Next‑Gen Healthcare: AI‑Powered IoT for Smart Hospitals

Guruprakash K. S., Nithya T. M., Deepak S., Devadharshini K. S.,

Dharshini R. and Dinesh Krishna S.

Department of Computer Science and Engineering, K.Ramakrishnan College of Engineering, Trichy, Tamil Nadu, India

Keywords: AI‑driven Bed Allocation, IoT‑Based, Hospital Management, Real‑Time Patient Monitoring, Infection Risk

Management, Emergency Response System.

Abstract: The healthcare industry faces challenges in patient monitoring, resource management, and emergency

response, necessitating an advanced AI and IoT-based hospital management system for real-time data

acquisition, automated decision-making, and remote monitoring. This system integrates body temperature,

heart rate, and blood oxygen sensors to continuously track patient vitals, with data processed by an Arduino

microcontroller and transmitted via Wi-Fi using NodeMCU, enabling healthcare professionals to monitor

patients remotely through a web or mobile interface. An AI-driven bed allocation system ensures optimal

resource utilization by analysing patient conditions, infection risks, and proximity to other patients,

automatically assigning beds to minimize cross-contamination and ensuring that infectious patients are

isolated appropriately. The system also considers patient severity, special medical needs, and ICU availability

to allocate resources efficiently. A pressure sensor detects hospital bed occupancy in real-time, further

enhancing resource management, while a buzzer alert system notifies staff of critical changes in patient

conditions, enabling immediate intervention. Additionally, AI-powered predictive analytics can forecast

patient deterioration based on historical and real-time data, allowing for proactive medical attention. Designed

with cost-effective, energy-efficient components, the system seamlessly integrates AI and IoT technologies,

making it scalable and adaptable for hospitals of all sizes, ultimately improving patient care, operational

efficiency, and emergency response.

1 INTRODUCTION

To effectively manage patient care, resource

allocation, and emergency response, modern

hospitals need clever, automated systems. The need

for real-time patient monitoring and efficient hospital

operations has grown as a result of the healthcare

sector's explosive expansion. Manual procedures are

frequently used in traditional hospital administration

systems, which can lead to mistakes, delays, and

inefficient use of hospital resources (Arul Kumar et

al., 2022). Slow reaction times can be fatal in

emergency scenarios, underscoring the need for a

more sophisticated, tech-driven strategy.

By combining automated bed distribution, sensor-

based real-time health monitoring, and emergency

warning systems, the proposed AI and IoT-based

smart hospital management system, Health Sphere,

seeks to address these issues. Using sensors, the

system continuously monitors vital indications such

blood oxygen levels, heart rate, and body

temperature, guaranteeing prompt identification of

anomalous conditions (Nithya et al., 2020). In order

to minimize cross-contamination, an AI-driven bed

allocation module makes sure that infected patients

are placed in segregated beds.

The system also has a touch sensor to track bed

availability in real time, removing human error from

resource management. While an IoT-based remote

monitoring, system enables medical personnel to

access patient data via a web or mobile interface, an

LCD display gives hospital staff immediate

information on patient health and resource condition

(Guruprakash et al., 2023). By guaranteeing that

hospital employees are promptly informed of

emergencies, a buzzer alert system greatly enhances

patient safety and reaction times. This system

optimizes hospital resource usage, improves patient

care, lowers human intervention errors, and

automates hospital operations by utilizing IoT and AI

S., G. K., M., N. T., S., D., S., D. K., R., D. and S., D. K.

Next-Gen Healthcare: AI-Powered IoT for Smart Hospitals.

DOI: 10.5220/0013944500004919

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 5, pages

833-839

ISBN: 978-989-758-777-1

833

technology (Arul Kumar et al., 2022). Hospitals of all

sizes can effectively apply the solution thanks to the

flawless data transmission made possible by the

combination of wireless communication (NodeMCU

& Arduino). This invention bridges the gap between

automation, real-time monitoring, and AI-driven

hospital management decision-making, marking a

significant advancement in smart healthcare.

2 RELATED WORK

The increasing reliance on the Internet of Things

(IoT), and other emerging technologies has

significantly advanced the healthcare industry. A

variety of intelligent systems and monitoring

frameworks have been proposed, leveraging these

technologies to improve efficiency and accuracy in

health monitoring and diagnostics.

