Optimizing Blood Supply Chains: AI-Enabled Smart Blood Management Solutions

International Research Journal of Engineering and Technology (IRJET)

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Volume: 10 Issue: 10 | Oct 2023

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Optimizing Blood Supply Chains: AI-Enabled Smart Blood Management Solutions Ambarish Shashank Kshirsagar1, Yash Mohan Phalle2 1Department of Computer Engineering

Vishwakarma Institute of Information Technology, Kondhwa, Pune, 411048, Maharashtra, India .

2Department of Computer Engineering Vishwakarma Institute of Information Technology, Kondhwa, Pune, 411048, Maharashtra, India.

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Abstract -In the realm of modern healthcare, a robust

supplied in times of need or overstacked which leads to wastage of blood.

and timely supply of blood is indispensable, with one in seven hospital admissions necessitating blood transfusions.

While blood banks play a pivotal role in this process, emergencies often pose challenges in swiftly acquiring specific blood types. To address this critical issue, our project introduces an innovative Blood Management Application. This application integrates cutting-edge AI technology to predict future blood demand based on extensive historical data. By employing machine learning models, our system alerts blood banks when the forecasted demand is likely to surpass the available supply, enabling proactive measures to be taken. Furthermore, our application features a targeted blood request system, driven

by AI algorithms trained on donor data. This intelligent feature prioritizes blood requests based on a donor's past history, ensuring that urgent requirements are met swiftly and efficiently. Through real-time alerts hospitals can promptly mobilize donors, saving crucial time and enabling timely, life-saving treatments.

To address this pressing issue, our idea introduces the Smart Blood Management System, a solution designed to transform the way blood supply chain management operates. At its core, our system integrates AI technology, leveraging the power of predictive analytics based on extensive historical data. By employing sophisticated machine learning models, our system tries to accurately forecast future blood demand, enabling proactive measures to be taken when the anticipated demand is likely to surpass the available supply. This foresighted approach ensures that hospitals and blood banks are wellprepared to meet the imminent needs of patients, saving invaluable lives in the process.

We have recognized the critical role of blood donors in this equation as well, our application features an intelligent and targeted blood request system. Driven by AI algorithms trained on available donor data, this feature analyses donors' past history to prioritise blood requests.

By identifying the most suitable donors swiftly and efficiently, urgent requirements are met with speed, enabling timely, life-saving treatments and significantly improving patient outcomes.

In our platform donors can pledge their commitment to donating blood, fostering a supportive community network.

By harnessing the potential of AI-driven predictions and targeted requests, our Blood Management Application not only revolutionizes blood supply chain management but also ensures a rapid, reliable, and targeted preventive response.

In our application, we have implemented a feature that allows users to request blood from donors during emergencies. This functionality proves invaluable in times of urgent need, enabling users to initiate smart and targeted requests to potential donors identified through our model.

Key Words: Blood supply chain management, Demand supply prediction models, Predictive Analytics, Healthcare Technology, AI in Healthcare, Blood Bank Inventory Management

In essence, our Blood Management Application represents more than just a technological advancement; it is a transformative approach to healthcare. By integrating AIdriven predictions, targeted donor requests, and community engagement, our application redefines the blood supply chain management paradigm. It ensures a rapid, reliable, and targeted response to blood requirements, safeguarding lives and providing hope in times of medical crises.

1.INTRODUCTION In the ever-evolving landscape of modern healthcare, the importance of a robust and timely blood supply cannot be overstated. With one in seven hospital admissions necessitating blood transfusions, the need for a streamlined and responsive blood management system is paramount. Blood banks, the backbone of this critical process, often face challenges, and are regularly under

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 10 | Oct 2023

p-ISSN: 2395-0072

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2. LITERATURE REVIEW:

2.

The existing literature on emergency blood supply systems reveals several deficiencies in the conventional methods employed by hospitals and blood banks. In times of crisis, the current approach involves a step-by-step process: hospitals check their inventory first, and if the required blood isn't available, they contact nearby blood banks. If unsuccessful, the search expands to distant facilities, patient relatives, or regular donors. This method, while established, is undeniably time-consuming and inefficient, often leading to delays in critical medical treatments.

