RA'ANANA, Israel, Dec. 15, 2020 /PRNewswire/ -- MedAware, a leader in AI-enabled decision support and patient safety solutions, today released the results from a study to be published by the Journal of the American Medical Informatics Association, showing that sleep-deprived, overworked junior physicians are at increased risk of erroneous prescribing and jeopardizing patient safety. The released results highlight the need for AI-driven, personalized decision support and the comprehensive safety net the MedAware platform provides.
Physician burnout is occurring at alarming rates, with 44% reported to be suffering from it according to a separate, recent survey of 15,000 physicians. A stressed physical and emotional state affects the quality of care patients receive and is a significant contributor to medication errors. Moreover, the COVID-19 pandemic continues to exhaust medical systems, propelling sleep-deprived and undertrained doctors into prescribing roles, further exacerbating the issue. This has resulted in the critical need for advanced decision support systems to help mitigate the risks of potentially fatal prescribing errors.
MedAware's machine-learning algorithms extract and analyze data gathered from millions of electronic health records (EHRs) to detect outlier prescribing behavior and evolving situations that would otherwise be missed. Acting as a safety layer within the EHR, MedAware's platform boasts an 85% accuracy rate -- compared to legacy solution rates of 16% or lower -- and ensures that hard-to-anticipate errors are caught at the earliest possible stage, avoiding dangerous and even fatal adverse drug events for patients.
"As expected, this study shows that long shifts with heavy workloads lead to increased physician prescribing errors," said Dr. Gidi Stein, Co-founder and CEO of MedAware and Co-author of the study. "Even in high-stress situations, our system is shown to ensure patient safety and prevent significant harm by accurately detecting and mitigating these risks. With the COVID-19 pandemic straining healthcare systems worldwide and pushing prescribers and clinical care teams to their limits, the need for advanced decision support systems is critical."
Currently available clinical decision support systems are rule-based and have several flaws including high false-alarm rates. Due to alert fatigue, prescribers and clinical care teams are left desensitized and loath to respond to even vital warnings, causing significant risk to patient safety. MedAware's algorithms work to curve this false positive rate by offering highly personalized decision support that accurately detects errors in real-time so prescribers and clinical care teams can act fast and save lives.
The purpose of the study was to assess the association of physician's workload, multiple work shifts, and level of experience with physicians' risk to medication-related errors when prescribing.
The large-scale study analyzed data from a premier medical center between 2014-2017, examining over one thousand physicians who each prescribed at least 100 prescriptions. Medication errors were flagged by MedAware's decision support system, which utilizes machine learning algorithms and big data analytics to detect potential medication-related risks. Prescribing errors were measured based on the physician's number of continuous shifts, overall workload and level of experience. Given its high accuracy rates, MedAware's identifications were used as surrogates for prescription errors for the purposes of the study.
- During the period of the study, 1.6 million orders were prescribed by the physicians with over 3,700 prescriptions flagged as erroneous by the system
- Those physicians with high workloads were 8X more likely to erroneously prescribe as compared to physicians with normal workloads
- Continuous back-to-back shifts were associated with 2X the risk of error when compared to single shift results
- Physicians were three times more likely to err when prescribing medications they seldom prescribed before
- 44% of the flagged errors were med-lab result-dependent irregularities
The findings show that less experience, longer hours and greater workloads increase the risk of medication-related errors. Moreover, nearly half of the errors were lab result-dependent irregularities, which most current clinical decision support systems do not address, highlighting a significant gap in care. Observations from the study stress the importance of incorporating probabilistic and personalized decision support tools in reducing medication-related risks and harm and improving patient safety.
MedAware's AI engine acts as a smart safety layer within any health information infrastructure to prevent dangerous medication-related risks. By leveraging advanced machine learning algorithms, its technology identifies medication errors, opioid dependency risk, and evolving adverse drug events during the patient encounter and beyond. Every day, MedAware is protecting providers, care teams and their patients by creating a safer prescribing and medication management environment. Founded in 2012, MedAware is headquartered in Ra'anana, Israel with offices in New York. For more information, please visit: https://www.medaware.com/
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