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Screenshots of Cerner and MedChart electronic prescribing systems. 

Screenshots of Cerner and MedChart electronic prescribing systems. 

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Article
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Objectives To compare the manifestations, mechanisms, and rates of system-related errors associated with two electronic prescribing systems (e-PS). To determine if the rate of system-related prescribing errors is greater than the rate of errors prevented. Methods Audit of 629 inpatient admissions at two hospitals in Sydney, Australia using the CSC...

Citations

... She still requires focused attention to choose among these textually similar options in a limited time. According to existing studies, about half of the data entry errors are caused by selection error [60,77] Example 2: Users require cognitive effort to match options with the actual value they plan to fill in. The second example is inspired by a use case of our industrial partner. ...
Preprint
Users frequently interact with software systems through data entry forms. However, form filling is time-consuming and error-prone. Although several techniques have been proposed to auto-complete or pre-fill fields in the forms, they provide limited support to help users fill categorical fields, i.e., fields that require users to choose the right value among a large set of options. In this paper, we propose LAFF, a learning-based automated approach for filling categorical fields in data entry forms. LAFF first builds Bayesian Network models by learning field dependencies from a set of historical input instances, representing the values of the fields that have been filled in the past. To improve its learning ability, LAFF uses local modeling to effectively mine the local dependencies of fields in a cluster of input instances. During the form filling phase, LAFF uses such models to predict possible values of a target field, based on the values in the already-filled fields of the form and their dependencies; the predicted values (endorsed based on field dependencies and prediction confidence) are then provided to the end-user as a list of suggestions. We evaluated LAFF by assessing its effectiveness and efficiency in form filling on two datasets, one of them proprietary from the banking domain. Experimental results show that LAFF is able to provide accurate suggestions with a Mean Reciprocal Rank value above 0.73. Furthermore, LAFF is efficient, requiring at most 317 ms per suggestion.
... In addition, drug-drug interactions are associated with increased hospital stay and costs (Moura et al., 2009). The literature shows important strategies for drug interaction and harm prevention, such as Software use for automatic drug interactions check (i.e., the Micromedex used in present paper); implementing computer prescription systems with electronic drug interaction alerts (Nuckols et al., 2014;Westbrook et al., 2013); multidisciplinary actuation (with an on-staff clinical pharmacist) (Jankovic et al., 2018;Ribeiro et, al 2019;Yasu et al., 2018); psychoeducation (Riblet et al., 2017;Solmi et al., 2020;Yanagida et al., 2017;Zhao et al., 2015); drug reconciliation (Day et al., 2017); the caregivers' involvement in patient treatment (Fulmer, 2016); as well as improvements in the qualification of health professionals. ...
Article
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Introduction Drug-drug interactions among people with suicidal behavior is a challenging topic, considering the harm it poses for patients already vulnerable and the lack of literature on the thematic. This aspect must not be neglected in research and clinical practice, and thus requires thorough investigation. Objective To investigate predictors of drug-drug interaction of prescribed drugs and the prescription of two or more drugs for people admitted due to suicidal behavior in a psychiatric emergency department (short-stay hospital ward). Method A cross-sectional study with retrospective approach, carried out in a Brazilian psychiatric emergency unit in 2015. Data about first and last medical prescriptions were collected from 127 patients' files. Descriptive statistics and the Zero Adjusted Logarithmic Distribution (ZALG) model were adopted, with the significance level α=0.05. Results Potential drug-drug interactions were found in most of the first and last prescriptions. The sample majority were female, with previous suicide attempts, being discharged from the hospital with three drugs (or more) prescribed, and without referral to any health service. Age and comorbidities were predictors of more drug prescriptions and the amount of prescribed drugs was the most important predictor of drug-drug interactions (quantity and severity). Conclusions The variables associated with drug-drug interactions and prescription of two or more drugs among people with suicidal behavior needs to be investigated in different contexts and addressed in interventions with the aim to promote patient safety.
