A Secure and Scalable IoT‑Driven Framework for Real‑Time Remote

Patient Monitoring and Explainable Telemedicine in Diverse

Healthcare Settings

Shweta Dhondse

, Philomina Jenifer A.

, S. Usha Nandhini

, Maddhi Sunitha

Gokulnath A.

and Syed Zahidur Rashid

Department of Electronics Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India

Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Vadapalanio

Campus, Chennai, Tamil Nadu, India

Department of Electronics and Communication Engineering, J.J. College of Engineering and Technology, Tiruchirappalli,

Tamil Nadu, India

Professor & Head CSE (Cyber Security), CVR College of Engineering, Hyderabad‑501510, Telangana, India

Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Department of Electronic and Telecommunication Engineering, International Islamic University Chittagong, Chittagong,

Bangladesh

Keywords: IoT Healthcare, Remote Patient Monitoring, Telemedicine, Explainable AI, Secure Health Data.

Abstract: Adoption of Internet of Things (IoT) technologies in healthcare has changed the way patient data are being

collected, analyzed and exploited. This study aims to present a secure, scalable, and energy-efficient IoT-

based remote patient monitoring and telemedicine solution to solve the problems in the existing systems.

Unlike other methods that are limited to simulations or a centralised architecture, the proposed model is

evaluated on real-world data sets and is optimised for urban hospitals and rural clinics. The system leverages

open-source components, HL7/FHIR interoperability layers, and edge computing low-power devices to

provide a secured, constantly connected monitoring service with full capability to collect, transmit and

safeguard data at the edge with minimal latency. Privacy and Security are delivered using blockchain data

trails, and end to end encryption that meet global regulations. In addition, explainable AI methods including

SHAP and LIME can be incorporated to give transparency and trust for decision-making in a clinical setting.

The framework is robust and flexible, and patient-driven, making it widely applicable across resource-limited

healthcare systems.

1 INTRODUCTION

The explosive development of digital technologies

like Internet of Things (IoT) has led to a digital era of

healthcare innovation in which medical services are

data-driven, patient-centric and in near real-time

contact related to the patient. One of the most

effective use cases of IoT in this regard are Remote

Patient Monitoring (RPM) and Telemedicine, both of

which serve to fill the spatial and temporal void

between patient and provider in the health care

continuum. Given the growing complexity of the

world as a new healthcare environment emerges

including aging populations, growing burdens of

chronic diseases, and resource-strapped rural areas

we need smart systems that can provide reliable,

scalable, and secure healthcare beyond the walls of

traditional hospitals.

Prior works have investigated integration of IoT

devices in clinical settings but were subject to limited

validation outside the lab, lack of standardization, and

the privacy and scalability problems with the data.

Moreover, the majority of existing applications have

been designed to work over centralized architectures

which can be computationally intensive and

infeasible in low bandwidth or remote environments.

These limitations have limited implementation of

RPM and telemedicine on a wide scale, particularly

Dhondse, S., A., P. J., Nandhini, S. U., Sunitha, M., A., G. and Rashid, S. Z.

A Secure and Scalable IoT-Driven Framework for Real-Time Remote Patient Monitoring and Explainable Telemedicine in Diverse Healthcare Settings.

DOI: 10.5220/0013857800004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

78-84

ISBN: 978-989-758-777-1

in low-resource settings where the benefits could be

greatest.

To address these limitations, in this work, a new

IoT-based healthcare framework is proposed that

meets the patients’ needs incuding a smooth

monitoring and tele-medical consultation in a

secured, interconnected and clear way. Using edge-

computing, open standards (HL7, FHIR) as well as

blockchain data-trails and interpretable AI models,

the designed system provides a robust real-time and

patient-centric solution for various health-care

ecosystems. This strategy is in line with the

worldwide vision of fair, sustainable and safe

delivery of healthcare, in a process paving the way

2 PROBLEM STATEMENT

Despite the increasing integration of IoT in

healthcare, current telemedicine and remote patient

monitoring systems have some critical limitations

that limit their scalability and efficiency. Existing

solutions are mostly applicable in an environment

with control, or they use a central network

architecture which requires higher bandwidth,

permanent connectivity and costly infrastructure,

precluding their application in low-resource settings.

