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School of Nursing and Midwifery, Faculty of Science, Engineering and Health, University of Southern Queensland, Salisbury Road, Ipswich, QLD, Australia
School of Nursing and Midwifery, Faculty of Science, Engineering and Health, University of Southern Queensland, Salisbury Road, Ipswich, QLD, Australia
School of Nursing and Midwifery, Faculty of Science, Engineering and Health, University of Southern Queensland, Salisbury Road, Ipswich, QLD, Australia
The medication administration process is particularly susceptible to errors due to the error being least likely to be captured before reaching patients. Nurses administer medications as part of everyday practice.
Aim
The purpose of this review is to identify if medication error rates are reduced during nursing administration when incorporating electronic medical administration records into medication management.
Methods
A systematic review was conducted of six electronic databases to identify original empirical research published between 2007 and 2020. An integrative review method using Strengthening the Report of Observational Studies in Epidemiology guidelines was used to direct this review.
Findings
Eighteen original research articles were identified and included in this review. Data were also collected using electronic data retrieval or chart review, incidence reports, or automated algorithms. Eight studies reported reduced medication errors after the implementation of electronic medication administration records, and two reported increases in medication errors. Studies reported between 2.8% and 16% of medication errors during nursing administration.
Discussion
Findings are mixed, some reported positive findings and reduction in medication errors, and other studies reported no reduction in medication errors or the introduction of new types of errors. Electronic medication administration records may not be as effective in paediatric and intensive care units and may require further adaptation. Barriers to successive integration of electronic medication errors are equipment, environment, lack of knowledge, and workload.
Conclusion
Evidence linking medication administration records use and reducing medication errors and patient safety is weak due to assessment techniques and reporting strategies. More rigorous research is needed.
The medication administration phase is particularly susceptible to errors due to the error being least likely to be captured before reaching patients. Nurses administer medications as part of their everyday practice.
What is already known
Electronic versions of medication orders have replaced the paper records previously used by many health care facilities in developed countries’ health care settings. Digital technologies and electronic medication administration records have emerged as methods of tracking, recording, storing, retrieving, and collating health care information. Electronic medication administration records have been suggested to increase communication, efficiency, improved drug accuracy, and patient safety.
What this paper adds
Despite the potential advantages offered by electronic medication administration records, there is a growing body of literature that suggests that successful use of electronic medication administration records has not reduced medication errors and has introduced negative outcomes. Evidence linking electronic medication administration records use and reductions in medication errors are weak because of study designs, assessment techniques, and reporting. Electronic medication administration records may not be as effective in some clinical settings, such as paediatrics and intensive care units, and may require further adaptation to meet these patients’ needs. Barriers for successful implementation of electronic medication administration records include lack of knowledge, understanding, skills and competencies of health care providers.
1. Introduction
The World Health Organization [WHO] estimates the cost of medication errors is approximately USD 42 billion per year (
). According to the WHO, medication errors occur when medication systems are insufficient and/or due to human errors such as fatigue, environment, inadequate staffing during prescribing, transcribing, dispensing, administration, or monitoring which results in death, disability and/or severe harm to the patient (
In line with the WHO Global Patient Safety Challenge, the Australian Government has targeted a reduction of 50% of medication errors, adverse medication incidents, and medication-related hospital admissions by 2025 (
). Eight different strategies have been suggested to reduce medication errors including education, electronic medical records, barcoded assisted medication administration, automated dispensing systems, prefilled syringes, medication protocols, prevention programs, and pharmacy-supported monitoring (
). Studies of medication errors by nurses conducted in Pakistan, Mexico, South Africa, Brazil, Chile, Turkey, Iran, Jordan, and Brazil have not reported the use of electronic medication administration records (
How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis.
Types of medication administration errors and comparisons among nursing graduands in Indonesia, Taiwan, and Thailand: a cross-sectional observational study.
). Studies in Australia, Spain, Belgium, United States, France, United Kingdom, and the Netherlands have reported the use of electronic medical records (
). The studies using electronic medical administration records reported lower medication errors than studies conducted where electronic medical administration records have not been reported.
According to the Australian Commission on Safety and Quality in Health
, Care electronic medication management can improve patient safety and health care quality by improving legibility and communication between health care professionals and people using health care. Components such as alerting, and clinical decision support (missed dose, erroneous dosing, duplication, prescribing and administration, reducing handwriting interpretation, and exceeding dosing) have been shown to reduce medication errors (
). Other components include dispensing systems, ordering and supplying solutions, electronic medication administration records have been shown to improve accuracy, visibility, legibility, and improve communications (
). To date the benefits of electronic medication administration records have focused on business and cost, there is limited evaluation of the quality and safety of patients that underpins nursing in Australia (
). Electronic medication administration records literature is rapidly evolving as digital information systems are implemented in the Australian Health Care System. Little is known about the direct and indirect benefits to patient safety of the use of electronic medical records on medication administration in nursing. The purpose of this review is to identify if electronic medication administration records improve patient safety when incorporated into medication administration. There is a gap in the knowledge regarding the implementation of electronic medication administration records into medication management systems for the safe administration of medications by nurses in reducing medication errors.
2. Research aim
This review aims to identify literature that reports evidence-based research on medication errors during nursing administration and patient safety in clinical settings that use electronic medication administration records.
3. Methods
An integrative review methodology was used as it allows for reviewing a small volume of literature that provides a complex undertaking of a specific phenomenon or health care problem. The review was guided by
framework. The six steps of an integrative review include preparing the research question, searching the literature, data collection, critical analysis, discussing of results, and presenting the integrative review that was used in this integrative review (
). Presenting data in nursing is complex and challenging, performing an integrative review using scientific evidence based on the inclusion of a rigorous approach reduces biases and errors (
). The Strengthening the Report of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies were chosen to appraise the quality of eligible studies. STROBE was chosen due to all of the reviewed studies used observational measures for collecting data. No study used a randomised control trial due to the nature of the phenomenon being measured.