... Les systèmes électroniques améliorent la qualité du processus d'utilisation des médicaments, mais ils peuvent introduire de nouvelles erreurs, telles que la sélection de la mauvaise dose ou du mauvais médicament à prescrire du menu déroulant(207). L'ampleur des erreurs liées au système informatisé varie selon les études (208) et est sujette à de nombreux facteurs, incluant la conception et l'implémentation du système(209,210).IV.4.3 Milieu communautaireLes psycholeptiques, les analgésiques et les médicaments utilisés en cas de diabète ont été fréquemment déclarés dans les erreurs médicamenteuses chez les personnes âgées. De même, Field et al. (205) ont identifié les médicaments hypoglycémiants et les analgésiques comme étant souvent impliqués dans les erreurs liées aux patients dans cette population. ...
Thesis
Les erreurs médicamenteuses représentent un défi mondial de santé publique lancé par l’Organisation Mondiale de la Santé en 2017. Ces erreurs peuvent survenir tout au long du processus médicamenteux mais le plus souvent au stade de l’administration, tant en milieu hospitalier que communautaire. Les erreurs médicamenteuses impliquent différents acteurs tels que les patients et les professionnels de santé, notamment le personnel infirmier. L’impact de ces erreurs est plus important pour les populations fragiles comme les enfants et les personnes âgées. En réponse à ce défi, il est important de quantifier les erreurs médicamenteuses et d’investiguer les sources d’hétérogénéité dans leurs taux, ainsi que d’identifier les déterminants de ces erreurs dans les populations à risque. En réalisant une revue systématique de la littérature et une méta-analyse, nous avons estimé la prévalence des erreurs d’administration chez les adultes hospitalisés, tout en explorant et maitrisant l’hétérogénéité. La prévalence moyenne d’au moins une erreur d’administration atteignait 26% avec maitrise d’hétérogénéité. La principale source d’hétérogénéité des taux poolés de cette méta-analyse était liée aux méthodes de calcul, spécifiquement le dénominateur. La standardisation de ces méthodes est donc une nécessité. La deuxième et la troisième études rétrospectives se basaient sur une analyse des déclarations des erreurs médicamenteuses au Guichet Français entre 2013 et 2017. Tout âge et dans milieux de soins confondus, les erreurs les plus fréquemment déclarées concernaient le stade d’administration. Au travers des analyses multivariées, nous avons identifié les déterminants des erreurs dans les populations pédiatrique et gériatrique par comparaison aux adultes. Dans les deux milieux hospitalier et communautaire, les antibactériens à usage systémique (Odds Ratio ajusté, ORa=2.54; Intervalle de Confiance 95%, IC95% : 1.57-4.11 et ORa=2.08 ; IC95% : 1.46-2.96, respectivement), et le type d’erreur de dose (ORa=2.51 ; IC95% : 1.71-3.68 et ORa=1.33 ; IC95% : 1.04-1.70, respectivement) étaient plus susceptibles d’être associés aux erreurs déclarées chez la population pédiatrique par comparaison aux adultes. D’autre part, dans ces deux milieux, les agents antithrombotiques (ORa=2.63 ; IC95% : 1.66-4.16 et ORa=4.46 ; IC95% : 2.70-7.37, respectivement) étaient plus susceptibles d’être associés aux erreurs médicamenteuses, ainsi qu’aux erreurs avec effet indésirable grave chez la population gériatrique par comparaison aux adultes. Les erreurs médicamenteuses représentent un risque majeur pour les patients et particulièrement les populations pédiatrique et gériatrique. Des stratégies de prévention ciblée sont nécessaires.
... For example, an order for an inappropriate drug located on a drop-down menu next to a likely drug selection is an SRE. 12 A pharmacist intervention (PI) due to an SRE is defined as any PI resulting from the identification of a prescribing error by a pharmacist that would probably not have occurred in the context of a handwritten prescription and of which at least one cause is related to the use of a computer (software system configuration issue, software functionality issue or software misuse). [13][14][15][16] Most studies concerning PIs triggered by systemrelated prescribing errors were conducted within a single hospital. [17][18][19] As a result, it is not possible to assess the extent of prescribing errors related to electronic systems or draw conclusions about subsequent PIs at a national level. ...
