Remote Patient Monitoring, or RPM, is not a widespread technology yet. However, continuous, non-invasive and unobtrusive monitoring of patients’ vitals can become an extremely efficient tool, beneficial both to patients and healthcare organizations. How to embrace the full power of this telehealth technology and what to start with when aiming at the long-term success? Here is our small guide.
Developing a Remote Patient Monitoring System: Asking Basic Questions
Insurances are getting more costly, the US population is aging, a consequent number of chronic diseases is increasing – all in all, these circumstances are forcing healthcare specialists to search for a solution, which would provide seniors and the elderly with comfortable and affordable conditions which will allow them to age decently. Telemedicine advances, and namely Remote Patient Monitoring (RPM), is expected to become such a solution.
No wonder: there are numerous benefits turning RPM projects into a multitool for today’s healthcare industry.
– Providing constant health control. This involves not only doctors’ better understanding of the treatment situation, but also greater patient engagement: now they have access to their care plan, and can follow and influence it.
– Cutting down on a number of visits for patients. This results in relief for staff, and cost optimization for patients, thus reducing the per capita cost of healthcare for the whole system.
– Focusing on prevention, rather than treatment. Regular tracking of vitals and daily disease condition monitoring provide clinicians with an opportunity to intervene early and take the necessary preventive measures.
Sure, finding a trustworthy technical partner among multiple IoT healthcare companies is the first step to starting your own development cycle. But before you contact them, it is better to define the project’s scope and get a clear project vision.
What: Figuring out the Way to Apply RPM
RPM projects are built for monitoring vital signs and transmitting information to a provider. They are collecting such data as Electrocardiogram (ECG), Electroencephalogram (EEG), heart beats and respiration rate, oxygen volume in blood or pulse oximetry, signals from the nervous system, blood pressure, body/skin temperature and blood glucose level. Besides, some of the systems gather information about the patient’s weight, their level of activity and sleep data.
Developing an IoT-driven RPM project usually starts with the question: which condition will this solution manage?
Most typically RPM systems belong to one of the following types:
- Heart and blood related diseases monitoring systems.
Remote monitoring of patients with heart failure is usually cited as one of the principal benefits which IoT can bring to healthcare. No wonder: heart diseases remain the No. 1 cause of death in the US, accounting for nearly 836,546 deaths (i.e., 1 of every 3 deaths).
Apart from connected sensors, the architecture of this solution includes: building software, which will empower the hardware part of the project and process the incoming data from EKG sensors in real time, and developing an app which will visualize the data at a suitable rate and in a readable form.
- Fall detection and mobility related disease monitoring systems. With falls being a major cause of injuries, it is getting important to develop such systems which won’t simply focus on factors like gait, vision, and cognition, but will integrate contextual information about patient behavior and environment for fall prediction. This is done via advanced sensors indicating sudden shifts experienced in a fall, fall detection algorithms, typically based on machine learning technologies, and an app sending a notification signal once a fall occurs.
- Monitoring system for brain, neurological system related diseases and mental health. The parameters under control in this case include, among others, speech, sleep activity and medication intake monitoring capabilities. However, biomarker data acquired by these solutions may differ significantly, depending on an end-user’s diagnosis, be it Parkinson ’s disease, epilepsy or dementia. It influences the system’s architecture, which generally includes sensors, data processing algorithms, end-terminal at the hospital, and the communication network.
- Diabetes monitoring system. Assisting patients with type 1 and type 2 diabetes, these RPM solutions present a powerful tool for those who are forced to constantly monitor their glucose level. Collecting real-time data, and sending it to a physician’s or healthcare providers facility allows to quickly react to any shifts in the chronic condition. Apart from the hardware element, and software empowering the whole solution, the RPM model needs an application, capable of retrieving and visualizing data coming from the client’s glucose meter, like the one developed by the R-Style Lab team.
For Whom: Defining Your Product’s Target Audience
For clearly defining the RPM system, it is essential to figure out who the product will be targeted at. Thus, the following questions should be considered:
- Type of a patient. The fact whether the RPM system’s audience consists of geriatric patients, pediatric patients or all patients with chronic conditions, correlates with their level of technological literacy, and cognitive skills needed to remember or perform certain tasks without extra coaching.
- Type of physical limitations and the patient’s mobility. Patients with hearing or vision limitations, limited mobility or other restrictions need to be treated in a particular way, when defining the project’s architecture.
- Type of monitoring. Will it be constant monitoring or a short-period one? At what time periods will it be aimed at: weeks, months or years?
- Type of home environment. RPM is aimed at being used in home conditions, thus, these conditions should be conducive to implementing these devices. This concerns, among all, tackling the connectivity issues.
How: Choosing the Most Appropriate RPM Type
Though each RPM project is unique, and there is no step-by-step roadmap for Internet of Things software development in the healthcare industry, there are some generalities lying at the basis of these solutions.
In their essence, Remote Patient Monitoring systems can be of two types:
· Contact-based RPM
The most wide-spread RPM systems include the following elements:
- Connected input device. Most often this is a sensor of any type, which can be either physically connected to the patient’s body or clothing, or embedded in their item: watch, clothing, etc. However, data acquisition can be performed not only by sensors but by other devices, like smart phones or computers. Data can be entered by patients manually or obtained via a wireless communications feed.
- Local data storage. This element of the RPM architecture is used for capturing and retaining data locally on the patient’s side.
- Central data repository. The repository aggregates and stores data from different devices on the provider’s side for their further processing.
- Data analytics software. Interpreting and analyzing acquired data, performed by this part of the system, leads to providing a solution or advice. Usually mobile apps fulfil this purpose, presenting the health data in the form of graphs, making any alteration from a normal value evident to end users.
- Connectivity protocol. This element is required for transmitting the collected data from an input device to a central data repository. Different types of protocols are used within the architecture, with the final choice depending on the project’s technical specification.
Despite being the most wide-spread one, this type implies certain limitations:
- They are hardly suitable for long-term monitoring, when a patient can simply forget to wear them or the device will face battery problems.
- They are not suitable for people who, for certain reasons, don’t want or cannot wear them, for example, dementia patients or the elderly not feeling comfortable with new technologies, or prisoners.
· Contactless RPM
In its turn, this system can be:
- Image-based methods. Image-based systems analyze images of patients to detect illnesses or falls.
- Radar-based methods. These systems use radio frequencies to get inputs for further processing and are often equipped with patient localization capability.
No smart healthcare solution can be deployed without specifying the project’s idea. Tapping into a specific condition and defining a corresponding patient group will help identify the right target for timely interventions and preventive actions. So far, this is one of the most evident and cost-effective ways to avoid the most typical pitfalls along the technology development way.
This post was sponsored by R-Style Lab
Alex Makarevich is Content Manager at R-Style Lab – a custom software development company (IoT, Web, Mobile) with a business office in San Francisco, CA and dev center in Belarus, Europe. Having worked in the publishing house Éditions Techniques de l'Ingénieur (Paris, France) and got experienced in editing texts both in English and French, she has switched now to topics associated with IoT, web and mobile development.