3.1 Literature search
To identify articles for inclusion, a search was conducted by two blind reviewers (authors 1 and 3) of six electronic databases; Cumulative Index to Nursing and Allied Health Literature, ScienceDirect, MEDLINE, PubMed, Web of Science, EBSCOHost as well as hand searches of Journal of Nursing Education, Teaching and Learning in Nursing, Nurse Education Today, Nurse Education Perspectives, International Journal of Nursing Studies, Nurse Education in Practice, Computer Informatics Nursing, Journal of Biomedical Informatics, and Nurse Educator were conducted to identify original empirical research published between 2007 and 2020. There was no limitation on the dates when the search was conducted. The earliest retrieved publication was accepted in 2007. The following search terms; MeSH (medical subject headings) terms or text words were used in various combinations and permutations (i) Evaluations terms: (‘electronic medical record’, OR ‘automated dispensing cabinets’ OR ‘barcoded medication alert’ OR ‘electronic’ OR ‘comp*’ or ‘health informatic systems (HIS)’ OR ‘medicat*’ OR, ‘drug*’, ‘error’, ‘safety’) (ii) Population terms: (‘nurs*’ OR ‘patient’) (iii) Design terms: (‘experimental’, ‘impact’, ‘evaluation’ effect). All potentially relevant articles were imported into the Endnote X9 library for review according to the inclusion and exclusion criteria. The data search was undertaken by authors 1 and 3 on the 24–28 June 2020.
The following question was developed to guide the search:
Does the implementation of electronic medication administration records reduce nursing administration medication errors?
3.1.1 Inclusion criteria
Articles describing and evaluating nursing, patient safety, and electronic medication administration records were of interest. Studies had to meet the following inclusion criteria (i) available in the English language; (ii) published in peer-reviewed scholarly journals; (iii) published between 2007 and 2020; (iv) to be quantitative or mixed methods; (v) sample included medication administration by nurses in a clinical health care setting; (vi) study outcomes measured medication errors.
3.1.2 Exclusion criteria
Articles were excluded if they (i) focused on prescribing errors; (ii) focused on dispensing errors; (iii) focused on development the of electronic medication administration records; (iv) described nurse education or curriculum design; (v) focused on the implementation of electronic medication administration; (vi) qualitative design; (vii) reported on staffing satisfaction, perceptions or attitudes; (viii) focused on workflow, productive, cost analysis, or unrelated cognition; (ix) electronic medication administration records were secondary related features and not the focus of the intervention such as display, observations, documentation, software, checklist, care plans, or decision support.
3.2 Data screening
A total of 3,581 articles were retrieved. Duplicates were removed by hand and by a reference software manager. The titles of the remaining 3,360 articles were screened according to the inclusion and exclusion criteria, and 3,160 by title were excluded. Two hundred and seventeen were reviewed 188 abstracts were excluded leaving 29 full-text publications retrieved that were assessed for eligibility. During full-text review, 14 articles were excluded based on inclusion criteria. The reference list of eligible records were reviewed articles that met the criteria were retrieved and reviewed to ensure data completeness (
). An additional three records were included from the reference list from the selected articles. A total of 18 articles met the eligibility criteria for this review. See Fig. 1 for the study selection process using the Joanna Briggs Institute Transparent Reporting of Systematic Reviews and Meta-Analysis (PRISMA) (
The STROBE Statement: guidelines for reporting observational studies were chosen to appraise the quality of eligible studies. The STROBE Statement: guidelines for reporting observational studies are designed to evaluate observational studies (
). It is difficult to randomise these studies due to the nature of the outcome measured (observations or frequency of medication errors). Each study was assessed according to the STROBE checklist including title and abstract. The introduction includes background/rationale and specific objectives. The methods section has the study design, setting, participants, variables, data sources, bias, study size, quantitative variables, and statistical methods. The results included the participants, descriptive data, outcome data, main results, and other analyses. The discussion reported key results, limitations, interpretation, generalisability, and funding (
). Incomplete reporting of research hampers the strengths and weakness of the studies in medical literature using STROBE for observational research that uses different study designs enhances the conclusions (
Eighteen studies met the eligibility criteria. See Table 1 for a summary of the included studies. Publication dates ranged from 2007 to 2020. Studies were from the United States (11), Singapore (1), Korea (1), Canada (1), Australia (1), and the Netherlands (1). Almost two-thirds of the studies (n = 11/18; 61%) used pretest post-test prospective design with or without nonequivalent control (
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
DesignPost-testRetrospectiveCross-sectionalWhen2008–2009Setting2603 medium to large (>100 beds) in US acute care nonfunded hospitals 16.2% ruralAMI, HF, pneumonia, surgical patients
ParticipantsHospitals coded:1. eMAR only (n = 986; 37.9%)2. CPOE only (n = 115; 4.4%)3. Both eMAR and CPOE (n = 804; 30.9%) with4. nonadopters (control n = 698; 26.8%)Medication managementOrdering, prescribing and administering of medications
Variables measuredCPOE/ eMAR usage from HISSS analytics +Medication quality scores from CMS Hospital Quality Alliance for input settingsUtilisation of medications for medical conditions AMI, HF, PN, SCIP +Electronic initiativeseMAR + COPE
StatisticsGeneralised linear model (logit link + binomial family) binomial logistic regressionData analysis conducted byStata V.11.1
Key findingEMar and IT use improves medication administration qualityMain results