... In addition, there are various definitions of PISREs in the literature. [13][14][15][16] This suggests that there is a certain level of subjectivity when a pharmacist characterises a PI as being related to a computer-generated prescription. Among hospitals that entered the PIs on Act-IP, 87 never qualified a PI as being an SRE. ...
Article
Full-text available
Objectives: Computerised physician order entry (CPOE) systems facilitate the review of medication orders by pharmacists. Reports have emerged that show conception flaws or the misuse of CPOE systems generate prescribing errors. We aimed to characterise pharmacist interventions (PIs) triggered by prescribing errors identified as system-related errors (PISREs) in French hospitals. Design: This was a cross-sectional observational study based on PIs prospectively documented in the Act-IP observatory database from January 2014 to December 2018. Setting: PISREs from 319 French computerised healthcare facilities were analysed. Participants: Among the 319 French hospitals, 232 (72.7%) performed SRE interventions, involving 652 (51%) pharmacists. Results: Among the 331 678 PIs recorded, 27 058 were qualified as due to SREs (8.2%). The main drug-related problems associated with PISREs were supratherapeutic (27.5%) and subtherapeutic dosage (17.2%), non-conformity with guidelines/contraindications (22.4%) and improper administration (17.9%). The PI prescriber acceptation rate was 78.9% for SREs vs 67.6% for other types of errors. The PISRE ratio was estimated relative to the total number of PIs. Concerning the certification status of CPOE systems, the PISRE ratio was 9.4% for non-certified systems vs 5.5% for certified systems (p<0.001). The PISRE ratio for senior pharmacists was 9.2% and that for pharmacy residents 5.4% (p<0.001). Concerning prescriptions made by graduate prescribers and those made by residents, the PISRE ratio was 8.4% and 7.8%, respectively (p<0.001). Conclusion: Computer-related prescribing errors are common. The PI acceptance rate by prescribers was higher than that observed for PIs that were not CPOE related. This suggests that physicians consider the potential clinical consequences of SREs for patients to be more frequently serious than interventions unrelated to CPOE. CPOE medication review requires continual pharmacist diligence to catch these errors. The significantly lower PISRE ratio for certified software should prompt patient safety agencies to undertake studies to identify the safest software and discard software that is potentially dangerous.
... Existing systematic reviews have also failed to draw attention to system-related errors (SREs), which have been cited to be frequent with electronic systems yet difficult to detect. 22 Moreover, reviews have consistently highlighted the low quality of individual studies. 10,23 Several systematic reviews have assessed the impact of EMS on medication errors that cause actual patient harm (ie, preventable adverse drug events or pADEs) or potential harm to patients. ...
Article
Full-text available
Objective To conduct a systematic review and meta-analysis to assess: 1) changes in medication error rates and associated patient harm following electronic medication system (EMS) implementation; and 2) evidence of system-related medication errors facilitated by the use of an EMS. Materials and Methods We searched Medline, Scopus, Embase, and CINAHL for studies published between January 2005 and March 2019, comparing medication errors rates with or without assessments of related harm (actual or potential) before and after EMS implementation. EMS was defined as a computer-based system enabling the prescribing, supply, and/or administration of medicines. Study quality was assessed. Results There was substantial heterogeneity in outcomes of the 18 included studies. Only 2 were strong quality. Meta-analysis of 5 studies reporting change in actual harm post-EMS showed no reduced risk (RR: 1.22, 95% CI: 0.18–8.38, P = .8) and meta-analysis of 3 studies reporting change in administration errors found a significant reduction in error rates (RR: 0.77, 95% CI: 0.72–0.83, P = .004). Of 10 studies of prescribing error rates, 9 reported a reduction but variable denominators precluded meta-analysis. Twelve studies provided specific examples of system-related medication errors; 5 quantified their occurrence. Discussion and Conclusion Despite the wide-scale adoption of EMS in hospitals around the world, the quality of evidence about their effectiveness in medication error and associated harm reduction is variable. Some confidence can be placed in the ability of systems to reduce prescribing error rates. However, much is still unknown about mechanisms which may be most effective in improving medication safety and design features which facilitate new error risks.
