Healthcare Prediction System for Avoidable Emergency Room Visits
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Solution Overview
Problem
Patients often unnecessarily utilize emergency room services, resulting in higher costs and worse outcomes due to a lack of information about alternative healthcare options, leading to a high propensity for repeated unnecessary emergency room visits.
Innovation Solution
A healthcare prediction system that includes a data warehouse and a server configured to receive patient requests, apply geospatial and claims processing engines to identify locally prevalent diseases and prior avoidable emergency room claims, predicting avoidable visits and transmitting alternative service requests based on location, identifier, and symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patients seek emergency room services for all medical problems, then they can access comprehensive healthcare services, but they experience higher costs, longer wait times, and worse outcomes
Solution Approach 1:
The system performs preliminary analysis of patient symptoms against historical claims data and geospatial disease prevalence before the patient arrives at the emergency room. By pre-evaluating whether the visit is avoidable and identifying appropriate alternative services, the system enables patients to seek care at more appropriate facilities, thereby reducing wait times and improving outcomes without compromising comprehensive care access
Solution Approach 2:
The healthcare prediction server acts as an intermediary between patients and healthcare services. It receives symptom information, analyzes it against multiple data sources including historical claims and local disease prevalence, and provides guidance on appropriate care settings. This intermediary function helps patients make informed decisions about where to seek care, reducing unnecessary emergency room visits while ensuring appropriate care is accessed
2Reliability
If patients use emergency room services without evaluating alternatives, then they can receive immediate care, but they incur higher costs and experience repeated unnecessary visits
Solution Approach 1:
The system incorporates feedback loops that analyze patient outcomes from previous emergency room visits and use this information to provide personalized recommendations. By evaluating historical claims data associated with the patient's identifier, the system learns from past unnecessary visits and provides targeted feedback to prevent repetition, thereby reducing costs while maintaining care quality through evidence-based guidance
Solution Approach 2:
The system performs preliminary cost-benefit analysis by evaluating patient history and symptom severity before the emergency room visit occurs. It identifies cases where alternative services would be equally effective and less costly, enabling patients to make informed decisions that reduce unnecessary spending while ensuring appropriate care is accessed when needed
3Measurement precision
If a prediction system analyzes historical claims data and geospatial prevalence, then it can identify avoidable visits accurately, but it requires complex data processing and multiple engines
Solution Approach 1:
The system segments the complex analysis task into distinct functional components: a geospatial prevalence engine that processes location-based disease data, a claims processing engine that evaluates historical claims, and a prediction module that integrates both. This segmentation allows each component to specialize in specific data types and analysis methods, improving overall prediction accuracy while making the system more manageable and maintainable through modular architecture
Data Source
AI summary
A method for providing prediction based healthcare recommendations is performed by a healthcare prediction server. The method includes receiving an emergency room services request from a patient. The method also includes receiving a first portion of the plurality of historical claims data. The first portion includes associated historical outcome data. The method further includes applying a geospatial prevalence engine to the first portion of the plurality of historical claims data and the requestor location to identify at least one locally prevalent disease. The method additionally includes applying a claims processing engine to the first portion of the plurality of historical claims data and the requestor identifier to determine whether the patient is associated with at least one prior avoidable emergency room claim. The method includes predicting that the emergency room services request is associated with an avoidable visit. The method further includes transmitting an alternative services request.