•
eMAR only hospital performed better 10/11 quality measures than hospital with COPE only
•
both eMAR and CPOE performed better 10/11 than hospital with COPE only
•
CPOE only hospital performed 2/11 of quality measures
•
Both eMAR and CPOE had higher adherence measures by 14-29% than eMAR only hospital
+ 2 yrs of use 6-15% ↑ compliance for eMAR + both
•
↑ adherence to medication guidelines
•
↑ adoption of eMAR alone + combination of CPOE + eMAR 14%–29% than nonadopters
•
Hospitals with CPOE alone showed little difference
Comments/limitations
•
Cross-sectional analysis does not represent causal relationship of IT use
Mean incidence difference = 0.72 hospital 2 ↑ no. med errors (n = 332) than hospital 1 (n = 94)
95% CI: [0.56, 0.88]
•
Mean incidence in med error = 0.06 pre (0.72/1000 per pt days) to post (0.78/1000 per pt/days) was not statistically significant (95%, CI: [0.26, 0.20], t = 0.17, P = 0.683)
•
Mean difference in medication errors of 0.06 between pre- and postintervention for 2 hospital not statistically significant
Nurse administration errors
•
Incorrect dose
hospital 1; 33.3%;hospital 2; 52.4%
•
Giving pt wrong med
hospital 1; 27%;hospital 2; 14.2%
•
Giving pt with known allergies hospital 1; (29.7%)
•
Wrong time hospital 2; 12%
Causes
•
Hospital 1: Lapse of nurse memory 35.1%; 21.0%
•
Hospital 2; Lack of nurse knowledge
15.9%, failure to check meds correctly; 49.6% inaccurate identification of medications 11.7%
DesignPre–Post Retrospective interrupted time seriesWhen2008–2013Setting1x 1500 bed hospital
Participants887, 303 cases of injectable antimicrobial prescriptions were analysedStudy typeMedication ManagementPrescribing, pharmacist instructions, nurse administration
Variable measuredeMAR + IPIS alerts CPOE, eMAR data retrieval for PRR + PDR rates and trendsIPIS alerts in time series analysis Reconstitution rate and dilutionElectronic initiativesIPIS + EMR + COPE
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
DesignRetrospective chart reviewdose records inWhen2011Setting3 adult hospitals; in medical wards
ParticipantsHospital (1) n = 1100 beds, (2) n = 550, (3), n = 650 beds; ∼20, 000 ptMedication managementNursing administration
Variables measuredAdministration records294,718 cross tabulation of omission data + route of med, frequency of med order, nursing shift time, dose schedule, hospital siteElectronic initiativeseMAR
DesignRetrospective chart reviewWhen2005-2006Setting2 hospitals in Victoria, Aust
ParticipantsHospital (1) n = 520 beds, (2) n = 223 beds in 2005 2006 private health service, with general surgical pts, ED, ICT, OT and paedsMedication managementNursing administration + prescribing
Variables measuredMedication incident reportsNo. of observations; descriptive analysis of voluntary incidence reports no. of MAEElectronic initiatives(1) Pen and paper MMS(2) COPE using MEDCHART by Halix Pty Ltd
Variables measuredNo of observations – work system modelObserved for tasks, technology,organisational factors, environment, patient, human factor engineer and pharmacistnurses' feedbackElectronic initiativesBCMA network server, handheld device and documentation
StatisticsDescriptive frequency sequence according to policyData analysis conducted byNot reported
Key findings10 (16%) unsafe medication administration – were potentially unsafe29 negative + 19 positive comments by nurses – negative performance and availability of device at times only 2 devices for 4 nurses, false sense of security, overly reliant of technology loss of critical thinkingProblems with physical environmentConstant interruptions not scanning the patient ID, equipment concerns, alarms + patient factors (contact isolation)Main resultsn = 59 recorded observations with 21 errors observed (34% occurred)mean duration of observation 7.7 minutes ± 5.9 mins5 (16%) observations of technology failure:42% alarm – for wrong dose (3x), double check needed (2x) disabled order (1x) barcode not readable (1x), missing medications (1x)Interruptions (32%)Family, pt, physician, medication task (looking for missing medications new BCMA ID required)Medication administration errors; med given without use of BCMA (medication not scanned)2 medications given without scanning ID2 medications given before scan ID2 undocumented admin, record of medication given but medication not given2 nurse scan patient chart rather than pt ID
Comments/limitations
•
Prospective observation allowed for identify issues
•
No statistical analysis
V & R
•
Not reported
Ethics
•
Granted by hospital
Bias
•
Observer training, type of observer, quality of data, use of pharmacist to observe nurses – not reported on nursing work
•
Missing nurse's viewpoint, general observation data
Variables measuredNurse, date, timeNo. of observation of medication administration accuracy rates AU MED system Reporting system Web based reporting Quantros Safety Event Manager v5.13Electronic initiativeCPOE, smart pumps, Meditech Magic 5.61- 5.64BCMA, eMAR, mobile medication charts
StatisticsDescriptive frequencyχ2 test with Yates correctionData analysis conducted byNot reported
Key findingsImplementation of BCMA-EMAR ↑ medication accuracy ratesAdministration errors of support medications, wrong dose, wrong drug Overall accuracy rate at hospital 1 ↑ significantly from phase 1 (89%) to phase 3 (90%) (p = 0.0015)Main results
•
Overall accuracy rate ↓ significantly in ICU from phase 1 (94%) to phase 3 (83%) at hospital 2; (p = 0.004) due to
↑ technical errors
•
Overall accuracy rate remained the same in Oncology unit from phase 1 (97%) to phase 3 (98%); 2 technical errors administration errors of support medications, wrong dose, wrong drug
•
Overall accuracy rate ↑ ED from phase 1 (86%) to phase 3 (95%) (p = 0015)
Comments/limitations
•
Direct observation
•
Voluntary reported
V & R
•
Not reported
Ethics
•
Not reported
Bias
•
Observer training, type of observer, quality of data, use of pharmacist to observe nurses – not reported on nursing work
•
7 pharmacist and 8 nurses were trained as observers
•
Observer reports reviewed by pharmacist or AUS MEDS specialist
•
Missing nurse's viewpoint, general observation data
•
Not discussed potential for Hawthorne effect
•
Did not include new types of errors
