Referral Provider Selection Through Structured POS Utilization Data
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Solution Overview
Problem
The referral process in healthcare networks faces challenges in maximizing patient outcomes due to inefficiencies in selecting referral providers, as referring providers lack timely and objective information about referral providers' access and value, which can change dynamically, leading to sub-optimal referrals that affect patient access, value, and network efficiency.
Innovation Solution
A system and method for structuring and processing data to analyze place of service utilization rates by comparing referral profiles with utilization logs, calculating POS utilization rates, and applying rulesets to score, rank, and qualify healthcare providers for referral selection, ensuring efficient and value-based referrals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If referrals are made based on habit or subjective criteria, then the referral process is simple and quick, but the patient outcome and access to value-based care deteriorate
Solution Approach 1:
The system pre-calculates and stores utilization rates and performance metrics for all referral providers before referral decisions are needed. This preliminary data preparation allows referring providers to quickly access objective quality metrics during the referral process, eliminating the need to conduct time-consuming assessments at the moment of referral while ensuring data-driven decision-making
Solution Approach 2:
The system introduces an intermediary data layer between the referring provider and the referral provider selection process. This intermediary layer aggregates, validates, and presents objective utilization rate data from multiple sources (payers, providers, patients) in a standardized format, enabling efficient comparison of referral providers without requiring direct assessments
2Measurement precision
If comprehensive data collection about referral providers is performed, then the quality of referral selection improves, but the time and resources required increase
Solution Approach 1:
The system performs comprehensive data collection and metric calculation in advance, storing utilization rates, access metrics, and quality indicators for all referral providers in a pre-computed database. This eliminates the need for real-time data gathering during the referral process, providing accurate assessment data instantly when needed
Solution Approach 2:
The system transforms complex, multi-dimensional provider performance data into simplified utilization rate parameters and ranked metrics that can be quickly compared. By changing the representation of provider quality from detailed narratives to standardized numerical parameters, the system enables rapid decision-making without sacrificing assessment depth
3Adaptability or versatility
If referral provider performance is evaluated in real-time, then the access and value information remains current, but the complexity of the evaluation system increases
Solution Approach 1:
The system implements periodic recalculation of utilization rates at defined intervals (e.g., monthly, quarterly) rather than continuous real-time evaluation. This periodic update mechanism maintains information currency while avoiding the computational complexity and resource demands of continuous real-time tracking, striking a practical balance between data freshness and system simplicity
Data Source
AI summary
Disclosed are a method, a device, and/or a system of enhancing patient referral outcome through structuring data for efficient place of service utilization rate analysis. In one embodiment, a device extracts a set of utilization logs that each describe a type of facility from a log data structure modeling healthcare providers associated by referral logs. A referral request agent receives a referral profile generated for a patient. The referral profile is compared by a profile matching engine to the set of utilization logs. A dataset reduction subroutine generates a reduced dataset that is compared to a minimum threshold of healthcare providers to determine a sufficient number are within the reduced dataset. The utilization rate routine then calculates a POS utilization rate, and a healthcare provider is selected based on criteria including the POS utilization rate. The selection can be transmitted to a computing device of a healthcare provider.