Description:FILED OF THE INVENTION:
The present invention relates to healthcare technology, specifically to an AI-enabled device and method for optimizing blood bank management. It focuses on centralized integration of blood bank systems, real-time monitoring of blood inventory, and ensuring compliance with safety standards to enhance blood donation processes and reduce wastage.
BACKGROUND :
Most hospitals in India depend on Blood Banks for donor blood as often hospitals and patients don’t have enough blood donors on their own. On the other hand, for patients and their relatives to find blood in an emergency situation is not easy task and this becomes even more challenging in case of rare blood groups.
To make this process simple for hospitals & patients and to monitor the inventory and quality of the Blood Banks across the City, there is need for a centralized Blood Inventory Monitoring (BIM)System which monitors the blood availability across the city and is owned by either City Health Department or any agency working on its behalf.
A country like India needs 12 million units - annually, collects - just 9 million units- with a gross deficit of 75% (http://www.financialexpress.com/article/healthcare/cover-story-healthcare/license-to-bill/83755). While the collection happens in the licensed blood banks – which are around 3042 in India, mostly in the public sector there are actually 6806 operating under variousheads(http://www.cdsco.nic.in/writereaddata/BLOOD%20BANKS%20INDIAfeb2015.pdf)
Multi-disciplinary hospital with 500 beds requires 5000-7000 units of blood per year of blood and an economically viable blood bank should have collection of 100,000 units of blood per year. Which means that a big blood bank should meet about 15-20 hospitals.
However, due to lack a state-run hospital had to throw away 717 units due to various reasons and an average of 26% blood is being wasted per year in India. This is predominantly due to either expiry of product or collecting from improper sources (like viral infected person).
Accidental blood born infections are a large social burden where the recipients have to live with HIV/ HBs/ HCV- lifelong besides Social stigma & Family impact- with cost of Rx. This not only is impacting logistics but also leads to a significant Morbidity with estimated loss of 96000 Man years annually and Cost of disease management (blood born infections) goes as high as 398Cr for a country like India. The estimated cost of destruction of the “unsafe blood” across India is approximately 12.6 Cr/ Annually leading to Non optimized wastage of blood costs- (=250000*800) – 20 Cr.
Further, current blood bank management practices, there are significant challenges in ensuring the efficient use of blood resources, maintaining quality and safety, and preventing malpractices. Blood banks often operate independently, leading to fragmented data, inefficient inventory management, and the risk of blood wastage due to overstocking or expiration. Moreover, existing systems lack a standardized approach to donor and patient tracking, which can result in inadequate oversight, improper storage, and the potential for unauthorized activities such as the mishandling or black-market sale of blood. These issues highlight the need for a more integrated, real-time solution to manage blood bank operations effectively.
To address these challenges, the invention introduces an AI-enabled device and method for the centralized optimization of blood bank processes. This system integrates multiple blood banks into a unified network, providing real-time data on blood inventory, donor eligibility, and patient utilization. By leveraging AI, the system optimizes inventory based on historical consumption patterns, anticipated demand, and demographic factors, significantly reducing blood wastage. Additionally, it ensures compliance with mandatory safety tests and standards, preventing the entry of unsafe blood into the supply chain. This comprehensive approach enhances the efficiency, safety, and reliability of blood bank operations across a city or region.
Summary of the Invention
This invention presents an advanced AI-enabled device and method designed to revolutionize the management of blood banks by centralizing and optimizing blood donation processes, inventory control, and safety compliance. The system integrates various blood bank management systems into a single, unified network, providing real-time monitoring and reporting of blood inventory, donor information, and patient data across all registered blood banks within a city or region. By centralizing these functions, the invention ensures standardized data entry, reduces the risk of errors, and enhances the overall efficiency of blood bank operations.
The system features several key modules, including a blood donation checkpoint for automated donor eligibility checks, an AI-driven inventory management system to predict and optimize blood supply levels, and a dashboard for real-time reporting and alerts. The integration module allows seamless communication between different blood banks, even those using varied management systems, while the administration module facilitates the management of users, compliance monitoring, and registration of new blood banks and hospitals. The inclusion of a web interface for hospitals and patients further enhances accessibility, allowing for easy search and location of available blood supplies.
This invention not only addresses the inefficiencies and risks associated with current blood bank practices but also provides a robust solution to prevent blood wastage and ensure the quality and safety of blood products. By leveraging AI and real-time data integration, the system significantly reduces the potential for malpractice, such as unauthorized blood storage or black-market sales, and enhances the ability to track blood donors and recipients. This comprehensive approach makes the invention a critical advancement in the field of blood bank management, offering a reliable, efficient, and safe solution for both donors and recipients.
The advantages of this invention includes but not limited to its use of cutting-edge AI and real-time data integration technologies. The AI-enabled inventory management system optimizes blood supply by accurately predicting demand based on historical data, demographics, and disease patterns, significantly reducing blood wastage and ensuring that fresh blood is available when needed. The centralized system improves oversight, reducing the risk of malpractice by providing real-time monitoring of blood quality and ensuring compliance with safety standards. Additionally, the integration of donor and patient tracking enhances communication and follow-up capabilities, while the standardized data management ensures consistency and reliability across all participating blood banks. Overall, this invention offers a comprehensive, efficient, and safe solution for blood bank management, benefiting both healthcare providers and patients.
