The 2019 coronavirus has wreaked havoc on our world, but it has also accelerated the adoption of telemedicine as a safe alternative to in-person appointments. One area of telemedicine that has gained a foothold over the past two years is remote monitoring.
Let’s take a look at what remote patient monitoring is and how artificial intelligence can save the world again.
Remote patient monitoring is a growing field in the healthcare industry that uses technology to collect patient data outside of the traditional doctor's office or hospital setting. Collect a variety of patient data including vital signs, activity levels, and more.
According to reports, the global remote monitoring patient equipment market is expected to exceed US$101 billion by 2028. The growing prevalence of chronic diseases such as diabetes, cardiovascular diseases, etc. is driving the market.
Remote monitoring of patients is an effective method of monitoring individuals or groups who cannot be monitored in person. In some cases, remote monitoring can be used to track a person's vital signs, such as blood pressure or pulse rate. Remote patient monitoring can also be used to monitor patients who are at risk for hypothermia or other medical conditions that require ongoing attention.
The cost saving potential of RPM solutions is huge. As a result, 69% of healthcare professionals rank RPM as the number one reducing factor in overall cost.
Remote monitoring allows patients to obtain professional diagnosis without spending time and money traveling to the hospital or clinic where they receive treatment. Additionally, teletherapy translates to:
● Optimize time with patients: No need to take routine vitals and questions since the data is already available.
● Improved communication due to increased accessibility of RPM solutions.
During the epidemic, hospitals have become centers for the spread of infectious diseases. Therefore, booking an appointment online becomes one of the safest options for getting professional consultation. Through remote monitoring of patients, doctors and nurses can monitor their patients at home, thus preventing individuals from contracting anything in the hospital.
Remote monitoring also helps improve the quality of care because it allows nurses and doctors to monitor patients' vital signs without visiting them in person. Having this information also allows patients with chronic diseases to be better treated because they can be monitored more frequently.
Since doctors and nurses can monitor data around the clock, this increases the likelihood of better adherence to treatment. Patients can also live more autonomously and be more involved in their treatment.
Finally, remote patient monitoring reduces the inequalities associated with traditional healthcare. Online monitoring solutions can also provide remote consultation and follow-up services to people living in rural areas.
There are many RPM systems on the market, and they come in all shapes and sizes. Some RPM systems are stand-alone devices, while others are integrated into existing electronic health records. But what all RPM systems have in common is the ability to collect patient-generated health data and then send the data to healthcare providers for monitoring.
● Standalone medical measurement devices such as patches, blood glucose concentrations, pulse oximeters, etc.
● Implantable devices, for example, cardiac implantable electronic devices.
● Digital platforms enable continuous patient monitoring and support around the clock, including telemedicine.
Typically, RPM solutions connect to the cloud, enabling compliant data sharing and seamless access to patient data.
● The patient is registered with the system so that the system can authenticate the specific device.
● The system initializes monitoring and data collection through medical equipment.
● The device collects and transmits data to the RPM server or cloud.
● Algorithms analyze patient data and the system generates reports and visualizations.
The doctor accesses the visualization and follows the corresponding actions, whether adjusting the treatment course, changing the treatment plan, or any other subsequent actions.
The significant impact of artificial intelligence on healthcare has led to the growth of the artificial intelligence market. By 2030, the value of artificial intelligence in the healthcare market is expected to exceed $187 billion.
The potential of artificial intelligence is also reflected in telemedicine and remote monitoring. Therefore, AI-driven technology has transformed RPM solutions from simple data aggregators into advanced data analysis platforms. Combined with analytics, the RPM platform allows physicians to integrate patient data into clinical workflows, generate accurate predictions, and flag individual patients at risk.
As a result, AI can enable proactive care and more personalized data-driven treatments. So, where does machine learning fit in?
According to data, remote health monitoring of diabetic retinopathy reduced patient visits by approximately 14,000 times. If we add artificial intelligence to the screening phase, we expect the number of visits and patient wait times to fall even further.
Thus, machine learning classification algorithms can analyze patient data in RPM solutions and flag patients who are at risk for certain diseases. Patients can also upload medical images to a secure server, where AI-based image recognition can spot abnormalities without professional help.
Artificial intelligence is also proving helpful in precision medicine. The AI-powered system compares the patient's medical images to a database of high-quality treatment options created by certified experts. It then combines these insights with personal health data to generate a personalized treatment plan.
According to IBM, the expert system can also group patients based on similar responses to treatments to produce optimal treatment options.
Keeping patients adhering to their medications or making timely appointments is another responsibility of artificial intelligence in monitoring patients remotely. By analyzing software data, AI can be used to generate action items, including appointment reminders, follow-up actions, and more. Powered by artificial intelligence and natural language processing technologies, chatbots are integral in automating communications and improving access to healthcare.
The complexity of chronic disease management has always been uncharted territory for the healthcare industry. However, AI can prevent chronic diseases such as diabetes, cancer and kidney disease by identifying early signs of these diseases in patient data. Therefore, the algorithm can identify patients with chronic kidney disease by stage and the presence of acute kidney injury.
Remote patient monitoring is a much-needed iteration of the traditional healthcare system, making professional diagnosis and treatment accessible to all. Artificial intelligence is gradually entering RPM software to enhance its data processing capabilities and transform it into a viable tool to complement offline treatment. Artificial intelligence supports the efficiency of disease diagnosis, personalized treatment and disease prevention to improve patient outcomes and proactive treatment.
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