Patient-Specific Medical Provider Recommendation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Patients often select medical care facilities based on simplistic criteria, such as physician recommendations and proximity, failing to consider the facility's expertise matching their specific medical needs, leading to suboptimal and costly choices, especially with the complexity introduced by Accountable Care Organizations (ACOs) and hospital networks.
Innovation Solution
A system and method that utilize patient-specific medical, geographical, and financial data to compare against multiple provider databases, generating recommendations for hospitals, hospital networks, or ACOs by matching current patient data with similar cases, incorporating user preferences, insurance, and provider incentives to optimize selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If patients rely on simplistic selection measures such as physician opinion and proximity, then the selection process is simple and quick, but the quality and cost-effectiveness of care is suboptimal
Solution Approach 1:
The patent introduces a recommendation system as an intermediary between patients and medical providers. This system processes complex data about patient medical history, provider expertise, and treatment outcomes to generate personalized recommendations, thereby maintaining ease of selection while improving care quality matching through data-driven insights
Solution Approach 2:
The patent replaces the mechanical/simple decision-making process (relying on physician opinion and proximity) with an information-based system that uses medical data, treatment outcome data, and algorithmic analysis to determine provider-patient matches, thereby improving reliability without significantly increasing operational complexity for the patient
2Reliability
If patients consider facility expertise matching their specific medical needs, then the care quality improves, but the selection process becomes more complex
Solution Approach 1:
The recommendation system performs the complex task of matching patient needs with provider expertise automatically without requiring patients to manually evaluate multiple providers' capabilities. The system self-services by gathering data, analyzing matches, and presenting recommendations, thereby improving expertise matching while keeping the patient's involvement simple
Solution Approach 2:
The system pre-processes and organizes complex information about provider expertise, treatment outcomes, and patient requirements before the selection moment. By preparing personalized recommendations in advance based on available data, the system reduces the complexity of the actual selection process while ensuring expertise matching is thoroughly considered
3Reliability
If the selection process considers the entire bundle of expertise offered by ACOs and hospital networks, then the cost-effectiveness improves, but the complexity of the selection process increases significantly
Solution Approach 1:
The patent segments the complex evaluation of ACOs and hospital networks into manageable components: patient medical data, provider expertise data, treatment outcome data, and cost data. By dividing the selection process into these discrete analytical segments that can be processed independently and then integrated, the system improves cost-effectiveness through comprehensive analysis while avoiding overwhelming complexity for the patient
4Reliability
If providers use incentives to influence patient choices, then the patient-provider alignment improves, but the transparency and simplicity of the selection process decreases
Solution Approach 1:
The recommendation system incorporates provider incentives and performance data as feedback mechanisms that inform patient recommendations. By systematically collecting and analyzing data about provider outcomes, costs, and incentive structures, the system translates complex provider motivations into actionable recommendations that improve patient-provider alignment while maintaining transparency through data-driven rationale
Data Source
AI summary
A system, method and computer readable storage medium for selecting a medical provider from a plurality of available medical providers. The selection being made by receiving medical data and one of geographical and financial data corresponding to a patient, comparing the medical data and the one of the geographical and financial data corresponding to the patient to corresponding data of a plurality of medical provider databases to determine a relationship therebetween and generating, via a recommendation generator, a recommendation of a first medical provider for the patient based on the medical data and the one of geographical and financial data and the comparison to the corresponding data of the medical provider databases.