AI Home-Visit Scheduling System for Healthcare Cost Reduction
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Solution Overview
Problem
There is a long-standing challenge in the healthcare industry to provide cost-effective, quality care to chronically ill and aging patients, particularly those who are home-bound or living in remote areas, as existing solutions like telehealth and disease management have failed to significantly reduce healthcare costs and improve patient outcomes due to barriers such as lack of access, compliance, and psychosocial issues.
Innovation Solution
A system and method that utilizes a computer-implemented approach combining machine learning and artificial intelligence to optimize home-visit appointments and travel, integrating real-time data collection and analysis to predict population risks and cost-saving opportunities, while bridging gaps between clinical primary care and managed care case management, using FDA-registered diagnostic equipment and onsite care coordinators to provide comprehensive, coordinated care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If home-visit appointments are optimized using AI and machine learning, then patient compliance and health outcomes are improved, but system complexity and implementation costs increase
Solution Approach 1:
The patent introduces an AI-based appointment optimization system as an intermediary between healthcare providers and patients. This system uses machine learning algorithms to analyze patient data, predict no-show risks, and optimize scheduling decisions, thereby improving compliance without requiring direct complex interventions in clinical practices
Solution Approach 2:
The system enables self-service through automated scheduling optimizations and predictive analytics that work autonomously to improve appointment attendance. The AI system continuously learns from data and adjusts scheduling parameters without manual intervention, reducing the need for complex human oversight while maintaining high compliance rates
2Loss of energy
If home-visit appointments are optimized using AI and machine learning, then healthcare costs are reduced, but data processing requirements and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and predictive analysis before appointments are scheduled. By pre-processing patient data and predicting outcomes in advance, the system reduces the need for intensive real-time computational resources during actual appointment management, thereby lowering overall energy consumption
Solution Approach 2:
The AI system uses periodic batch processing to train models and update predictions rather than continuous real-time processing. This periodic action reduces computational overhead and energy usage while still maintaining accurate predictions for cost optimization
3Measurement precision
If comprehensive patient data is collected and analyzed, then population risks and cost-saving opportunities are predicted accurately, but patient privacy concerns and data security requirements increase
Solution Approach 1:
The system extracts only the specific data elements necessary for prediction purposes while leaving out unnecessary personal information. By selectively extracting relevant features for analysis and excluding sensitive data that is not needed for predictions, the system maintains high accuracy while minimizing privacy risks
Solution Approach 2:
The system uses anonymized copies of patient data for training and analysis purposes rather than processing actual personal information. These synthetic or anonymized copies preserve the statistical properties needed for accurate predictions while eliminating identifiable personal data, thus addressing privacy concerns
Data Source
AI summary
A system and a computer-implemented method employ an appointment optimization and route planning system (AORPS) for optimizing home-visit appointments and related travel for delivering patient care. The AORPS receives registration and patient data from patients and client input including information about healthcare providers, onsite care coordinators, health plans, appointment types, and success rates from a client. The AORPS collates the patient data and generates an input matrix from the client input and the collated patient data. The AORPS generates a predictive model for appointments, capitation, and return on investment for delivering patient care based on appointment and patient history, feedback, and healthcare data. The AORPS generates an appointment schedule with travel routes dynamically based on optimization factors derived from the client input, the collated patient data, the input matrix, the healthcare data, and the predictive model, incorporating real-time changes in patient data, the client input, the optimization factors, and appointments.