Dataset Information

The safety of electronic prescribing: manifestations, mechanisms, and rates of system-related errors associated with two commercial systems in hospitals.

ABSTRACT: 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 MedChart and Cerner Millennium e-PS. System related errors were classified by manifestation (eg, wrong dose), mechanism, and severity. A mechanism typology comprised errors made: selecting items from drop-down menus; constructing orders; editing orders; or failing to complete new e-PS tasks. Proportions and rates of errors by manifestation, mechanism, and e-PS were calculated. RESULTS: 42.4% (n=493) of 1164 prescribing errors were system-related (78/100 admissions). This result did not differ by e-PS (MedChart 42.6% (95% CI 39.1 to 46.1); Cerner 41.9% (37.1 to 46.8)). For 13.4% (n=66) of system-related errors there was evidence that the error was detected prior to study audit. 27.4% (n=135) of system-related errors manifested as timing errors and 22.5% (n=111) wrong drug strength errors. Selection errors accounted for 43.4% (34.2/100 admissions), editing errors 21.1% (16.5/100 admissions), and failure to complete new e-PS tasks 32.0% (32.0/100 admissions). MedChart generated more selection errors (OR=4.17; p=0.00002) but fewer new task failures (OR=0.37; p=0.003) relative to the Cerner e-PS. The two systems prevented significantly more errors than they generated (220/100 admissions (95% CI 180 to 261) vs 78 (95% CI 66 to 91)). CONCLUSIONS: System-related errors are frequent, yet few are detected. e-PS require new tasks of prescribers, creating additional cognitive load and error opportunities. Dual classification, by manifestation and mechanism, allowed identification of design features which increase risk and potential solutions. e-PS designs with fewer drop-down menu selections may reduce error risk.

SUBMITTER: Westbrook JI

PROVIDER: S-EPMC3822121 | biostudies-literature | 2013 Nov-Dec

REPOSITORIES: biostudies-literature

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The safety of electronic prescribing: manifestations, mechanisms, and rates of system-related errors associated with two commercial systems in hospitals.

Westbrook Johanna I JI Baysari Melissa T MT Li Ling L Burke Rosemary R Richardson Katrina L KL Day Richard O RO

Journal of the American Medical Informatics Association : JAMIA 20130530 6

<h4>Objectives</h4>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.<h4>Methods</h4>Audit of 629 inpatient admissions at two hospitals in Sydney, Australia using the CSC MedChart and Cerner Millennium e-PS. System related errors were classified by manifestation (eg, wrong dose), mechanism, and severity. ...[more]

PMID: 23721982

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Project description:BACKGROUND: Considerable investments are being made in commercial electronic prescribing systems (e-prescribing) in many countries. Few studies have measured or evaluated their effectiveness at reducing prescribing error rates, and interactions between system design and errors are not well understood, despite increasing concerns regarding new errors associated with system use. This study evaluated the effectiveness of two commercial e-prescribing systems in reducing prescribing error rates and their propensities for introducing new types of error. METHODS AND RESULTS: We conducted a before and after study involving medication chart audit of 3,291 admissions (1,923 at baseline and 1,368 post e-prescribing system) at two Australian teaching hospitals. In Hospital A, the Cerner Millennium e-prescribing system was implemented on one ward, and three wards, which did not receive the e-prescribing system, acted as controls. In Hospital B, the iSoft MedChart system was implemented on two wards and we compared before and after error rates. Procedural (e.g., unclear and incomplete prescribing orders) and clinical (e.g., wrong dose, wrong drug) errors were identified. Prescribing error rates per admission and per 100 patient days; rates of serious errors (5-point severity scale, those ?3 were categorised as serious) by hospital and study period; and rates and categories of postintervention "system-related" errors (where system functionality or design contributed to the error) were calculated. Use of an e-prescribing system was associated with a statistically significant reduction in error rates in all three intervention wards (respectively reductions of 66.1% [95% CI 53.9%-78.3%]; 57.5% [33.8%-81.2%]; and 60.5% [48.5%-72.4%]). The use of the system resulted in a decline in errors at Hospital A from 6.25 per admission (95% CI 5.23-7.28) to 2.12 (95% CI 1.71-2.54; p<0.0001) and at Hospital B from 3.62 (95% CI 3.30-3.93) to 1.46 (95% CI 1.20-1.73; p<0.0001). This decrease was driven by a large reduction in unclear, illegal, and incomplete orders. The Hospital A control wards experienced no significant change (respectively -12.8% [95% CI -41.1% to 15.5%]; -11.3% [-40.1% to 17.5%]; -20.1% [-52.2% to 12.4%]). There was limited change in clinical error rates, but serious errors decreased by 44% (0.25 per admission to 0.14; p?=?0.0002) across the intervention wards compared to the control wards (17% reduction; 0.30-0.25; p?=?0.40). Both hospitals experienced system-related errors (0.73 and 0.51 per admission), which accounted for 35% of postsystem errors in the intervention wards; each system was associated with different types of system-related errors. CONCLUSIONS: Implementation of these commercial e-prescribing systems resulted in statistically significant reductions in prescribing error rates. Reductions in clinical errors were limited in the absence of substantial decision support, but a statistically significant decline in serious errors was observed. System-related errors require close attention as they are frequent, but are potentially remediable by system redesign and user training. Limitations included a lack of control wards at Hospital B and an inability to randomize wards to the intervention.

