Evidence map›Paper›PMID 34822165›Full record

SynthesisThe Cochrane database of systematic reviews2021

Reducing medication errors for adults in hospital settings.

Agustín Ciapponi, Simon E Fernandez Nievas, Mariana Seijo, María Belén Rodríguez, Valeria Vietto, Herney A García-Perdomo, Sacha Virgilio, Ana V Fajreldines, Josep Tost, Christopher J Rose and 1 more

Abstract readSystematic Review
In one paragraph

Synthesis in The Cochrane database of systematic reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  17. Real-World Complexity of Prescribing Cascades.Basic & clinical pharmacology & toxicology · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Agustín CiapponiArgentine Cochrane Centre, Institute for Clinical Effectiveness and Health Policy (IECS-CONICET), Buenos Aires, Argentina.
Simon E Fernandez NievasQuality and Patient Safety, Institute for Clinical Effectiveness and Health Policy, Buenos Aires, Argentina.
Mariana SeijoQuality of Health Care and Patient Safety, Institute for Clinical Effectiveness and Health Policy (IECS), Buenos Aires, Argentina.
María Belén RodríguezHealth Technology Assessment and Health Economics Department, Institute for Clinical Effectiveness and Health Policy (IECS), Ciudad Autónoma de Buenos Aires, Argentina.
Valeria ViettoFamily and Community Medicine Service, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Herney A García-PerdomoDepartment of Urology, Universidad del Valle, Cali, Colombia.
Sacha VirgilioInstituto de Efectividad Clínica y Sanitaria (IECS), Ciudad Autónoma de Buenos Aires, Argentina.
Ana V FajreldinesQuality and Patient Safety, Austral University Hospital, Buenos Aires, Argentina.
Josep TostUrgencias � Calidad y Seguridad de pacientes, Consorcio Sanitario de Terrassa, Barcelona, Spain.
Christopher J RoseNorwegian Institute of Public Health, Oslo, Norway.
Ezequiel Garcia-ElorrioQuality and Safety in Health Care, Institute for Clinical Effectiveness and Health Policy (IECS), Buenos Aires, Argentina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedication errors are preventable events that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the healthcare professional or patient. Medication errors in hospitalised adults may cause harm, additional costs, and even death.

objectivesTo determine the effectiveness of interventions to reduce medication errors in adults in hospital settings. SEARCH

methodsWe searched CENTRAL, MEDLINE, Embase, five other databases and two trials registers on 16 January 2020.  SELECTION CRITERIA: We included randomised controlled trials (RCTs) and interrupted time series (ITS) studies investigating interventions aimed at reducing medication errors in hospitalised adults, compared with usual care or other interventions. Outcome measures included adverse drug events (ADEs), potential ADEs, preventable ADEs, medication errors, mortality, morbidity, length of stay, quality of life and identified/solved discrepancies. We included any hospital setting, such as inpatient care units, outpatient care settings, and accident and emergency departments. DATA COLLECTION AND ANALYSIS: We followed the standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care (EPOC) Group. Where necessary, we extracted and reanalysed ITS study data using piecewise linear regression, corrected for autocorrelation and seasonality, where possible.  MAIN