Detailed Description:
The present invention relates to an AI-enabled device and method for optimizing blood bank processes, designed to address inefficiencies and risks associated with current blood bank management systems. The invention integrates multiple blood banks into a centralized network, providing real-time data on blood inventory, donor eligibility, and patient utilization. It includes several key modules that work together to enhance the efficiency, safety, and reliability of blood bank operations across a city or region. This system not only optimizes inventory management through AI-driven predictions but also ensures compliance with mandatory safety standards, reduces blood wastage, and prevents malpractices.
Blood Donation Checkpoint Module:
The Blood Donation Checkpoint Module is a critical component of the system, designed to ensure that all safety parameters are met before blood is collected. This module automatically verifies the eligibility of each donor by cross-referencing with a centralized database. The system checks for factors such as recent donations, medication history, and any potential infections. Donors who are deemed ineligible based on these criteria are automatically disqualified from donating, preventing unsafe blood from entering the system.
The donor registration process is enhanced with the use of facial recognition technology and phone number verification, ensuring donor anonymity and privacy while maintaining a unique donor ID. This ID is used across the system for tracking and audit purposes. The module also includes a phlebotomist override function, allowing medical professionals to manually override the system's disqualification in exceptional cases, with proper reasoning recorded for future audits.
Blood Bank Integration Module
The Blood Bank Integration Module is designed to integrate with various blood bank management systems, enabling the seamless transfer of data between individual blood banks and the centralized system. Given the diversity of existing blood bank management systems, the invention includes a synchronization software, referred to as “BIM Sync,” that can be customized to work with each unique system. This software ensures that blood, donor, and patient information is regularly updated in the centralized database.
For blood banks without an existing management system, the invention offers a standardized Blood Bank Module that can be directly adopted. This module provides real-time data on blood inventory, donor details, and patient information, ensuring uniformity across the network. This standardization is particularly beneficial for smaller blood banks that may lack the resources to develop or maintain their own management systems.
AI-Enabled Inventory Management Module
The AI-Enabled Inventory Management Module is central to the invention’s ability to optimize blood bank operations. This module uses artificial intelligence to analyze historical consumption patterns, demographic data, and disease trends to predict future blood demand. By accurately forecasting needs, the system can optimize blood inventory levels, ensuring that sufficient blood is available while minimizing the risk of overstocking and wastage.
The system also includes features for real-time monitoring of blood inventory, generating alerts for low stock levels, and notifying relevant authorities, impacted blood banks, and regular donors via SMS or email. This proactive approach helps maintain an optimal balance of blood supplies across the network, reducing the likelihood of shortages or excesses.
Dashboard and Reporting Module
The Dashboard and Reporting Module provides a user-friendly interface for accessing real-time data and generating reports on blood bank operations. This module offers a comprehensive overview of blood stock availability by city or individual blood bank, donor and patient details, blood freshness, and wastage reports. It also includes advanced search functionalities, allowing users to locate specific blood types, donor IDs, blood bag numbers, and other relevant data.
The reporting capabilities of this module are designed to support decision-making processes at both the administrative and operational levels. For instance, blood bank administrators can use the system to identify trends in blood usage, monitor compliance with safety standards, and track the effectiveness of donor recruitment campaigns.
Web Interface for Hospitals and Patients
The Web Interface Module extends the system’s functionality to external stakeholders, such as hospitals and patients. This module provides a platform for hospitals to search for and locate available blood supplies across the network in real-time. Patients and donors can also use the interface to find the nearest blood bank, schedule donation appointments, and track their donation history.
This interface enhances the accessibility and transparency of the blood bank system, making it easier for hospitals to secure the blood they need and for patients to access life-saving resources. By offering real-time updates on blood availability, the system reduces the time and effort required to locate suitable blood supplies, improving the overall efficiency of the healthcare system.
Administration Module
The Administration Module is responsible for managing the overall operation of the system. It allows system administrators to register new blood banks and hospitals, update existing records, create and manage user accounts, and assign user roles. The module also includes tools for monitoring compliance with blood safety regulations, rating blood banks based on their adherence to standards, and conducting audits of blood bank operations.
This module ensures that the system operates smoothly and efficiently, with clear oversight and control mechanisms in place. By centralizing administrative functions, the system reduces the burden on individual blood banks and ensures that all participants adhere to the same high standards of safety and quality.
Blood Bank Management Module
The Blood Bank Management Module is an optional component of the system, designed to support the day-to-day operations of individual blood banks. This module includes features for donor registration, testing, bar code generation, and component data management. Donor registration is enhanced with automated checks that cross-reference centralized databases to prevent ineligible donors from donating. Testing results for each blood bag are uploaded in formats such as JPEG or PDF for future audit verification.
The module also supports the management of blood components, such as plasma and platelets, and includes tools for tracking the expiration dates of blood bags. By integrating these functions into a single platform, the module simplifies the management of blood bank operations and ensures that all necessary data is recorded and accessible.
In one of the embodiments Centralized Integration and Standardization of blood bank systems. Unlike existing solutions, which often operate in isolation, this invention brings together all registered blood banks within a city or region into a single, standardized network. This integration ensures that data is consistently recorded, easily accessible, and updated in real-time across the entire network.
The use of a standardized blood bank management module for smaller or less technologically advanced blood banks further enhances this integration, providing them with the tools they need to manage their operations effectively while ensuring that their data is fully compatible with the centralized system.