... 21,22 Many TGEs are system-specific, and with increasing use of locally customized commercial systems, site-, setting-, and system-specific studies are critical. 16,23 Objectives The primary objective was to determine the incidence, distribution, and severity of infusion-related MEs associated with interfaced smart-pumps in PICUs. A secondary objective was to identify contributory factors. ...
Article
Background: Processes for delivery of high-risk infusions in pediatric intensive care units (PICUs) are complex. Standard concentration infusions (SCIs), smart-pumps, and electronic prescribing are recommended medication error reduction strategies. Implementation rates in Europe lag behind those in the United States. Since 2012, the PICU of an Irish tertiary pediatric hospital has been using a smart-pump SCI library, interfaced with electronic infusion orders (Philips ICCA). The incidence of infusion errors is unknown. Objectives: To determine the frequency, severity, and distribution of smart-pump infusion errors in PICUs. Methods: Programmed infusions were directly observed at the bedside. Parameters were compared against medication orders and autodocumented infusion data. Identified deviations were categorized as medication errors or discrepancies. Error rates (%) were calculated as infusions with errors and errors per opportunities for error (OEs). Predefined definitions, multidisciplinary consensus and grading processes were employed. Results: A total of 1,023 infusions for 175 patients were directly observed over 27 days between February and September 2017. The drug library accommodated 96.5% of infusions. Compliance with the drug library was 98.9%. A total of 133 infusions had ≥1 error (13.0%); a further 58 (5.7%) had ≥1 discrepancy. From a total of 4,997 OEs, 153 errors (3.1%) and 107 discrepancies (2.1%) were observed. Undocumented bolus doses were most commonly identified (n = 81); this was the only deviation in 36.1% (n = 69) of infusions. Programming errors were rare (0.32% OE). Errors were minor, with just one requiring minimal intervention to prevent harm. Conclusion: The error rates identified are low compared with similar studies, highlighting the benefits of smart-pumps and autodocumented infusion data in PICUs. A range of quality improvement opportunities has been identified.
... [75][76][77] Researchers found that such system-related errors occur when prescribers need to select items from drop-down menus, construct orders, edit information within the system, and/or perform new tasks not previously required. 78 Selection errors were the most frequent type of error, accounting for 43 percent of total system-related errors, while editing orders accounted for 21 percent. In addition, failure to complete new tasks found to be accounted for 32 percent of all system-related errors. ...
... In addition, failure to complete new tasks found to be accounted for 32 percent of all system-related errors. 78 It is important to note that such a failure is generally related to the limited functionality of the system, which leads to developing workaround processes and adopting hybrid systems (i.e. using computer-based orders along with some paperbased format). 79 Constructing or editing orders may also create risks and jeopardize patient care and safety. ...
... Enabling prescribers to use free-text instructions to enter orders may lead to inconsistencies between narrative text and structured order information. 78,80,81 For instance, Zhou et al. 82 found that around 17 percent of free-text medication order entries contained misspellings and led to additional errors. ...
Article
Full-text available
The Institute of Medicine estimates that 7,000 lives are lost yearly as a result of medication errors. Computerized physician and/or provider order entry was one of the proposed solutions to overcome this tragic issue. Despite some promising data about its effectiveness, it has been found that computerized provider order entry may facilitate medication errors. The purpose of this review is to summarize current evidence of computerized provider order entry -related medication errors and address the sociotechnical factors impacting the safe use of computerized provider order entry. By using PubMed and Google Scholar databases, a systematic search was conducted for articles published in English between 2007 and 2019 regarding the unintended consequences of computerized provider order entry and its related medication errors. A total of 288 articles were screened and categorized based on their use within the review. One hundred six articles met our pre-defined inclusion criteria and were read in full, in addition to another 27 articles obtained from references. All included articles were classified into the following categories: rates and statistics on computerized provider order entry -related medication errors, types of computerized provider order entry -related unintended consequences, factors contributing to computerized provider order entry failure, and recommendations based on addressing sociotechnical factors. Identifying major types of computerized provider order entry -related unintended consequences and addressing their causes can help in developing appropriate strategies for safe and effective computerized provider order entry. The interplay between social and technical factors can largely affect its safe implementation and use. This review discusses several factors associated with the unintended consequences of this technology in healthcare settings and presents recommendations for enhancing its effectiveness and safety within the context of sociotechnical factors.
