Abstract
Background
Medication administration errors (MAEs) occur frequently in hospitals and may compromise patient safety. Preventive strategies are needed to reduce the risk of MAEs.
Objective
The primary aim of this study was to assess the effect of central automated unit dose dispensing with barcode-assisted medication administration on the prevalence of MAEs. Secondary aims were to assess the effect on the type and potential severity of MAEs. Furthermore, compliance with procedures regarding scanning of patient and medication barcodes and nursing staff satisfaction with the medication administration system were assessed.
Methods
We performed a prospective uncontrolled before-and-after study in six clinical wards in a Dutch university hospital from 2018 to 2020. MAE data were collected by observation. The primary outcome was the proportion of medication administrations with one or more MAEs. Secondary outcomes were the type and potential severity of MAEs, rates of compliance with patient identification and signing of administered medication by scanning and nursing staff satisfaction with the medication administration system. Multivariable mixed-effects logistic regression analyses were used for the primary outcome to adjust for confounding and for clustering on nurse and patient level.
Results
One or more MAEs occurred in 291 of 1490 administrations (19.5%) pre-intervention and in 258 of 1630 administrations (15.8%) post-intervention (adjusted odds ratio 0.70, 95% confidence interval 0.51–0.96). The rate of omission fell from 4.6% to 2.0% and of wrong dose from 3.8% to 2.1%, whereas rates of other MAE types were similar. The rate of potentially harmful MAEs fell from 3.0% (n = 44) to 0.3% (n = 5). The rates of compliance with scanning of patient and medication barcode post-intervention were 13.6% and 55.9%, respectively.
The median overall satisfaction score of the nurses with the medication administration system on a 100-point scale was 70 (interquartile range 63–75, n = 193) pre-intervention and 70 (interquartile range 60–78, n = 145) post-intervention (P = 0.626, Mann–Whitney U test).
Conclusion
The implementation of central automated unit dose dispensing with barcode-assisted medication administration was associated with a lower probability of MAEs, including potentially harmful errors, but more compliance with scanning procedures is needed. Nurses were moderately satisfied with the medication administration system, both before and after implementation. In conclusion, despite low compliance with scanning procedures, this study shows that this intervention contributes to the improvement of medication safety in hospitals.
Keywords: medication errors, patient safety, medication administration error, barcode, medication systems, hospital
Introduction
A recent meta-analysis has shown that at least 1 in 20 patients is affected by preventable patient harm in healthcare settings and that approximately 12% of preventable harm causes permanent disability or patient death [1]. Medication-related incidents account for the highest proportion of preventable harm [1]. Thus, although drug therapy remains a cornerstone for the treatment of many diseases, possible process difficulties may compromise patient safety. Medication administration errors (MAEs) occur in about 10%, ranging from approximately 5% to 20% of medication administrations in hospitals [2, 3]. Many interventions to prevent these errors have been implemented [4–9]. However, the remaining high MAE rates warrant additional system defences.
Promising interventions include automated unit dose dispensing (ADD) and barcode-assisted medication administration (BCMA) [4–6, 9–13]. Combining central ADD with BCMA may have a synergistic effect on medication errors by facilitating a closed-loop system, when combined with an electronic medical record (EMR) or a computerized physician order entry (CPOE) system. Studies on central ADD have shown relative reductions in MAE rates of approximately 50–60% [14, 15], while most studies on the effect of BCMA have shown relative reductions of 30–60% [9, 12]. So far, only two studies, performed in acute medical or haematological wards, have examined the combined effect of central ADD with BCMA on MAEs, with CPOE already in place [16, 17]. These studies have shown relative MAE reduction values of 62% [16] and 94% [17], respectively.
As only limited evidence is available, we performed a before-and-after study aimed to assess the effect of central ADD with BCMA on the prevalence, type and severity of MAEs; compliance with patient identification and signing of administered medication by scanning; and nursing staff satisfaction with the medication administration system.
Methods
Study design
We performed a prospective before-and-after study in Erasmus MC, University Medical Center Rotterdam in Rotterdam, the Netherlands. The local Medical Ethics Review Committee waived approval for this study (reference number MEC-2018-1532) in accordance with the Dutch Medical Research Involving Human Subjects Act. Nursing staff gave verbal informed consent to participate in this study. Data were handled confidentially according to the Dutch General Data Protection Regulation. The Strengthening the Reporting of Observational Studies in Epidemiology Statement guideline was used for reporting (Supplementary File 1) [18].