In another embodiment, The AI-enabled inventory management system analyzes a wide range of data inputs, including historical blood usage, demographic trends, and disease patterns, the AI system can accurately predict future blood demand and adjust inventory levels accordingly. This predictive capability reduces the risk of both shortages and overstocking, ensuring that blood is available when and where it is needed, while minimizing wastage.

The system's ability to generate real-time alerts for low stock levels and notify relevant stakeholders further enhances its effectiveness, allowing for rapid responses to potential shortages and improving overall blood supply management.
In another embodiment, Enhanced Donor and Patient Tracking expands the scope of blood bank management by incorporating comprehensive donor and patient tracking capabilities. By integrating donor information with patient records, the system improves the ability to monitor and communicate with both donors and recipients. This tracking capability is particularly valuable for ensuring that donors are not overburdened, preventing unsafe donations, and enabling follow-up with patients who have received blood products.
The use of facial recognition technology and phone number verification for donor registration enhances privacy and security, while the centralized database ensures that donor eligibility is accurately assessed across all blood banks in the network.
In another embodiment , Robust Quality Assurance and Compliance system includes automated checks at multiple stages of the blood donation and storage process to ensure that all safety standards are met. These checks include verifying donor eligibility, confirming that mandatory tests have been conducted, and monitoring the storage conditions of blood products.
The system’s centralized reporting and audit capabilities allow administrators to monitor compliance across the network, identify potential issues, and take corrective action as needed. The ability to rate blood banks based on their adherence to standards further incentivizes compliance and helps maintain high levels of quality across the system.
Further , The present invention deals with the issue of malpractices in blood bank management. By centralizing data and providing real-time oversight, the system reduces the risk of unauthorized activities such as the storage of untested or unsafe blood, or the illegal sale of blood products. The system’s robust tracking and reporting capabilities make it easier to detect and prevent such activities, ensuring that all blood products meet the necessary safety standards before they are used.
In another embodiment, The inclusion of a web interface for hospitals, patients, and donors significantly improves the accessibility and transparency of the blood bank system. By providing real-time updates on blood availability and facilitating easy access to blood donation and tracking services, the system enhances the overall user experience and improves the efficiency of the healthcare system.
Hospitals can quickly locate the blood supplies they need, reducing the time and effort required to secure critical resources, while donors can easily find blood banks and schedule appointments, encouraging more frequent donations and improving the overall availability of blood.
This invention represents a comprehensive solution for the optimization of blood bank processes, offering significant advancements over existing systems in terms of integration, efficiency, safety, and compliance. By leveraging AI and real-time data integration, the system not only optimizes blood inventory management but also ensures that all blood products meet the highest standards of quality and safety. The invention’s robust tracking and reporting capabilities further enhance oversight and reduce the risk of malpractices, making it a critical tool for improving blood bank operations and ensuring the safety and well-being of both donors and recipients.
Figures:
Figure 1 : : illustrates the various eco-systems from where blood can be obtained for optimizing blood bank process.
Figure 2 : illustrates the method to optimize the blood bank process
Figure description :
Figure 1 : The ecosystem from where blood can be obtained (100) for optimizing blood bank process includes but not limited to Mobile donation site (300); fixed donation site (400); Hospital blood bank (500); hospital remote inventory (600); Further all the blood bank ecosystem is integrated with the central optimizing method (200); where in the decision support and alert delivered in real time.
Figure 2: Figure 2 discloses the A method for optimizing the blood bank process(200), comprising a centralized integrating system (201) equipped with a database (201A) for storing and retrieving donor and recipient information. The centralized system further includes an API -Application programing interface (201B)to facilitate real-time data exchange across various blood banks. The method also comprises a network of blood banks(202), each equipped with scanners(202A) for reading donor blood unit and recipient identification codes. The network further includes a monitoring system (202B), which incorporates reaction monitoring devices (204) to detect and record adverse reactions during blood transfusions. Data from the reaction monitoring devices is transmitted to the centralized system(205), which includes both an API interface (206)for seamless data transmission and data processing units(207) to analyze and interpret the received data.
Additionally, the method includes an auto-matching system (203) that uses machine learning algorithms to match donor and recipient blood types. The centralized system generates reports(208) based on real-time data analysis, and provides decision support by issuing alerts and recommendations. Specifically, threshold-based alerts are triggered when certain risk thresholds are exceeded, such as when a patient with a history of allergic reactions is about to receive a high-risk blood product. In such cases, the AI system sends real-time alerts to the medical staff. The system may also offer recommendations for alternative actions, such as selecting a different blood product or conducting further tests before proceeding with the transfusion, thus optimizing patient safety and the overall efficiency of blood bank operations.

Working Model :
Stage 1 : Patient Registration and Digital Automation
One of the embodiments of the present invention focuses on a system for automating patient registration and digital record management using advanced AI technologies. This system leverages Optical Character Recognition (OCR) for digitizing paper forms and facial recognition for identity verification, aiming to enhance accuracy and efficiency in patient data handling. Main objective of this stage is to automate the digitization and structuring of patient records to minimize manual entry errors.
Process:
1. Data Extraction Using OCR:
o Technology: OCR converts scanned images of paper forms into machine-readable text.
o Example: Google Cloud Vision API can be used to scan patient forms and extract text.
o Formula: T=OCR(I)T = \text{OCR}(I)T=OCR(I) Where:
? III = Image of the patient form.
? TTT = Extracted text containing patient details.