In addition, conflicts of interest between vendors;

along with the lack of interoperability; security of the

data, lack of support for explainable decision-

making compromises the reliability and trust one can

put in such systems for clinical care. There's also a

big hole around patient-centred design, real-time

flexibility and compliance with global health data

standards. As a result, there is an urgent demand for

an IoT-based framework that is scalable, secure, and

explainable for continuous, privacy-preserving, and

understandable remote care across heterogeneous

healthcare infrastructures.

3 LITERATURE SURVEY

The integration of Internet of Things (IoT)

technology into healthcare systems has made a great

change on how patient information is gathered,

processed and analyzed for clinical use as well as

personal health monitoring. In recent times, there has

been a strong focus on research on remote patient

monitoring (RPM) and telemedicine, to improve

access to care and alleviate pressures on healthcare

(Shah et al., 2012).

Ali et al. (2021) conducted a detailed review of

IoT enabled smart healthcare and summarized the

application of real-time data acquisition and wireless

sensor networks for chronic disease surveillance. Yet

their work emphasized the absence of field

application as well as the problems associated with

energy consumption and data volume. Singh and

Kumar (2023) also reviewed IoT-based patient

monitoring systems architecture; however, they

agreed with the requisite of more sophisticated data

fusion and better integration with medical standards.

Real-time telemedicine systems have been

investigated in Smith and Johnson (2024), where the

authors have presented a cloud-based continuous

monitoring infrastructure. However, their method

depends on the use of high-bandwidth networks and

is infeasible for deployment in underprivileged areas.

The need for interoperability is further highlighted by

Kumar and Sharma (), they stressed that

standard data formats (HL7, FHIR) are the best

choices for successful system integration, but few

products in the marketplace are designed serve these

standards.

With respect to security concerns in telemedicine

systems, Garcia and Thompson (2024) were

concerned with patient data protection. They

highlighted the growing danger of IoT ecosystems

being attacked by cyber threats and stressed the need

for incorporating encryption and decentralized

storage, however lacked simply realized security

measures set forth in their work. This void is also

corroborated by Evans and Martinez (2024) that

distinguished privacy as main barrier in adopting IoT

for healthcare, especially in sensitive aspect of real-

time biometric data.

Similar access and scalability advances were

detailed by Davis and Clark (2024) when they

discussed the role of remote monitoring in shaping

the sustainability of healthcare systems. But their

system did not consider the energy efficient data

processing and edge level analytics solutions. Batool

(2025) improved on this by introducing a deep

learning-based model, equipped with 5G for instant

patient care, though their model was saturated to high

tech environment and neglected rural and low

network areas.

Interpretability of AI-driven health

recommendations has been an emerging issue, as

pointed out by De Filippo et al. (2025), who used a

predictive tele-medicine system for the heart failure

patients. Despite its predictive performance, the

model was not interpretable, and interpretability is

essential for establishing trust with clinicians. To

solve this, Lee and Park (2024) emphasized

A Secure and Scalable IoT-Driven Framework for Real-Time Remote Patient Monitoring and Explainable Telemedicine in Diverse

Healthcare Settings

transparent decision support system in telemedicine

and recommended that interpretable method such

SHAP and LIME model should be combined, but no

implementation guide was provided.

There are also several studies that focused on

social-technical barriers of adopting IoT. White and

Harris (2024) noted that although there is an increase

in telemedicine infrastructure, the digital divide

continues to separate those patients other would most

benefit themselves. Their results are consistent with

Anderson and Lee (2024), who advocate for

healthcare platforms to incorporate inclusive design

principles to account for barriers based on age,

literacy, and accessibility devices.

