at large. The connected system, compute capacities, edge delivery, ethics, data trust, legal, and privacy concerns are some of the complex challenges in need of gaining maturity. The highlight being the connected systems in the ecosystem – Edge computing and IoT play a big role in strengthening the connected ecosystem.
AI and healthcare management are strongly aligned with the variety and veracity of incoming data analyzed and fed back into operational systems to enhance the patient experience, population health, and well-being of the healthcare team while reducing healthcare costs. Data federation of decentralized data assets brings all connected systems together. Meeting the needed standards and maturity in privacy and legal will bring it into the mainstream while maintaining balance with governance. The governance aspect is needed for accurate implementations and expanding the footprint in more communities and countries.
Conclusion
All-encompassing edge computing, IoT, and a connected AI based healthcare ecosystem will evolve new dimensions in healthcare and improve the experience for patients, providers and all systems involved.