Hospital Infection Care Evaluation via Length-of-Stay Estimation
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Solution Overview
Problem
Evaluating hospital care for infection patients is challenging due to varying host responses and recovery trajectories, making it difficult to identify priorities and bottlenecks in patient transition decisions, especially for infectious diseases, as existing systems struggle to effectively combine clinical and operational data into actionable information.
Innovation Solution
A cloud-based patient logistics management solution employing a length of stay (LoS) estimation model, trained using historical patient encounter records, which estimates recovery time for infection patients based on attributes such as vital signs, demographics, laboratory data, and medication administration, and generates evaluation metrics by comparing estimated and actual LoS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a hospital manages patient flow for infection patients, then patient transition decisions can be made, but priorities and bottlenecks become difficult to identify leading to inconsistent care delivery
Solution Approach 1:
The system uses evaluation metrics that compare actual patient length of stay against predicted length of stay to provide feedback on care quality. This feedback mechanism enables continuous improvement of care delivery consistency by identifying deviations from expected recovery trajectories.
Solution Approach 2:
The patent replaces manual assessment methods with an automated machine learning-based prediction system that processes clinical and operational data to generate actionable insights, thereby improving both productivity and care consistency.
2Measurement precision
If the LoS estimation model processes multiple patient attributes, then recovery time estimation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments patient attributes into distinct categories (vital signs, demographics, laboratory data, infection type, medication administration) and processes them through specialized data extraction and feature engineering modules, making the complex model more manageable and maintainable.
Solution Approach 2:
The patent introduces an intermediary layer of feature engineering that transforms raw patient attributes into meaningful features for the machine learning model. This intermediary processing layer simplifies the relationship between input data and model predictions, reducing overall system complexity.
3Productivity
If historical patient encounter records are used to train the model, then evaluation metric generation capability improves, but data processing requirements increase
Solution Approach 1:
The system extracts relevant features and evaluation metrics from historical patient encounter records using targeted data extraction techniques. This allows the model to generate meaningful evaluation metrics without processing the entire historical dataset, reducing data processing requirements while maintaining productivity.
Solution Approach 2:
The patent applies partial data processing by selecting and processing only the necessary portions of historical records required for training and evaluation. This partial action approach enables effective model training without the need to process all available historical data, thereby reducing computational resources required.
Data Source
AI summary
Systems and methods for infection treatment evaluation is provided. The system receives patient encounter records including patient attribute data and patient LoS data. The system generates, via an LoS estimation model, LoS estimations based on the patient attribute data of each of the patient encounter records. The patient attribute data includes vital sign data, patient demographic data, laboratory data, medical condition data, infection type data, medication administration data, and/or discharge type. The system further generates, via a metric analyzer, evaluation metrics based on the LoS estimations and the patient LoS data of each of the patient encounter records. The system may also include a user interface configured to display at least a portion of the one or more evaluation metrics. The system may also train the LoS estimation model with a historical encounter records. Each of the historical encounter records includes historical attribute data and historical recovery data.