•
Total errors accuracy measured by observation by nurse + pharmacist
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
Participants84 bed Oncology Medical CentreEMR based handoff/over was used in 9,274 of 10, 910 (85%) patient treatment visitN = 42 nurses presurvey; n = 27 postsurveyMedication ManagementHandovers/offs for communication between nurses for administration
Variables measuredPareto Chart on workfloweMAR based WCI handoff toolPre–postFor MAE in the WCI tool and Incident reportsChart reviewHandoff tool utilisation ratePatient waiting times, nursing satisfactionElectronic InitiativeseMAR
StatisticsBivariateT test &χ²/Fisher's exact testData analysis conducted bySAS v9.4
Key findingsProportion of medication errors ↓ 60%Medication errors rates 32% pre to 86% postHandover tool used 85% patient treatment visitsFrom 32% pre to 86% postNurse reported new tool reduced medication errorsreduced fromMain results
•
Pt waiting time average ↓ of 2 mins/pt/month
•
MAE from ineffective handoffs ↓ from 10/17 (60%) pre to 11/34 (32%) post (p =0.07)
Comments/limitation
•
Not included contribution to errors
V & R
•
Not reported
Ethics
•
Not reported
Bias
•
Handover form not standardised
•
Bias single nurse manager data collector
•
Self-report of medication errors
•
↓ incidence due to workload, lack of time not true reflection
Participants2 medical-surgical units (22 beds & 26 beds), 1 medical ICU (13 beds), and 1 surgical ICU (20 beds) in 386 bedN = 888 pre + n = 697 post in medical surgical ward, n =374 pre + n = 394 post no. of observationMedication ManagementNursing administration + prescribing
Variables measuredObservations of nurses were conducted by 2 pharmacists and 6 pharmacy studentsCalNOCElectronic InitiativesCPOE BCMA, eMAR &ADC
Statistics Nominal data Chi-square Fisher's exact test Continuous data Unpaired t-test Data analysis conducted by Stata 10
Key findingsUse of tool for accuracy and MAO assessmentICUs, the charting of medication administration improved after BCMA implementation, but total medication errors and wrong-time errors did not change No difference for overall error rate (12.6% pre to post 13.5%)Main results
•
↓ 58% of errors
•
Few errors ↓in ICU nonsignificant
•
Med-surg wards no. of wrong errors ↑ timing
•
↑ ID checks ward
•
ICU compliance ↓ after BCMA
•
Most common route of medication errors oral (82.9% in med surgical ward, 68.2% in ICU pre; 83.4% in med surgical ward, 66.2% in ICU post)
Comments/limitationV & R
•
Not reported
Ethics
•
Not reported
Bias
•
BCMA medication administration limited no. of indicators
•
Only show ↓ medication errors if large
•
Observations pharmacy students
•
Nonexperienced observers
•
Observers entered 50% of DE bias
•
Not severity of MAO
Power calculation
•
Based on similar study 6.3% an α = 0.05 power 80% = 654 medication observations
Design Pre–Post Prospective When 2011 Setting 1 large hospital
Participants 70 bed ED 1,978 (996 pre‐BCMA and 982 post‐BCMA) Medication Management Nursing administration
Variables measured No of observations of medication administration errors (wrong drug, dose, route, medication administration without order; severity of error, medications associated with error) Electronic Initiatives eMAR & BCMA
Statistics Fisher's Exact test Error rate of medication using logistic regression Data analysis conducted by Excel spreadsheet SAS v9.2
Key findings
BCMA in ED ↓
Wrong dose errors decreased by 90.4% (p < 0.0001), and medication administrations with no physician order decreased by 72.4% (p = 0.057)Most errors discovered were of minor severityAntihistamine medications were associated with the highest error rateMain resultsPre
•
BCMA medication administration error rate 6.3% to post BCMA error rate 1.2% represents 80.7% ↓ (p < 0.0001), with wrong dose errors representing 66.7% of observed errors ↓ 90.4% (p < 0.0001)
Comments/limitation Blinded observers compared to physician order, pharmacist + 3 pharmacy studentsV & R
•
Not reported
Ethics
•
Granted by hospital
Bias
•
Observer bias Hawthorne effect
Power calculation
•
Baseline error of 10% expected reduction of 40%, 951 medication administration required α = 0.05 + 90% power
Design Pre–Post Prospective When 2011 -2012 Setting 1 hospital
Participants 59 NICU in 1 hospital in US 38, 282 medication orders, in 2011, 37, 439 in 2012, eMar records in 180,595 in 2011 210, 231 in 2012 Medication Management Nursing administration
Variables measured Automated algorithms identified MAE Data extracted from the eMAR data warehouse including medication order history and incidence report Medication audit trail data eMar data, lab results, enteral feeding, communication orders trigger tool for measuring adverse drug events Risk MonitorPro incident report tool Electronic Initiatives eMAR with clinical decision making, BMAS, smart pump
Statistics Descriptive frequency Data analysis conducted by Not reported
Key findingsError rate after eMAR 2.8%Main resultsPre
•
46 errors/1000 pt days, 103 errors per 100 NICU admission
Post
•
45 errors/1000 pt days, 102 errors per 100 NICU admission
Fluid admin error rates were similar IV 3.2% (273/8567); parenteral nutrition 3.2% (649/20124) and lipid administration 1.3% (203/15227), 13 insulin administration errors with 2.9% (13/456)
Comments/limitation
•
Data from HER
•
Self-reported incident report
•
Data warehouse
•
Incident reports may not identify errors
V & R
•
Not reported
Ethics
•
Not reported
Bias
•
Reviewers indicated error in eMAR records
•
Algorithm specification reviewed
•
Reviewer blinded to algorithm error assessment at time of review
Ethics
•
Granted by hospital
Bias
•
Mitigated by use of unbiased dataset
Power calculation
•
Sample size of 1000 medication orders = power sensitivity of 99%, 2 sided 95% CI = 97.9%–100%
Design Pre- and postprospective without control group When Not stated which year published 2020 Setting 1 hospital
Participants 1 x 55 bed ED in an acute community hospital; pre n = 676 medication administration, post n = 656; Medication Management Nurse administration
Variables measured Direct observation of No. and type of errors MAS – NS survey Electronic Initiatives BCMA
Statistics χ²/Fisher's exact test Data analysis conducted by Excel spreadsheet → SPSS v20
