JMIR Publications

ABSTRACT

Background:

In the contemporary realm of healthcare, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as excessive costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable healthcare. For example, an echocardiogram is a type of laboratory test that is extremely important and not easily accessible. The increasing demand for echocardiograms underscores the imperative for more efficient scheduling protocols. Despite this pressing need, limited research has been conducted in this area.

Objective:

The study aims to develop an interpretable machine learning model for determining the urgency of patients requiring echocardiogram, thereby aiding in the prioritization of scheduling procedures. Furthermore, this study aims to glean insights into the pivotal attributes influencing the prioritization of echocardiogram appointments, leveraging the high interpretability of the machine learning model.

Methods:

Empirical and predictive analyses have been conducted to assess the urgency of patients based on a large real-word echocardiogram appointment dataset (i.e., 34,293 appointments) sourced from electronic health records (EHR) encompassing administrative information, referral diagnosis, and underlying patient conditions. We employed a state-of-the-art interpretable machine learning algorithm, the Optimal Sparse Decision Tree (OSDT), renowned for its high accuracy and interpretability, to investigate the attributes pertinent to echocardiogram appointments.

Results:

Our method demonstrated satisfactory performance (F1 improvement: 1.79% and F2 improvement: 0.79%) in comparison to the commonly used machine learning algorithms. Moreover, due to its high interpretability, the results provide valuable medical insights regarding the identification of urgent appointments through the extraction of decision rules from the OSDT model.

Conclusions:

Our method demonstrated state-of-the-art predictive performance, affirming its effectiveness. Furthermore, we validate the decision rules derived from the OSDT model by comparing them with established medical knowledge. These interpretable results (e.g., attribute importance and decision rules from the OSDT model) underscore the potential of our approach in prioritizing patient urgency for echocardiogram appointments and can be extended to prioritize other laboratory test appointments using EHR data.