2. Data Parsing and Structuring:
o Technology: Text parsing algorithms or Natural Language Processing (NLP) are used to structure the extracted data.
o Example: Regex can be used to extract fields like Name, Date of Birth, and Blood Type.
o Formula: F=Parse(T,{f1,f2,…,fn})F = \text{Parse}(T, \{f_1, f_2, \dots, f_n\})F=Parse(T,{f1,f2,…,fn}) Where:
? FFF = Structured fields extracted from text TTT.
3. Error Detection and Correction:
o Technology: AI models and validation algorithms are applied to identify and correct errors.
o Example: If the blood type “O” is extracted but the patient’s history indicates “B+”, the system flags this discrepancy.
o Formula: F'=Correct(F)F' = \text{Correct}(F)F'=Correct(F) Where:
? F'F'F' = Corrected data fields.
4. Update Digital Records:
o Technology: Database management system updates records with corrected data.
o Example: Using SQLite to update patient records with validated details.
o Formula: DB.Update(F')\text{DB.Update}(F')DB.Update(F') Where:
? DB.Update\text{DB.Update}DB.Update = Database update operation with corrected fields F'F'F
II. Smart Kiosks for Self-Registration:
Process:
1. Capture and Preprocess the Image:
o Technology: A camera captures the patient’s image, which is then preprocessed for analysis.
o Example: Ensure the image is clear and in a compatible format.
2. Use Amazon Rekognition to Detect and Compare Faces:
o Technology: Facial recognition via Amazon Rekognition detects facial features and compares them with stored images.
o Formula: Similarity=CompareFaces(Isource,Itarget)\text{Similarity} = \text{CompareFaces}(I_{\text{source}}, I_{\text{target}})Similarity=CompareFaces(Isource,Itarget) Where:
? IsourceI_{\text{source}}Isource = Stored reference image.
? ItargetI_{\text{target}}Itarget = New image captured at registration.
3. Verify Identity:
o Technology: Identity verification is based on similarity scores from facial recognition.
o Example: If similarity is above 90%, the identity is verified.
o Formula: Verified=(Similarity>90)\text{Verified} = (\text{Similarity} > 90)Verified=(Similarity>90)
4. Handle Identity Verification:
o Technology: Update or store the verified image in the database.
o Example: Store the verified patient image for future comparisons or prompt for re-capture if verification fails.
The above embodiments integrates OCR and facial recognition technologies to automate patient registration and improve data accuracy. By reducing manual data entry errors and enabling efficient identity verification, this invention offers a robust solution for modern healthcare facilities.
Stage 2: Linking Mobile Numbers and Blood Group Tagging
In one of the embodiments, there is an automated system for linking patient mobile numbers with their records and ensuring accurate blood group tagging. This system utilizes AI and natural language processing (NLP) for seamless integration and verification, while data matching algorithms ensure correctness in blood group data.
1. Automated Linking of Mobile Numbers
This reduces the manual intervention.
Process:
1. Set Up Dialogflow Agent:
o Objective: Create an AI agent to handle interactions for mobile number collection, OTP sending, verification, and linking.
o Steps:
? Create New Agent: Configure an agent in Dialogflow.
? Define Intents:
? Collect Phone Number Intent:
? Intent Name: CollectPhoneNumber
? Training Phrases: Examples include "I need to register," "My phone number is [phone number]."
? Action and Parameters:
? Parameter: phone_number (with regex pattern \d{10})
? Response: "Thank you! We will send an OTP to [phone number] shortly."
? Send OTP Intent:
? Intent Name: SendOTP
? Fulfillment: Connect to a webhook that triggers an OTP service (e.g., Twilio).
? Response: "An OTP has been sent to your mobile number. Please enter it to proceed."
? Verify OTP Intent:
? Intent Name: VerifyOTP
? Training Phrases: Examples include "My OTP is [otp]."
? Fulfillment: Verify OTP using an external service.
? If correct: "OTP verified successfully. Your mobile number is now linked."
? If incorrect: "The OTP is incorrect. Please try again."
? Link Mobile Number Intent:
? Intent Name: LinkMobileNumber
? Fulfillment: Link the verified mobile number to the patient's record in the database.
2. Webhook Setup for OTP and Linking:
o Webhook for OTP Generation:
? Endpoint: https://your-domain.com/api/send-otp
? Logic:
? Generate a random 6-digit OTP.
? Use Twilio API to send the OTP.
? Store OTP with expiry time in the database.
3. Integrate Webhooks in Dialogflow:
• Enable webhooks in Dialogflow for intents like SendOTP and VerifyOTP.
• Add webhook URLs to Dialogflow’s Fulfillment section.
4. Test the System:
• Simulate user interactions in Dialogflow.
• Debug and refine intents and the NLP model using Dialogflow’s test console.
II. Blood Group Tagging
1. Levenshtein Distance (Edit Distance):
Formula: Minimum number of single-character edits required to transform one string into another.
Application: Compare entered blood group (e.g., "B+") with historical records. A small distance indicates a possible match.
2. Jaccard Similarity:
Formula: Size of intersection divided by the size of the union of character sets.
Application: Compare blood group strings. A similarity score close to 1 indicates a good match.
3. Soundex Algorithm:
Formula: Encodes strings by their sounds, useful for matching similarly pronounced names.
Application: Use Soundex to match similarly pronounced blood group names.
Example :
Blood Group Matching: For a blood group entry "B+", compare using Levenshtein Distance, Jaccard Similarity, and Soundex against historical records. Each method helps in identifying and preventing potential mismatches due to entry errors or phonetic variations.
