The study also found that applying a federated learning approach can further improve accuracy of the model.
A study has demonstrated the international transferability of a Taiwanese artificial intelligence model for detecting medication errors in EHR systems in the United States.
The study was jointly conducted by Taiwan-based medical AI startup Aesop Technology, Taipei Medical University, Harvard Medical School and Brigham and Women's Hospital. Its results were announced last week in a press release.
WHY IT MATTERS
The "biggest challenge" in data-driven medicine is the successful implementation of data-driven applications in clinical practice from local to global settings without compromising patient safety and privacy, according to Dr Yu-Chuan Jack Li, a professor at Taipei Medical University.
The study, whose findings were published in the Journal of Medical Internet Research - Medical Informatics in January, found "good" transferability of Aesop's machine learning model in the EHR systems of two training schools under Harvard Medical School – Brigham and Women’s Hospital and Massachusetts General Hospital.
A federated learning (FL) approach was applied to the model which enhanced its performance. The said approach is an emerging technique that addresses the issues of isolated data islands and privacy.
"FL provides the solution by training algorithms collaboratively without exchanging the data itself," Dr Yu-Chuan said.
"The study has shown that the model trained by federated learning achieves remarkable performance comparable to the other two models trained by individual data sets," Aesop Technology co-founder and CEO Jim Long also said.
Incorporated in Aesop's MedGuard system, the AI medication safety model was trained using the 1.3 billion prescription data set from the National Health Insurance Administration in Taiwan.
In the statement, Aesop said its system can "immediately" provide adaptive suggestions to help doctors better complete their prescriptions. The AI model has since been expanded to eastern and western hospitals in the US.
THE LARGER TREND
Despite the wide adoption and optimisation of EHR systems in US hospitals, those systems still pose risks given varying safety performance, according to a 2020 study by researchers from the University of Utah and the Brigham and Women’s Hospital.
Medical errors are costing the US around $20 billion each year, leading to over 250,000 deaths. These can occur at any stage of the medication process with errors in prescribing happening half of the time.
The use of an AI system in preventing medication errors was already validated as early as 2017 by researchers at Harvard Medical School. In the same year, MedAware, the Israel-based startup which developed the algorithmic system, raised $8 million to scale its AI-powered solutions.
ON THE RECORD
"Reducing medication errors at the source is crucial. However, to help physicians be better informed and make better decisions, they need more accurate suggestions and alerts. This is where machine learning can help to make better decisions and improve patient safety and quality of care," said Dr David W. Bates, Chief of General Internal Medicine and Primary Care at Brigham and Women's Hospital and Professor of Medicine at Harvard Medical School.
New study paves the way for collaboration on artificial intelligence modelling and medication error reduction globally
Researchers at Harvard Medical School, Brigham and Women's Hospital, Taipei Medical University, and Aesop Technology, a Taiwan-based startup, announced today the results of a new joint study into the international transferability of machine learning (ML) models to detect medication errors. The results were recently published in the Journal of Medical Internet Research - Medical Informatics.
Working to Reduce Medication Errors
Medication errors are a growing financial and healthcare burden that results in economic costs of around US$ 20 billion and more than 250,000 deaths annually in the U.S. alone.
Medication errors can occur during any stage of the medication process, including prescribing, dispensing, administration, and monitoring, with errors in prescribing accounting for 50% of the total.
When medicating patients, physicians go through complex decision-making processes to accurately write a prescription. First, they must clearly define the patient's problem and list the therapeutic objective before selecting an appropriate drug therapy based on age, gender, and possible allergies. They must also consider dosing, drug-drug interaction, potential discontinuation of the drug, drug cost, and other therapies — and all of these need to be done instantly and simultaneously.
"Reducing medication errors at the source is crucial. However, to help physicians be better informed and make better decisions, they need more accurate suggestions and alerts. This is where machine learning can help to make better decisions and improve patient safety and quality of care," said Dr. David W. Bates, Chief of General Internal Medicine and Primary Care at Brigham & Women's Hospital and Professor of Medicine at Harvard Medical School.
For technology to assist in solving these problems, it is critical that machine learning understands these variables. For this to be successful, data must be properly collected, organized, and maintained.