Prediction Models for Placeholder Values in Data Analysis
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Solution Overview
Problem
Current data analysis systems often omit data from the current or latest period to avoid changes, leading to significant errors due to lag periods in key performance indicator settlements, particularly in real-time data analysis, such as in medical insurance claims where costs and reimbursement amounts can change dramatically.
Innovation Solution
A system and method that generate prediction models for placeholder values using historical information, including placeholder values, updated values, and timing information, to predict potential further revisions within a given time window, thereby providing stable predictions and reducing lag periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data from current or latest period is included in real-time data analysis, then the timeliness and relevance of analysis results is improved, but the accuracy and reliability deteriorates due to placeholder values being subject to change
Solution Approach 1:
The system performs preliminary actions by generating prediction models in advance using historical data before final settlement occurs. These models predict final values for placeholder data, allowing the system to incorporate current period data into analysis while compensating for potential changes through predictive adjustments.
Solution Approach 2:
Prediction models serve as intermediaries between placeholder values and final settled values. The models translate current placeholder data into predicted final values, enabling the system to use timely data while maintaining accuracy through the mediating predictive layer.
2Productivity
If placeholder values are used in data analysis, then the timeliness of analysis is improved, but the precision and accuracy worsens due to revisions and changes in placeholder values
Solution Approach 1:
The system changes the parameter state of placeholder values by transforming them through prediction models. Instead of using raw placeholder values directly, the system applies predictive transformations that adjust the values based on historical patterns, thereby improving precision while maintaining the timeliness of using current data.
3Reliability
If data analysis systems avoid current period data to maintain accuracy, then the reliability of analysis is improved, but the loss of time and timeliness deteriorates
Solution Approach 1:
The system performs preliminary actions by establishing prediction models before final settlement. This allows current period data to be included in analysis with predictive adjustments already in place, eliminating the need to wait for final settlement while maintaining accuracy through pre-established predictive mechanisms.
Data Source
AI summary
The present disclosure relates to a method and non-transitory machine-readable storage medium encoded with instructions for using prediction models for predicting values, the medium comprising instructions for receiving a plurality of entry identifiers, instructions for receiving a value for each of the plurality of entry identifiers, instructions for determining whether the value for each of the plurality of entry identifiers has changed and a magnitude of the change, instructions for building a model for predicting a time-to-value change, instructions for building a model for predicting a future magnitude of change, instructions for performing a simulation using the model for predicting the time-to-value change and the model for predicting the future magnitude of change and instructions for outputting a confidence interval.