Two technologies have changed how healthcare services are offered and
managed in the previous decade: patient monitoring systems and remote
health devices. Today, the healthcare industry provides patients with
various new technologies to monitor their health remotely. These can
be various outpatient care devices, from wearable devices that monitor
basic parameters like heart rate and blood pressure to complex remote
patient monitoring solutions that allow prolonged observation of a
patient’s well-being using stationed monitors from miles away. These
innovations are invaluable and help us to provide better patient care.
Real-time data monitoring identifies problems before they become
critical and avoids hospitalization in time. In addition, remote
patient monitoring reduces the time patients have to spend in
hospitals, giving them more access to medical services. It is also a
convenient approach for healthcare providers because it does not limit
them to the hospital rooms.
Some notable recent innovations are wearable health devices such as
smartwatches and fitness trackers to monitor heart rate,
electrocardiogram (ECG), or sleep patterns. Remote patient monitoring
systems have been advanced so much that they can also be configured to
enable telehealth interaction, and healthcare providers can provide
care from a distance of many miles. Artificial intelligence (AI) and
machine learning (ML) can assist in analyzing huge chunks of health
data to provide customized feedback and predictive analytics to detect
or prevent potential health issues.
These transformations are not only reshaping the patient experience
but also rewriting the economics of home healthcare. For one, they
enable increased monitoring and data-driven decision-making, which can
help patients remain out of the hospital for longer. Letting patients
rebound at home reduces readmissions back to the hospital, making the
overall healthcare delivery more efficient and reducing significant
economic pressure on healthcare systems. The more widely accepted
these innovative systems become, the brighter the future looks.
In addition to supplementing clinical care, wearable health devices have become viable patient monitoring tools. These tools include smartwatches, fitness trackers, and biosensors. Smartwatches like Apple Watch and Fitbit have heart-rate monitors, electrocardiogram (ECG) capabilities, and sleep trackers. These data-generating devices can track vital signs and physical activity, giving wearers a handy snapshot of their health on their wrists. These devices, such as the Garmin Vivosmart and the WHOOP Strap, monitor a person’s physical activity, sleep, and fitness level in real-time, offering data on metrics that include steps taken, calories burned, and heart rate variability. Embedded in clothing or worn as patches, biosensors offer a more specialized kind of monitoring. For example, patients with diabetes who wear soil-thin needles that detect blood glucose levels in real-time can manage their condition more effectively because their devices offer real-time readings through a continual glucose monitor or CGM.
Remote Patient Monitoring (RPM) technologies track patients’ health
status in situations where they are outside a standard clinical
setting. RPM technologies comprise a sprawling set of tools and
technologies ranging from customized mobile apps with text and audio
capabilities to in-home remote sensors and connected devices that
track health parameters and with various assessments and capabilities.
These RPM systems, in real-time, capture diverse parameters such as
patient blood-glucose levels, weight, blood pressure, heart rate, and
oxygen saturation and transmit them to health workers to adjust and
enhance their monitoring and treatment programs and plans.
For chronic care and post-operative care, the benefits of RPM become
even clearer. For chronic conditions such as hypertension or diabetes,
RPM can monitor the patient’s vital signs on a daily basis, allow for
early identification of complications, and allow the physician to
adjust the treatment plan without frequent visits to the clinic or
in-hospital stays – and offer personalized treatment to each
participant. For patients recovering from surgery, RPM allows for
keeping track of recovery, early identification, and prevention of
complications, reducing readmissions to the hospital, and offering
interventions in true ‘real-time’ based on data.
Applying artificial intelligence (AI) and machine learning (ML)
algorithms to patient monitoring deepens the intelligence of current
systems and equips them with advanced data analytics and predictive
analytics capabilities. To prevent loco-regional complications from
developing into life-threatening problems such as sepsis or local
infection, predictive capabilities can detect the changes earlier and
deliver more personalized treatment based on individual diffused
patient data.
AI-assisted monitoring consists of applications such as early disease
alert systems, personalized health advice, and warnings of abnormal
readings, such as the emergence of irregular heartbeats. Arrhythmias,
for example, can be detected by algorithms that crunch ECG data. At
the same time, other machine learning models might predict heart
failure long before it hits the critical stage, with clinical
recommendations personalized to an individual patient’s historical
data and real-time monitoring.
