Reading the prescription label and expiration date of the medication is also another best practice. For example, a nurse forgetting to document an as needed medication can result in another dosage being administered by another nurse since no documentation denoting previous administration exists. A lack of proper documentation for any medication can result in an error. This includes proper medication labeling, legible documentation, or proper recording of administered medication. Place a zero in front of the decimal point.Ī dosage of 0.25mg can easily be construed as 25mg without the zero in front of the decimal point, and this can result in an adverse outcome for a patient. Names such as Johnson and Johnston can lead to easy confusion on the part of nursing staff, so it is for this reason that name alerts posted in front of the MAR can prevent medication errors.Ħ. Some institutions use name alerts to prevent similar sounding patient names from potential medication mix up. This process can also be carried out from one nurse to the next whereby a nurse reads back an order transcribed to the physician’s order form to another nurse as the MAR is reviewed to ensure accuracy. This is a process whereby a nurse reads back an order to the prescribing physician to ensure the ordered medication is transcribed correctly. Have the physician (or another nurse) read it back. Some institutions have a chart flag process in place to highlight charts with new orders that require order verification.Ĥ. This is a process whereby another nurse on the same shift or an incoming shift reviews all new orders to ensure each patient’s order is noted and transcribed correctly on the physician’s order and the medication administration record (MAR) or the treatment administration record. Double check-or even triple check-procedures. There are several forms for medication reconciliation available from various vendors.ģ. Often not all elements of a medication record are available for easy verification, but it is of paramount importance to verify with every possible source-including the discharging or transferring institution/unit, the patient or patient’s family, and physician-to prevent potential errors related to improper reconciliation. Nurses must compare this to the medication administration record (MAR). Review and verify each medication for the correct patient, correct medication, correct dosage, correct route, and correct time against the transfer orders, or medications listed on the transfer documents. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.ĭrug discovery QSAR artificial intelligence de novo drug design synthesis prediction.Institutions must have mechanisms in place for medication reconciliation when transferring a patient from one institution to the next or from one unit to the next in the same institution. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Areas covered: The current status of AI in chemoinformatics is reviewed. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery.
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