... 20 Systematic reporting of TGEs, many of which are site-and system-specific, supports shared learning and system enhancement. 4,21,22 Diversity in TGE terminology is adding to the recognized difficulties in comparing medication error studies. 21,23 IntelliSpace Critical Care and Anesthesia (ICCA, Philips, United Kingdom) is a commercially available clinical information management system, widely used in both adult and pediatric hospitals in Ireland and the United Kingdom (Personal Communication, Philips, September 2019). ...
... Heterogeneity of TGEs definitions is also problematic, with differences in: inclusion criteria, for example, duplicate orders; and terminology, for example, "CPOE-related incidents" or "system-related errors" and use of terms such as "selection errors" which require knowledge of the intention of the prescriber. 20,22,42 A recent systematic review by Korb-Savoldelli et al reported that 6.1 to 77.7% of prescription errors were CPOE related. 20 In contrast, Potts et al reported no TGEs or duplicate orders with their commercially available CPOE system. ...
... 48 Westbrook et al describe similar problems with autocompleted ancillary information and edited orders with two commercial CPOE systems. 22 Singh et al identified higher rates of errors of "inconsistent communication" in prescriptions with free-text comments. 49 The use of "free-text" was involved in several "lack of clarity" TGEs in both Epochs 3 and 4; three involved instructions to either "give PRN and max 4hourly" on 8-hourly regular orders, or "give 8-hourly PRN" on a 12-hourly order. ...
Article
Background Increased use of health information technology (HIT) has been advocated as a medication error reduction strategy. Evidence of its benefits in the pediatric setting remains limited. In 2012, electronic prescribing (ICCA, Philips, United Kingdom) and standard concentration infusions (SCIs)—facilitated by smart-pump technology—were introduced into the pediatric intensive care unit (PICU) of an Irish tertiary-care pediatric hospital. Objective The aim of this study is to assess the impact of the new technology on the rate and severity of PICU prescribing errors and identify technology-generated errors. Methods A retrospective, before and after study design, was employed. Medication orders were reviewed over 24 weeks distributed across four time periods: preimplementation (Epoch 1); postimplementation of SCIs (Epoch 2); immediate postimplementation of electronic prescribing (Epoch 3); and 1 year postimplementation (Epoch 4). Only orders reviewed by a clinical pharmacist were included. Prespecified definitions, multidisciplinary consensus and validated grading methods were utilized. Results A total of 3,356 medication orders for 288 patients were included. Overall error rates were similar in Epoch 1 and 4 (10.2 vs. 9.8%; p = 0.8), but error types differed (p < 0.001). Incomplete and wrong unit errors were eradicated; duplicate orders increased. Dosing errors remained most common. A total of 27% of postimplementation errors were technology-generated. Implementation of SCIs alone was associated with significant reductions in infusion-related prescribing errors (29.0% [Epoch 1] to 14.6% [Epoch 2]; p < 0.001). Further reductions (8.4% [Epoch 4]) were identified after implementation of electronically generated infusion orders. Non-infusion error severity was unchanged (p = 0.13); fewer infusion errors reached the patient (p < 0.01). No errors causing harm were identified. Conclusion The limitations of electronic prescribing in reducing overall prescribing errors in PICU have been demonstrated. The replacement of weight-based infusions with SCIs was associated with significant reductions in infusion prescribing errors. Technology-generated errors were common, highlighting the need for on-going research on HIT implementation in pediatric settings.
... 7 However, electronic pre scribing systems are likely to have changed quite substantially over the past 10 years, with more robust clinical decision support. 8 Furthermore, studies [9][10][11] have also shown how these systems can con tribute to new types of errors, specifically those associated with use of the system (eg, drop-down menu selection errors). Up-to-date evidence from the UK about the incidence and types of errors occurring in hospitals is needed to guide the decisions of policy makers and managers. ...