Key findingsBCMA in ED ↓MAE74.2% ↓ in MAEWrong dose pre n = 16; 2.37%, post n = 5; 0.76%
•
most common ↓ 1.97% Fisher exact p = 0.03
•
↑ MAS-Nurses Assessment of Satisfaction
Main resultsPre
•
Total errors n = 20; 2.96%
Post
•
Total errors n = 5; 0.76% statistically significant difference ↓ of 74.2% Fisher exact p < 0.01
Comments/limitation
•
Nurse and pharmacist observers
•
2 observers
•
Must achieve 100% before eligible to observer medication round
•
Consent obtained by nurses
V & R
•
MAS-NS Cronbach's alpha 0.86
•
V & R
Ethics
•
Granted by hospital
Bias
•
Hawthorne effect
•
Observers 14 external reviewer to reduce bias by peers
•
Interrater reliability before observation + education and training
Power calculation
•
Priori power analysis G* Power 3.1 expected error ↓ 50% 748 medication administration events needed α = 0.05 to achieve 80% post did not reach adequate power
•
Post-hoc detected adequate α = 0.05 Power of 84.4%
•
Convenience sample power analysis not conducted for survey
Design Pre–Post Prospective observational and retrospective chart review When 2011–2012 Setting 1 hospital
Participants 40 bed medical/oncology unit of 1 x 357 bed hospital 156 cases of medication administration activities 78 pre and 78 post were observed Medication Management Nurse administration
Variables measured Utilising time and motion technique pre and post-eMAR Review of the hospital Midas and medication error database for rates of errors eMAR Electronic Initiatives eMAR
Statistics Descriptive Bivariate Independent t-t test Multivariate analysis Hierarchical multiple regression model Data analysis conducted by Excel spreadsheet SPSS v20 level of significance 0.05
Key findings↓ Medication ErrorsDistractions/interruptions during medication administration processes are significant predictorMain resultsPre
•
Mean hospital medication errors ↓ from 11. 0 to 5.3 events per month
•
Mean medication administration time ↑ pre 11.3 to 14.4 mins significant difference
Post
•
5.3 events/month (p = 0.034)
•
14.4 mins (P= 0.039)
Comments/limitation
•
Convenience sample
•
Random direct observations by nurse chief investigator
V & R
•
Face validity by nurse expert panel for time motion
Design Pre–Post Prospective When 2006–2007 Setting 1 hospital
Participants 38 bed adult medical ICU of 744 bed teaching hospital 1465 medication administrations were observed (775 preintervention and 690 postimplementation) for 92 patients (45 preintervention and 47 postimplementation) Medication Management Nurse administration + physician order sent to pharmacist who entered into EMAR
Variables measured No. of observations of medication errors, Electronic Initiatives BCMA + CPOE + automated dispensing cabinet + EMAR
Statistics Bivariate Pearson's chi-square test Student's t-test and Mann-Whitney U Data analysis conducted by SPSS v13 α = 0.05
Key findingsBCMA ↓ no. of wrong administration time errors 56%
•
Wrong administration time 95.4% pre ↓ 86.7% post
•
Omission 3.9% pre ↑ 11.7%
•
Wrong drug 0.7% pre ↑3.3%
•
Wrong dose 0 pre ↑ 3.3% post
•
Wrong route 0 pre ↑ 1.7% post
•
Drug without order 0.7% pre ↑ 1.7% post
•
Document error 13.1% pre 26.7% post
•
Nurse mean years as nurse pre 2.3 ± 0.6; post 2.1 ± 0.7
•
Nurse year in ICU pre 1.5 (0.1–23) post (0.1–35)
•
No. of medications on EMAR pre 21 ± 7.8 post 24.4 ± 7.2
Design Pre–Post Prospective When 2009–2010 Setting 1 hospital
Participants 28 bed surgical ward in a 555 bed Medication Management Nurse administration
Variables measured No. of observations, frequency of medication administration, severity of errors, frequency of errors per ATC Electronic Initiatives CPOE, BCMA & eMAR
Statistics χ² Data analysis conducted by Not reported
Key findings68 medication errors including time errors in 945 administrations frequency 7.2% pre vs 36 errors 3.2% in 1001 administrations post-BMCAClass C + D 79.4% of 20.6% pre and 88.9% of 11.1% post50% reduction in frequency of medication administrationMedication errors ↓ from 7.2% to 3.6% post-BCMANo. of errors per category pre- and post-BCMA intervention were as follows: omission, 2 (0.2%) and 1 (0.1%); logistic omission, 12 (1.3%) and 2 (0.2%); administration too early, 36 (3.8%) and 30 (3.0%); administration too late, 4 (0.4%) and 2 (0.2%); wrong dose, 12 (1.3%) and 1 (0.1%); extra dose, 2 (0.2%) respectively (p < 0.0001 over all categories; χ2 test)Main resultsPre
•
Exclusion of time errors resulted in 28 (3.0%)
Post
•
Exclusion of time errors resulted in 4 (0.4%) (RR, 0.13; 95% CI, 0.05– 0.38)
Comments/limitation
•
Noncompliance was not measured
•
Observational
•
Prospective
V & R
•
Not reported
Ethics
•
Not granted as it was within the quality-of-care improvement not necessary according to Dutch legislation
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
Design Pre‐Post Prospective When 2003–2004 Setting 1 hospital
Participants 28 bed general surgical ward Medication Management Nurse administration, prescribing
Variables measured No. of observations Frequency Administration errors omission, wrong drug, wrong dose, extra dose, wrong route and fast IV bolus Electronic Initiatives Closed loop electronic prescribing, automated dispensing, BCMA & eMAR (ServeRx v1.13: MDG Medical Israel)
Statistics χ²/Fisher's exact test t-test Mann Whitney U test Data analysis conducted by Not reported
Key findingsNurse administration time ↓Patient identity was not checked for 82.6% of 1344 doses pre‐intervention and 18.9% of 1291 afterwards(p<0.001; χ2 test)Main resultsPre
•
MAEs occurred in 7.0% of 1473 non‐intravenous doses
•
Time per drug administration round ↓ 50 min
•
Nursing time on medication tasks outside of drug rounds ↑21.1%
Post
•
4.3% of 1139 afterwards (p = 0.005; χ2 test)
•
40 min (p = 0.006; t test)
Nursing time on medication tasks outside of drug rounds 28.7% (p = 0.006; χ2 test)
Comments/limitation
•
Data collection by pharmacist
•
Potential severity of MAE assessed by 4 judges
•
Pharmacist self-reported
•
Nurses time recorded
•
Signalling device (JD-7, Divilbiss electronics, Chanute Kansas USA used to identify 32 random samples
V & R
•
Reported use of valid method but not described tool, reliability, validity, expert review
Ethics
•
Not reported
Bias
•
No reported training or education
•
