Dynamic Blood Donor Screening via Personalized Questionnaires
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Solution Overview
Problem
Current blood donation screening processes are inefficient and prone to safety issues due to uniform questionnaires that fail to account for individual donor factors, leading to potential misidentification of donor eligibility and wastage of blood supplies.
Innovation Solution
A system that constructs personalized user profiles based on user data, generates context-specific questions, and analyzes responses using machine learning models to determine eligibility and recommend actions, such as blood test prioritization, through a cloud computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a uniform precompiled questionnaire is used for all donors, then the screening process is simple and quick, but the accuracy of donor eligibility assessment deteriorates due to inability to account for individual donor factors
Solution Approach 1:
The questionnaire dynamically adapts based on donor responses and profile data. The system transitions from a static uniform questionnaire to a dynamic personalized one, where questions are selected and presented based on the donor's specific characteristics, risk factors, and previous answers, thereby maintaining screening speed while improving assessment accuracy.
Solution Approach 2:
The system changes the parameters of the questionnaire by selecting different questions, varying the depth of inquiry, and adjusting the focus based on donor-specific parameters such as age, medical history, lifestyle factors, and risk indicators. This parameter adaptation allows the same screening system to efficiently handle diverse donor profiles with appropriate precision.
2Measurement precision
If professional assessment of donor responses is performed manually, then eligibility determination is accurate, but the screening process becomes time-consuming and inefficient
Solution Approach 1:
The manual mechanical assessment process by professionals is replaced with an automated computer-based system that uses algorithms, machine learning models, and decision support tools. This substitution maintains high accuracy in eligibility determination while dramatically reducing the time required for assessment, as the automated system can process and analyze donor responses instantaneously.
Solution Approach 2:
The system enables self-service by allowing donors to complete the questionnaire and receive preliminary eligibility assessment automatically without requiring extensive manual review by professionals. The automated system performs the initial screening and eligibility determination, freeing professionals to focus only on complex cases that require human judgment.
3Reliability
If comprehensive blood safety checks are performed, then blood safety is improved, but the cost and complexity of the blood supply chain increase
Solution Approach 1:
The system performs preliminary screening and risk assessment actions before blood collection and processing. By identifying high-risk donors early through personalized questionnaires and automated assessment, the system prevents unnecessary blood collection from ineligible donors, thereby simplifying subsequent processing steps and reducing overall supply chain complexity while maintaining safety.
Solution Approach 2:
The blood supply chain is segmented into distinct risk-based pathways. Donors are categorized into different risk groups based on their profile and responses, allowing the system to apply appropriate levels of screening and processing complexity to each segment. This segmentation ensures comprehensive safety checks for high-risk cases while streamlining processes for low-risk donors, reducing overall system complexity.
4Ease of operation
If unnecessary blood transfusions are performed, then patient care is provided, but blood wastage increases and safety risks are introduced
Solution Approach 1:
The system incorporates feedback loops where donor data, outcomes, and results are continuously collected and used to refine the screening algorithms and eligibility criteria. This feedback mechanism improves the accuracy of eligibility determination over time, reducing false positives that lead to unnecessary transfusions and blood wastage, while ensuring appropriate care is provided to eligible donors.
Data Source
AI summary
A first set of user data is received and a user profile is constructed based on the user data and in accordance with a sensitive service involving the user. A situational context is analyzed based on the first set of data. Personalized questions are generated, responsive to the user profile and to the situational context. The personalized questions are presented to a user corresponding to the user data and responses to same are received, including detection of user micro-expressions. The responses are analyzed, according to one or more machine learning models. A neural network model selects an action to be performed in response to analyzing the responses from the user; the action is a sensitive service involving the user. An apparatus is triggered to send a simple message service (SMS) message to a point of care service professional; the message recommends performance of the sensitive service on the user.