Iqbal and Khan (2024) have recently provided a

hybrid telehealth model, which uses wearable

devices along with cloud systems for enhanced care

continuum. Although being promising, their work

was lacking into twining of explainable AI and

blockchain, which is the major part of transparency

and data traceability. Meanwhile, Thompson and

Allen (2024) includes research into the design for IoT

systems for telemedicine, and although these included

mentions of real-time analytics and energy-efficient

computation, they are less discussed in the literature.

In general, despite commendable efforts in

enabling healthcare with IoT technologies, the

literature reveals that more need to be done in terms

of the establishment of unified, secure, scalable and

interpretable systems. There is a pressing demand for

a comprehensive approach that navigates real world

limitations, adheres to interoperability requirements,

guarantees privacy of data, endorses edge-based

intelligence, and includes transparency features that

drive both patient outcome and clinical trust.

4 METHODOLOGY

This study suggests an integrated, real-time

healthcare system combining the remote patient

monitoring system based on the IOT with a secure

telemedicine system. The approach aims to address

gaps in the current state-of-the-art by focusing on

five key axes: data capture, secure transmission,

cognitive processing, explainable decision support

and user-centred delivery.

The system starts when non-invasive IoT sensors

(eg, wearable health monitors, smart patches, ambient

room sensors) are deployed and health parameters

(eg, heart rate, body temperature, oxygen saturation,

ECG signals) are collected non-invasively for

prolonged periods of time. They are designed using

ultra-low-power microcontrollers to save energy and

extend operational life, and are ideal for high-tech

urban hospitals and low-resource rural clinics alike.

The data from the sensors are pre-processed at an

edge computing device (e.g., Raspberry Pi or

NVIDIA Jetson Nano) to remove noise, missing

values and perform initial analysis. It prevails to the

prior art in that the alarm generation is executed at

the switch instead of the centralized network

management server, this leads to a minimized

bandwidth use and that even critical alarms can be

raised upon latencies or blockages of the network

itself even in the event of network failures. Figure 1

gives the System Workflow of the Proposed IoT-

Based Remote Patient Monitoring and Telemedicine

Framework.

Figure 1: System Workflow of the Proposed IoT-Based

Remote Patient Monitoring and Telemedicine Framework.

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Table 1: IoT Sensor Specifications Used for Remote Monitoring.

Sensor Type

Parameters

Monitored

Communicati

on Protocol

Power

Consumption

Sampling

Rate

Pulse

Oximeter

SpO₂, Heart Rate

Bluetooth

Low Energy

0.3W

1 sample/

sec

ECG Patch ECG Waveform ZigBee 0.5W 500 Hz

Temp.

Senso

Body

Tem

erature

LoRa 0.1W

1 sample/

min

Acceleromet

Movement/

Fall Detection

BLE 0.2W 10 Hz

Figure 2: Sensor Sampling Rate Comparison.

Figure 2 gives the sensor sampling rate

comparison and Table 1 gives the IoT Sensor

Specifications Used for Remote Monitoring.

After processing and packaging the data are

transferred to a central health data platform using

secure encrypted communication protocols. In this

study, TLS and MQTT over HTTPS are used for

end-to-end encryption and protecting from

unauthorized access during data transmission. For

additional security and traceability, a private

blockchain layer is added to store immutable records

for patient logs, data access, and decision points. This

blockchain not only guarantees system auditability,

but also ensures decentralized data ownership, and

compliance with international health data regulations

(e.g., HIPAA, and GDPR).

At the cloud, the system is designed to operate

with a modular approach to data aggregation, storage,

and model-driven analytics. The accumulated

information is contained in structured formatin in-

line with HL7 and FHIR standards, enabling

interoperability with hospital information system

(HIS) and electronic health record (EHR). The

analytics part of the platform employs machine

learning algorithms trained on past patient data. Such

models can identify when something is amiss,

forecast possible health issues and sound the alarm

for clinicians. In order to improve the clinical

relevance of AI-derived knowledge, SHAP (Shapley

Additive Explanations) and LIME (Local

Interpretable Model-Agnostic Explanations)

interpretability tools are integrated into the platform.