Telehealth and telemedicine, all made possible through technological
advances, completely changed a traditional perspective of accessing
healthcare. They both use digital technologies to deliver healthcare
services to patients remotely. It allows medical experts to consult,
assess, and prescribe medications or therapies without bringing
patients to their clinics. This particular type of online healthcare
service is often combined with remote monitoring devices. Using
special digital technologies and wearable biomedical sensors,
telehealth can offer efficient and comprehensive care to patients.
When combined with remote monitoring, virtual consultation with a
physician includes tracking a patient’s health metrics in real-time.
For example, a patient with a chronic illness can use a remote blood
glucose monitor that provides the same data for the physician
participating in the virtual visit. This would decrease the time for
accurate and individualized assessments and provide more efficient and
precise chronic disease management and personalized treatment plans.
It vastly improves healthcare services in remote and underserved areas
by reducing the need to travel to distant specialty care facilities,
and it increases engagement among patients by implicating individuals
in their healthcare management.
Home health devices allow patients to increase self-care and
monitoring, which are traditionally limited to the hospital. Devices
such as blood pressure monitors, glucometers, and pulse oximeters now
allow patients to manage disease from their homes. Examples are: Blood
pressure monitors (home) easily allow the individual (in this case,
hypertensive) to ‘measure’ their blood pressure and track
changes/trends over time. Glucometers (home) are used for people with
diabetes to help them monitor their condition and keep blood sugars in
the normal range. Pulse oximeters (home/hospital) measure blood oxygen
saturation in real-time.
With the power of monitoring vital signs at home, more patients can
take charge of their own healthcare and proactively manage their
diseases. Early awareness of any problems aids the patient in adhering
to a treatment strategy to avoid complications and hospitalizations.
The home health devices will also provide rich information that can be
shared with caregivers, which might lead to a quicker and more
insightful medical decision.
Mobile health apps are a fundamental part of the they are here to
stay. There are now apps for nearly every condition or aspect of
wellbeing. Some apps manage single conditions – for example, diabetes
management apps where users input their blood sugar level and check in
when taking medications – and apps that monitor physical activity,
diet, and mental health more generally.
Mobile health apps also become more helpful and valuable if integrated
with other devices and health information systems. For example, an app
can communicate with other devices, such as smartwatches and other
wearable health-related gadgets, so that information and
health-related data in various health-tracking apps can be aggregated.
Moreover, many health apps send data to electronic health records
(EHR) and remote monitoring systems today. This connectivity allows
for better coordination of care between patients and health
professionals, will enable people to track health more accurately when
data about similar patients are available, and allows health
professionals to make better decisions if they have access to
real-time data.
Improved patient monitoring and remote health devices allow patients to receive improved care by continuously monitoring them and providing early detection of potentially serious health problems. As users of these devices can provide real-time data for their health metrics, users can be constantly monitored and health metrics read early, particularly if abnormalities or new conditions are emerging. This will be important for early intervention and, by reducing the severity and frequency of health crises, lead to improved health outcomes for patients with chronic or acute conditions. For example, continuous glucose monitors for diabetes will read blood sugar more continuously, which can help to prevent dangerous ups and downs in blood sugar numbers and can adjust treatments more accurately.
Innovative devices improve access and convenience, especially for patients in rural and service deserts. Access can be further enhanced by innovative new health devices that increase the accessibility and convenience of healthcare services. For instance, patients living in rural or service deserts can remain in the comfort of their homes, thanks to telehealth platforms or remote monitoring of their condition. This ease of access minimizes the need to travel long distances to consult with doctors and nurses to seek medical services, especially disadvantaged residents who can’t physically do so or cannot afford the cost of transportation. Subsequently, patients can follow up on their medical condition more frequently, at different times, without causing any disruption to their regular tasks.
Remote health devices collect and analyze data, which has the potential to give us a better understanding of patient information. Tracking data on different health metrics allows for a personalization that wouldn’t otherwise be possible. For example, aggregating data from cardiac patients would allow healthcare providers to concoct more precise treatment plans according to the individual’s needs and conditions. Thus, data-driven care could greatly help improve patient outcomes and quality of care.
Primary among these challenges, particularly for patient monitoring
and remote health devices, is data security and protection. Monitoring
a person's health involves collecting sensitive and personal
information that must be available only to authorized parties,
particularly protecting it from data breaches and the sale of said
information to predatory third parties. Regulations such as the Health
Insurance Portability and Accountability Act (HIPAA) and General Data
Protection Regulation (GDPR) require that healthcare providers and
device manufacturers employ robust security measures for storing,
manipulating, and disseminating data.
