Clinical Scheduling Data Cleansing via Standardized Mapping
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Solution Overview
Problem
Current scheduling systems are inadequate in optimizing clinical scheduling due to their reliance on custom interfaces and APIs, limiting their ability to operate with various scheduling databases and resulting in wasted opportunities and delayed care.
Innovation Solution
A scheduling system and method that includes hardware processors configured to receive, cleanse, and optimize clinical record data by mapping it to standardized formats, purging errors and artifacts, and applying configurable logic to create optimized scheduling templates that align resource availability with variables like visit complexity and provider preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom interfaces and APIs are used for each electronic record, then scheduling systems can operate with specific databases, but adaptability to various scheduling databases is limited
Solution Approach 1:
The scheduling system employs a universal interface layer that can connect to multiple different scheduling databases without requiring custom-specific interfaces for each database type. This standardized universal interface enables the system to work with various electronic health record systems, improving adaptability while reducing the complexity of maintaining multiple custom integrations.
2Productivity
If scheduling systems lack optimization capabilities, then system simplicity is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing and cleansing clinical record data before it enters the optimization engine. This includes validating data formats, removing duplicates, and preparing scheduling parameters in advance, which reduces the computational burden on the optimization engine and improves overall resource utilization efficiency without proportionally increasing system complexity.
Solution Approach 2:
The optimization engine incorporates self-service mechanisms that automatically adjust scheduling parameters based on historical data patterns and real-time availability, reducing the need for manual intervention and complex user configuration. The system self-tunes optimization parameters to maximize resource utilization while maintaining operational simplicity.
3Measurement precision
If data cleansing is performed manually, then data accuracy can be verified, but time consumption increases
Solution Approach 1:
The system replaces manual data cleansing operations with automated computational processes that validate, clean, and transform clinical record data. The optimization engine automatically detects and corrects data errors, removes duplicate records, and ensures data consistency through algorithmic processing, achieving high data accuracy while dramatically reducing the time required compared to manual verification methods.
Data Source
AI summary
A scheduling system and method for data cleansing may be used to optimize clinical scheduling. The present disclosure describes receiving clinical record data, in an agnostic manner, from a system including a source scheduling database containing the clinical record data; mapping the clinical record data to a desired format; conforming the clinical record data to standardized scheduling elements of the scheduling system; cleansing, in a manner configurable by a user, the clinical record data to purge portions of the clinical record data; providing the clinical record data to an optimization engine for optimization of the clinical record data; optimizing the clinical record data by applying configurable logic to the clinical record data; and uploading one or more newly defined optimized scheduling templates via an outbound connection back to the scheduling system.