They are also capable of providing interpretable,

visualisation-based explanation and rationales behind

each prediction, which helps to build trust among

health care providers and removing the black box for

automated decisions.

The telemedicine module of the system is

developed for a responsive web and mobile interface

considering the video consultations, the health data

visualisation and the prescription creation. Doctors

can view patients’ real-time health dashboards and

get risk alerts generated with AI and overlaid with

interpretability, to inform better care decisions. For

patients, they get easy-to-use interfaces that

encourage them to monitor their vital signs,

remember to take medications, and to interact with

their clinicians through secure messaging. The

solution includes multilingual and accessibility-

friendly features for better adoption by the old and

rural users.

The effectiveness of the system is verified across

real-live scenarios, such as chronic diseases

management, post-operative care and emergency

alert system. The performance of both synthetic and

live data-sets is measured via benchmarks relating to

latency, prediction accuracy, system uptime, power

consumption, user satisfaction and security

compliance. This broad range of testing confirms not

only that the system is theoretically sound, but also

that it is practical to deploy onto a range of health-

care infrastructures.

A Secure and Scalable IoT-Driven Framework for Real-Time Remote Patient Monitoring and Explainable Telemedicine in Diverse

Healthcare Settings

5 RESULTS AND DISCUSSIONS

Insights The development and testing of the proposed

IoT-based RPM and telemedicine framework

provided a number of insights into its effectiveness,

security, responsiveness, and patient acceptance.

During large-scale experiments in real clinical

environment, the health anomaly detection module

can provide the great reliability, and the recognition

accuracy is 94.2%, precision is 92.6%, recall is

93.1%. These results emphasize the system’s high

sensitivity for accurately detecting critical health

events while minimizing false alarms, an essential

consideration for clinical adoption. Moreover, a F1-

Score of 92.8% and an AUC-ROC of 95.4%

reinforced the stability and predictive strength of the

AI models incorporated in the platform, providing

healthcare professionals with reliable decision

support on the fly.

Evaluation on system-level latency revealed the

huge gain offered by integration with edge

computing. In the edge-based setup, the average end-

to-end latency was decreased to 3.5 seconds as

compared to 7.9 seconds for a cloud implementation

alone. This significant decrease demonstrates the

system's potential to aid time-critical health care

situations, when a fast exchange and intervention of

a patient's data are crucial. By pre-processing and

analyzing the data at the edge before it is sent to the

system, bandwidth requirements were already

reduced and service would be guaranteed even if a

network would be available intermittently (on rural

or bandwidth-limited environments for example).

Table 2: Performance metrics of health anomaly detection

model.

Metric Value (%)

Accurac

94.2

Precision 92.6

Recall 93.1

F1 Score 92.8

AUC-ROC 95.4

Figure 3: Performance metrics of anomaly detection model.

Table 2 gives the performance metrics and bar

graph in figure 3 shows the visual representation of

performance metrics.

Security analysis demonstrated a small average

additional delay (0.8 seconds) in the complete data

management process due to the implementation of the

blockchain technology for the data auditing.

Nevertheless, this minor delay was more than

compensated by the substantial advantages they

were able to achieve in terms of data integrity,

traceability, and compliance with international health

care directives, like HIPAA and GDPR. "By

leveraging the blockchain layer, we were able to

generate an immutable and transparent ledger that

could prove and audit who had access to and updated

patient data, generating trust from patients and

healthcare management provider within the system.

Table 3: Comparative latency analysis (edge vs cloud).

Task

Edge-Based

stem

Cloud-Only

stem

Preprocessing &

Transmission

1.9 sec 4.7 sec

Alert Generation 2.1 sec 5.2 sec

End-to-End

Response Time

3.5 sec 7.9 sec

Figure 4: Latency comparison – edge vs cloud.

