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Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

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Confidence-based laboratory test reduction recommendation algorithm.

Tongtong Huang¹, Linda T Li^1,2, Elmer V Bernstam^1,3

¹School of Biomedical Informatics, UTHealth, Houston, TX, USA.

BMC Medical Informatics and Decision Making

|May 10, 2023

Summary

A new deep learning model can identify unnecessary hemoglobin (Hgb) tests in hospitalized patients, reducing risks and costs. This AI approach improves healthcare efficiency by flagging superfluous Hgb testing.

Keywords:

Confidence based Deep learning Lab test reduction

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Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Clinical Decision Support

Background:

Unnecessary laboratory testing contributes to increased healthcare costs and potential patient harm.
Hemoglobin (Hgb) testing is a common laboratory test where potential for unnecessary utilization exists.

Purpose of the Study:

To develop and validate a deep learning model for identifying unnecessary hemoglobin (Hgb) tests in hospitalized patients.
To reduce healthcare expenditures and mitigate patient risks associated with superfluous Hgb testing.

Main Methods:

Utilized internal and external patient datasets (MIMIC III) for model training and validation.
Employed a "select and predict" strategy with prediction confidence estimation to ensure reliability.
Incorporated handling of irregularly sampled observational data, considering variable correlations and temporal dependencies.

Main Results:

Achieved high performance with AUCs of 95.89% for normality and 95.94% for Hgb stability.
Recommended a 9.91% reduction in unnecessary Hgb tests.
Demonstrated strong generalization capabilities on external patient data.

Conclusions:

The novel deep learning model effectively identifies unnecessary Hgb tests in hospitalized patients.
This approach holds significant potential for reducing healthcare costs and improving patient outcomes.