Study setting
The study was performed in six clinical wards (internal oncology, neurology, pulmonary medicine, haematology, neurosurgery and hepatopancreatobiliary surgery). Pre-intervention, an EMR system, a CPOE system and an electronic medication administration record (eMAR) system were in place using HiX® software (ChipSoft B.V., Amsterdam, the Netherlands). An additional CPOE system, Practocol® (Practocol B.V., Rotterdam, the Netherlands), was used for medication prescription and administration in chemotherapy protocols.
The intervention consisted of central ADD (in the hospital pharmacy) and BCMA, which were integrated in HiX®. ADD and BCMA were gradually implemented in the entire hospital, starting mid-2019. Detailed setting characteristics are shown in Table 1.
Table 1.
Setting characteristics before and after the implementation of central automated unit dose dispensing and barcode-assisted medication administration
| Characteristics | Pre-intervention | Post-intervention |
|---|---|---|
| EMR systema | HiX® | HiX® |
| CPOE systema | HiX®, Practocol® | HiX® |
| Central ADDa | Not applicable |
|
| Medication supply |
|
|
| Medication stocking | Ward-based stock (tailored): emergency medication, commonly used medication and patient-specific medication (for several days) | As pre-intervention, but a smaller range of commonly used medication |
| Medication cart fillingb | By nurses, generally for 24 h |
|
| BCMA | ||
| Patient identification by scanning | Possible, but not standard practice | Yes |
| Medication identification by scanning | No | Yes |
| BCMA features of medication identificationa | Not applicable |
|
| Workstations | On mobile medication carts with scanners | On mobile medication carts with scanners |
| Use of patient’s own medication or self-administration | Not standard practice, only under strict protocols | Not standard practice, only under strict protocols |
| Signing of administered medicationa | In eMAR HiX®: manually by nursing staff | In eMAR HiX®: manually or by scanning medication by nursing staff |
HiX® version 6.1 (ChipSoft B.V.; Amsterdam, the Netherlands); Practocol® version 2.0.9.3 and 2.1.5.1 (Practocol B.V.; Rotterdam, the Netherlands) for medication in chemotherapy protocols (e.g. dexamethasone); Pillpick® (Swisslog; Buchs, Switzerland).
Procedures differed between wards because central filling was hampered by limited human resources.
Inclusion and exclusion criteria
Medication administrations to patients performed by nursing staff were included. Excluded were medication administrations that were terminated during the observation, medication administrations with the medication name missing on the data collection form or medication administrations that were declined by patients for other reasons than being erroneous.
Data collection
Data were collected from October 2018 to February 2019 pre-intervention and from September 2020 to December 2020 post-intervention. All post-intervention measurements took place at least 3 months after complete implementation. Data on medication administration were collected using the disguised observation method [19–21], meaning that nursing staff did not know the exact purpose of the observations. Nursing staff were informed that the purpose of the observations was to study the medication process to minimize the effect of the observer on the observed (Hawthorne effect). Eight observers, one pharmacist and seven students with a pharmaceutical or medical background, that had completed a training programme of several days shadowed nursing staff and recorded details of every medication administration on data collection forms. Observers used the convenience sampling method to select nurses to be observed when present in the ward and asked them for verbal consent before initiating an observation. Observers were instructed to intervene if they perceived a serious error to be occurring (wrong patient, wrong drug or a dose deviation of at least 20%). Data collected during observation were compared with medication prescriptions and protocols after the observation and not during observation, which is in accordance with the gold standard of medication error detection methods [20]. Initially, one pharmacist (J.J.) and hospital pharmacist (N.H.) independently reviewed 200 data collection forms to assess the presence, type and potential severity of MAEs. For this sample, the Cohen’s kappa for the presence of one or more MAE was calculated at 0.72, which indicates substantial interrater reliability. Therefore, the remaining data collection forms were reviewed by one reviewer (J.J.) to determine the presence of an MAE. The type and potential severity of each MAE were classified by J.J. and N.H. based on the literature and experience, while disagreement was resolved by consensus.
Nurses were asked to fill in a questionnaire to collect data on their background characteristics (i.e. gender, age, degree type, educational level, experience and employment type) after the completion of observation rounds in a particular ward. For each observed medication administration, J.J. assessed the day of the week and whether the medication was signed as administered in the eMAR (signed as administered in HiX®: yes, no; scanned medication barcode according to HiX®: yes, no). Patient-characteristic data (i.e. gender, birth date and number of prescribed medications) were collected by J.J. from Practocol® and HiX®.
Collected data of the medication administrations were entered in OpenClinica® version 2.1 (OpenClinica LLC, Waltham, Massachusetts, USA).
Nursing staff satisfaction questionnaire
The Medication Administration System—Nurses Assessment of Satisfaction (MAS-NAS) scale [22] was translated to Dutch. This questionnaire was presented twice to the nursing staff. Pre-intervention, MAS-NAS measured the satisfaction with the medication administration system without ADD-BCMA and post-intervention, it measured the satisfaction of the system with ADD-BCMA. The questionnaire consisted of a question concerning overall satisfaction, 15 statements (on efficacy, safety and access) and an open-ended question. Nurses indicated overall satisfaction on a visual analogue scale from 0 (dissatisfied) to 100 (satisfied). Responses on the 15 statements were given on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree). The open-ended question invited additional remarks. Trained students visited the clinical wards and presented the questionnaire on an iPad® (Apple Inc.; Cupertino, California, USA) to the nursing staff present.
Definition and classification of MAE
An MAE was defined as a deviation from medication orders used by the nursing staff to administer medication; a deviation from general medication administration protocols; and if local protocols were not available, a deviation from the medication information sheet provided by the manufacturer. Timing errors were not within the scope of the study because they are generally considered not to be clinically relevant [2, 3]. Intravenous admixture preparation errors, such as wrong solvents and hygiene errors, as well as procedural errors, such as labelling and documentation errors, were not considered as MAEs.
MAEs were classified into the following types [19, 23]: wrong administration technique (too fast administration, incompatibility of parenteral medication and other), wrong medication handling (e.g. not shaking suspensions, wrong infusion fluid/infusion fluid volume or combining medication solutions), omission, wrong dose, unordered drug, wrong dosage form, wrong route of administration, expired medication and other. Omissions were defined as medication not administered during the day of observation. This was the case if all of the following conditions were met: (i) the observer observed the entire medication round for the patient (e.g. of 8 a.m.), (ii) the nurse did not administer the medication and (iii) the medication was not signed as administered in the eMAR by another nurse or outside the observed period. For wrong dose, too fast administration and wrong infusion fluid volume, a deviation of more than 10% were marked incorrect because a maximum of 10% deviation from the declared dose of pharmaceutical products within the shelf life is widely accepted (e.g. by manufacturing guidelines). For the classification of the potential severity of MAEs, the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) severity index was used, which ranges from category A (circumstances that have the capacity to cause error) to I (death) [24]. Errors classified in NCC MERP class E or higher were considered potentially harmful.
Outcomes
The primary outcome was the proportion of medication administrations with one or more MAEs using the total opportunities for error as the denominator (i.e. the number of observed administrations plus the identified omissions). Secondary outcomes were the type and potential severity of MAEs, rates of compliance with patient identification by scanning and electronic signing of administered medication by scanning, and nursing staff satisfaction with the medication administration system.
Sample size calculation
To identify a reduction in MAEs from 10% to 5% [2, 3, 10, 12, 25], a sample size of 868 medication administrations both pre-intervention and post-intervention would be required, based on a two-sided chi-square test using α of 0.05 and β of 0.2. The same dataset was planned to be used for a study on potential risk factors for MAEs, for which sample sizes of 2000 pre-intervention and 4000 post-intervention were calculated. Therefore, this larger number of medication administrations was pursued to be included. Ninety-six observation rounds in each measurement period were planned beforehand, distributed over different time windows and days of the week.
Data analysis
MAE rates were compared using univariable and multivariable mixed-effects logistic regression analysis (generalized linear mixed models). The dependent variable in these models was a dichotomous variable indicating whether a medication administration had one or more MAEs (yes, no); and the independent variables were setting (pre-intervention and post-intervention) and the covariates pharmaceutical form (oral solid, oral liquid, infusion, injection, nebulizing solution, ointment, suppository/enema and miscellaneous), time window of administration (7 a.m.–10 a.m., 10 a.m.–2 p.m., 2 p.m.–6 p.m. and 6 p.m.–7 a.m.), clinical ward type, nursing degree type (nurse, specialised nurse and other) and nursing educational level (secondary vocational education, higher professional education and other). A model with and without nurse characteristics was presented because data on nurse characteristics were only available for 189 of 359 observed nurses (52.6%). We included two random effects, i.e. a random intercept by staff member and a random intercept by patient to account for repeated measurements and within-subject correlations. Complete case analyses were performed. The results of the mixed-effects logistic regression analyses are reported as adjusted odds ratios (ORadj) with 95% confidence intervals (95% CIs).
The Mann–Whitney U test was used to compare the overall nursing staff satisfaction scores, which were not normally distributed. Descriptive statistics were used for other secondary outcomes. A two-tailed P value <0.05 was considered statistically significant. Data analyses were performed with R Statistics® version 4.0.2. (The R Foundation, Vienna, Austria) for the mixed-effects logistic regression analyses and with SPSS Statistics® version 25 (IBM Corporation, Armonk, New York, USA) for other analyses.