Deepa et al., proposed an AI-based intelligent

system for healthcare analysis utilizing the ridge-

Adaline stochastic gradient descent classifier. This

approach demonstrated enhanced performance for

healthcare-related data analysis and prediction tasks,

contributing to efficient decision-making in

healthcare systems. Similarly, Islam and Rahaman

developed a smart healthcare monitoring system in an

IoT environment, providing a practical solution for

real-time health data monitoring and analysis.

Masud et al., introduced a deep learning-based

intelligent face recognition system designed for IoT-

cloud environments. Their work highlights the

integration of deep learning techniques for secure and

efficient health data access and authentication. Bhat

et al. presented a comprehensive review of IoT-based

health monitoring systems, emphasizing the potential

benefits of IoT in improving patient care and

operational efficiency in healthcare facilities. Gogate

and Bakal implemented a healthcare monitoring

system using wireless sensor networks for cardiac

patients, focusing on the early detection and

prevention of cardiac events. Their work underscores

the importance of sensor-based solutions in critical

healthcare applications. This body of work

collectively showcases the diverse applications of AI,

IoT, and sensor technologies in enhancing healthcare

systems, with a focus on improving patient outcomes

and addressing challenges in traditional healthcare

practices.

3 PROBLEM DESCRIPTION

Hospitals face significant challenges in patient

monitoring, resource management, and emergency

response, which directly impact efficiency and patient

safety. Traditional patient monitoring methods rely on

manual supervision, leading to delays in detecting

critical health changes and increasing the risk of

medical emergencies. Additionally, manual data entry

is prone to errors, which can result in misdiagnosis or

incorrect treatment. Resource management,

particularly bed allocation, is often inefficient,

causing delays in patient admission and leading to

overcrowding in emergency wards.

The lack of an automated system also increases

the risk of infection spread, as patients are not always

assigned beds based on their health conditions and

infection risks. Moreover, emergency response

mechanisms in hospitals are often slow due to the

absence of real-time alerts, making it difficult for

medical staff to respond quickly to deteriorating

patient conditions.

Existing systems use standalone sensors that are

not integrated, limiting their ability to monitor

multiple health parameters simultaneously. The

absence of an AI-driven approach for patient

monitoring, bed allocation, and predictive analytics

further reduces operational efficiency. To address

these challenges, a smart hospital management

system integrating AI and IoT is required to enable

real-time monitoring, automated decision-making,

and efficient resource utilization.

4 RISK ASSESSMENT

IoT based health risk monitoring system seeks to

continually track a variety of personal health metrics,

anticipate possible health concerns, and potentially

avert unfavourable health outcomes via early

detection. Implementing a health risk monitoring

system using IoT involves various risks that need to

be assessed and mitigated to ensure the system's

effectiveness, security, and compliance. Here are

some key aspects for a health risk monitoring system

using IoT:

Abnormal Vital Signs: Sudden spikes or drops

in essential indicators including respiration rate,

blood pressure, and heart rate & also a drastic change

in physical activity levels, especially a sudden

decrease or increase.

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Temperature Fluctuations: Sudden temperature

changes either elevated or subnormal may signal an

infection or other underlying health issues. Such

changes could be indicative of a decline in health or

an acute event.

Emergency Response and Contingency

Planning: Develop and regularly test emergency

response plans & to have contingency measures in

place for system failures, including data backup and

recovery procedures.

Reliability and Accuracy: Implement quality

assurance processes for data accuracy. Calibrate and

validate sensors regularly. Establish redundancy and

failover mechanisms to ensure continuous

monitoring. Design for redundancy, fault tolerance,

and disaster recovery to minimize downtime and

ensure continuous operation.

Resource Constraints: Healthcare facilities are

often Authorized licensed use limited resources to

handle the increasing demand for medical services.

This leads to longer wait times and overburdened

medical staff, making it challenging to provide

immediate care.

Regulatory Compliance: Assure that healthcare

data management, privacy, and security needs are met

in accordance with industry standards and legal

regulations. To reduce the legal and regulatory

concerns related to IoT health monitoring devices,

stay current on the rules and guidelines that are

always changing.

Figure 1: Death comparison due to health issues.