No reported consent or volunteering
•
Hawthorne effect
Power calculation
•
Identify 2%–1% required 2319 new orders in each phase, identify reduction in MAE 5%–2.5% required 906 observation of administration in each phase needed 56 medication rounds
•
α = 0.05 + β = 0.02
•
adequate power
Generalisability
•
Single site
•
Unable to generalise
Funding
•
Funded by MDG Medical + Department of Health Public Safety Research Program
Design Retrospective cross-sectional study When 2010–2013 Setting 1 hospital
Participants 56, 428 paediatric patients, 110, 435 encounters ∼ 54% were under 3 years, ∼ 33% were neonates Medication Management Prescribing, dispensing and administering Prescribing, dispensing, administration,
Variables measured Dose omissions Enterprise Data Warehouse + EMAR system, medication + patient encounters MedEx to extract data from text + ATC Electronic Initiatives eMAR
Statistics χ² test Data analysis conducted by Statistical package R 64 bit v2.15.2 (2012 – 10-26)
Key findings
•
596 dose omissions for 56 000 pt < 0.05% incidences of nonadministration
•
Nursing staff compliance 97.35%
Reasons
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Nursing administration
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35.5% due to already given
•
8.3% due to given by another
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Schedule change 7%
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Medication discontinued 7%
•
Other pt or family refused 11.35%
Main results
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596 missed-admin orders without corresponding administration records less than 0.05% of 1.6 million orders for 56 million patients
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40,999 orders with corresponding administration records indicating nonadministration (<3% of orders)
•
Medication not available 0.02%
Comments/limitation
•
Retrospective
•
Data warehouse
•
Reminder to nurses to document and administer
•
Validation phase lost due to extract-transform-load (ETL) processes that transfer from CPOE, MAR, PIS
•
Data on omissions do not take into account other medication errors
•
Only orders serviced by pharmacy included in the review
V & RUsed validated measuresEthics
•
Granted by hospital
Bias
•
Retrospective
Power calculation
•
Not reported but large sample
Generalisability1 hospitalUnable to generaliseFunding
•
Funded by National Library of Medicine Grant
Note: ADC = Automated Dispensing Cabinets; AMI = Acute Myocardial Infarction; ATC = Anatomical Therapeutic Chemical; BCMA = Bar Coded Medication Alert; CalNOC = California Nursing Outcome Coalition Tool; CI = confidence interval; CMS = Centres for Medicaid and Medicare Services; CPOE = Computerised Physician Order Entry; DAT = Drug Assessment Tool; DDI = drug to drug interactions; ED = emergency department, EDW = Enterprise Data Warehouse; EHR = electronic health records; EMR = electronic medical records; eMAR = Electronic Medical Administration Records, FMEA = failure mode and effects analysis; HF = Heart Failure; HIS = Health Information System; HIMSS = Health Information and Management System Society; ICU = intensive care unit; ID = identity number; IHIS = Integrated Health Information System; IPIS = Intravenous Preparation Information System; IQR = interquartile rating; MAS- NS = Medication administration system -nurse assessment of satisfaction; MAE = medication administration error; MAO = medication administration omissions; MMS = Medication Management System; NBM = nil by mouth; NICU = neonatal intensive care unit; No. = number; OSMC Adverse DAT = Ohio State Medical Centre Adverse Drug Assessment Tool; QFD = Quality function deployment; PRR = proper reconstitution rate; PDR = proper dilation rate; PIS = Pharmacy Information System; SAS = Statistical Analysis Software; Stata = Stata Corp LP College TX; SPSS = Statistical Package for Social Sciences; TACMMS = Transmission Alerts Centre for Medication and Medicare Services; UK = United Kingdom; US = United States; WCI = Wilmot Cancer Institute; V & R = validity and reliability; χ² = chi-squared test
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
The main method of assessing medication administration was by the number of observations, frequency, or descriptive analysis of incidences of medications administration errors or accuracy rates and types of errors; these observations were conducted by pharmacists or pharmacy students, human factor engineer, or expert nurses (
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
). Observations comprised of frequency of medication administration errors, wrong medication, incorrect route, medication administration without order, severity of error, additional doses, faster intravenous bolus doses, and the medications associated errors (
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
Chart reviews of medication administration rates of errors, errors of omission, route of medication errors, unauthorised dose errors, wrong dose errors, and frequency of medication order were also conducted (
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
Medication administration errors tools used by these studies included the Medication Quality Scores from Centres for Medicaid and Medicare Services Tool (
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
This review has acknowledged mixed findings. Some studies reported positive findings. A large study of 2603 hospitals found that hospitals with electronic medication administration records performed better on quality measures and compliance than hospitals with computerised physician order alone or hospitals without electronic medication administration records and or computerised physician order (
). One study conducted in a 70-bed emergency department of a large hospital concluded that pre-electronic medication administration records and barcoded medication alerts medication administration errors rates of 6.3% were reduced significantly to post barcoded medication alerts errors rates of 1.2% representing a decrease of 80.7% (p < 0.0001) (
). Another study performed in a 55-bed emergency department found the total number of errors preintervention 2.96% reduced statistically to 0.76% (Fisher's exact p < 0.01).