By integrating these AI-driven processes for linking mobile numbers and ensuring accurate blood group tagging, the system aims to enhance the reliability and efficiency of patient data management.
Stage 3 : Verification of Donor Safety
I. Health History Analysis Using AI
1. Collect Donor Data
• Sources: Electronic Health Records (EHRs), online forms, or surveys.
• Formats: Structured data (databases) or unstructured data (free-text).
2. Prepare the Data
• Text Preprocessing:
o Tokenize: Break text into individual words.
o Remove Stopwords: Eliminate common, unimportant words.
o Lemmatize/Stemming: Convert words to their base form.
3. Extract Features
• Bag of Words (BoW): Count word frequencies.
• TF-IDF: Measure word importance across documents.
4. Analyze Risk
• Model Training:
o Use models like Logistic Regression, Random Forest, or Neural Networks to identify risks.
o Train the model on historical data.
• Prediction: Apply the model to new donor data to identify risks.
5. Use Watson NLP (Optional)
• Analyze Text: Use IBM Watson to process and understand donor responses, identifying specific risk factors.
6. Generate Outputs
• Risk Profile: Create profiles indicating potential risks (e.g., smoking, chronic diseases).
• Alerts: Trigger alerts if risks are detected for further evaluation.
7. Continuously Improve
• Update Model: Retrain with new data to enhance accuracy over time.
Predictive Analytics for Safety
1. Gather and Prepare Data
• Collect: Health history, previous donations, and lab results.
• Clean: Handle missing values and normalize data.
2. Engineer Features
• Select Features: Identify key features for risk prediction.
• Transform Features: Create new features or interaction terms.
3. Build and Train Model
• Choose Model: Options include Logistic Regression, Random Forest, or SVM.
• Train Model: Fit the model with training data.
• Validate: Use cross-validation to test model performance.
4. Evaluate and Optimize
• Metrics: Assess using accuracy, precision, recall, etc.
• Tune Hyperparameters: Adjust settings to improve model performance.
5. Deploy Model
• Integration: Deploy model in the blood bank system for real-time risk assessment.
Example Calculation For a simple logistic regression model:
• Formula: log(P1-P)=-2.5+1.2×Smoking+1.5×Chronic Disease\text{log}\left(\frac{P}{1-P}\right) = -2.5 + 1.2 \times \text{Smoking} + 1.5 \times \text{Chronic Disease}log(1-PP)=-2.5+1.2×Smoking+1.5×Chronic Disease
• Probability PPP for a smoker with chronic disease: 11+e-0.2˜0.55\frac{1}{1 + e^{-0.2}} \approx 0.551+e-0.21˜0.55, indicating a 55% risk.
This streamlined flow covers the main steps involved in verifying donor safety using AI tools and predictive analytics.
Stage 4 : Workflow for Blood Batch Safety and QR Code Integration
1. Predict Adverse Reactions
a. Collect Data
• Gather batch characteristics like reaction count and total units.
b. Prepare Data
• Calculate reaction frequency: reaction_frequency = reaction_count / total_units
c. Build Models
• Clustering: Use KMeans to group batches based on reaction frequency.
• Classification: Train a Random Forest model to predict reaction likelihood.
• Anomaly Detection: Apply Isolation Forest to identify unusual reaction patterns.
d. Interpretation
Visualize Results: Use tools like Tableau or Matplotlib to analyze clusters and patterns.
Alerts: Flag batches with high anomaly scores for review.
2. QR (Quick Response) Code Integration
a. Encode QR Code
• Data points: Blood Product ID, Expiration Date, Blood Group, Viral Testing Results, Cross-Match Result, Packaging Date, Storage Temperature, Batch Number.
• Integration with AI
• Real-Time Monitoring: Continuously assess data and make real-time decisions.
• Machine Learning: Use past data to refine decision-making algorithms.
This structured approach improves safety and efficiency in blood banks by automating critical checks and using predictive analytics to manage potential risks.
Stage 5 : Screening and Grouping with AI
Steps:
a. Data Acquisition
• Formula: X=f(Images, Sensors, Test Results)X = f(\text{Images, Sensors, Test Results})X=f(Images, Sensors, Test Results)
• Explanation: Collect data from blood samples using imaging technology, sensors, and biochemical test results.
• Example: X={Image1,Image2,…,Imagen,Hb_level,WBC_count,…}X = \{\text{Image}_1, \text{Image}_2, \ldots, \text{Image}_n, \text{Hb\_level}, \text{WBC\_count}, \ldots\}X={Image1,Image2,…,Imagen,Hb_level,WBC_count,…}
b. Preprocessing
• Formula: Xclean=Preprocess(X)X_{\text{clean}} = \text{Preprocess}(X)Xclean=Preprocess(X)
• Explanation: Clean and preprocess the data to remove noise and standardize it.
• Example: Xclean={Preprocessed_Images,Normalized_Test_Results}X_{\text{clean}} = \{\text{Preprocessed\_Images}, \text{Normalized\_Test\_Results}\}Xclean={Preprocessed_Images,Normalized_Test_Results}
c. Feature Extraction
• Formula: F=Extract_Features(Xclean)F = \text{Extract\_Features}(X_{\text{clean}})F=Extract_Features(Xclean)
• Explanation: Extract features like cell size and shape from images and key biochemical markers.
