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Numerous studies have shown the high percentage of medication errors in hospitals and have evaluated their causes. While the majority of errors (around one-half) are categorised as prescribing errors, administration errors at the bedside comprise around one-third of medication errors.
Electronic Medical Record (EMR) software has been offered as a solution for reducing prescribing errors. However, it is also clear (to some extent) that EMRs contribute to medication errors, for example, via selection errors from a pick list of medications. EMR features such as the use of Order Sets and Order Favourites have been introduced so that the appropriate medication for a patient is prescribed by the clinician using embedded clinical decision support.
Unfortunately, while EMRs can show the Medication Administration Record at the bedside, they cannot directly address the drug selection errors that lead to incorrect Medication Administration.
Traditional medication distribution systems include ‘imprest’ where a room is set aside for the most commonly used medications on a particular ward, and ‘unit dose’, where the pharmacist packages a patient’s drugs into single use packages. The disadvantage of imprest is that selection of medication, which requires a nurse’s focus, occurs in a noisy environment with frequent interruptions that are conducive to error. Unit Dose distribution systems, using pharmacy robots and/or human technicians, are expensive.
Solutions to these dilemmas include Automated Dispensing Cabinets (ADCs) at the ward level and newer technologies to assist nurses identify medication at the
patient’s bedside. ADCs are comprised of modular cabinets for the storage of medications. When interfaced to an EMR, the ADC ‘knows’ which medications have been prescribed for the patient and will open the appropriate drawer. Some models have a series of guiding lights to lead the nurse to the right drawer. Widely used systems include those from Becton- Dickinson and Omnicell.
So, problem solved right? Not really! The literature is equivocal on the benefits of ADCs in patient safety. While reducing the time taken by nurses in inventory activities (but increasing pharmacy time), the clinical benefits of reducing errors that can cause harm to the patient are still unproven.
A significant issue relates to the process of carrying medication from the central ADC to the bedside safely. Traditional methods used by nurses include the use of a small cup for tablets or by using a dedicated drawer in a portable medication cart. Both methods have potential for error, given the number of interruptions that a nurse may be subjected to as they move from an ADC to the patient’s bedside.
New technology has recently emerged that performs a validation check on the medication at the patient’s bedside. These systems use Machine Learning to identify solid dose forms (e.g. tablets and capsules). Having learned the identity of each medication, the system uses a small camera mounted on the medication trolley to confirm the medication at the bedside. Medications can be identified even when cut in half and the device is capable of reading syringes and infusion pumps.
When interfaced to an EMR, by using HL7 FHIR for example, the camera device assists in confirming the medication administration within the Medication Administration Record (MAR) of the EMR. One example of this new technology comes from MedEye which has had rapid acceptance in Northern Europe and the United Kingdom.
Combined with bar-code identification on the patient’s wristband, this final check of the identity of the medication and the patient may act like the “Catcher in the Rye” in Salinger’s novel and prevent selection errors at this vital stage of medication administration.
