Patient Feedback Keyword Cloud for Early Complication Detection
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Solution Overview
Problem
Current healthcare systems face inefficiencies in collecting and processing patient-reported information, which is often imprecise and inaccurate, affecting diagnosis, treatment, and post-procedure monitoring, leading to increased costs and mortality rates due to undetected complications.
Innovation Solution
A processor-based system that structures patient feedback data into a keyword cloud, correlating symptoms, complications, and conditions using previous patient data entries, providing a user-friendly interface for patients to report their status and allowing caregivers to track insights for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static generic surveys are used to collect patient feedback, then the system is simple to implement, but the accuracy and relevance of patient-reported information deteriorates
Solution Approach 1:
The feedback collection process is segmented into multiple interaction stages rather than a single static survey. Patients engage in iterative conversations with the NLP system, allowing feedback to be collected in structured segments that improve accuracy while maintaining ease of use through automated processing.
Solution Approach 2:
The system implements feedback loops where patient responses are processed by NLP algorithms that learn and adapt from previous interactions. This feedback mechanism refines the precision of information collection over time without increasing implementation complexity, as the learning is automated.
2Productivity
If patients are asked to self-report symptoms and conditions, then the system requires minimal caregiver intervention, but the precision and accuracy of reported information deteriorates due to patient misunderstanding
Solution Approach 1:
An NLP-based intermediary system is introduced between the patient and the feedback collection process. This intermediary translates patient language into structured medical data, maintaining high caregiver efficiency while improving reporting precision through automated interpretation and validation of patient responses.
Solution Approach 2:
The mechanical process of manual survey administration and interpretation is replaced with an automated NLP system. This substitution maintains productivity by eliminating caregiver intervention while improving precision through consistent, algorithmic processing of patient responses.
3Reliability
If comprehensive patient monitoring is implemented to detect complications early, then patient outcomes improve, but the complexity of the information processing system increases
Solution Approach 1:
The monitoring system performs self-service through automated NLP processing that independently collects, structures, and analyzes patient feedback without requiring complex manual intervention. This automation maintains high reliability for complication detection while minimizing system complexity by using intelligent algorithms rather than elaborate manual processes.
Solution Approach 2:
The system changes parameters dynamically by adapting its questioning and analysis based on patient responses and risk factors. This parameter adaptation enables comprehensive monitoring for improved reliability while keeping the base system relatively simple, as the complexity is managed through flexible parameter adjustment rather than fixed complex structures.
4Reliability
If detailed patient feedback is collected to improve diagnosis and treatment, then care quality improves, but the time required for data collection and processing increases
Solution Approach 1:
Manual data collection and processing mechanics are replaced with automated NLP processing. This substitution enables detailed feedback collection that improves care quality while reducing time loss, as the automated system processes information faster and more efficiently than manual methods.
Solution Approach 2:
The feedback collection is integrated into continuous patient interactions rather than being a separate time-consuming process. The NLP system processes feedback continuously during natural patient-caregiver interactions, maintaining care quality through detailed data collection while minimizing time loss by making the process continuous and seamless.
Data Source
AI summary
An apparatus system and method for processing patient data pursuant to a monitoring period, wherein received patient feedback data is processed and structured to provide a selectable keyword cloud to a display. The keyword cloud may include a plurality of at least one of symptoms, complications and patient conditions, wherein the keyword cloud is structured by a processor for display in accordance with previous patient feedback data during at least part of the monitoring period.