• Example: F={Cell_Size,Cell_Shape,Hb_Level,WBC_Trend}F = \{\text{Cell\_Size}, \text{Cell\_Shape}, \text{Hb\_Level}, \text{WBC\_Trend}\}F={Cell_Size,Cell_Shape,Hb_Level,WBC_Trend}
d. Classification
• Formula: Ypred=Classifier(F)Y_{\text{pred}} = \text{Classifier}(F)Ypred=Classifier(F)
• Explanation: Use machine learning models (SVM, CNN) to classify the blood sample.
• Example:
o SVM: Ypred=sign(w·F+b)Y_{\text{pred}} = \text{sign}(w \cdot F + b)Ypred=sign(w·F+b)
o CNN: Ypred=Softmax(CNN(F))Y_{\text{pred}} = \text{Softmax}(\text{CNN}(F))Ypred=Softmax(CNN(F))
o Labels: Normal, Anemia, Leukemia, etc.
e. Post-Processing and Decision Making
• Formula: Yfinal=Decision_Rules(Ypred,Thresholds)Y_{\text{final}} = \text{Decision\_Rules}(Y_{\text{pred}}, \text{Thresholds})Yfinal=Decision_Rules(Ypred,Thresholds)
• Explanation: Refine predictions and decide on actions like manual review or alerts.
• Example:
o If Ypred="Anemia"Y_{\text{pred}} = \text{"Anemia"}Ypred="Anemia" and Hb_Level<12\text{Hb\_Level} < 12Hb_Level<12 then Yfinal="Flag for Review"Y_{\text{final}} = \text{"Flag for Review"}Yfinal="Flag for Review"
o If Ypred="Leukemia"Y_{\text{pred}} = \text{"Leukemia"}Ypred="Leukemia" and WBC_Count>11000\text{WBC\_Count} > 11000WBC_Count>11000 then Yfinal="Alert"Y_{\text{final}} = \text{"Alert"}Yfinal="Alert"
f. Reporting and Integration
• Formula: Report=Generate_Report(Yfinal)\text{Report} = \text{Generate\_Report}(Y_{\text{final}})Report=Generate_Report(Yfinal)
• Explanation: Automatically generate and integrate reports with the hospital’s database.
• Example: Report = "Patient XYZ: Detected Anemia, Recommend Further Testing"
II. QR Code Tagging
AI Tool: QR Code Generation and Tagging
Steps:
a. Data Collection and Structuring
• Data Includes: Blood Group, Expiry Date, Temperature, Screening Results, Unique Blood Bag ID
• Example Data:
o Blood Group: "B+"
o Expiry Date: "2024-12-31"
o Temperature: "4°C"
o Screening Results: "HIV Negative, Hepatitis B Negative"
o Blood Bag ID: "BB1234567890"
b. Concatenate Data
• Formula: QR_Data=Blood Group+"|"+Expiry Date+"|"+Temperature+"|"+Screening+"|"+Blood Bag ID\text{QR\_Data} = \text{Blood Group} + "|" + \text{Expiry Date} + "|" + \text{Temperature} + "|" + \text{Screening} + "|" + \text{Blood Bag ID}QR_Data=Blood Group+"|"+Expiry Date+"|"+Temperature+"|"+Screening+"|"+Blood Bag ID
• Example: QR_Data="B+|2024-12-31|4°C|HIVNegative,HepatitisBNegative|BB1234567890"\text{QR\_Data} = "B+|2024-12-31|4°C|HIV Negative, Hepatitis B Negative|BB1234567890"QR_Data="B+|2024-12-31|4°C|HIVNegative,HepatitisBNegative|BB1234567890"

3. Temperature Monitoring
AI Tool: Temperature Monitoring with AI
• Example: Monnit
Steps:
a. Temperature Monitoring with Smart Sensors
• Explanation: Smart sensors measure temperature and report data.
b. Temperature Formula and Monitoring
• Formula: ?T={T-Tmaxif T>TmaxTmin-Tif T T_{\text{max}} \\ T_{\text{min}} - T & \text{if } T < T_{\text{min}} \\ 0 & \text{if } T_{\text{min}} \leq T \leq T_{\text{max}} \end{cases}?T=???T-TmaxTmin-T0if T>Tmaxif T2Falseif ?T=0 and |Z|=2\text{Alert} = \begin{cases} \text{True} & \text{if } \Delta T \neq 0 \text{ or } |Z| > 2 \\ \text{False} & \text{if } \Delta T = 0 \text{ and } |Z| \leq 2 \end{cases}Alert={TrueFalseif ?T?=0 or |Z|>2if ?T=0 and |Z|=2
e. Temperature Control Adjustments
• PID Controller: u(t)=Kpe(t)+Ki?0te(t)dt+Kdde(t)dtu(t) = K_p e(t) + K_i \int_0^t e(\tau) d\tau + K_d \frac{de(t)}{dt}u(t)=Kpe(t)+Ki?0te(t)dt+Kddtde(t)
• Explanation: Adjust cooling systems to maintain the desired temperature.
The use of AI in blood banking includes efficient screening, accurate QR code tagging, and continuous temperature monitoring. AI tools ensure timely and precise handling of blood products, improving overall safety and efficiency in blood banks.
Stage 6 : Blood Issue and Ward-Level Management
• Auto-Matching
• Objective: Minimize human error in blood matching using AI.
• Approach: Train models using encoded blood group data to ensure perfect matches.
• Tools: Logistic Regression or Neural Networks (e.g., TensorFlow/Keras).