resultsWe included 65 studies: 51 RCTs and 14 ITS studies, involving 110,875 participants. About half of trials gave rise to 'some concerns' for risk of bias during the randomisation process and one-third lacked blinding of outcome assessment. Most ITS studies presented low risk of bias. Most studies came from high-income countries or high-resource settings. Medication reconciliation -the process of comparing a patient's medication orders to the medications that the patient has been taking- was the most common type of intervention studied. Electronic prescribing systems, barcoding for correct administering of medications, organisational changes, feedback on medication errors, education of professionals and improved medication dispensing systems were other interventions studied. Medication reconciliation Low-certainty evidence suggests that medication reconciliation (MR) versus no-MR may reduce medication errors (odds ratio [OR] 0.55, 95% confidence interval (CI) 0.17 to 1.74; 3 studies; n=379). Compared to no-MR, MR probably reduces ADEs (OR 0.38, 95%CI 0.18 to 0.80; 3 studies, n=1336 ; moderate-certainty evidence), but has little to no effect on length of stay (mean difference (MD) -0.30 days, 95%CI -1.93 to 1.33 days; 3 studies, n=527) and quality of life (MD -1.51, 95%CI -10.04 to 7.02; 1 study, n=131).  Low-certainty evidence suggests that, compared to MR by other professionals, MR by pharmacists may reduce medication errors (OR 0.21, 95%CI 0.09 to 0.48; 8 studies, n=2648) and may increase ADEs (OR 1.34, 95%CI 0.73 to 2.44; 3 studies, n=2873). Compared to MR by other professionals, MR by pharmacists may have little to no effect on length of stay (MD -0.25, 95%CI -1.05 to 0.56; 6 studies, 3983). Moderate-certainty evidence shows that this intervention probably has little to no effect on mortality during hospitalisation (risk ratio (RR) 0.99, 95%CI 0.57 to 1.7; 2 studies, n=1000), and on readmissions at one month (RR 0.93, 95%CI 0.76 to 1.14; 2 studies, n=997); and low-certainty evidence suggests that the intervention may have little to no effect on quality of life (MD 0.00, 95%CI -14.09 to 14.09; 1 study, n=724).  Low-certainty evidence suggests that database-assisted MR conducted by pharmacists, versus unassisted MR conducted by pharmacists, may reduce potential ADEs (OR 0.26, 95%CI 0.10 to 0.64; 2 studies, n=3326), and may have no effect on length of stay (MD 1.00, 95%CI -0.17 to 2.17; 1 study, n=311).  Low-certainty evidence suggests that MR performed by trained pharmacist technicians, versus pharmacists, may have little to no difference on length of stay (MD -0.30, 95%CI -2.12 to 1.52; 1 study, n=183). However, the CI is compatible with important beneficial and detrimental effects. Low-certainty evidence suggests that MR before admission may increase the identification of discrepancies compared with MR after admission (MD 1.27, 95%CI 0.46 to 2.08; 1 study, n=307). However, the CI is compatible with important beneficial and detrimental effects. Moderate-certainty evidence shows that multimodal interventions probably increase discrepancy resolutions compared to usual care (RR 2.14, 95%CI 1.81 to 2.53; 1 study, n=487). Computerised physician order entry (CPOE)/clinical decision support systems (CDSS) Moderate-certainty evidence shows that CPOE/CDSS probably reduce medication errors compared to paper-based systems (OR 0.74, 95%CI 0.31 to 1.79; 2 studies, n=88).  Moderate-certainty evidence shows that, compared with standard CPOE/CDSS, improved CPOE/CDSS probably reduce medication errors (OR 0.85, 95%CI 0.74 to 0.97; 2 studies, n=630). Low-certainty evidence suggests that prioritised alerts provided by CPOE/CDSS may prevent ADEs compared to non-prioritised (inconsequential) alerts (MD 1.98, 95%CI 1.65 to 2.31; 1 study; participant numbers unavailable). Barcode identification of participants/medications Low-certainty evidence suggests that barcoding may reduce medication errors (OR 0.69, 95%CI 0.59 to 0.79; 2 studies, n=50,545). Reduced working hours Low-certainty evidence suggests that reduced working hours may reduce serious medication errors (RR 0.83, 95%CI 0.63 to 1.09; 1 study, n=634). However, the CI is compatible with important beneficial and detrimental effects. Feedback on prescribing errors Low-certainty evidence suggests that feedback on prescribing errors may reduce medication errors (OR 0.47, 95%CI 0.33 to 0.67; 4 studies, n=384). Dispensing system Low-certainty evidence suggests that dispensing systems in surgical wards may reduce medication errors (OR 0.61, 95%CI 0.47 to 0.79; 2 studies, n=1775). AUTHORS'

conclusionsLow- to moderate-certainty evidence suggests that, compared to usual care, medication reconciliation, CPOE/CDSS, barcoding, feedback and dispensing systems in surgical wards may reduce medication errors and ADEs. However, the results are imprecise for some outcomes related to medication reconciliation and CPOE/CDSS. The evidence for other interventions is very uncertain. Powered and methodologically sound studies are needed to address the identified evidence gaps. Innovative, synergistic strategies -including those that involve patients- should also be evaluated.

Indexed as

Medication ErrorsMedication ReconciliationAdultHospitalizationHospitalsHumansPharmacists

Identifiers

PMID34822165
PMCPMC8614640

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.