Associations Between Interruptions and Medication Administration Errors Among Nurses in Hospital Settings: A Scoping Review of Quantitative Studies

ABSTRACT

Aim

To provide an overview of quantitative research studies that report inferential statistics on the associations between interruptions and medication administration errors among nurses in hospital settings.

Design

A scoping review guided by the Joanna Briggs Institute methodology for scoping reviews.

Methods

Quantitative research studies conducted among nurses in hospital settings and published in English were sought. Covidence software was used by two authors to independently screen titles, abstracts, and full‐text articles. Two authors performed an independent data extraction using a standardised extraction template.

Data Sources

Ovid Medline, EMBASE (Elsevier), CINAHL (EBSCOHost), Web of Science, and Scopus databases were searched from the database inception through October 2024. Citation searching was also used to locate relevant studies.

Results

Twenty‐two studies met the review criteria. Studies were conducted in nine different countries, often in more than one hospital, and in various nursing units. Definitions of interruption and medication administration error frequently differed across studies or were not provided. Data were collected via direct observation, self‐report survey, a combination of direct observation and self‐report, or retrospective review of records. Most (n = 16; 73%) studies reported a statistically significant positive association between interruptions and medication administration errors.

Conclusion

Globally, interruptions are prevalent during nurse medication administration, and a majority of studies report a positive association between interruptions and medication administration errors. Continued investigation of this association using standard definitions of interruption and medication administration error, as well as consistent methods, is needed to strengthen research in this area.

Implications

The findings from this review can be used to inform future primary research, potential systematic reviews, and the development of targeted interventions to enhance medication safety and nursing practice in hospital environments worldwide.

Reporting Method

Reporting was guided by Joanna Briggs Institute scoping review method and the PRISMA extension for Scoping Reviews.

Patient or Public Contribution

No patient or public contributions were made to this scoping review.

Summary.

• What does this paper contribute to the wider global clinical community?

  • ○
    Studies included in this review were conducted in nine countries, reflecting the global nature of this patient safety concern.
  • ○
    Findings raise awareness of the positive associations between interruptions and medication administration errors.
  • ○
    Specific recommendations for future research and interventions are made.

1. Introduction

Worldwide, medication errors are a leading cause of avoidable patient harm (World Health Organization (WHO) ²⁰²³). A meta‐analysis of 81 studies conducted in high and low‐middle income countries found that 3% of patients experienced preventable medication harm, with at least one fourth of harm being severe or life threatening (Hodkinson et al. 2020). In the United States (U.S.), medication errors harm approximately 1.3 million people annually and cause a minimum of one death each day (World Health Organization, 2017). In England, it has been estimated that 237 million medication errors occur each year that result in an approximated cost of £98.5 million to the National Health Service, utilise nearly 182,000 bed‐days, and contribute to over 1700 deaths (Elliott et al. 2021).

A medication error is defined by the National Coordinating Council for Medication Error Reporting and Prevention as “any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient, or consumer.” (National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) ²⁰²⁵) (p.1) Medication errors can occur at any stage in the medication use process from prescribing through administering (Tariq et al. 2024).

1.1. Medication Administration Errors

Nurses employed in hospital settings frequently administer medication to patients, allowing many opportunities for errors to occur. For example, in the U.S., nurses who work on medical units typically care for five to six patients per shift and administer an average of 16 medications per patient per day (Hawkins and Morse 2022). In Brazil, nurses care for five to seven patients per shift, with each patient prescribed an average of nine medications (Magalhães et al. 2019).

The medication administration (MA) process includes three steps–preparation, administration, and documentation. A medication administration error (MAE) is defined as a medication error during the administration process, and involves a violation of one or more of the “rights”–right patient, drug, dose, route, time, and documentation–of MA safety (Hanson and Haddad 2023; Yoost and Crawford ²⁰²⁰). Omission errors, wrong technique (e.g., administration of an intravenous medication at a rate that falls outside of guidelines), and incorrect form (e.g., liquid form prescribed but tablet form administered) are also often classified as MAEs. Despite error‐reducing efforts, such as the use of technologies like computerised order entry and bar‐code medication administration, MAEs remain prevalent among nurses (MacDowell et al. ²⁰²¹).

Recent systematic reviews and meta‐analyses indicate that MAE rates in hospital settings (reported as rates per medication or per nurse) range between 32.1% and 56% (Alemu and Cimiotti 2023; Bifftu and Mekonnen ²⁰²⁰; Fathizadeh et al. ²⁰²⁴; Sutherland et al. ²⁰²⁰). Nurses cite various factors, such as lack of medication knowledge, fatigue, heavy workloads, and interruptions as causes of MAEs. Interruptions are cited by nurses as one of the most frequent contributors to MAEs (Schroers et al. ^2021a).

1.2. Interruptions

An interruption is defined by Brixey et al. ²⁰⁰⁷ (p.38) as “a break in the performance of a human activity initiated by a source internal or external to the recipient… This break results in the suspension of the initial task by initiating the performance of an unplanned task with the assumption that the initial task will be resumed.” The terms distraction and interruption are often used interchangeably; however, a distraction only directs attention away from an ongoing activity. As defined by D'Esmond (D'Esmond ²⁰¹⁶), a distraction is a “diversion of cognitive resources that draws some attention away from your current activity (primary focus), making it difficult to think clearly, focus, or pay attention”. Unlike an interruption, a distraction does not, in and of itself, cause a person to stop their primary task to do something else.

Interruptions can be externally or internally initiated (Brixey et al. 2007). An external interruption, for instance, can be caused by another person or the sound of an alarm. An internal interruption, often referred to as a self‐interruption, occurs when a person's own thought processes cause them to stop mid‐task to attend to something else (Schroers et al. 2023). Interruptions, whether externally or internally initiated, can cause confusion, loss of focus, and forgetfulness that increase the risk for errors.