In summary, while progress has been made, the complexities of emergency blood supply demand continuous exploration and innovation. By focusing on the areas outlined above, researchers and practitioners can collectively contribute to the development of more efficient, responsive, and inclusive emergency blood supply systems, ultimately saving lives and improving healthcare outcomes.

In analysing the existing research landscape, it becomes evident that the area of blood supply chain management requires significant attention, as highlighted in [3]. This study indicates the need for extensive work in the supply management domain, emphasising the gaps in the existing methodologies and the urgency for innovation.

3.PROPOSED SYSTEM: As we have discovered main problems are centred around: 1.

[1][5][7] have collectively made strides in centralising databases, encompassing comprehensive information about blood banks, donors, and blood donation camps.

This digital transformation represents a noteworthy departure from the traditional practice of maintaining manual log journals. Building on this progress, [4] and [6] have introduced a Geographic Information System (GIS) approach. This geospatial technology enhances the effectiveness of retrieving information from blood bank databases, enabling more precise and rapid location-based searches for blood donors and supplies.

2.

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An Automated AI Enabled system for managing Blood inventory

2.

Smart Donor Identification and targeted efforts

3.

Platform Features

f.

Display Real-time data of available bloodstock Request Blood Organizing a blood donation camp Blood bank directory Dashboard for blood banks to track the predicted demand and predicted expected supply.

Alert system when predicted demand will be more than predicted supply.

1)Automated Management:

Establishment of a Centralised and AI-Enabled System for Automated Blood Inventory Management: Creating an intelligent, centralised system that utilises AI algorithms to predict, monitor, and manage blood inventory in real time is paramount. This system should factor in various variables such as blood type, demand patterns, expiry dates, and geographic distribution to ensure optimal blood supply at all times.

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1.

a. b. c. d. e.

However, despite these advancements, there remains a pressing need for extensive research and development in the following critical areas:

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Establishment of a centralised and AI-enabled system for automated blood inventory management.

Development of methods to broaden the scope of blood search efforts

To address these problems we are proposing our system:

Furthermore, [2][4] have developed platforms enabling users to search for blood in nearby blood banks based on specific requirements, marking a significant advancement from the conventional methods. Taking a step further, [6] introduced a feature that allows users to request blood even when it is unavailable in the immediate vicinity, showcasing the evolving nature of technology in addressing emergent needs.

1.

Development of Methods to Broaden the Scope of Blood Search Efforts: Innovative approaches are required to expand the reach of blood search efforts.

This could involve harnessing social media platforms, community outreach programs, and incentives to encourage regular blood donations.

AI-Enabled

Blood

Inventory

For Inventory management, It is crucial to account for the expected demand and expected supply of blood taking into account several factors like geolocation, blood group/type, timestamp, unit requirement, and expiry information for different blood components (plasma, RBC, WBC, platelets).

The Demand Model predicts blood demand for the upcoming predefined interval, taking into account various factors. On the other hand, the Supply Model forecasts the expected blood donations for the same period.

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e-ISSN: 2395-0056

Volume: 10 Issue: 10 | Oct 2023

p-ISSN: 2395-0072

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Demand Prediction Model: Utilizing historical data, our AI system forecasts blood demand for upcoming intervals.

Factors considered include cyclic patterns in blood donation, seasonal trends, and area-specific demand variations.

3.

Supply Prediction Model: Predicts expected blood donations for predefined intervals. By comparing demand and supply forecasts, the system identifies potential shortages and surpluses.

5.

4.

6.

If the demand exceeds the supply, the system will pinpoint specific geographical locations where certain blood groups are anticipated to be in short supply in the upcoming days, along with the extent of scarcity measured in quantities.

In summary, our proposed system combines cutting-edge AI technology with a user-friendly interface, addressing the challenges of blood inventory management and donor engagement. By integrating predictive analytics, targeted donor requests, and real-time alerts, our solution ensures a proactive, efficient, and reliable blood supply chain management process.

2) Smart Donor Identification and Targeted Efforts: To address immediate blood supply needs, it is highly effective to promptly identify the most compatible donors and prioritize them for expedited blood donation. Our AIpowered donor identification system ensures efficient and timely outreach to potential donors.