The information in figure1 displays the total no.of

deaths occurred in percentage due to health issues

without having an immediate medical response. The

risk levels that the health care patients encountered

from 2012 to 2022 are summarized. However the

no.of deaths occurring seems to be decreased at some

point but it increases gradually year by year with a

considerable amount of death taking place. The

information clearly suggests that nearly 30 to 40

percent of death takes place due to health issues.

Figure 2 portrays the data recorded by various sensors

used to demonstrate the level of risk it handles. It

monitors the risk and analyses the chances of the

occurring risk in percentage and produces the

graphical representation which depends on each and

every individual health condition. This risk analysis

clearly defines the amount of risk that a person is

exhibiting while using the health monitoring system

and produces accurate and other precautionary

measures to help in treatment.

Figure 2: Risk analysis chart.

Outcomes demonstrated that the device's

measurements produced 99.43% accuracy for body

temperature and 99.59% accuracy for oxygen level,

99.76% accuracy for pulse rate, and 99.85% for heart

rate which is pictorially represented in Figure 3.

Figure 3: Performance analysis.

Next-Gen Healthcare: AI-Powered IoT for Smart Hospitals

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5 FEATURES AND

FUNCTIONALITIES

5.1 Real-Time Patient Monitoring

The system continuously tracks patient vitals,

including body temperature, heart rate, and blood

oxygen levels, using advanced sensors. Data is

processed via an Arduino microcontroller and

transmitted through Wi-Fi (NodeMCU), allowing

healthcare professionals to remotely monitor patient

health in real time via a web or mobile application.

This ensures early detection of abnormalities, leading

to timely medical intervention.

5.2 AI-driven Bed Allocation System

The system utilizes AI to assign hospital beds

efficiently based on patient condition, infection risk,

and ICU availability. By analyzing real-time and

historical patient data, it optimizes resource

utilization while minimizing cross-contamination

risks. Infectious patients are automatically assigned to

isolated beds, ensuring a safer hospital environment.

5.3 Smart Bed Occupancy Detection

A pressure sensor detects hospital bed occupancy in

real time, updating bed availability status on the

system. This eliminates manual tracking and

improves patient admission efficiency by providing

hospital staff with an up-to-date view of available

resources, thus reducing waiting times and optimizing

hospital space utilization.

5.4 Automated Emergency Alerts

In case of a critical change in patient vitals, an

integrated buzzer alert system notifies hospital staff

immediately. This ensures quick response times

during emergencies, allowing medical personnel to

intervene before a situation worsens. The alert system

is crucial for high-risk patients, improving overall

safety and care quality.

5.5 Remote Monitoring via IoT

Healthcare professionals can monitor patient vitals

and hospital resource usage remotely through an IoT-

enabled platform. The system provides real-time data

visualization via LCD displays and cloud-based

dashboards, allowing for informed decision-making

and reducing the need for manual supervision.

6 METHODLOGY

6.1 Data Acquisition Using Sensors

The system collects real-time patient data using

multiple sensors, including blood oxygen,

temperature, heart rate, and touch sensors for bed

occupancy. These sensors are connected to an

Arduino microcontroller, which gathers vital health

parameters for continuous monitoring.

6.2 Data Processing and Transmission

The Arduino processes the sensor data and transmits

it through an IoT module NodeMCU. The data is then

displayed on an LCD screen for local monitoring and

sent to a mobile application for remote access by

healthcare professionals.

6.3 IoT-Based Real-Time Monitoring

The IoT module ensures seamless transmission of

patient vitals to a web-based dashboard and mobile

app. This enables doctors and nurses to remotely

monitor patient health, reducing the need for physical

presence and allowing for early detection of critical

conditions.

6.4 Emergency Alert System

If a patient's vitals cross a critical threshold, an

automated buzzer alerts hospital staff immediately.

Simultaneously, real-time notifications are sent to

medical personnel via a mobile application, ensuring

a swift response to emergencies.

6.5 Automated Bed Occupancy

Detection

Touch sensors installed on hospital beds detect

occupancy in real time and update bed availability on

the hospital’s management system. This eliminates

manual tracking, optimizing resource allocation and

ensuring efficient patient admission.