Project description:ImportanceThe majority of US states have passed mandates requiring the use of electronic prescribing of controlled substances (EPCS) as a tool to reduce rates of opioid prescribing. It is not known whether increasing use of EPCS will have the intended effect.ObjectiveTo assess the association between use of EPCS and trends in opioid prescribing.Design, setting, and participantsIn this retrospective, longitudinal cohort study of all patients and prescribers in the 50 US states and the District of Columbia from 2010 to 2018, changes in state-level use of EPCS and concurrent changes in opioid prescribing in each state are described. Then the association between changes in the use of EPCS and opioid prescribing are estimated using state and year fixed-effects models that include covariates for policy change and state demographic change. Data Analysis was performed on May 5, 2020.Main outcomes and measuresThe proportion of controlled substances in each state prescribed using EPCS based on opioid prescriptions per 100 persons and morphine milligram equivalents (MME) of opioids.ResultsIn 2018, the population-weighted percent of opioids prescribed using EPCS was 27%, up from 0% as of 2013. National rates of opioid prescriptions decreased from 78 prescriptions per 100 persons in 2013 to 53 in 2018. Over the same period, there was a decrease from 64 071 MME per 100 persons in 2013 to 40 906 MME per 100 persons in 2018, representing 36% of the 2013 level. By 2018, EPCS increased to 69.4% in states with mandates for its use and 23.6% in states without mandates. In multivariable models, a 10 percentage-point increase in the use of EPCS was associated with an additional 2 prescriptions per 100 persons (95% CI, 1.3-2.8) and a 0.8% (95% CI, 0.06%-1.5%) increase in MME per 100 persons.Conclusions and relevanceThese data suggest that an increased use of EPCS was not associated with decreased opioid prescribing or a decrease in the amount prescribed and may have been associated with a small increase in opioid prescribing. Opioid prescribing is associated with a variety of social and public health factors, and thus, despite the appeal, EPCS adoption alone may be insufficient to reduce opioid prescribing. Policy makers should consider levers to ensure that EPCS is integrated with outside data and that information is actively used to inform prescribing decisions.

Project description:Importance:The indication (reason for use) for a medication is rarely included on prescriptions despite repeated recommendations to do so. One barrier has been the way existing electronic prescribing systems have been designed. Objective:To evaluate, in comparison with the prescribing modules of 2 leading electronic health record prescribing systems, the efficiency, error rate, and satisfaction with a new computerized provider order entry prototype for the outpatient setting that allows clinicians to initiate prescribing using the indication. Design, Setting, and Participants:This quality improvement study used usability tests requiring internal medicine physicians, residents, and physician assistants to enter prescriptions electronically, including indication, for 8 clinical scenarios. The tool order assignments were randomized and prescribers were asked to use the prototype for 4 of the scenarios and their usual system for the other 4. Time on task, number of clicks, and order details were captured. User satisfaction was measured using posttask ratings and a validated system usability scale. The study participants practiced in 2 health systems' outpatient practices. Usability tests were conducted between April and October of 2017. Main Outcomes and Measures:Usability (efficiency, error rate, and satisfaction) of indications-based computerized provider order entry prototype vs the electronic prescribing interface of 2 electronic health record vendors. Results:Thirty-two participants (17 attending physicians, 13 residents, and 2 physician assistants) used the prototype to complete 256 usability test scenarios. The mean (SD) time on task was 1.78 (1.17) minutes. For the 20 participants who used vendor 1's system, it took a mean (SD) of 3.37 (1.90) minutes to complete a prescription, and for the 12 participants using vendor 2's system, it took a mean (SD) of 2.93 (1.52) minutes. Across all scenarios, when comparing number of clicks, for those participants using the prototype and vendor 1, there was a statistically significant difference from the mean (SD) number of clicks needed (18.39 [12.62] vs 46.50 [27.29]; difference, 28.11; 95% CI, 21.47-34.75; P < .001). For those using the prototype and vendor 2, there was also a statistically significant difference in number of clicks (20.10 [11.52] vs 38.25 [19.77]; difference, 18.14; 95% CI, 11.59-24.70; P < .001). A blinded review of the order details revealed medication errors (eg, drug-allergy interactions) in 38 of 128 prescribing sessions using a vendor system vs 7 of 128 with the prototype. Conclusions and Relevance:Reengineering prescribing to start with the drug indication allowed indications to be captured in an easy and useful way, which may be associated with saved time and effort, reduced medication errors, and increased clinician satisfaction.

