Alejandra Salazar

Computerized prescriber order entry–related patient safety reports: analysis of 2522 medication errors

Objective: To examine medication errors potentially related to computerized prescriber order entry (CPOE) and refine a previously published taxonomy to classify them. Materials and Methods: We reviewed all patient safety medication reports that occurred in the medication ordering phase from 6 sites participating in a United States Food and Drug Administration–sponsored project examining CPOE safety. Two pharmacists independently reviewed each report to confirm whether the error occurred in the ordering/prescribing phase and was related to CPOE. For those related to CPOE, we assessed whether CPOE facilitated (actively contributed to) the error or failed to prevent the error (did not directly cause it, but optimal systems could have potentially prevented it). A previously developed taxonomy was iteratively refined to classify the reports. Results: Of 2522 medication error reports, 1308 (51.9%) were related to CPOE. Of these, CPOE facilitated the error in 171 (13.1%) and potentially could have prevented the error in 1137 (86.9%). The most frequent categories of “what happened to the patient” were delays in medication reaching the patient, potentially receiving duplicate drugs, or receiving a higher dose than indicated. The most frequent categories for “what happened in CPOE” included orders not routed to or received at the intended location, wrong dose ordered, and duplicate orders. Variations were seen in the format, categorization, and quality of reports, resulting in error causation being assignable in only 403 instances (31%). Discussion and Conclusion: Errors related to CPOE commonly involved transmission errors, erroneous dosing, and duplicate orders. More standardized safety reporting using a common taxonomy could help health care systems and vendors learn and implement prevention strategies.

Incorporating medication indications into the prescribing process

Purpose The incorporation of medication indications into the prescribing process to improve patient safety is discussed. Summary Currently, most prescriptions lack a key piece of information needed for safe medication use: the patient‐specific drug indication. Integrating indications could pave the way for safer prescribing in multiple ways, including avoiding look‐alike/sound‐alike errors, facilitating selection of drugs of choice, aiding in communication among the healthcare team, bolstering patient understanding and adherence, and organizing medication lists to facilitate medication reconciliation. Although strongly supported by pharmacists, multiple prior attempts to encourage prescribers to include the indication on prescriptions have not been successful. We convened 6 expert panels to consult high‐level stakeholders on system design considerations and requirements necessary for building and implementing an indications‐based computerized prescriber order‐entry (CPOE) system. We summarize our findings from the 6 expert stakeholder panels, including rationale, literature findings, potential benefits, and challenges of incorporating indications into the prescribing process. Based on this stakeholder input, design requirements for a new CPOE interface and workflow have been identified. Conclusion The emergence of universal electronic prescribing and content knowledge vendors has laid the groundwork for incorporating indications into the CPOE prescribing process. As medication prescribing moves in the direction of inclusion of the indication, it is imperative to design CPOE systems to efficiently and effectively incorporate indications into prescriber workflows and optimize ways this can best be accomplished.

Outpatient CPOE orders discontinued due to ‘erroneous entry’: prospective survey of prescribers’ explanations for errors

Background Computerised prescriber order entry (CPOE) systems users often discontinue medications because the initial order was erroneous. Objective To elucidate error types by querying prescribers about their reasons for discontinuing outpatient medication orders that they had self-identified as erroneous. Methods During a nearly 3 year retrospective data collection period, we identified 57 972 drugs discontinued with the reason ‘Error (erroneous entry).” Because chart reviews revealed limited information about these errors, we prospectively studied consecutive, discontinued erroneous orders by querying prescribers in near-real-time to learn more about the erroneous orders. Results From January 2014 to April 2014, we prospectively emailed prescribers about outpatient drug orders that they had discontinued due to erroneous initial order entry. Of 2 50 806 medication orders in these 4 months, 1133 (0.45%) of these were discontinued due to error. From these 1133, we emailed 542 unique prescribers to ask about their reason(s) for discontinuing these mediation orders in error. We received 312 responses (58% response rate). We categorised these responses using a previously published taxonomy. The top reasons for these discontinued erroneous orders included: medication ordered for wrong patient (27.8%, n=60); wrong drug ordered (18.5%, n=40); and duplicate order placed (14.4%, n=31). Other common discontinued erroneous orders related to drug dosage and formulation (eg, extended release versus not). Oxycodone (3%) was the most frequent drug discontinued error. Conclusion Drugs are not infrequently discontinued ‘in error.’ Wrong patient and wrong drug errors constitute the leading types of erroneous prescriptions recognised and discontinued by prescribers. Data regarding erroneous medication entries represent an important source of intelligence about how CPOE systems are functioning and malfunctioning, providing important insights regarding areas for designing CPOE more safely in the future.

Analysis of variations in the display of drug names in computerized prescriber-order-entry systems

PURPOSE The variations in how drug names are displayed in computerized prescriber-order-entry (CPOE) systems were analyzed to determine their contribution to potential medication errors. METHODS A diverse set of 10 inpatient and outpatient CPOE system vendors and self-developed CPOE systems in 6 U.S. healthcare institutions was evaluated. A team of pharmacists, physicians, patient-safety experts, and informatics experts created a CPOE assessment tool to standardize the assessment of CPOE features across the systems studied. Hypothetical scenarios were conducted with test patients to study the medication ordering workflow and ways in which medications were displayed in each system. Brand versus generic drug name ordering was studied at 1 large outpatient system to understand why prescribers ordered both brand and generic forms of the same drug. RESULTS Widespread variations in the display of drug names were observed both within and across the 6 study sites and 10 systems, including the inconsistent display of brand and generic names. Some displayed drugs differently even on the same screen. Combination products were often displayed inconsistently, and some systems required prescribers to know the first drug listed in the combination in order for the correct product to appear in a search. It also appeared that prescribers may have prescribed both brand and generic forms of the same medication, creating the potential for drug duplication errors. CONCLUSION A review of 10 CPOE systems revealed that medication names were displayed inconsistently, which can result in confusion or errors in reviewing, selecting, and ordering medications.

Using drug knowledgebase information to distinguish between look-alike-sound-alike drugs

Objective To extract drug indications from a commercial drug knowledgebase and determine to what extent drug indications can discriminate between look-alike-sound-alike (LASA) drugs. Methods We extracted drug indications disease concepts from the MedKnowledge Indications module from First Databank Inc. (South San Francisco, CA) and associated them with drugs on the Institute for Safe Medication Practices (ISMP) list of commonly confused drug names. We used high-level concepts (rather than granular concepts) to represent the general indications for each drug. Two pharmacists reviewed each drugs association with its high-level indications concepts for accuracy and clinical relevance. We compared the high-level indications for each commonly confused drug pair and categorized each pair as having a complete overlap, partial overlap or no overlap in high-level indications. Results Of 278 LASA drug pairs, 165 (59%) had no overlap and 58 (21%) had partial overlap in high-level indications. Fifty-five pairs (20%) had complete overlap in high-level indications; nearly half of these were comprised of drugs with the same active ingredient and route of administration (e.g., Adderall, Adderall XR). Conclusions Drug indications data from a drug knowledgebase can discriminate between many LASA drugs.