Right drug, wrong patient. Wrong drug, right patient. Believe it or not, mix-ups like these do occur. And when they do, outcomes can be tragic. In the US alone, adverse drug events are responsible for 1 million emergency department visits every year.
Clinical decision support tools have become a widely adopted solution to providing medication alerts for dosing errors or drug-drug interactions, notifying the physician when a medication may interact in a harmful way with another on a patient’s active medication list. However, these tools fail to identify situations where a wrong medication was prescribed or ordered that didn’t align with the patient profile. What happens, for example, if an antipsychotic is mistakenly given to a patient without a history of mental illness? A seemingly obvious error such as this may simply be the result of a medication with a similar name accidentally being selected from a picklist. Yet, current tools fail to account for these types of medication errors and many other prescribing anomalies.
Using advanced machine learning algorithms, MedAware’s medication safety monitoring platform analyzes key patient data to identify outlier prescribing behaviors and notifies clinicians when the wrong drug may be ordered for the wrong patient. It provides an added layer of protection to existing medication alerting systems, preventing avoidable medication errors and giving physicians peace of mind. In fact, physicians using MedAware’s technology have been shown to change their prescribing behavior over 40% of the time.
The following three examples are real situations where MedAware’s technology identified an erroneous medication that had been missed by a health system’s traditional alerting systems. While these notifications were presented to the provider at the time of medication ordering, similar notifications can be implemented at other critical points of medication access, such as within pharmacy management or dispensing systems.
Case #1: Elevated Heart Rate
A hospitalized 89-year-old woman was meant to receive Deralin (propranolol) as treatment for a rapid heart rate. However, the clinician accidentally ordered the ADHD medication, Adderall. MedAware’s AI identified the mistake and the order was promptly canceled. If given, the patient’s heart rate could have increased even more and put her at serious risk.
Case #2: Chemotherapy for an Eye Infection
A 68-year-old man was hospitalized due to a serious corneal infection that required urgent antibiotic treatment. The medical staff intended to prescribe the antibiotic, Doxy 100 (doxycycline). However, a highly-toxic chemotherapy drug, Doxil (doxorubicin), was prescribed by mistake. MedAware’s AI identified the error and the order was canceled. If given, the patient may have risked suffering from toxic side effects of chemotherapy, while losing his eyesight due to untreated infection.
Case #3: Overactive Parathyroid
An 80-year-old patient with a history of hyperparathyroidism (overactive parathyroid gland) was mistakenly prescribed mercaptizole, a medication used to treat hyperthyroidism (overactive thyroid gland). MedAware’s AI algorithm identified the error and the order was promptly canceled by the physician. If given, the patient may have experienced dangerously low white blood counts, heart rate abnormalities, and mental deterioration.
Why can MedAware catch these errors while others can’t? Put simply, these mistakes are rare, so no rules-based system could practically have all the necessary if/then scenarios. Yet, the consequences of not addressing these errors in time could be devastating.
MedAware’s AI technology can identify outlier prescriptions and inconsistencies because it knows what normal prescribing patterns look like (based on analysis of millions of patient records), and thus, can identify outliers to the norm as potential errors without needing a comprehensive list of examples. It’s the difference between memorizing answers vs understanding the material.
Ensuring the right patients get the right medications is a basic function of practicing medicine. Integrating MedAware’s AI technology within existing systems provides care teams the patient-specific insights they need to keep patients safe from accidental harm.
Editor's Note: The examples provided above are part of MedAware's social media campaign, #MedSafetyMonday. Follow us on Twitter and LinkedIn to see a new case each Monday!
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