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7 ARCHITECTURE DIAGRAMS

Figure 4 illustrate the Schematic of the suggested system.

7.1 Blood Oxygen Sensor

A Pulse Oximeter (SpO2 sensor) is a medical device

that determines the blood's oxygen saturation level. It

is a crucial factor in determining an individual's

respiratory health. The ratio of oxygenated to

deoxygenated haemoglobin can be found by placing

a non-invasive device on the finger and measuring

light wavelengths. Pulse oximeters utilize the idea of

light absorption to measure oxygen saturation. The

sensor emits two different wavelengths of light,

typically red and infrared, through a translucent part

of the body.

Figure 4: Schematic of the suggested system.

7.2 Temperature Sensor

A device that senses temperature can be employed to

take frequent readings of the body’s temperature and

convert this information into an electrical signal. By

transferring the electrical resistance over a diode into

usable measurements like Fahrenheit, Celsius, or

Centigrade, it also determines the relative humidity of

an item. The voltage across the diode is exactly

proportional to the temperature change. These sensors

are used to detect the interior temperature of

structures such as homes, bridges, dams, and power

plants in environmental monitoring.

7.3 Heart Rate Sensor

A gadget called a pulse sensor is used to determine a

person's heart rate. commonly by detecting the

pulsatile blood flow through arteries, which are

widely used to monitor heart rate in real time. Pulse

Monitor emits infrared, red, or green light (~550 nm)

towards the body and measures the amount of light

reflected using a photodiode which provides the pulse

rate of the patient. The pulse sensor operates by

means of two surfaces that are connected to an LED

and an ambient light sensor. Pulse rates may be

established by monitoring the minute variations in

light over a period of time.

7.4 Buzzers

Buzzers refers to simple devices that produce a

continuous buzzing or beeping sound when an

electric current pass through them. These are often

used in alarms, timers, and other signalling

applications. They are frequently employed to signal

the end of an activity or to notify others of an

impending event. Buzzers operate on the premise of

applying an alternating current voltage at the

element's resonance frequency, which causes the

element to vibrate and produce sound.

8 RESULT

The implementation of the AI and IoT-based hospital

management system demonstrated significant

improvements in real-time patient monitoring,

resource allocation, and emergency response. The

integration of blood oxygen, heart rate, temperature,

and bed occupancy sensors with an Arduino

microcontroller and IoT module enabled seamless

data collection and transmission. The system

successfully provided remote patient monitoring via

a mobile application, ensuring timely alerts for

critical conditions. Additionally, the AI-driven bed

allocation system optimized hospital resource

utilization, reducing patient waiting time and

minimizing cross-contamination risks. The

emergency alert mechanism efficiently notified

hospital staff of deteriorating patient conditions,

enhancing response time and medical intervention

efficiency.

Next-Gen Healthcare: AI-Powered IoT for Smart Hospitals

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9 CONCLUSIONS

The proposed IoT-based hospital management system

enhances patient care, operational efficiency, and

resource management in hospitals. By integrating

real-time monitoring, automated alerts, AI-driven bed

allocation, the system reduces manual intervention

and improves decision-making for healthcare

professionals. Its cost-effective, scalable, and energy-

efficient design makes it suitable for hospitals of all

sizes.

The advancements in AI, IoT, and sensor-based

technologies have paved the way for transformative

changes in healthcare. Studies such as those by Li and

Chiu highlight the importance of remote healthcare

systems, improving accessibility for underserved

areas. Rahimoon et al. emphasized the need for cost-

effective, non-invasive monitoring with their

contactless body temperature measurement system.

Reza et al. showcased how mobile technologies can

enhance cardiovascular monitoring through portable

and affordable solutions.

These innovations contribute significantly to

creating efficient and scalable healthcare solutions.

By integrating remote monitoring, non-invasive

technologies, and real-time data analysis, healthcare

systems can become more patient-centric and

effective. Future research should address challenges

like data security, interoperability, and accessibility to

ensure broader adoption of these technologies and

drive global advancements in healthcare.

Future enhancements may include AI-based

diagnostics, robotic automation, and expanded IoT

functionalities for even more comprehensive

healthcare management. Overall, this smart

healthcare system significantly contributes to better

patient outcomes, reduced hospital workload, and

improved emergency response capabilities.

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