“Data is variously described as the oxygen of the digital economy or the new raw material of the 21st century.“-Nigel Shadbolt
There are more than a few issues with today’s medication order entry systems. However, in this post I want to focus on only two.
First, alert fatigue. As a pharmacist that has entered his fair share of orders I can tell you that alert fatigue is real. Order entry systems, including CPOE, are designed to indiscriminately alert users of every possible problem associated with the patient’s profile and the order being entered. When entering orders for a patient with complex medical conditions, this can become a bit frustrating because a majority of these alerts are of little to no value. After a while you begin to blow through alerts because so many are simply a waste of your time. Unfortunately, when this happens you will occasionally miss something important. It happens.
Second, the “perfect medication error”.(1) This occurs when a physician inadvertently utilizes CPOE to order the wrong medication for a patient – or the right drug for the wrong patient – but the order meets all the necessary checks and balances to end up on the medication profile, i.e. no allergies, meets all appropriate dosing parameters, there are no drug-drug interactions, labs are fine, and so on. This is an issue that appeared on my radar while performing an FMEA for a CPOE implementation when I was still working as an IT pharmacist.
The issue with excessive, inappropriate alerts during order entry is real, and it’s been a thorn in the side of healthcare providers for quite some time. Fortunately the growth of data analytics and business intelligence has given rise to more potential solutions than ever before. Work such as that being done by Dr. Criag Rusin at Baylor College of Medicine is a good example. Dr. Rusin presented on real-time clinical decision support at the ASHP Summer Meeting in June. The work focuses on using machine learning to provide real-time, patient-specific feedback to the end user, minus all the unnecessary information. Promising work, but how long it will take to become mainstream is anybody’s guess.
Another promising development in this area comes by way of Dr. Gidi Stein, Professor of Medicine and Molecular Engineering at Tel Aviv Universityis. Dr. Stein’s work focuses on utilizing a big-data software platform called MedAware that integrates with a hospital’s EHR to detect prescription errors before they happen. According to an article in Modern Healthcare: “[MedAware] draws from patterns in millions of patient records to flag medication-order outliers. If a physician chooses a drug that doesn’t match any condition in the patient’s record or diverges from how other patients with similar histories have been treated, the discrepancy is flagged. The system blocks the drug order until the doctor confirms its accuracy or cancels and re-enters the order. Typically, EHR alert systems are programmed to spot dangerous drug interactions, higher-than-normal doses and duplicate prescriptions. But these systems have not used large volumes of aggregated data to determine in real time the likelihood that the wrong drug was selected for a particular patient.”
Both real-time clinical decision support utilizing machine learning that can adjust on the fly, and predictive analytics that can preemptively stop order entry errors are promising. I would like to see both implemented sooner rather than later. One has to wonder at what point order verification performed by a pharmacist will become obsolete? Given the potential of the work being done by Dr. Rusin and Dr. Stein, it’s easy to believe that pharmacists may finally be able to move out of that role into something with more substance.
(1) This actually happened during one of my mothers hospital admissions. I caught the error only after reviewing her medications on discharge.