• Example Implementation:
o Use one-hot encoding for blood groups.
o Train models to classify matches based on feature matrix X and target variable y.
o Evaluate and implement using Scikit-Learn or TensorFlow/Keras.
• Reaction Monitoring and Reporting
• Objective: Detect adverse reactions and automatically generate reports.
• Approach: Use AI to compare patient data with historical data and flag reactions.
• Tools: Haemovigilance Systems integrated with AI (e.g., BloodTrack by Haemonetics).
• Formulas:
o Reaction Score: Computes the reaction severity.
o Automated Reporting: Sends structured data to haemovigilance portals.
o Blocking: Flags and blocks donor-recipient pairs with previous severe reactions.
Stage 7: Integrating With State/Central Network:
1. Data Flow Overview
1. Central System Integration:
o The central system coordinates data exchanges between blood banks and manages critical information.
2. Local Blood Bank Operations:
o Blood banks send data to the central system and receive updates on donor-recipient compatibility.
2. Components Involved
1. Central System API:
o API Endpoints:
? /update_reaction: Update reaction records for donor-recipient pairs.
? /check_pair: Verify if a donor-recipient pair is blocked or flagged.
? /update_inventory: Update blood inventory levels.
2. Database Management:
o Reactions Table:
? Stores records of adverse reactions.
? Fields: donor_id, recipient_id, reaction_occurred, reaction_type.
o Inventory Table:
? Manages blood inventory levels.
? Fields: blood_type, quantity.
o Blocked Pairs Table:
? Tracks pairs with severe reactions to prevent future transfusions.
? Fields: donor_id, recipient_id.
3. Real-Time Monitoring:
o Monitors blood inventory and tracks supply chain logistics.
o Uses WebSockets or a message queue for real-time updates.
3. Data Integration Process
1. Updating Reaction Records:
o Step 1: Blood bank detects an adverse reaction during a transfusion.
o Step 2: Blood bank sends a POST request to the central system’s /update_reaction endpoint.
? Data Sent: donor_id, recipient_id, reaction_occurred, reaction_type.
o Step 3: Central system updates the reaction record in its database.
o Step 4: If a severe reaction is recorded, the system adds the donor-recipient pair to the blocked pairs list.
2. Checking Donor-Recipient Pair:
o Step 1: Blood bank prepares for a new transfusion.
o Step 2: Blood bank sends a GET request to the central system’s /check_pair endpoint with donor_id and recipient_id.
o Step 3: Central system checks if the pair is blocked or flagged.
o Step 4: Central system returns the status to the blood bank (blocked, allowed, or review needed).
3. Updating Inventory Levels:
o Step 1: Blood bank updates blood inventory after donation or usage.
o Step 2: Blood bank sends a POST request to the central system’s /update_inventory endpoint.
? Data Sent: blood_type, quantity.
o Step 3: Central system updates the inventory database.
4. Real-Time Inventory Monitoring:
o Step 1: Central system or blood banks use WebSockets or message queues to push updates about inventory changes.
o Step 2: Inventory data is updated in real-time across all connected systems.
4. Example Workflow
1. Adverse Reaction Handling:
o During a transfusion, an adverse reaction is detected.
o The blood bank sends reaction details to the central system.
o The central system updates its records and blocks the donor-recipient pair if the reaction is severe.
2. Transfusion Preparation:
o Before proceeding with a new transfusion, the blood bank checks the donor-recipient pair’s status.
o The central system responds with the pair’s status, guiding whether to proceed, review, or reject the match.
3. Inventory Management:
o Blood inventory is updated after a donation.
o The central system reflects these changes in real-time, ensuring accurate inventory tracking across all blood banks.
5. Security and Error Handling
1. API Security:
o Ensure all API communications are encrypted using HTTPS.
o Implement authentication and authorization to restrict access.
2. Error Handling:
o Implement logging for API requests and database operations.
o Handle errors gracefully and provide meaningful responses to users.
3. Testing:
o Conduct unit and integration testing for API endpoints and integration code.
o Validate data consistency and accuracy across systems.

Process for Blood Collection and Management:
1. Donor Registration:
o A donor visits a Blood Bank and registers by providing personal information, including contact details, medical history, and consent for donation.
2. Blood Collection:
o Blood is collected from the donor into sterile blood bags. A sample of the blood is taken for laboratory testing.
3. Blood Testing:
o The blood sample undergoes rigorous testing for infections, diseases, and blood type classification. This includes screening for common pathogens and assessing blood group and Rh factor.
4. Result Processing:
o Once test results are confirmed, and if the blood is deemed safe, a barcode is generated. This barcode contains donor information and blood group details.
5. Labeling and Storage:
o The blood bag is labeled with the barcode and stored in a refrigerated environment. Blood is categorized based on the age of the bag as follows:
? < 5 days: Very Fresh
? 5 to 30 days: Fresh
? 30 to 90 days: Usable
? > 90 days: Near to Expiry
6. Component Extraction (Optional):
o If required, the blood can be separated into components such as red blood cells, platelets, plasma, and cryoprecipitate. Details of the extracted components are updated in the blood database.
7. Inventory Management:
o The blood and its components are tracked and managed within a centralized database, which records their age, category, and availability.
8. Request Handling:
o Blood is stored under refrigeration until a request is received from hospitals or patients. The request triggers a retrieval process where the blood or components are prepared for dispatch.
9. Patient Matching and Administration:
o Before dispatch, patient details are recorded to ensure proper matching and compatibility. Blood is administered to the patient as per the requirement.