Project description:To evaluate the prevalence, type and severity of prescribing errors observed between grades of prescriber, ward area, admission or discharge and type of medication prescribed.Ward-based clinical pharmacists prospectively documented prescribing errors at the point of clinically checking admission or discharge prescriptions. Error categories and severities were assigned at the point of data collection, and verified independently by the study team.Prospective study of nine diverse National Health Service hospitals in North West England, including teaching hospitals, district hospitals and specialist services for paediatrics, women and mental health.Of 4238 prescriptions evaluated, one or more error was observed in 1857 (43.8%) prescriptions, with a total of 3011 errors observed. Of these, 1264 (41.9%) were minor, 1629 (54.1%) were significant, 109 (3.6%) were serious and 9 (0.30%) were potentially life threatening. The majority of errors considered to be potentially lethal (n=9) were dosing errors (n=8), mostly relating to overdose (n=7). The rate of error was not significantly different between newly qualified doctors compared with junior, middle grade or senior doctors. Multivariable analyses revealed the strongest predictor of error was the number of items on a prescription (risk of error increased 14% for each additional item). We observed a high rate of error from medication omission, particularly among patients admitted acutely into hospital. Electronic prescribing systems could potentially have prevented up to a quarter of (but not all) errors.In contrast to other studies, prescriber experience did not impact on overall error rate (although there were qualitative differences in error category). Given that multiple drug therapies are now the norm for many medical conditions, health systems should introduce and retain safeguards which detect and prevent error, in addition to continuing training and education, and migration to electronic prescribing systems.

Project description:Although electronic prescribing (e-prescribing) holds promise for preventing prescription errors in the ambulatory setting, research on its effectiveness is inconclusive.To assess the impact of a stand-alone e-prescribing system on the rates and types of ambulatory prescribing errors.Prospective, non-randomized study using pre-post design of 15 providers who adopted e-prescribing with concurrent controls of 15 paper-based providers from September 2005 through June 2007.Use of a commercial, stand-alone e-prescribing system with clinical decision support including dosing recommendations and checks for drug-allergy interactions, drug-drug interactions, and duplicate therapies.Prescribing errors were identified by a standardized prescription and chart review.We analyzed 3684 paper-based prescriptions at baseline and 3848 paper-based and electronic prescriptions at one year of follow-up. For e-prescribing adopters, error rates decreased nearly sevenfold, from 42.5 per 100 prescriptions (95% confidence interval (CI), 36.7-49.3) at baseline to 6.6 per 100 prescriptions (95% CI, 5.1-8.3) one year after adoption (p < 0.001). For non-adopters, error rates remained high at 37.3 per 100 prescriptions (95% CI, 27.6-50.2) at baseline and 38.4 per 100 prescriptions (95% CI, 27.4-53.9) at one year (p = 0.54). At one year, the error rate for e-prescribing adopters was significantly lower than for non-adopters (p < 0.001). Illegibility errors were very high at baseline and were completely eliminated by e-prescribing (87.6 per 100 prescriptions at baseline for e-prescribing adopters, 0 at one year).Prescribing errors may occur much more frequently in community-based practices than previously reported. Our preliminary findings suggest that stand-alone e-prescribing with clinical decision support may significantly improve ambulatory medication safety.ClinicalTrials.gov, Taconic Health Information Network and Community (THINC), NCT00225563, http://clinicaltrials.gov/ct2/show/NCT00225563?term=Kaushal&rank=6 .

Project description:Healthcare providers previously using older electronic health records (EHRs) with electronic prescribing (e-prescribing) are transitioning to newer systems to be eligible for federal meaningful use incentives. Little is known about the safety effects of transitioning between systems.To assess the effect of transitioning between EHR systems on rates and types of prescribing errors, as well as provider perceptions about the effect on prescribing safety.Prospective, case study of 17 physicians at an academic-affiliated ambulatory clinic from February 2008 through August 2009. All physicians transitioned from an older EHR with minimal clinical decision support (CDS) for e-prescribing to a newer EHR with more robust CDS.Prescribing errors were identified by standardized prescription and chart review. A novel survey instrument was administered to evaluate provider perceptions about prescribing safety.We analyzed 1298 prescriptions at baseline, 1331 prescriptions 12 weeks post-implementation, and 1303 prescriptions one year post-implementation. Overall prescribing error rates were highest at baseline (35.7 per 100 prescriptions, 95% confidence interval (CI) 23.2-54.8) and lowest one year post-implementation (12.2 per 100 prescriptions, 95% CI 8.6-17.4) (p?<?0.001). Improvement in prescribing safety was mainly a result of reducing inappropriate abbreviation errors. However, rates for non-abbreviation prescribing errors were significantly higher at 12 weeks post-implementation than at baseline (17.7 per 100 prescriptions, 95% CI 9.5-33.0 versus 8.5 per 100 prescriptions, 95% CI 4.6-15.9) (p <0.001) and no different at baseline than one year (10.2 per 100 prescriptions, 95% CI 6.2-18.6) (p?=?0.337). Survey results complemented quantitative findings.Results from this case study suggest that transitioning between systems, even to those with more robust CDS, may pose important safety threats. Recognizing the challenges associated with transitions and refining CDS within systems may help maximize safety benefits.

Dataset Information

The safety of electronic prescribing: manifestations, mechanisms, and rates of system-related errors associated with two commercial systems in hospitals.

Publications

The safety of electronic prescribing: manifestations, mechanisms, and rates of system-related errors associated with two commercial systems in hospitals.

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