SMART Algorithm for Physician Attribution Accuracy
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Solution Overview
Problem
Current methods for attributing patient care in hospitals often inaccurately assign responsibility to physicians who were not substantially involved, as they are listed as attending at discharge, leading to misinterpretation of care and decision-making impact, particularly in reducing Length of Stay (LOS).
Innovation Solution
A computer-implemented system using machine learning to develop the SMART algorithm, which analyzes electronic health record (EHR) data to predict physician attribution by learning from predefined decisions and computing attribution scores based on predictor parameters, ensuring accurate attribution of primary physician involvement in patient care and LOS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the current method of attributing patient care to the attending physician at discharge is used, then the attribution process is simple and quick, but the accuracy of identifying the truly involved physician is poor
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the EHR data and the attribution decision. This model processes multiple variables including physician communication patterns, order entry activities, and care plan development to identify the truly involved physician, rather than relying directly on the attending physician field alone.
Solution Approach 2:
The patent replaces the simple mechanical rule of attributing to the attending physician at discharge with an intelligent system using machine learning algorithms. The system automatically analyzes patterns in EHR data to determine attribution, substituting manual administrative review with automated computational analysis.
2Measurement precision
If machine learning algorithms are used to predict physician attribution, then the accuracy of identifying involved physicians improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing EHR data into structured variables before feeding them to the machine learning model. This includes extracting communication patterns, order entry activities, and care plan developments in advance, which simplifies the actual prediction process and improves computational efficiency.
Solution Approach 2:
The patent transforms raw EHR data into meaningful parameters and variables that the machine learning algorithm can process effectively. By changing the form of data from unstructured clinical notes to structured variables representing physician activities and communication patterns, the system achieves both accuracy and computational feasibility.
3Reliability
If multiple variables from EHR data are analyzed to determine physician attribution, then the reliability of attribution decisions improves, but the time and resources required for data processing increase
Solution Approach 1:
The patent enables continuous processing of EHR data by integrating the machine learning model into the existing healthcare IT infrastructure. The system continuously analyzes incoming EHR data streams and generates attribution decisions in real-time or near-real-time, eliminating batch processing delays and maintaining continuous operational flow.
Solution Approach 2:
The machine learning model performs self-service by automatically selecting and weighting the most important variables from EHR data without requiring manual intervention. The system autonomously processes multiple data sources, weighs their importance, and generates attribution decisions independently, reducing both time and resource requirements compared to manual multi-variable analysis.
Data Source
AI summary
Various embodiments of a computer-implemented system for generating an algorithm configured to compute an attribution output to enhance provider attribution are disclosed.