Essential steps in securing sensitive health data are about facing
today’s hurdles. As the proliferation of connected devices creates
more potential attack surfaces, we need more robust encryption and
more secure communication protocols. We also need more proactive
network surveillance to enhance security as threats evolve. To prepare
for the future, we need to be more proactive in protecting the vast
amounts of data that patients entrust to their doctors by effectively
encrypting data, securing access, and performing regular security
audits.
However, we’re going to have some big challenges with these devices.
Integrating and working with the existing health systems is a concern.
These new patient monitoring devices have to be seamlessly connected
with EHRs (electronic health records), health information systems,
clinic healthcare, clinical tools, and more—collecting, processing,
and transferring accurate data quickly and effectively.
However, the range of medical devices and various systems employed to
manage patient data might be different from one another or with some
of the data standards, protocols, or formats that healthcare may
require. Healthcare organizations will benefit from investing in
developing and adopting standardized interfaces and integration
frameworks that promote the efficient sharing of patient data across
diverse systems. Collaborating with medical device manufacturers and
technology providers (e.g., EHRs) to facilitate compatibility with
receiving platforms is essential to improving interoperability and
providing a unified view of patient information.
User adoption and training: Promoting the implementation of new
monitoring technologies requires user buy-in. Patients and providers
have to understand how to operate new devices and how to use the
information they provide most effectively and efficiently. Training
programs should address both the mechanical aspects of operating the
device and best practices for interpreting the data and incorporating
it into clinicians’ work processes.
Overcoming resistance to new monitoring systems can sometimes require
deliberately extra time and effort. Some patients and providers could
be reluctant to trust new technologies that they find too complex,
unreliable, or nosy. All of these concerns must be considered
conscientiously before recommending a new device and mitigating in the
instructions you provide to users. For example, if you support a new
blood-pressure-monitoring app for patients, you must provide clear,
user-friendly instructions and support resources. In addition, slowly
incorporating users into developing and testing new technology and
showing them the benefits through pilot programs or case studies can
allow for smoother integration.
The data collected from these sensors could revolutionize future patient monitoring devices and remote health technology if sensor technology can improve significantly. As sensor capabilities continue to advance, there could be an increase in sensitive and specific sensors that could monitor various physiological and behavioral parameters with better accuracy and precision. For instance, wearable health monitors, such as smartwatches and biosensors, could continue reliably measuring and recording health-related data, such as heart rate, blood pressure, blood oxygen levels, and activity levels.
Predictive healthcare is already made possible with artificial
Intelligence (AI) and abundant big data reality, providing powerful
early warning systems to anticipate digital health databases can
health and anticipate by decoding patterns and fluctuating health
conditions. This information could be used earlier to steer the person
toward a lifestyle to avoid or delay illness.
Big data adds urgency to these capabilities by pooling electronic
health records, vital measurements from wearable devices, genomic
information, and other sources to magnify the power of the data pool
to craft more precise, tailored predictions that enable healthcare
providers to develop and apply more effective anticipatory and
targeted avenues of preventive care and treatment. Integrating AI with
big data in patient monitoring systems drives the potential for
earlier detection, better outcomes, and greater healthcare delivery
efficiencies.
Additionally, blockchain technology has the potential to securely and
transparently share patient data, potentially overcoming many of the
challenges of keeping data private and secure today. Taking advantage
of a decentralized, tamper-proof, and immutable ledger means that
using the blockchain minimizes the risk of security breaches exposing
private data or instances where data changes hands without the
patient’s knowledge or consent.
In the health arena, by its ability to enable the secure sharing of
health information (but only with explicit consent to certain
providers or stakeholders), with precise control over every access
conducted, blockchain has the potential to enhance the trust that
patients place in the institutions that collect and process
information which is vital to their health and care. It can also
transform processes, reduce potential errors, and improve the accuracy
and quality of health records shared with the appropriate care
providers.
Innovations in patient monitoring systems and remote health devices empower doctors and patients to engage in new ways – with increased scope for better care of patients, as well as more effective management of health in various contexts. Recent advancements in sensor technology are making devices more accurate and sensitive than ever. The interplay between artificial intelligence (AI) and big data analytics can offer a new paradigm of predictive healthcare. This leads to enhanced opportunities, such as providing care tailored to individual needs and enabling more accurate and early interventions. Moreover, combining AI and blockchain technology, essentially a cloud-based shared record of transactions and events, could enhance data security and transparency, building trust in data sharing.