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The COVID-19 pandemic has emphasized the need for innovations in remote patient care, which is more critical than ever due to social-distancing norms. Amazon and Microsoft have invested heavily in digital healthcare, specifically in Remote Patient Monitoring (RPM).

RPM innovations are also beneficial to patients with chronic conditions such as heart disease and diabetes. Damage caused by these conditions accelerates death. Diagnosticians could reduce the impact of this damage with timely medical intervention with the help of Artificial Intelligence-assisted analytics. The right Electronic Health Record (EHR) system and cloud computing technologies can also help create a strong RPM network.

RPM Reduces the Risk of Contagion

Prior to COVID-19, researchers observed that the probability of spreading viruses and hospital-acquired infections increased with the length of a patient’s stay in the hospital. Data from the US Centers for Disease Control and Prevention (CDC) shows that hospital stays increased infection risks by 17.6%, with a 1.6% daily increase. Telemedicine software, therefore, is more crucial than ever before in delivering secure healthcare. The majority of hospitals still monitor a patient’s condition manually every 4–6 hours, which makes matters worse. The condition of a patient may deteriorate for extended periods without being noticed.

The most current patient information is not always available to healthcare professionals. The nurses’ shift timings change, complicating things even further.

The Benefits of RPM for Medical Experts

RPM with real-time data analytics powered by AI is essential for addressing all of the issues listed above. RPM data helps physicians prioritize care efficiently and make informed decisions. In overcrowded wards, this ability to prioritize is the only way to appropriately triage patients and treat the most serious illnesses first.  An AI-powered analytics platform organizes health data derived from RPM devices such as oximeters, spirometers, and apnea monitors into manageable layers. In this case, a hospital’s resources and the opinion of a medical expert can be applied.

Thousands of patients can be analyzed simultaneously and in real-time using such data analytics.

In the future, AI-powered RPM will enable the following capabilities for the US Centers for Medicare and Medicaid Services (CMS):

 

    Reduction of 38% in readmissions

    25% reduction in emergency room visits

    Increase patient satisfaction by 25%

    Reduce overhead by over 17%, and

    13% increase in Medicaid compliance

RPM’s Major Elements

It is possible to segment an RPM the same way you would segment an Internet of Things (IoT) setup. Therefore, RPM can be broken down into the following high-level components:

Typically, these devices include a Bluetooth module to capture all relevant patient data. Wearable devices that monitor many health factors, such as blood pressure and heart rate, can be implanted under or over the skin. US Food and Drug Administration (FDA) authorization is currently limited to non-invasive devices measuring a basic range of physiological phenomena.

 Health care providers must be able to access patient metrics from wearable devices. This transfer is most commonly accomplished via Bluetooth Low Energy (BLE) communication. Patient data is collected from wearable devices and sent to a clinician via a mobile app. To safeguard against connectivity issues, these apps need to be compatible with BLE data exchange networks and have caching mechanisms.

The application should be integrated with the healthcare provider’s system through a secure API built on Fast Healthcare Interoperability Resources (FHIR) standards.

In order to make an application engaging for patients, it usually features informative visuals and top-notch UX design. A video calling feature, medication reminders, and educational content are all available on these apps

A cloud repository can be used to store raw data from a patient’s wearable device. An AI-driven analytics system stratifies this data into manageable, labeled clusters.

RPM devices can connect directly to the cloud, which allows all information captured by the device to be deposited there. A hospital-side web app is similar to a patient-side app and is compliant with HIPAA regulations. The system interacts with the hospital administration’s Electronic Medical Records (EMR) system using FHIR APIs. Individual departmental data silos are also accessible through the app.

Additionally, the RPM setup includes some or all of the following modules:

i)Decision support: This module copies the patient’s vital signs from the repository and compares them to the normal values before sending them to the physician.

ii)Reporting: The RPM system generates reports from the relevant patient metrics and manually entered data.

iii)Notification: When an abnormality in the patient’s data is detected, the decision support module sends an alert message to the respective physician or consultant.

(iv)Analytics: Using Business Intelligence (BI) and Artificial Intelligence (AI) technologies, real-time inferences are drawn from patient data. Using these inferences, doctors can predict risks and make well-informed treatment decisions.

Upcoming prototypes and popular RPM devices

By the end of 2022, more than 4 million patients will be monitored remotely, according to a 2020 survey by IHS Markit. There will be a 34% increase in use of RPM over the next year alone because of Big Tech’s innovations. Here are some of the devices that have taken the medical device market by storm:

In recent years, remote specimen collection devices have grown increasingly popular, particularly those based on the Volumetric Absorptive Microsampling (VAMS) technique. Due to new analytics technologies, blood from a fingertip is sufficient since its volume has less than 5% variation. By using VAMS, there is no need for venous blood collection or conventional phlebotomy.

The devices detect high blood sugar levels by monitoring blood sugar levels using a sensor that is inserted under the skin. In addition to physicians using these devices remotely, parents of children with Type-I Diabetes can also monitor their children’s levels and take action when dangerous levels are reached. Using these devices, surgeons can reach places inside the body with greater precision than they would otherwise be able to. Many companies such as Riverfield and Stryker are developing strategies to mass-produce surgical tools at affordable prices using Robotic Process Automation (RPA) technologies.

They would be able to visualize the target organ in magnified form and use advanced instruments to reach it safely. Artificial intelligence will enhance computer vision by the end of 2021, which can be applied to get faster results for the visual aspects of patient vitals.

 During or after childbirth, this technology will be used to estimate blood loss or hemorrhaging in mothers. Particularly in hard-to-reach regions and rural areas, where gynecological intervention is limited, this technology will become crucial.

Summary

In addition to allowing doctors to treat a greater number of patients, RPM systems also enable patients to participate more actively in their own health care. We can expect unexpected accessibility issues to crop up in the coming years, thereby increasing the relevance of these technologies. The use of AI-enabled data analytics and cloud computing in RPMs will also enable doctors to improve diagnosis and treatment precision.

 

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