Article
Full-text available
Background: WHO's Third Global Patient Safety Challenge, Medication Without Harm, focused on reducing the substantial burden of iatrogenic harm associated with medications by 50% in the next 5 years. We aimed to assess whether the number and type of medication errors changed as an electronic prescribing system was optimised over time in a UK hospital. Methods: We did a prospective observational study at a tertiary-care teaching hospital. Eight senior clinical pharmacists reviewed patients' records and collected data across four adult wards (renal, cardiology, general medical, and orthopaedic surgical) over a 2-year period (from Sept 29, 2014, to June 9, 2016). All medication errors and potential and actual adverse drug events were documented and the number of medication errors measured over the course of four time periods 7-10 weeks long. Pharmacists also recorded instances where the electronic prescribing system contributed to an error (system-related errors). A negative-binomial model and a Poisson model were used to identify factors related to medication error rates. Findings: 5796 primary errors were recorded over the four time periods (period 1, 47 days [Sep 29-Dec 2, 2014]; period 2, 38 days [April 20-June 12, 2015, for the renal, medical, and surgical wards and April 20-June 15, 2015, for the cardiology ward]; period 3, 35 days [Sep 28-Nov 27, 2015] for the renal ward, 37 days [Sep 28-Nov 23, 2015] for the medical ward, and 40 days [Sep 28-Nov 20, 2015] for the cardiology and surgical wards; and period 4, 37 days [Feb 22-April 15, 2015] for the renal and medical wards and 39 days for the cardiology [April 13-June 7, 2015] and surgery [April 18-June 9, 2015] wards; unanticipated organisational factors prevented data collection on some days during each time period). There was no change in the rate of primary medication errors per admission over the observation periods: 1·53 medication errors in period 1, 1·44 medication errors in period 2, 1·70 medication errors in period 3, and 1·43 medication errors in period 4, per admission. By contrast, the overall rate of different types of medication errors decreased over the four periods. The most common types of error were medicine-reconciliation, dose, and avoidable delay-of-treatment errors. Some types of errors appeared to reduce over time (eg, dose errors [from 52 errors in period 1 to 19 errors in period 4, per 100 admissions]), whereas others increased (eg, inadequate follow-up of therapy [from 12 errors in period 1 to 24 errors in period 4, per 100 admissions]). We also found a reduction in the rates of potential adverse drug events between the first three periods and period 4. 436 system-related errors were recorded over the study period. Interpretation: Although the overall rates of primary medication errors per admission did not change, we found a reduction in some error types and a significant decrease in the rates of potential adverse drug events over a 2-year period, during which system optimisation occurred. Targeting some error types could have the added benefit of reducing others, which suggests that system optimisation could ultimately help improve patient safety and outcomes. Funding: No funding.
... 15 In the one large-scale Australian study to quantify the rate at which these system-related errors occur, approximately 42% of prescribing errors were related to the use of an electronic prescribing system -that is 78 system-related errors per 100 patient admissions. 16 The most frequent type of error was selection error, where prescribers made the wrong selection from a drop-down menu. An interesting result was that, although the study was undertaken at two hospitals, each using a different electronic prescribing system, the overall rate of system-related errors was equivalent at both sites. ...
... Placing the most frequently used items at the top of a drop-down menu is likely to minimise selection errors, as is limiting the number of options on a list. 16,17 In a study that explored the use of lists of antibiotic orders in an electronic prescribing system, a doctor said 'Sometimes there are a lot of options…I know my colleagues have accidentally clicked the wrong dose just because there are a million different regimens or dosages'. 18 As expected, the more choices a user is presented with, the longer they take to make a selection (the Hick-Hyman Law 19 ). ...
Article
The implementation of computerised prescribing can result in large reductions in prescribing error rates the flow-on effects to patient outcomes are not well studied: The reduction in errors is dependent on prescribers becoming proficient in using the electronic prescribing system all potential safety benefits are therefore not expected to be achieved immediately: Electronic prescribing systems introduce new types of errors most frequently errors in selection some of these errors can be prevented if the system is well designed: Computerised decision support embedded in electronic prescribing systems has enormous potential to improve medication safety however current support systems have a limited capacity to provide context-relevant advice to prescribers: