Alert Types and Frequencies During Bar Code-Assisted Medication Administration: A Systematic Review

Factors Influencing Medication Administration Outcomes Among New Graduate Nurses Using Bar Code-Assisted Medication Administration

Paramount to patient safety is the ability for nurses to make clinical decisions free from human error. Yet, the dynamic clinical environment in which nurses work is characterized by uncertainty, urgency, and high consequence, necessitating that nurses make quick and critical decisions. The aim of this study was to examine the influence of human and environmental factors on the decision to administer among new graduate nurses in response to alert generation during bar code-assisted medication administration. The design for this study was a descriptive, longitudinal, observational cohort design using EHR audit log and administrative data. The study was set at a large, urban medical center in the United States and included 132 new graduate nurses who worked on adult, inpatient units. Research variables included human and environmental factors. Data analysis included descriptive and inferential analyses. This study found that participants continued with administration of a medication in 90.75% of alert encounters. When considering the response to an alert, residency cohort, alert category, and previous exposure variables were associated with the decision to proceed with administration. It is important to continue to study factors that influence nurses' decision-making, particularly during the process of medication administration, to improve patient safety and outcomes.

Describing Medication Administration and Alert Patterns Experienced by New Graduate Nurses During the First Year of Practice

The aim of this study was to describe medication administration and alert patterns among a cohort of new graduate nurses over the first year of practice. Medical errors related to clinical decision-making, including medication administration errors, may occur more frequently among new graduate nurses. To better understand nursing workflow and documentation workload in today's clinical environment, it is important to understand patterns of medication administration and alert generation during barcode-assisted medication administration. Study objectives were addressed through a descriptive, longitudinal, observational cohort design using secondary data analysis. Set in a large, urban medical center in the United States, the study sample included 132 new graduate nurses who worked on adult, inpatient units and administered medication using barcode-assisted medication administration. Data were collected through electronic health record and administration sources. New graduate nurses in the sample experienced a total of 587 879 alert and medication administration encounters, administering 772 unique medications to 17 388 unique patients. Nurses experienced an average medication workload of 28.09 medications per shift, 3.98% of which were associated with alerts, over their first year of practice. In addition to high volume of medication administration, new graduate nurses administer many different types of medications and are exposed to numerous alerts while using barcode-assisted medication administration.

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload

OBJECTIVE

The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow.

METHODS

Using 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time.

RESULTS

As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2  = 0.237; marginal R2  = 0.199; intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs.

CONCLUSION

Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.

Reducing medication errors for adults in hospital settings

BACKGROUND

Medication 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.

OBJECTIVES

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

SEARCH METHODS

We 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 RESULTS: We 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' CONCLUSIONS

Low- 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.