reported 596 missed-administration orders accounting for less than 0.05% of 1.6 million orders for 56, 428 paediatric patients indicating <3% of administration orders were missed.
reported significant reductions in medical/oncology hospital medication errors from 11.0 to 5.3 events per month postelectronic medication administration records implementation (p = 0.034). However, the nurse's administration time increased from 11.3 minutes to 14.4 minutes (p = 0.039). Medication administration errors occurred in 7.0% of 1473 nonintravenous doses pre-electronic Computerised Physician Order, electronic medication administration records, bar coded medication alerts, and Automated Dispensing Cabinets interventions, and 4.3% of 1139 postinterventions (p = 0.005) (
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
). Other studies report reductions in medication administration error rates from 19.7% to 8.7% post barcoded medication alerts (p < 0.001); however, the error rates of 8.7% are alarming (
). Medication administration errors from ineffective handoffs reduced from 60% pre-electronic medication administration records and Wilmot Cancer Institute Handoff Tool to 32% postintervention (
Ensuring effective care transition communication: implementation of an electronic medical record–based tool for improved cancer treatment handoffs between clinic and infusion nurses.
reported improvements in medication administration accuracy rates for hospital 1 from pre- to postintervention from 89% in phase one to 90% in phase three (p = 0.0015); however, the overall accuracy rate did not statistically improve from phase one to three for hospital 2 from 93% to 96% (p = 0.015). In this study, the special care unit's accuracy rates, in fact, decreased significantly after bar coded medication alerts implementation from 94% to 83% from phase one to three (p = 0.004). The analysis presented a large number of technical errors. The accuracy rate for the oncology unit remained the same with the accuracy rate from 97% in phase 1 to 98% in phase 3. In the emergency department, accuracy rates increased from 86% to 95% (p = 0.0015).
found medication errors were 7.2% before bar-coded medication alerts and 3.6% postimplementation in a 28-bed surgical ward of one hospital. However, in the same study, Class C and D medication administration errors occurred in 79.4% and 20.6% of all medication administrations before bar coded medication alerts and increased to 88.9% and 11.1% after bar coded medication alerts (OR 0.48; 95% CI, 0.15–1.59). Class C refers to the cardiovascular system and D to dermatologicals of the Anatomical Therapeutic Chemical (ATC) Classification (
reported 359 medication incidents with the majority of errors made by nurses. This same study reported the number of errors at the hospital using paper-based were 68% compared to 88% for a hospital using electronic medication administration records.
found no difference in overall medication administration error rates in intensive care units (12.6% pretechnology and 13.5% postinterventions). The charting of medication administration improved; however, the total errors and wrong time errors did not change.
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
used automated algorithms to identify medication administration errors reporting no difference between pre (46 errors/1000 patient days, 103 errors per 100 neonatal intensive care unit admission; from 38, 282 medication orders) and post (45 errors/1000 patient days, 102 errors per 100 neonatal intensive care unit admission; from 37,439 medication orders). One study found no statistically significant difference in mean incidence of medication errors between pre- and postinterventions for one hospital that used electronic medication administration records compared to another hospital that used paper-based medication charts (
). Another study reported that initially electronic alerting systems improved proper reconstitution rates of antibiotics and proper dilution rates by 42% (p < 0.001) but after a few months it reduced to 0.9% (p = 0.013) indicating that the passive alerts were ineffective over time (
4.5 Types and causes of errors reported by studies
The listed types of medication administration errors by nurses include administration of the incorrect dose, giving the patient the wrong medications, giving patients with allergies the wrong medication, giving medications at the wrong time, and medication administration without a doctor's order (
). Types of errors reported given without scanning identification, undocumented administration, and record of medication given but medication not given (
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
). The causes of medication errors include lapse of nurse memory, failure to check medication correctly, lack of nurse's knowledge, inaccurate identification of the patient, not double-checking medications with another nurse, and missing medications (
Peer-reviewed literature supports the use of electronic medication administration records compared to paper-based medication administration records, as it is more efficient, reduces adverse medication events, promotes patient safety, and improves communication (
). Though, according to this review, there are still unacceptable numbers of preventable medication administration errors occurring and there is the introduction of technical errors. Any medication error has the potential to cause mortality and increase morbidity (
A review of these studies suggests there is a reduction in the number or frequency of observed medication administration errors; however, the results were not as favourable as expected with the percentage of errors remaining between ∼ 25% and 3.5%. This finding is comparable to other studies with reported errors in the electronic medication administration records system occurring 25.6% of cases (
). The clinical setting of the studies reported favourable results in adult's hospitals, general wards, medical-surgical units, oncology and emergency departments however not so advantageous in paediatrics, neonatal intensive care units, special care and critical care units. Children have unique medication prescribed (
), require dosing according to weight-based, and may need a paediatric medication administration function within the electronic medication administration records (
). Clinical settings were included to ensure heterogeneity of the studies. Heterogeneity is important individual studies in a review may seem to evaluate the same variables but may have outcomes that are inconsistent with each other. In this review, some showed improvements in patient safety and reduce medication errors using eMAR, while others showed no benefits or different types of errors.
