Unfortunately, preventable medical errors are not rare, and they are often the result of failures in electronic health information systems. According to the Sheba researchers, statistics show that avoidable errors account for 1 out of 131 outpatient deaths and 1 out of 854 inpatient deaths in the United States. These errors are responsible for direct annual costs of over $20 billion and liability costs of more than $13 billion.
The Sheba study is titled “Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine learning-based clinical decision support system in an inpatient setting,” and it was published in the Journal of American Medical Informatics Association (JAMIA) on August 7, 2019.
Led by Dr. Gadi Segal, Head of Internal Medicine “T,” the research analysis assessed the accuracy, impact, and overall quality of MedAware’s medication safety platform. MedAware was integrated hospital-wide into Sheba’s existing EMR system. The platform monitored all medical prescriptions that had been issued over a 16-month period, and physicians analyzed the results in a single medical division. All alerts were evaluated for accuracy, clinical validity and usefulness, and the doctors’ real-time responses to the alerts were recorded.
“Today’s widely used rule-based systems for prevention of medication risks, including prescription errors and adverse drug events, are unsuccessful and associated with a substantial false alert burden. These alerts are ignored in nearly 95 percent of cases,” explains Dr. Segal. “Our study demonstrates that MedAware’s patient safety platform, which leverages a probabilistic, machine learning approach based on outlier detection can significantly minimize such risks, with high physician acceptance of MedAware warnings that results in physician behavior change and increased patient safety.”
The findings, which showed a low overall alert burden, demonstrated:
MedAware generated warnings for only 0.4% of all prescriptions
60% of warnings were generated after a medicine was already dispensed following changes in patient status
89% of all alerts were regarded as accurate
80% of all alerts were regarded as clinically useful
43% of alerts led to changes in subsequent medical orders
In response to Sheba’s study, Dr. Gidi Stein, co-founder and CEO of MedAware, said, “We were always confident that our advanced patient safety platform would help physicians provide the highest level of care for their patients in a live inpatient setting, and our performance at Sheba, one of the top hospitals in the world, confirms our ability to protect physicians and their patients from avoidable medication-related errors and risks, thereby creating a safer prescribing environment.”
MedAware is changing the standards of patient safety through clinical decision support solutions that are empowered by AI. MedAware’s platform can accurately detect and prevent medication related risks, such as prescription errors, opioid dependency, and adverse drug events and contraindications. Sheba’s researchers verified that the MedAware system enhances patient safety and minimizes preventable risks and costs each day.
Sheba has a steadfast commitment to advancing medical technologies, as attested to by their long-standing partnership with MedAware.
“Sheba Medical Center, recently named by Newsweek as one of the top ten hospitals in the world, prides itself on prioritizing the safety and wellbeing of our patients. One way in which the leadership at Sheba does this is by remaining open to innovation – always searching for the newest, most cutting-edge technologies to improve the care of our patients,” said Dr. Eyal Zimlichman, Deputy Director and Chief Medical Officer at Sheba Medical Center. “Given the challenge of medication safety and its significant impact on patient care, we elected to work with MedAware when the company was still proving its concept. After years of partnership, our research team set out to assess the clinical impact of the live implementation of MedAware’s platform, and the results speak for themselves.”
