What is it about?
Laboratory tests are considered an essential part of patient safety as patients' screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice. The objective of this study was to develop an artificial intelligence-based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs.
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Why is it important?
Laboratory tests are key components of the health care system and patient safety. These tests assist physicians, helping them make many important decisions related to prevention, diagnosis, treatment, and management of chronic diseases. However, in recent years, laboratory error rates have increased significantly, which has raised serious concerns about patient’s safety. Compared with other types of medical errors, laboratory errors have received little attention, despite these errors often causing significant harm to the patients. Previous studies have reported that indiscriminate and inappropriate use of laboratory tests puts a significant and unnecessary burden on the health care system . The value and associated cost of such inappropriate tests in the diagnostic and management process thus need to be determined.A quick and accurate laboratory test is crucial for patient’s safety through successful diagnosis and proper treatment of diseases. Because DL algorithms can easily handle hundreds of thousands of attributes and are capable of detecting and utilizing their interaction, developing an automated recommendation tool is always appreciable to improve proper clinical decisions. Accordingly, our study developed and evaluated a DL algorithm–based automated recommendation system using variables available in electronic health records (EHRs). We hypothesized that the DL algorithm can capture high-dimensional, nonlinear relationships among clinical features and a laboratory test recommendation system can be developed that can help physicians prescribe laboratory tests to individual patients more accurately as well as ensure safety of these patients.
Perspectives
We used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity.The developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.
Md.Mohaimenul Islam
Taipei Medical University
Read the Original
This page is a summary of: Development of an Artificial Intelligence–Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database, JMIR Medical Informatics, November 2020, JMIR Publications Inc.,
DOI: 10.2196/24163.
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