Barcoding may be an effective technology to improve health care practices; however, few were conducted in the paediatric setting (
found medication accuracy rates decreased after bar coded medication alerts in telemetry patients of hospital 1 and ICU of hospital 2, their analysis found an increase in technique errors. Intensive care patients have complex care needs, changes in the frequency and titrations of the doses of medication, interoperability between smart pumps and electronic medication administration records could prove beneficial in the administration and monitoring of continuous infusions (
). Few studies have been located testing the impact of electronic medication administration records in aged care facilities on medication errors and patient safety. One study reviewed medication rounds in an Australian residential care facility and discovered no difference in time, documentation, and communication of medication administration records when compared to paper-based (
The length of time that the setting has commissioned electronic medication administration records reported that intensive care nurses were more accepting of electronic medication administration records at 12 months compared to 3 months indicating that electronic medication administration records usability and computerised physician order usefulness. As the push toward electronic health records continues more hospitals will face issues of technology acceptance (
found increased use and compliance for electronic medication administration records and computerised physician order after each additional 2 years of technology use.
There are many different research study designs, the ‘time frame of data collection includes the use of retrospective, prospective and ambispective designs (
). In retrospective studies, the events have occurred before the onset of the study, in prospective design, the events have not occurred this provides the ideal opportunity to maximise the accurate collection of data after the interventions are enabled adding to the scientific rigour (
). Due to the nature of the reviewed articles and outcomes measured it may be difficult for the researcher to undertake a randomised control trial design (gold standard of research); therefore, the results of these studies do not have strong scientific validity and it is difficult to draw firm conclusions from these study's results (
). The retrospective chart review method is used in approximately one-quarter of research studies; this was similar to this review with approximately 30% of the studies using this design (
). Chart reviews can be from electronic databases, notes, and incident reports which can provide valuable data to direct subsequent prospective studies (
Though, these studies were difficult to compare as they calculated adverse medication incidences differently. Many of the studies used electronic medication administration records as data collection methods; however,
used automated algorithms for the detection of medication errors through the electronic medication administration records and suggested that this may be a more feasible method for current reporting systems. It has been acknowledged that accident models and classification systems for the detection of errors can provide beneficial evidence in health care including medication errors resulting in contributing factors, there is a need to improve the reliability and validity of these models (
). There is a lack of psychometric properties in the instruments used in the reviewed studies. There are few of the reviewed studies that describe the sample size calculations (
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
). Prescribing medication is related to writing the order, transcribing is related to the nurse transferring the order from paper to paper-based medication administration of medication (
). The pharmacist's role in the medication management cycle is dispensing, preparing and the storage of medications they have not received the same education or have the same clinical experience in the administration process as nurses (
) and may therefore miss some medication administration errors. Barriers to interprofessional collaboration in health care are both structural and cultural with an under-appreciation of each other's roles (
Collaboration between pharmacists, physicians and nurse practitioners: a qualitative investigation of working relationships in the inpatient medical setting.
Journal of Interprofessional Care.2009; 23: 169-184
). None of the reviewed studies reported the years of experience of the nurses. The competency development of nurses has been found to have two distinctive periods; rapid growth and development in the early stages of the nurse's career and a slower increase later (
The relationship between the levels of nurse's competence and the length of their clinical experience: a tentative model for nursing competence development.
). The high number of occurrence of medication errors is a concern and nurses are often involved as they administer or instruct patients, to enable a nurse to be able to safely administer medications, education on safe practice must start early in their training, this can be achieved by medication simulations, technology aids and online learning in their undergraduate nursing programs (
). The new generation of nurses will be required to develop an understanding of the effective and safe use of electronic medication administration records, the adoption of electronic medication administration records systems in simulation has identified medication administration errors generated during simulations and they include difficulty using technology, lack of knowledge and use of the skill of medication administration using electronic systems (
). Therefore, it is essential that electronic medication administration records be incorporated into the Bachelor of Nursing programs.
Technology-based medication administration is gaining acceptance to prevent medication errors; however, studies suggest that the improper use of technology can lead to unsatisfactory error reduction and the introduction of new potential medication errors. Clinical override alerts for approximately 23% of administrations include failure to scan identification barcodes and scanning barcodes after the dose is removed (
). Medication alerts generated by electronic medical records can contribute to alarm fatigue ("EHRs Remain Near Top of ECRI's Top Health IT Hazards," 2016; "
Most of the studies in this review sought to evaluate nurses’ medication administration, doctor prescription, or pharmacist dispensing errors were not included in this review. Qualitative studies were not included in this review as often they measured nurses’ satisfaction and effect on workload rather than incidences of medication errors. Many of the studies had small sample sizes and used retrospective chart reviews methods. Many of the studies did not explore contributing factors or causes of medication errors. Future research should focus on how improving the training of undergraduate and registered nurses on retrieval, organising, synthesising, and communicating using digital information systems to reduce medication administration errors using electronic medical records. Practical research should include integrating electronic medication administration records into simulations in undergraduate programs. Further research is needed in the clinical settings including residential care facilities.
7. Conclusion
This comprehensive review of the literature is the first to attempt to summarise and evaluate the impact of electronic medication administration records on the incidences of medication errors and therefore patient safety. This review identified a few studies that focused on the nursing administration of electronic medication administration records and patient safety including medication errors using quantitative methods.
Findings from this review challenge the notion that electronic medication administration records reduce medication errors. The findings of this review are that electronic medication administration records create unconsidered negative outcomes. The functionality of electronic medication administration records in some clinical settings such as paediatric, neonates, and intensive care units may not be as in effective as general acute adult settings.
CRediT authorship contribution statement
Snezana Stolic: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft. Linda Ng: Data curation, Writing – review & editing. Georgina Sheridan: Writing – review & editing.
Funding
No funding was required for this review.
Ethical statement
Ethics approval was not required for this integrative review of literature
Conflict of interest
None.
References
AHC M.
Poor EMR usability linked to patient safety concerns, clinician burnout.
Use of an electronic medication administration record (eMAR) for surveillance of medication omissions: results of a one year study of antimicrobials in the inpatient setting.
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.
How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis.
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