Interruptions to nurses' work are an international occurrence. Recent studies conducted in various countries, including China, Saudi Arabia, and South Korea, indicate that interruptions are highly prevalent to nurses in hospital settings, reporting that nurses are interrupted between 6 and 14 times per hour (Shan et al. 2023; Eid et al. ²⁰²²; Kwon et al. ²⁰²¹). Team members, patients, and visitors often interrupt nurses to relay information or make requests. In addition, phones ring, and devices alarm to signal to nurses that their attention is needed. One of the most frequently interrupted nursing tasks is MA (Eid et al. 2022; Kwon et al. ²⁰²¹).

Observational studies conducted in China and Australia found that nearly all, 94.5%–99%, of nurse medication rounds are interrupted (Zhao et al. 2019; Johnson et al. ²⁰¹⁷). Findings from an integrative review (Schroers 2018) revealed that nurses are interrupted at least once when in MA. Researchers of a U.S. study asserted that MA “inherently includes interruptions” (Hawkins and Morse 2022 (p.3)). While some interruptions to nurses are essential and may lead to improved patient care (e.g., notification of a critical patient condition), research frequently demonstrates that interruptions to nurses have a negative impact on patient safety, quality of care, and can lead to increased financial costs to healthcare organisations (Kwon et al. 2021; Cole et al. ²⁰¹⁶).

1.3. The Review

Medication errors are a serious global patient safety issue, and interruptions, which are known to be highly prevalent during nurse MA (Shan et al. 2023; Eid et al. ²⁰²²; Kwon et al. ²⁰²¹), are implicated as contributing to MAEs (Schroers et al. ^2021a). Research on the associations between interruptions to nurses during MA and resulting MAEs exists, yet the evidence is fragmented. A scoping review was therefore conducted to provide a synthesis of the evidence to inform future primary research, potential systematic reviews, and the development of targeted interventions to enhance medication safety and nursing practice in hospital environments.

A search for published reviews and registered protocols of the associations among interruptions to nurses and MAEs revealed only one review. The Biron et al. (Biron et al. 2009) review was published in 2009, and had objectives to examine the evidence of interruption rates, characteristics of interruptions, and associations between interruptions and MAEs. The review included 23 original studies with publication dates of between 1980 and 2008. Only one study (Scott‐Cawiezell et al. ²⁰⁰⁷), which was conducted in a long‐term care facility, reported a statistical association between interruptions and MAEs; the association was found to be positive when wrong time MAEs were excluded. The Biron et al. review found no evidence of statistical associations between interruptions to nurses during MA in hospital settings and MAEs, even though most of their included studies (n = 21; 91%) were conducted in hospitals.

Since the publication of the 2009 Biron et al. (Biron et al. 2009) review, studies have examined the associations between interruptions to nurses in hospital settings and MAEs, warranting an updated review. A scoping review was performed to systematically chart existing studies, clarify how interruptions and MAEs are conceptualised, and examine the strength and direction of reported statistical associations.

1.4. Aims

The primary aim of this scoping review was to answer the following review question: What is the extent and nature of quantitative research studies that report inferential statistics on the associations between interruptions and MAEs among nurses in hospital settings? The secondary aims that guided the mapping process were: (a) How do studies define interruption?; (b) How do studies define and categorise MAEs?; and (c) What methods are used to collect data on interruptions and MAEs?

2. Methods

2.1. Design

This review was guided by the Joanna Briggs Institute (JBI) methodology for scoping reviews (Peters et al. 2024) and the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis Extension for Scoping Reviews (PRISMA‐ScR) (Tricco et al. 2018). A protocol was developed a priori. The protocol was not registered as registration is not required of scoping review protocols (Peters et al. 2024).

2.2. Search Methods

A librarian (EH) searched Ovid Medline, EMBASE (Elsevier), CINAHL (EBSCOHost), Web of Science, and Scopus to locate literature related to the review question. These databases were selected because their subject coverage of health sciences, psychology, and nursing is related to the key concepts of the review question. Many databases lacked specific and relevant controlled vocabulary (subject headings) for the concept of interruption. Therefore, the search strategies were broadened to include concepts of attention and distraction. Terms that described these concepts included: attention, disruption, disturbance, diversion, and distraction. Search strategies also included terms that described nurses and medication errors, the other key concepts of the review question. All terms were searched as controlled vocabulary, when available, and keywords/textwords. All search strategies and corresponding database details are outlined in the Appendix A. The strategies were modelled after a search strategy executed in EMBASE (Elsevier):

((‘attention’/de OR ‘alertness’/de OR ‘distractibility’/de OR (interrupt*:ab,ti) OR (disrupt*:ab,ti) OR (disturb*:ab,ti) OR (diversion:ab,ti) OR (diver:ab,ti) OR (distract*:ab,ti)) AND ((‘drug therapy’/exp/dd_ad) OR (‘medication error’/syn)) AND (‘nurse’/syn OR ‘nursing’/syn)) AND [english]/lim

2.3. Inclusion and Exclusion Criteria

To meet inclusion criteria, a study was required to evaluate the statistical significance of associations between interruptions to nurses during MA in hospital settings and MAEs. Studies that only reported results among student nurses were excluded. Only English‐language studies published in peer‐reviewed journals were sought.

2.4. Screening

All located articles were uploaded into the Covidence web‐based platform for review. Two authors (GS, JO) independently screened the title and abstract to first identify relevant articles. Discrepancies between the authors were identified through the Covidence platform, and agreement was reached through discussion. The same process was followed for full‐text review, with authors selecting a reason for excluding any articles. Common reasons for full‐text exclusion included studies that evaluated MAE rates but failed to associate errors directly with interruptions and studies that did not apply inferential statistics.

2.5. Data Extraction

Extracted data were selected based on relevance to the scoping review's aims. Key areas of data extraction included study characteristics (e.g., study aim, setting, sample, methods), interruption and MAE definitions, MAE categories, data collection methods, and associations between interruptions and MAEs. The MAE categories (e.g., wrong patient, wrong route) were determined by those identified in the included studies. Using an inductive approach, some categories were grouped together, such as expired or deteriorated medication (these were often grouped together in the original studies) and unauthorised or unordered drug. An MAE category of “other” was used to group any MAE type that was labelled in an original study as “other” or infrequently reported across the studies. Infrequent report was determined as three or fewer studies that included an MAE category. For example, only one study (Verweij et al. 2014) included “wrong reason” as an MAE, thus this was categorised as “other”.

An independent data extraction was performed by two authors (GS, JO) using a standardised extraction template built into the Covidence platform. Using a randomly selected article, the data extraction template was pilot tested. After minor modifications of the template, the authors completed data extraction of the full set of articles. The two authors met to resolve any discrepancies. A third author (FS) was available, as needed, to resolve any conflicts.

2.6. Data Analysis and Presentation

As scoping reviews are not intended to synthesise data, all data were analysed descriptively (Peters et al. 2024). Study characteristics were summarised using frequency counts and percentages. Results are presented narratively and in tables.

3. Results

3.1. Study Selection

A total of 1671 articles were retrieved from the five databases searched. After duplicates were removed, 794 articles remained for title and abstract screening. Following the title and abstract screening, 113 full‐text articles were assessed for eligibility. Ninety‐one articles were excluded during full‐text review, primarily for not reporting outcomes of associations between MAEs and interruptions. Two studies were discovered through citation searching. A total of 22 studies met the criteria for inclusion in the review. Figure 1 illustrates the study selection process.

FIGURE 1.

PRISMA flow diagram for study selection.

3.2. Study Characteristics

The earliest study included in this review, although no limits on publication years were placed, was published in 2010 by Westbrook and colleagues (Westbrook et al. 2010). Half (n = 11) of the studies were published more recently, between 2020 and 2024. Studies were conducted in nine countries, across six continents. Nine studies were conducted in Africa, with most in Ethiopia (n = 8). Only one study was conducted in Asia, and one in Australia. Three studies were conducted in North America (all in the U.S.), two in South America (both in Brazil), and six in Europe. Most studies included more than one site (n = 15; 68%) and setting (n = 18; 82%) for data collection. Data were collected in a variety of nursing units, including medical, surgical, emergency, paediatrics, intensive care, mental health, and others. A majority of studies (n = 16; 73%) included medical and/or surgical nursing units. Some studies reported the sample as the number of nurses, the number of medications given, and the number of patients, while others only included one or two of these variables. Table 1 provides a summary of the study characteristics.

TABLE 1.

Summary of study characteristics.

Author and publication year	Country	Title	Study aim	Site and setting	Sample	Data collection methods	MAE rate	Interruption rate	Association between interruptions and MAEs
Alemu et al. (2017)	Ethiopia	Medication administration errors and contributing factors: A cross sectional study in two public hospitals in Southern Ethiopia	To quantify the prevalence of MAEs, to assess the degree of reporting MAEs, and to identify the contributory factors to MAEs	2 Hospitals Units: Medical, surgical, paediatrics, ob‐gyne, outpatient, operating room, “others”	130 nurses 139 observed medication doses	MAES: DO and Self‐report Interruptions: Self‐report	Self‐report: 71% DO: 99.3%	Self‐report: 58%	Positive p < 0.05 AOR = 5.615 (1.713–18.403)
Assunção‐Costa et al. (2022)	Brazil	Observational study on medication administration errors at a University Hospital in Brazil: incidence, nature and associated factors	To identify the prevalence and nature of medication errors, including the associated factors, in a public university hospital in Brazil	1 Hospital Units: Medical, surgical	Nursing staff/technicians (number not provided) 561 medications	DO	36.2%	14.8%	Positive p = 0.001 OR 1.594 (1.255–2.024)
Berdot et al. (2012)	France	Evaluation of drug administration errors in a teaching hospital	To assess the frequency, type, potential clinical significance and determinants of MAEs detected by direct observation in adult in‐patients	1 Hospital Units: Immunology‐cardiology, nephrology, vascular medical, cardiovascular	28 nurses 415 medications 108 patients	MAEs: DO Interruptions: Not specified	27.6%	*4.4%	None p = 0.82 OR 0.92 (0.47–1.82)
Berdot et al. (2021)	France	Effectiveness of a ‘do not interrupt’ vest intervention to reduce medication errors during medication administration: a multicenter cluster randomised controlled trial	To evaluate the impact of a ‘Do not interrupt’ vest on MAEs in four French hospitals using a randomised controlled design. The secondary objectives were to evaluate the types and potential clinical impact of errors, the association between errors and several risk factors (such as interruptions), and nurses' experiences.	4 Hospitals Units: Medical, surgical, critical care	178 nurses 8472 doses	DO	*6.3%	*18.32%	None p = 0.191 OR 1.19 (0.92–1.55)

Blignaut et al. (2017)	South Africa	Medication administration errors and related deviations from safe practice: an observational study	To determine the incidence of MAEs and deviations from safe practice, as well as associated factors, by means of direct observation in medical and surgical units of public hospitals in the Gauteng Province of South Africa	8 Hospitals Units: Medical, surgical	36 nursing staff (included student nurses) 1847 medications 315 patients	DO	94%	External: 21% Internal: 14%	Inverse association with wrong‐dose MAEs p < 0.05 OR − 2.56
Bonafide et al. (2020)	USA	Association between Mobile Telephone Interruptions and Medication Administration Errors in a Paediatric Intensive Care Unit	To examine whether incoming mobile telephone call and text message interruptions are associated with subsequent MAEs among paediatric intensive care unit (PICU) nurses	1 Children's Hospital Unit: PICU (med‐surg)	257 nurses 238,540 medications 3308 patients	Retrospective review of tele‐communications and EHR data	3.2%	Incoming calls: 8.3% Incoming text messages: 44.7%	Incoming calls: positive p < 0.001 OR 1.20 (1.11–1.30) Incoming text messages: None p = 0.65 OR 0.99 (0.94–1.04)
Cottney and Innes (2015)	England	Medication‐administration errors in an urban mental health hospital: a direct observation study	To identify the incidence, type, and potential clinical consequence of medication‐administration errors made in a mental health hospital, and to investigate factors that might increase the risk of error	Inpatient Mental Health Hospitals (number not provided) 43 mental health wards: Acute adult, forensic, adult psychiatric intensive care, child & adolescent	Nurses (number not provided) 172 medication rounds 1422 doses	DO	3.3%	Not Provided	Positive p = 0.003 RR = 1.48 (1.14–1.93)
Donaldson et al. (2014)	USA	Improving medication administration safety: using naïve observation to assess practice and guide improvements in process and outcomes	To (a) describe the CALNOC naïve observation MA accuracy assessment method, (b) examine nurse adherence to six fundamental safe practices during MA, (c) examine the prevalence of MA errors in adult acute care, and (d) explore associations between nurse deviation from fundamental safe practices and MAEs in adult acute care	43 Hospitals Units: Medical‐surgical, step down, critical care	Nurses (number not provided) 33,425 doses 8594 patients	DO	0.32%	Distractions & interruptions (undefined) reported together: 22.89%	Positive p = 0.0189
Feleke et al. (2015)	Ethiopia	Medication administration error: Magnitude and associated factors among nurses in Ethiopia	To assess the magnitude and associated factors of MAEs among nurses at the Felege Hiwot Referral Hospital inpatient department	1 Hospital Units: Medical, surgical, paediatric, emergency	82 nurses 360 medications 263 patients	DO	56.4%	*59.7%	Positive p < 0.05 AOR 1.5 (1.14–3.21)
Gebrye et al. (2023)	Ethiopia	Magnitude and Predictors of Medication Administration Errors Among Nurses in Public Hospitals in Northeastern Ethiopia.	To assess the magnitude of MAEs and its associated factors among nurses working in public hospitals in the south Wollo zone, northeastern Ethiopia	5 Hospitals Units: Medical, surgical, adult ICU, NICU, paediatrics, orthopaedics, emergency	416 nurses	Self‐report questionnaire	55%	18.8%	Positive p < 0.05 AOR 4.943 (2.088–11.712)
Jessurun et al. (2022)	Netherlands	Prevalence and determinants of intravenous admixture preparation errors: A prospective observational study in a university hospital	To assess the prevalence, type, and severity of intravenous admixture preparation errors (IAPEs) as well as the determinants associated with the occurrence of IAPEs	1 Hospital Units: haematology, internal oncology, neurosurgery, paediatrics	109 nursing staff members 614 intravenous admixture preparations	DO	59.8%	16.6%	Positive p < 0.05 AOR 2.32 (1.13–4.74)
Jessurun et al. (2023)	Netherlands	Prevalence and determinants of medication administration errors in clinical wards: A two‐centre prospective observational study	To identify the prevalence, type and potential severity of MAEs, as well as determinants of MAEs in two Dutch hospitals that have several supportive electronic medication systems (i.e., EMR, CPOE and eMAR) in place	2 Hospitals Units: Internal oncology, neurology, pulmonary medicine, haematology, neurosurgery, hepatopancreatic‐biliary surgery	235 nursing staff 2576 medications 416 patients	DO	13.7%	10.2%	None OR 0.84 (0.52–1.36)
Mekonen et al. (2020)	Ethiopia	Magnitude and associated factors of medication administration error among nurses working in Amhara Region Referral Hospitals, Northwest Ethiopia	To assess the magnitude and associated factors of MAE among nurses at Northwest Amhara Region Referral Hospitals	2 Hospitals Units: Medical, surgical, paediatrics, emergency, ICU, outpatient	DO: 200 nurses Self‐report questionnaire: 332 nurses	MAES: DO and Self‐report questionnaire Interruptions: Self‐report questionnaire	DO: 95% Self‐report: 54%	Self‐report: 50.3%	Positive p < 0.05 AOR 4.70 (2.42–9.10)

Mohammed et al. (2022)	Ethiopia	Medication administration errors and associated factors among nurses in Addis Ababa federal hospitals, Ethiopia: A hospital‐based cross‐sectional study	To assess the magnitude and contributing factors of medication administration errors among nurses in federal hospitals in Addis Ababa, Ethiopia	3 Hospitals Units: Medical, surgical, paediatrics, emergency obstetrics and gynaecology, ICU, outpatient, “others”	402 nurses	Self‐report questionnaire	59.9%	61.4%	Positive p < 0.05 AOR 2.42 (1.30–4.49)
Raja et al. (2019)	Pakistan	Association of medication administration errors with interruption among nurses in public sector tertiary care hospitals	To determine the association of MAEs with interruption among nurses working at public sector tertiary care hospitals in Karachi, Pakistan	2 Hospitals Units: Not provided	204 nurses 716 medications	Questionnaire	82%	*69.1%	Positive p < 0.001
Tassew et al. (2022)	Ethiopia	Magnitude of medication administration error and associated factors in adult intensive care units of public hospitals in Addis Ababa, Ethiopia, 2019	To investigate the extent of MAEs and associated factors in adult intensive care units of public hospitals in Addis Ababa	3 Hospitals Units: Adult ICUs	97 nurses 419 medications 41 patients	DO	61.1%	*25.3%	Positive p < 0.05 AOR 3.4 (1.64–7.04)
Thomas et al. (2017)	USA	Impact of Interruptions, Distractions, and Cognitive Load on Procedure Failures and Medication Administration Errors	To (a) describe interruptions, distractions, and cognitive load experienced by registered nurses (RNs) during administration of medications, (b) examine the relationship of interruptions and distractions on cognitive load, and (c) investigate the impact of cognitive load on procedural failures and MAEs	9 Hospitals Units: Medical‐surgical	79 nurses 857 episodes	DO	8.31%	*55.8%	None
Tsegaye et al. (2020)	Ethiopia	Medication administration errors and associated factors among nurses	To assess medication administration errors and associated factors among nurses in referral hospitals in Amhara Ethiopia	5 Hospitals Units: Medical, surgical, gynaecology, emergency, paediatrics, “others”	DO: 42 nurses & 109 doses Self‐report questionnaire: 414 nurses	MAES: DO & Self‐report questionnaire Interruptions: Self‐report	DO: 89% Self‐report: 57.7%	Self‐report: 51.2%	Positive p < 0.05 AOR 3.37 (2.15–5.28)
Verweij et al. (2014)	Netherlands	Quiet please! drug round tabards: Are they effective and accepted? A mixed method study	To evaluate the effect of drug round tabards on (a) the frequency and type of interruptions, (b) the number and type of MAEs, and (c) the magnitude of the relation between interruptions and MAEs during the process of preparation, distribution, and administration of medication in hospital wards	1 Hospital Units: Neurology neurosurgery, dermatology, ophthalmology‐ENT	Nurses – number not provided 313 medication administrations	DO	Not Provided	Not Provided	Positive p < 0.05 R 10.4%
Volpe et al. (2014)	Brazil	Medication errors in a public hospital in Brazil	To describe the frequency, type and risk factors relating to errors in the preparation and administration of medications in patients admitted to a public hospital in Brasilia Federal District, Brazil	1 Hospital Unit: Medical	8 nurses 16 nurse technicians 484 medications	DO	85.9%	58.8%	Positive p = < 0.000 OR 3.92 (2.44–6.35)
Westbrook et al. (2010)	Australia	Association of interruptions with an increased risk and severity of medication administration errors	To test the hypothesis that interruptions increase the risk of MAEs in hospitals	2 Hospitals Units: Medical, surgical, geriatrics, respiratory medicine, renal/vascular medicine, orthopaedics, neurology	98 nurses 4271 medications 720 patients	DO	25%	53.1%	Positive p < 0.001 12.7 (5.3–20.5)
Wondmieneh et al. (2020)	Ethiopia	Medication administration errors and contributing factors among nurses: A cross sectional study in tertiary hospitals, Addis Ababa, Ethiopia	To assess the magnitude and contributing factors of MAEs among nurses in tertiary care hospitals, Addis Ababa	3 Hospitals Units: Medical, surgical, emergency	DO: 225 doses Self‐report questionnaire: 298 nurses	MAES: DO & Self‐report questionnaire Interruptions: Self‐report	Self‐report: 68.1%	Self‐report: 54%	Positive p < 0.05 AOR 2.42 (1.30–4.49)

Note: Numerals in parenthesis = 95% confidence intervals; * = calculation performed by the first author.

Abbreviations: AOR, adjusted odds ratio; DO, direct observation; EHR, electronic health record; Episodes, one or more medications administered to one patient; MAE, medication administration error; OR, odds ratio; PICU, paediatric intensive care unit; RR, relative risk.

3.3. Interruption Definitions

Most studies (n = 16; 73%) did not provide a definition of an interruption, defined the term interruption as interchangeable with distraction, or instead described a distraction. Of the six studies that provided a definition of an interruption, four defined interruption similarly with the use of terms like “halting,” “break,” and “ceased” that indicated that an interruption involves stopping an ongoing task. See Table 2 for interruption definitions.

TABLE 2.

Conceptual definitions of interruption.

Author (year)	Interruption conceptual definition
Berdot et al. (2021)	“…the halting of an ongoing task to respond to an external stimulus before completing the task” (p. 4)
Mekonen et al. (2020)	“Interruptions involve anything that disturbs an individual from the current task by averting one's attention. Noise (alarms, ringing phones, and other clinicians) and other people, or electronic devices (text messages, e‐mails, or other communication technologies) are considered sources of interruptions. 39” (p. 157 in Discussion section)
Mohammed et al. (2022)	“…an unexpected disturbance that destroys the continuity of care, diverts attention and increases the time required to perform activities, reduces work efficiency, and increases negligence and human errors…46–48” (p. 6 in Discussion section)
Thomas et al. (2017)	“A break in the performance of a human activity initiated by a source internal or external to the recipient with occurrence situated within the context of a setting or location resulting in the suspension of an initial task to perform an unplanned task with the assumption that the initial task be resumed.” (p. 311, Supplemental file)
Verweij et al. (2014)	“An interruption or a distraction was defined as an event initiated by another professional(s) or something else, and when a nurse interrupted him‐ or herself. In this study, the term interruption was used for distractions as well as for interruptions.” (p. 341)
Westbrook et al. (2010)	“…situations in which a nurse ceased the preparation or administration task in order to attend to an external stimulus.” (p. 684)

3.4. Medication Administration Error Definitions and Categories

Among the 12 studies that provided a conceptual definition of MAE, most (n = 8) used terms such as “deviation”, “difference”, or “mismatch” from the provider order/prescription in the definition. Four studies only provided a definition of a medication error. Six studies did not provide a conceptual definition of MAE. See Table 3.

TABLE 3.

Conceptual definitions of medication administration error.

Author (year)	Medication administration error conceptual definition
Alemu et al. (2017)	No definition provided
Assunção‐Costa et al. (2022)	“the administration of a dose of medication that differs from the prescription, as written in the medical record, or from standard hospital policy and procedures.” (p 3)
Berdot et al. (2012)	“…types of errors defined by the American Society of Health‐system Pharmacists (ASHP).” (p. 2)
Berdot et al. (2021)	“Administration error is defined as a deviation from the prescriber's medication order as written on the patient's chart (Hodkinson et al. 2020) and concerns the nurse administering the medication to the patient.” (p. 2)
Blignaut et al. (2017)	No definition provided
Bonafide et al. (2020)	No definition provided
Cottney and Innes (2015)	“a dose administered differently than as prescribed on the patient's medication chart” (p. 67)
Donaldson et al. (2014)	“a dose administered differently than ordered by the physician” (p. 59)
Feleke et al. (2015)	“any difference between what the patient received or was supposed to receive and what the prescriber intended in the original order.”. (p 2)
Gebrye et al. (2023)	“The National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) a medication error (ME) as any easily preventable error that may lead to or result in inappropriate drug utilisation or potentially harm a client's health if chosen to be taken by them or on the order of healthcare workers.” (p. 1)
Jessurun et al. (2022)	“An IAPE was defined as any error in the preparation of an intravenous admixture, i.e., a deviation from the medication order, a deviation from the local electronic admixture preparation instructions, or a deviation from the medication information sheet provided by the manufacturer in case local protocols were not available.” (p. 46)
Jessurun et al. (2023)	“…any error during the administration of medication by nursing staff, that is a deviation from medication orders used by the nursing staff to administer medication, a deviation from local medication administration protocols, or a deviation from the medication information sheets provided by the manufacturer if local protocols were not available.” (p. 210)
Mekonen et al. (2020)	“…a deviation from the prescriber's medication order as written on the patient's chart, manufacturers' administration instructions, or relevant institutional policies.” (p. 151)
Mohammed et al. (2022)	“…any preventable events that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the healthcare professionals, patients or consumers.” (p. 1)
Raja et al. (2019)	No definition provided
Tassew et al. (2022)	“Medication administration error: a mismatch between the medication given to the patient and the drug therapy prescribed by the doctor.” (p. 3)
Thomas et al. (2017)	“Medication administration errors are specific to the period of administration and will be classified under the following categories: None, Wrong Patient, Wrong Drug, Wrong dose, Wrong Route, Wrong time, Omission, Error in documentation.” (p. 311, Supplemental file)
Tsegaye et al. (2020)	“…any preventable act that contributes to the failure of proper medication use in the treatment process resulting in harm for the patient to the extent of disability and death.” (p. 621)
Verweij et al. (2014)	“… a breach of one of the seven rights of medication administration: correct patient, drug, dose, time, route, reason, and documentation…” (p. 341)
Volpe et al. (2014)	“The US National Coordinating Council For Medication Error Reporting And Prevention (NCCMERP) (2001) defines errors in the medication process as any event arising from improper use or lack of medication, which culminates in injury and harm to the user.” (p. 552)
Westbrook et al. (2010)	No definition provided
Wondmieneh et al. (2020)	No definition provided

All studies reported the MAE categories used, which varied across studies. All reported wrong dose errors, and most reported wrong patient (n = 13), wrong medication (n = 14), wrong route (n = 18), wrong time (n = 19), and omission (n = 14) errors. See Table 4.

TABLE 4.

Medication administration error categories.

Author (year)	Wrong patient	Wrong med/drug	Unauthorised/unordered drug	Wrong dose	Wrong route	Wrong time	Wrong doc	Wrong technique	Wrong form	Omission	Expired or deteriorated	Other
Alemu et al. (2017)	x	x		x	x	x	x
Assunção‐Costa et al. (2022)		x		x	x	x		x	x	x
Berdot et al. (2012)			x	x		x		x	x	x	x	x
Berdot et al. (2021)			x	x				x	x	x	x	x
Blignaut et al. (2017)	x	x		x	x	x				x
Bonafide et al. (2020)	x	x		x		x		x		x
Cottney and Innes (2015)	x	x		x	x	x		x	x	x		x
Donaldson et al. (2014)			x	x	x	x		x	x	x
Feleke et al. (2015)			x	x	x	x	x	x		x
Gebrye et al. (2023)	x			x	x	x		x		x		x
Jessurun et al. (2022)		x		x				x				x
Jessurun et al. (2023)			x	x	x			x	x	x	x	x
Mekonen et al. (2020)	x	x		x	x	x	x
Mohammed et al. (2022)	x	x		x	x	x	x					x
Raja et al. (2019)				x	x	x	x			x		x
Tassew et al. (2022)	x		x	x	x	x	x	x		x
Thomas et al. (2017)	x	x		x	x	x	x			x
Tsegaye et al. (2020)	x	x		x	x	x	x					x
Verweij et al. (2014)	x	x		x	x	x	x					x
Volpe et al. (2014)	x	x		x	x	x		x	x	x	x	x
Westbrook et al. (2010)		x	x	x	x	x			x			x
Wondmieneh et al. (2020)	x	x		x	x	x	x

Note: Medication administration error categories used by four or more studies are presented; med = medication; doc = documentation; “other” varied among studies.

3.5. Data Collection Methods

A majority of studies (n = 18; 82%) used an observational design to collect MAE data, with four of these also incorporating self‐report data from a questionnaire. Observational data collection of MAEs was often described as collected by trained observers who followed a nurse during MA, documented data, then compared the MA data with the provider order to identify errors. Data on interruptions were collected via direct observation (n = 13), self‐report (n = 7), retrospective review of tele‐communications data, or not described.

3.6. Associations Between Interruptions and MAEs

Of the 22 studies, most (n = 16; 73%) reported a statistically significant positive association between interruptions and MAEs, four found no association, one found an inverse association, and one study reported a positive association between a specific source of interruption–telephone calls–and MAEs but no association between incoming text messages and MAEs. Where odds ratios were provided, nurses were often reported as two to three times more likely to commit a medication error when interrupted, with Alemu et al. (2017) reporting over five times greater odds of making an error when interrupted. Westbrook et al. (Westbrook et al. 2010) described medication error rates by the number of interruptions using logistic regression. They found that medication administrations with one interruption had an error rate of 22.5% (95% CI, 20.3%–24.7%) and those with four or more interruptions had an error rate of 30.4% (95% CI, 22.0%–38.8%), concluding that each interruption was associated with a 12.7% increase in errors. In contrast, Blignaut et al. (2017) found that when interrupted, the odds of making a wrong‐dose error were 2.56 times less (OR = −2.56, p < 0.05).

4. Discussion

Studies across numerous countries have investigated interruptions to nurses and resulting errors, reflecting a global nature of this patient safety issue. In this review, studies were conducted in nine different countries and published between 2010 and 2023. Curiously, many studies were conducted in Ethiopia, with publication dates of between 2017 and 2023. The reason for the interest in this topic more so in Ethiopia, as compared to other countries, is unknown. However, it is possible that interest was influenced by the seminal 2010 Westbrook et al. (2010) study that provided strong evidence that interruptions to nurses were associated with MAEs, and the considerable amount of research on MAEs in Ethiopia published between 2010 and 2018 (Bifftu and Mekonnen 2020).

Twenty‐two studies met the review inclusion criteria. Half were published in the past 5 years, indicating a recent increase of research in this area. A majority of the included studies reported a statistically significant positive association between interruptions and MAEs, which is consistent with studies from other disciplines (Chen et al. 2024; Westbrook et al. ²⁰¹⁸). There were conceptual and methodological limitations among the studies, as Biron et al. (2009) also reported in their 2009 review on the same topic.

4.1. Definitions and Categories

Many studies in this review did not provide a definition of interruption, which seems likely to have been due to the aim of the specific study. As can be seen in Table 1, many studies had an aim of investigating broad “factors” or “contributors” to MAEs among nurses in hospital settings. Among the six studies that provided a definition of interruption, three had an aim that included investigating interruptions during the MA process. Those three studies provided comparable definitions in regards to the requisite of an interruption involving stopping of a task, which aligns with the Brixey et al. (2007) definition. One study defined interruption as interchangeable with distraction, and another defined a distraction as an interruption. While it is not uncommon for the term distraction to be used synonymously with interruption (Brixey et al. 2007; D'Esmond ²⁰¹⁶), these are distinct terms that should not be used interchangeably.

Many studies provided a conceptual definition of MAE, yet some only provided a definition of medication error. An MAE is a type of medication error; however, medication errors can occur across the medication use process, which encompasses prescribing, transcribing, dispensing, administering, and monitoring (Tariq et al. 2024). MAEs are specific to the administration process.

All studies reported the categories of MAEs used in data collection. The MAE categories were diverse, as can be seen in Table 4, with most studies including the traditional “rights of MA safety” in their data collection. Dose and documentation errors are common MAEs made among nurses (Bifftu and Mekonnen 2020; Mulac et al. ²⁰²¹), thus it was not surprising that all studies collected data on dose errors; however, only 10 studies collected data on documentation errors. It is important to include documentation errors when collecting data on MAEs. Documentation errors can lead to dose (extra and missed doses) errors, as well as other types of MAEs, as found in a recent study by Roberts et al. (2023). In addition, documentation MAEs are prevalent among nursing students (Fusco et al. 2021; Schroers et al. ^{2022, 2025}), highlighting an area in need of reinforcement when teaching MA skills.

4.2. Data Collection Methods

A majority of the studies used direct observation to collect data. Direct observation is the preferred method to collect behavioural (e.g., interruptions) and MAE data due to its improved accuracy over self‐report (Allan and Barker 1990; Henry Basil et al. ²⁰²⁵; Flanagan and Beck ²⁰²⁵). Self‐report data raises concerns regarding validity and accuracy as MAEs are underreported, partly because nurses are not always aware that an MAE occurred (Henry Basil et al. 2025). Observational data, however, also has its weaknesses. Observational data collection can introduce the issue of reactivity. Reactivity, also referred to as the Hawthorne effect, occurs when people alter their behaviours due to awareness of being observed (Flanagan and Beck 2025).

4.3. Associations Between Interruptions and MAEs

The majority of studies in this review found a positive association between interruptions and MAEs. The 2009 review by Biron et al. (2009) reported only one study (Scott‐Cawiezell et al. ²⁰⁰⁷) that evaluated an association, which was positive when wrong time errors were excluded. The Scott‐Cawiezell et al. (2007) study was conducted in a long‐term care facility; thus, it did not meet the inclusion criteria for this review. Of the studies in this review that did not find an association between interruptions and MAEs, or found an inverse association, many of the authors provided potential reasons for their findings.

Bonafide et al. (2020) reported a positive association between incoming telephone calls and MAEs but no association with incoming text messages. The authors surmised that the text message interruptions may not have been associated with errors due to their frequency (they preceded nearly half of all MA in their study) and nurses' adaptation to them, or due to the ability to easily delay responding to text messages. It is important to note that the authors examined retrospective data that assumed the calls and text alerts caused an interruption. Some nurses may have, as Bonafide et al. stated, delayed responding to the call or text alert until their MA was completed. Thus, what are termed interruptions in the Bonafide et al. study may not have, in accordance with the Brixey et al. (2007) definition of an interruption, actually caused a nurse to stop mid‐task to do something else.

Jessurun et al. (2023) explained that many of the MAEs in their study appeared to be routine deviations from practice, such as administration of intravenous medication too quickly, as opposed to unintentional mistakes, thus could have been a reason for the lack of an association. Thomas et al. (2017) offered three possible reasons for the lack of an association in their study: (1) there was no relationship, (2) other variables mitigated MAEs, or (3) lack of sufficient power in their study.

Only one study (Blignaut et al. 2017) reported an inverse (negative) relationship between interruptions and wrong‐dose MAEs. Blignaut et al. (2017) discussed that “medication administrators” often rechecked the prescription after an interruption occurred, and speculated that this may have led to the decrease in wrong‐dose errors. Blignaut et al.'s study included student nurses, enrolled nurses, and registered nurses in their sample and did not differentiate the occurrence of MAEs nor who performed a recheck of medication after an interruption among the different populations. Prior research has found that student nurses often recheck medication after being interrupted (Schroers et al. ^2021b), thus it is possible that Blignaut et al. may have observed the rechecking of medication primarily among the students in their study. The findings of an inverse association between interruptions and MAEs may or may not have applied to practicing nurses.

4.4. Recommendations

Due to the diversity of conceptual definitions as well as data collection methods across the included studies in this review, there are several recommendations for future work. First, large studies are needed that use common definitions of interruption and MAE. For instance, distinguishing a distraction from an interruption is important when defining interruption. In addition, variability of the types of errors that can be categorised as an MAE, as seen in Table 4, should be standardised across studies. The traditional rights are accepted globally as a foundation of MA safety (Hanson and Haddad 2023) and can be a starting point to operationalise MAEs.