4. Results and Discussion The prototype was developed by leveraging advanced AI algorithms and predictive analytics to enhance and improve blood supply chain management. The prototype of AI-Enabled Smart Blood Management Systems was implemented and tested on a smaller scale. Harnessing extensive historical data, the system adeptly predicted future blood demand and efficiently managed blood inventory. The Demand Prediction Model, incorporating cyclic patterns, seasonal trends, and area-specific variations, alongside the Supply Prediction Model, accurately forecasted expected blood donations for predefined intervals. The AI-driven Donor Identification Model prioritized compatible donors based on comprehensive historical data, ensuring precise and timely blood requests.

Donor Identification Model: Utilizes a dataset comprising donor information such as age, blood type, donation history, area, and health status. AI algorithms identify the most suitable donors based on their history and compatibility.

Targeted Blood Requests: Prioritises potential donors for targeted blood requests. Blood banks and individual users can utilise this feature to send requests to the most likely donors.

3) Platform Features:

The prototype's performance, validated with real-time data, has showcased significant accuracy in predicting the dynamic patterns of blood demand and supply.

Furthermore, the prototype has demonstrated a decent accuracy in identifying the most suitable blood donors as well, ensuring efficient matching between donors and recipients. Together, these results signify the prototype's potential to significantly enhance the efficiency and effectiveness of blood management within healthcare organizations.

Fig. 1- Platform Architecture 1.

2.

5. CONCLUSIONS

Real-time Blood Stock Display: Displays live data of available blood stock, enabling real-time decisionmaking.

Blood Request Functionality: Allows users to request blood based on their requirements, connecting them with potential donors.

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Organizing Blood Donation Camps: Enables Blood banks to facilitate the organization of blood donation camps, optimizing donor participation.

Blood Bank Directory: Provides a comprehensive directory of blood banks, enhancing accessibility and coordination.

Dashboard for blood banks:track the predicted demand and predicted expected supply.

Alert system: Alter system in place to notify the blood bank when predicted demand will be more than predicted supply.

In summary, our research emphasizes the crucial role of an efficient blood management system in modern healthcare. We introduced the Smart Blood Management System, integrating AI and predictive analytics to address existing challenges faced by traditional blood banks.

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 10 | Oct 2023

p-ISSN: 2395-0072

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Through real-time stock monitoring, targeted donor requests, and predictive analysis, our system ensures swift responses to urgent blood needs. By fostering community engagement and shared responsibility among donors, our solution bridges gaps in the conventional methods, safeguarding lives.

While our system provides a robust foundation, continuous adaptation to evolving healthcare technologies is essential. Ongoing efforts and collaborations can further enhance our system, making substantial contributions to advancing healthcare practices and ensuring timely, adequate support for patients in need.

REFERENCES [1] Shah, Aman, et al. "Blood Bank Management and Inventory Control Database Management System."

Procedia Computer Science, vol. 198, 2021,pp.404409, https://doi.org/10.1016/j.procs.2021.12.261.

Accessed 21 Oct. 2023.

[2] Abhijeet Gaikwad | Nilofar Mulla | Tejashri Wagaj | Raviraj Ingale | Prof. Brijendra Gupta | Prof. Kamal Reddy "Smart Blood Finder" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1, December 2017, pp.1027-1032 [3] Baş Güre, Seda & Carello, Giuliana & Lanzarone, Ettore & Ocak, Zeynep & Yalcindag, Semih. (2016).

Management of Blood Donation System: Literature Review and Research Perspectives. 169. 121-132.

10.1007/978-3-319-35132-2_12.

[4] P. Priya1, V. Saranya2, S. Shabana3, Kavitha Subramani4, “The Optimization of Blood Donor Information and Management System by Technopedia”. Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, India, Volume 3, Special Issue 1, February 2014.

[5] F A. Clemen Teena, K. Sankar and S. Kannan (2017) “A Study on Blood Bank Management System” ISSN (online): 2455-1457 03, (1).

[6] Muhammad Nur Sahid Ramadhan et al (2019)” Blood Bank Information System Based on Cloud Computing In Indonesia” J. Phys.: Conf. Ser. 1179 012028.

[7] Kayode, A.Anthonia., Adeniyi, A.Emmanuel., Ogundokun, R.Oluwaseun., Ochigbo, S.Agaba. (2019) “An Android based blood bank information retrieval system” Journal of Blood Medicine 10: 119-125.

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