Cognitive System Predicting Engagement Items for Care Providers
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Solution Overview
Problem
Healthcare providers face challenges in optimizing the limited time spent with patients, often under 16 minutes, leading to inefficiencies in addressing patient concerns and missed opportunities for preventive services, especially in busy clinical settings.
Innovation Solution
A data processing system that predicts engagement items for care providers by detecting scheduled appointments, scanning patient communication patterns, generating relevant questions, and presenting seed topics to doctors based on patient responses, optimizing the doctor-patient interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If doctors spend more time with patients to address all concerns and provide preventive services, then patient care quality improves, but productivity decreases due to limited appointment time
Solution Approach 1:
The system performs preliminary actions by analyzing patient communication patterns and generating prioritized engagement items before the appointment occurs. This allows doctors to receive pre-sorted information about patient concerns and preventive services in advance, enabling them to focus during the limited appointment time on the most critical issues without needing to spend more time reviewing all patient data.
2Reliability
If doctors review comprehensive patient history and communication patterns, then engagement quality improves, but time consumption increases during the limited appointment
Solution Approach 1:
The system extracts and isolates the most relevant information from comprehensive patient histories and communication patterns. By using natural language processing to identify key themes, concerns, and preventive service needs, the system separates essential engagement items from redundant information, providing doctors with a condensed, prioritized overview that maintains engagement quality while reducing review time requirements.
Solution Approach 2:
The system performs the time-consuming analysis of patient history and communication patterns before the appointment occurs. By pre-processing and organizing this information into prioritized engagement items, the system eliminates the need for doctors to spend significant time reviewing comprehensive patient data during the limited appointment window.
3Measurement precision
If the system analyzes all patient communication patterns and generates comprehensive questions, then prediction accuracy improves, but processing time and system complexity increase
Solution Approach 1:
The system segments the complex task of analyzing patient communication patterns into distinct processing stages: collecting communication data, identifying key themes through natural language processing, prioritizing engagement items based on patient concerns and preventive service needs, and generating targeted questions. This segmentation allows the system to achieve high prediction accuracy by focusing analysis on the most relevant aspects of communication patterns rather than processing all data uniformly.
Solution Approach 2:
The system applies different levels of analysis to different aspects of patient communication patterns. Instead of uniformly processing all communications, it identifies and intensifies analysis on specific themes and patterns that are most predictive of patient engagement needs, while applying lighter processing to less relevant communications. This local quality approach maintains high prediction accuracy while reducing overall processing complexity.
Data Source
AI summary
A mechanism is provided in a data processing system to implement a healthcare cognitive system which operates for predicting engagement items for care providers. An engagement item prediction component executing within the healthcare cognitive system detects a scheduled appointment between a patient and a doctor. The engagement item prediction component scans communication pattern and details of patient communications for indicators of a medical condition of the patient. The healthcare cognitive system generates a set of one or more questions related to the medical condition. The engagement item prediction component presents the set of one or more questions to the user and receives one or more responses to the set of one or more questions from the patient. The healthcare cognitive system generates one or more seed topics based on the one or more responses and presents the one or more seed topics to the doctor for the scheduled appointment.