Example :
Scenario: Near Miss in Blood Transfusion
Situation: A patient with a blood type of B+ is scheduled to receive a blood transfusion in a hospital. Due to a labeling error during manual data entry, the blood bank issues a unit of blood with type A+ instead of the correct B+.
Present Invention to Prevent This Near Miss:
Step 1: Patient Registration and Blood Group Tagging
AI Tools Utilized: Optical Character Recognition (OCR) and Facial Recognition Algorithms
Technical Prevention Measures: During the patient registration process, the patient's blood group is verified and electronically tagged within their digital medical record using AI-driven facial recognition technology. This ensures an automated cross-verification with the patient’s historical medical data, thereby eliminating the potential for manual data entry errors that could result in a mismatch of blood group information.
Step 2: Blood Bank Issue and Transfusion Preparation
AI Tools Utilized: QR Code Integration and Auto-Matching Algorithms
Technical Prevention Measures: Each blood unit is encoded with a unique QR code containing critical information, including the blood type (e.g., A+). Prior to the transfusion, the nurse scans the QR code at the point of care, where the AI system automatically cross-references the blood unit’s data against the patient’s digital medical record. The AI-powered auto-matching algorithm detects any discrepancies between the blood unit’s type (A+) and the patient’s registered blood group (B+). Upon detecting a mismatch, the system immediately triggers an alert, halting the transfusion process.
Step 3: Real-Time Alerts, Logging, and Investigation
AI Tools Utilized: Real-Time Monitoring Systems and Automated Alert Mechanisms
Technical Prevention Measures: The AI system dispatches an instantaneous real-time alert to the medical staff and logs the incident in the Haemovigilance system for further investigation. This process not only prevents the incorrect blood type from being transfused but also ensures that the error is meticulously documented, contributing to ongoing quality assurance and improvement measures within the hospital.
Outcome: The AI-driven system effectively identifies the blood type mismatch prior to transfusion, thereby averting a potentially fatal error. The error is promptly reported, allowing for immediate corrective action, which includes the re-issuance of the correct blood unit (B+) and the initiation of an investigation to prevent future occurrences.
Further, The AI system offers continuous learning by updating its algorithms based on incidents like this, thereby improving future matching accuracy. Additionally, it automatically generates compliance reports that are sent to relevant authorities, such as the Haemovigilance portal, contributing to broader safety monitoring efforts. This example underscores the role of AI as a critical safeguard in blood bank operations, preventing human errors that could lead to serious consequences. By integrating AI into the workflow, near misses can be detected and prevented, ultimately enhancing patient safety and ensuring the highest standards of care.
, Claims:. A method for optimizing the blood bank process (200) comprising:
(a) A centralized integrating system (201) to manage donor and recipient records, including a server with a database (201A) for storing and retrieving records.
(b) A network (202) of blood banks equipped with scanning devices(202A) to read quick response codes on donor blood units, system for monitoring(202B) and recipient identification cards.
(c) An auto-matching system (203) utilizing machine learning algorithms )implemented on the centralized server (203A) to match donor and recipient blood types, wherein the hardware comprises computational units capable of running these algorithms.
2. The method of claim 1, wherein the centralized system is further configured to:
(a) Include an Application programing interface (201B) for real-time communication between the central system and individual blood banks.
(b) Utilize network routers and secure communication protocols to ensure data integrity and privacy during data exchange.
3. The method of claim 1, wherein the auto-matching system includes:
(a) Computational hardware such as servers or cloud-based systems for processing data and running machine learning models.
(b) Data storage devices to maintain a feature matrix and target variables used for training the machine learning models.
4. The system for monitoring (202B) adverse reactions in blood transfusions comprising:
(a) Reaction monitoring devices(204) connected to patient monitoring systems to detect and record adverse reactions in real-time.
(b) A centralized system (205) configured to receive data from the monitoring devices (204), process it using an AI-based analysis engine running on computational hardware, and generate reports (208) for a haemovigilance portal.
5. The system of claim 4, wherein the centralized system includes:
(a) An API (206) interface for updating reaction records and submitting reports to a haemovigilance portal.
(b) Data processing units (207) to analyze reaction data and compare it with historical records to identify patterns and flag problematic donor-recipient pairs.
6. A method for integrating hardware components with a central network to optimize blood bank operations, comprising:
(a) Using scanning devices at blood banks to read QR codes and update the central database.
(b) Employing networked inventory management systems with real-time data synchronization to track and manage blood supply levels.
(c) Implementing centralized servers to run machine learning models for auto-matching and reaction monitoring.
7. The method of claim 6, wherein:
(a) The centralized system is equipped with high-performance computing hardware to handle large datasets and perform real-time analysis.
(b) The system includes hardware-based security measures to ensure data protection and compliance with regulations.
8. A method for preventing adverse reactions in blood transfusions comprising:
(a) A blocking and flagging mechanism implemented on a centralized server, utilizing data storage devices to keep records of adverse reactions.
(b) Data retrieval and processing hardware to determine if a donor-recipient pair should be blocked or flagged based on historical data.
9. The centralized optimizing method (200) as claimed in claim further comprises:

real-time data synchronization between the mobile donation site (300), fixed donation site (400), hospital blood bank (500), and hospital remote inventory (600);a decision support system utilizing machine learning algorithms to predict blood supply levels and optimize inventory management; and an alert system configured to notify medical personnel of critical blood shortages or mismatches in donor-recipient compatibility.