Observation of MAEs and interruptions in a clinical setting is likely the strongest approach for investigating associations between the two; however, valid and reliable methods of data collection are essential. Common weaknesses of the observational data collection method include the risk of participant reactivity, observer bias, and errors (Flanagan and Beck 2025). Efforts can be made to minimise reactivity, such as using disguised observation (Flanagan and Beck 2025), or observers can undertake practice sessions with the study participants so that participants gain comfort with the observers (Westbrook et al. 2010). Observer bias and errors can be addressed by having more than one independent observer and calculating intra‐and inter‐rater reliability. Lastly, clear conceptual and operational definitions are imperative to guide data collection. Without clear definitions, the validity and reliability of the research findings are unknown.

Interventions that mitigate the negative impacts caused by interruptions are needed. Some healthcare settings have implemented quiet areas, often referred to as “no interruption zones” (NIZs), to prepare medications, visual displays such as “no talking” signage, or nurses wearing “do not interrupt vests” during MA. However, strong support is lacking for these methods (Berdot et al. 2021; Raban and Westbrook ²⁰¹⁴). The Institute for Safe Medication Practices (ISMP) (Institute for Safe Medication Practices (ISMP) ²⁰²³) has made many organisational‐level recommendations to improve medication safety that include identifying critical tasks that should be free from interruptions followed by educating staff on the dangers of interruptions during critical tasks (e.g., MA). However, the ISMP also acknowledges that some interruptions during the medication use process are necessary.

Due to the necessity of some interruptions and the inability to prevent all interruptions (e.g., phones and alarms), interruption management strategies should be taught to nursing students and nurses. Interruption management refers to how an attempted interruption is handled. Evidence from human factors (Falkland et al. 2020) and medicine (Falkland et al. 2022) suggests that interruption management strategies, such as the use of associative cues (e.g., forming a placeholder before giving attention to the interruption source), can improve the safety of interrupted tasks. While limited, some studies have investigated teaching specific interruption management strategies to nurses and nursing students and show promising results (Henneman et al. 2018; Vital and Nathanson ²⁰²³; Schroers et al. ²⁰²⁴).

4.5. Strengths and Limitations

The primary strengths of this review include following rigorous methodology with a multi‐person team and updating prior knowledge on an important topic. Scoping reviews, however, have inherent limitations. A critical appraisal of the quality and risk of bias of the included studies was not performed, as this is not conducted in scoping reviews (Peters et al. 2024), which limited the ability to assess the reliability of the evidence. As a result, limited guidance can be provided for evidence‐based decision‐making in clinical or policy contexts. In addition, the aim of this review was limited to English language and hospital sites, which may have excluded studies that could have added to the body of evidence. Lastly, studies with non‐significant results of associations between MAEs and interruptions may not have been published, which could have biased the overall results.

5. Conclusion

This review addressed a common and costly global patient safety concern, and highlighted a growing interest in the link between interruptions and MAEs. A majority of the included studies reported statistically positive associations between interruptions and MAEs; however, there were conceptual and methodological limitations among many of the studies. Continued investigation using standard definitions of interruption and MAE, as well as consistent methods, is needed to strengthen research in this area. The findings from this review can be used to inform future primary research as well as potential systematic reviews, and the development of targeted interventions to enhance medication safety and nursing practice in hospital environments worldwide.

Author Contributions

All authors have agreed on the final version and meet at least one of the following criteria: (1) substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; (2) drafting the article or revising it critically for important intellectual content.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A.

Database and Search Strategy Details

Web of Science Core Collection (Science Citation Index Expanded, Social Sciences Citation Index, Arts and Humanities Citation Index, & Emerging Sources Citation Index [2020]), Clarivate, 1980 – October 28, 2024.

TS = ((interrupt*) OR (disrupt*) OR (disturb*) OR (diversion) OR (diver) OR (distract*)) AND TS = ((medic* OR drug OR pharmaceutical) NEAR/1 (error)) AND TS = (nurs*) and English (Languages).

312 results

Scopus, Elsevier, 1788 through October 28, 2024.

TITLE‐ABS‐KEY ((interrupt*) OR (disrupt*) OR (disturb*) OR (diversion) OR (diver) OR (distract*)) AND TITLE‐ABS‐KEY ((drug OR medication OR pharmaceutical) W/1 (error)) AND TITLE‐ABS‐KEY (nurs*) AND (LIMIT‐TO (LANGUAGE, “English”)).

343 documents

Ovid MEDLINE(R), Wolters Kluwer, (Epub Ahead of Print, In‐Process, In‐Data‐Review & Other Non‐Indexed Citations, Daily and Versions), 1946 to October 25, 2024.

1 (exp Attention/or interrupt*.ab,ti. or disrupt*.ab,ti. or disturb*.ab,ti. or diversion.ab,ti. or diver.ab,ti. or distract*.ab,ti.) and (medication error/or Pharmaceutical Preparations/ad or ((medic* or pharmaceutical or drug) adj1 error).ab,ti.) and (exp Nurses/or exp. Nursing/or nurs*.ab,ti.) 249

2 limit 1 to english language 238

EMBASE, Elsevier, 1947 through October 28, 2024.

((‘attention’/de OR ‘alertness’/de OR ‘distractibility’/de OR (interrupt*:ab,ti) OR (disrupt*:ab,ti) OR (disturb*:ab,ti) OR (diversion:ab,ti) OR (diver:ab,ti) OR (distract*:ab,ti)) AND ((‘drug therapy’/exp/dd_ad) OR (‘medication error’/syn)) AND (‘nurse’/syn OR ‘nursing’/syn)) AND [english]/lim.

491 results

CINAHL, EBSCOHost, 1937 through October 28, 2024.

((MH Attention+) OR (TI interrupt*) OR (TI disrupt*) OR (TI disturb*) OR (TI diversion) OR (TI divert*) OR (TI distract*) OR (AB interrupt*) OR (AB disrupt*) OR (AB disturb*) OR (AB diversion) OR (AB divert*) OR (AB distract*)) AND ((MH “Medication Errors+”) OR (TI medic* N1 error) OR (AB medic* N1 error)) AND ((MH Nurses+) OR (MH Nursing+) OR (TI nurs*) OR (AB nurs*))

Limiters: English Language, Peer Reviewed Journals.

285 results

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.