Over the past two years, many people have been apprehensive about their health due to COVID-19. Because of the risk of getting COVID-19 during in-person medical visits, it is not unexpected that more people are interested in using technology to receive healthcare; nearly three-quarters of Americans think the pandemic has boosted their enthusiasm to try the virtual treatment. Telehealth has seen a 38x rise in venture capital investment in digital health in 2020 compared to 2017. It is due to consumers' and providers' enhanced preparedness to adopt the technology.
Healthcare firms may now conduct remote patient monitoring at scale and enhance patient outcomes thanks to artificial intelligence (AI). To comprehend the value that conversational AI solutions and virtual assistants may provide to healthcare organizations, one needs to consider the COVID-19 situation. Patients of varying severity and risk overrun hospitals as infected cases increase exponentially. Machine learning (ML), and artificial intelligence (AI) technologies, such as deep learning (DL), natural language processing (NLP), and others, may outperform humans in specific domains, such as disease detection. Undoubtedly, artificial intelligence in healthcare has demonstrated the ability to learn and do jobs like humans.
In this blog :
What is Remote Patient Monitoring or RPM?
Remote Patient Monitoring (RPM) is a practice where healthcare delivery uses the latest advances in AI and information technology to gather patient data outside traditional healthcare settings. By employing technology to bridge the conventional physical healthcare setting and where people live daily, remote patient management aims to move more healthcare services outside that setting.
RPM platform closely resembles the smartphones and tablets that are currently popular among users of all ages, including elders, and it uses technology to encourage patients to take an active role in managing their health. With AI in remote patient monitoring services, patients take an active role and care for their health, and clinicians can better comprehend and handle their patients' health situations thanks to a more continuous stream of data that paints a much clearer picture of their health. AI in Remote patient monitoring can aid in enhancing the standard of treatment. Thanks to quality RPM models.
RPM's role in senior citizens
The most common age group of senior citizens is between 65 to 80 years old. Senior citizens face many health risks, such as chronic diseases, declining cognitive abilities, and frailty. To address the risks that senior citizens face, they need frequent monitoring by their care providers, with the help of AI in remote patient monitoring services.
Remote patient monitoring (RPM) is a growing field in healthcare, and it's particularly well-suited for senior citizens. In the past, RPM was done through in-person visits to the doctor's office or clinic. However, with the recent advances in telemedicine and the advent of AI in remote patient monitoring, senior citizens can now be monitored remotely using AI and deep learning technologies. RPM allows senior citizens to live independently for extended periods while still receiving the necessary care. Let us further look at the use cases of deep learning in healthcare.
Use cases of Deep Learning and AI in Healthcare
AI and Deep Learning can fill in the gaps that prevent developing nations with limited infrastructure and resources from accessing healthcare. Its features, such as digital self-assessments, remote patient monitoring, and data extraction, make healthcare available to everyone. The healthcare sector is where AI and Deep Learning have already broken-down barriers. AI and Deep Learning can effectively address some of the medical sector's most prevalent but essential issues. artificial intelligence in healthcare may be widely used in the medical industry, making it routine and speeding up processes.
1. Aid in the development of new drugs
In the medical industry, finding new medications is an ongoing endeavor with great potential for combating chronic or recently discovered disorders. Drug discovery benefits from the speed and accuracy with which AI and Deep Learning can do tasks using machine learning. AI-based models may run many programs at once, locate the appropriate resources, and take control of every clinical trial phase to monitor, analyze, and generate reliable data in a shorter amount of time.
2. Monitor patients using virtual nurses or bots
AI in remote patient monitoring services can guarantee continuous healthcare support, whether it be through AI in patient monitoring at a hospital or remote monitoring in distant areas. For instance, AI-powered virtual nurses can monitor the behaviors of vital patients in intensive care units (ICUs). They may also be used to deliver blood results or to analyze photos taken from patients in far-off locations for guidance and diagnosis. Additionally, AI-based virtual assistants provide constant patient communication outside hospital visits, including concerns about admission, doctor availability, assistance with nutrition plans, and more.
3. Manage medical data and maintain patient records
Data are the lifeblood of the healthcare sector and losing them could be expensive and prevent advancement and innovation. AI with deep learning is used to record doctor-patient interactions, lines of therapy, etc., for future analysis and to keep current electronic health records (EHR). Massive amounts of pertinent data may be intelligently processed, categorized, and securely stored for later use.
4. Perform robot-assisted surgery
Robotic surgery is not a recent development in the medical field. However, throughout the past few decades, sophisticated or lengthy procedures have successfully deployed AI robots to reduce complications, time, and effort that can be taxing on the individuals involved. Real-time data of AI in patient monitoring provides medical assistance to personnel via robots, which can also intelligently direct a surgical operation while delivering a magnified and comprehensive image of the operative site.
5. Streamline administrative workflows
Robotic process automation (RPA) and other technologies can collect the necessary medical data and automate tedious and repetitive operations, relieving the administrative personnel of some of their workload and enabling more efficient healthcare support systems. In the healthcare sector, automating administrative duties can lead to huge savings. Doctors and other healthcare professionals may effectively take notes during procedures and consultations, order lab tests, and design treatment plans for individual patients using AI technology like voice-to-text transcriptions.
6. Data-Driven Clinical Decision Making
Real-time data support clinical decision-making. AI can instantly give real-time data to keep valuable and accurate decision-making. Machine Learning algorithms identify potential threats, send status alerts on urgent or critical patients, prevent diagnostic mistakes, and promote positive doctor-patient relations.
7. Enhancing healthcare processes
AI in remote patient monitoring services and Deep Learning make better operations and task automation possible by AI, bringing several advances in these areas. AI makes scheduling appointments, surgeries, and follow-up visits simpler and less chaotic, tracking medical histories, updating patient information from various departments, and examining and resolving incorrect insurance claims.
8. Support Medical Research
Investing in medical research to improve disease prevention or develop novel medications costs billions of dollars, not to mention the time and effort needed. The availability of pertinent data that AI can quickly gather from many sources is at the heart of it. It can exchange data across numerous networks and contribute to life-changing medical breakthroughs by providing real-time information.
Technology is evolving at a rapid pace. The use of AI in remote patient monitoring services has enabled clinicians to make decisions based on data in real-time, improving patient experience. The future of artificial intelligence in healthcare promises more precise diagnoses, lower doctor burnout rates, and easier hospital visits, thanks to these new advancements. Even though implementing AI in patient monitoring presents several difficulties, including the complexity of the data, ethical considerations, security concerns, and other factors, functioning similarly to humans can close accessibility gaps in healthcare and promise adequate healthcare availability to improve people's lives.