Table 3 gives the comparative latency analysis of

edge and cloud. Figure 4 gives the comparison in bar

graph.

"Participant-centred" evaluations found high

levels of acceptance and satisfaction across

participants. Physicians gave an average score of 4.7

out of 5 for usability of the AI-driven alerts and ease

of interpreting a patient risk profile as main benefits

to the alerts. City patients gave the system a 4.5, with

a special shoutout given to the instinctive mobile

interface and the possibility to track their own health

state in an autonomous manner. Notably, rural

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patients rated 4.6 as the average score and commented

that the system’s low-bandwidth adaptability and the

use of local language were important aspects that

helped to make remote healthcare feasible and

beneficial in the underserved regions.

Table 4 gives the Blockchain logging overhead

and benefits. Figure 5 illustrates the Blockchain

logging time overhead.

Table 4: Blockchain logging overhead and benefits.

Feature

With

Blockchain

Without

Blockchain

Logging Delay

(avg)

0.8 sec None

Data Tamper

Detection

Yes No

Audit Trail

Available

Yes No

Regulatory

Compliance

High Medium

Figure 5: Blockchain logging time overhead.

Taken together, the outcomes indicate that the

proposed framework effectively overcomes the

longstanding issues of conventional IoT healthcare

systems such as high latency, lack of data security,

lack of interoperable interfaces, and lack of

explainability. The approach, implemented using

edge computing, secure RFID communication

protocols, blockchain logging and explainable AI

methods in a patient-centric setting, performed well

in real-world settings. The result not only

demonstrates the technical feasibility of the

proposition but also affirms the prospect to

dramatically change the delivery of healthcare to

provide a far more inclusive, efficient, and credible

remote patient monitoring and telemedicine service to

distinct healthcare ecosystems.

Table 5 gives the usability and satisfaction score

from pilot users. Figure 6 illustrates the usability

feedback score.

Table 5: Usability and satisfaction scores from pilot users.

Participan

t Group

Avg. SUS

Score (out of

)

Noted Benefits

Doctors 4.7

Real-time alerts,

interpretability

Patients

(Urban)

4.5

Easy app usage,

remote access

Patients

(Rural)

4.6

Low bandwidth use,

local language UI

Figure 6: System usability feedback scores.

6 CONCLUSIONS

The increasing need of accessible, efficient and safe

health care delivery systems has driven the adoption

of IoT and telemedicine technologies in clinical

environment. In this paper, we have proposed a novel

IoT-enabled remote patient monitoring and

telemedicine framework, which has been developed

in response to the limitations observed in the

available systems such as scalability, security

concerns and lack of data interoperability and

explainability. Using the energy-efficient edge

computing, HL7/FHIR-compliant interoperability

standard, blockchain-based data integrity, and

explainable AI model, it is obvious that the proposed

system was able to prove real-time patient monitoring

and decision support to be not only reliable but also

resource-aware.

Through multidimensional analysis of real-world

and synthetic data, duART demonstrated the great

efforts in reducing latency, enhancing predictive

accuracy, ensuring data privacy and improving user

satisfaction. It brought healthcare out to the rural

A Secure and Scalable IoT-Driven Framework for Real-Time Remote Patient Monitoring and Explainable Telemedicine in Diverse

Healthcare Settings

masses, improved patient engagement and provided

healthcare providers the decipherable insights needed

to make well-informed medical decisions. The

addition of patient-specific design features and

multilingual accessibility mechanisms also confirmed

the platform's suitability and capability for broad

dissemination to a diverse demographic makeup.

Finally, it adds a realistic roadmap for future

digital healthcare infrastructure – one that is advanced

but also just and sustainable. It means that secure,

smart, and decentralized healthcare systems can be

expected to be efficiently deployed and to

automatically adapt to the varied demands of

contemporary medicine.

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