Results
A total of 3191 medication administrations administered were observed. Seventy-one observations were excluded because patients declined administration. Observers intervened in six administrations pre-intervention (omission, n = 1; wrong dose, n = 2; unordered drug [wrong patient], n = 3) and in one administration post-intervention (omission, n = 1). The characteristics of the included 3120 medication administrations are shown in Table 2.
Table 2.
Characteristics of included medication administrations before and after implementation of central automated unit dose dispensing and barcode-assisted medication administration
| Characteristics | Pre-intervention N = 1490 medication administrations | Post-intervention N = 1630 medication administrations |
|---|---|---|
| Patient characteristics | ||
| Patients, n | 245 | 253 |
| Male, n (% of patients) | 145 (59.2) | 128 (50.6) |
| Age, median (IQR) | 62 (50–70) | 61 (47–69) |
| Prescribed medications per day, median (IQR) | 13 (10–16) | 13 (10–17) |
| Medication characteristics | ||
| Pharmaceutical forma, n (% of administrations) | ||
| Oral solid | 936 (62.8) | 1021 (62.6) |
| Oral liquid | 66 (4.4) | 87 (5.3) |
| Infusion | 252 (16.9) | 239 (14.7) |
| Injection | 136 (9.1) | 202 (12.4) |
| Nebulizing solution | 47 (3.2) | 35 (2.1) |
| Ointment | 10 (0.7) | 11 (0.7) |
| Suppository/enema | 17 (1.1) | 8 (0.5) |
| Miscellaneousb | 25 (1.7) | 26 (1.6) |
| Ward characteristics | ||
| Clinical ward, n (% of administrations) | ||
| Internal oncology | 252 (16.9) | 285 (17.5) |
| Neurology | 196 (13.2) | 218 (13.4) |
| Pulmonary medicine | 375 (25.2) | 278 (17.1) |
| Haematology | 234 (15.7) | 215 (13.2) |
| Neurosurgery | 281 (18.9) | 351 (21.5) |
| Hepatopancreatobiliary surgery | 152 (10.2) | 283 (17.4) |
| Time characteristics | ||
| Day of the week, n (% of administrations) | ||
| Weekday | 985 (66.1) | 1097 (67.3) |
| Weekend | 505 (33.9) | 533 (32.7) |
| Time of administration, n (% of administrations) | ||
| 7 a.m.–10 a.m. | 454 (30.5) | 497 (30.5) |
| 10 a.m.–2 p.m. | 236 (15.8) | 248 (15.2) |
| 2 p.m.–6 p.m. | 273 (18.3) | 335 (20.6) |
| 6 p.m.–7 a.m. | 527 (35.4) | 550 (33.7) |
| Workload characteristics | ||
| Patient-to-nurse ratioc, median (IQR) | 5 (4–7) | 5 (4–7) |
| Interruptions | ||
| Yes | 96 (6.4) | 70 (4.3) |
| Staff characteristics | ||
| Observed staff members, n | 179 | 180 |
| Staff members, personal data available, n (% of staff) | 107 (59.8) | 82 (45.6) |
| Male, n (% of staff) | 7 (6.5) | 6 (7.3) |
| Aged, median (IQR) | 30 (25–50) | 27 (23–35) |
| Degree type, n (% of staff) | ||
| Nurse | 68 (63.6) | 53 (64.6) |
| Specialized nurse | 27 (25.2) | 18 (22.0) |
| Student nurse | 10 (9.3) | 10 (12.2) |
| Other | 2 (1.9) | 1 (1.2) |
| Educational levele, n (% of staff) | ||
| Secondary vocational education | 46 (43.4) | 40 (48.8) |
| Higher professional education | 49 (46.2) | 42 (51.2) |
| University education | 1 (0.9) | 0 |
| Other | 10 (9.4) | 0 |
| Experience since nursing diploma, n (% of staff) | ||
| 0–1 year | 18 (16.8) | 11 (13.4) |
| 1–5 years | 20 (18.7) | 33 (40.2) |
| More than 5 years | 60 (56.1) | 27 (32.9) |
| Not applicable | 11 (10.3) | 11 (13.4) |
| Experience in healthcare settingse, n (% of staff) | ||
| 0–1 year | 1 (0.9) | 3 (3.7) |
| 1–5 years | 34 (32.1) | 32 (39.0) |
| More than 5 years | 71 (67.0) | 47 (57.3) |
| Employment typee, n (% of staff) | ||
| Non-temporary | 97 (91.5) | 74 (90.2) |
| Temporary | 6 (5.7) | 8 (9.8) |
| Other | 3 (2.8) | 0 |
IQR, interquartile range.
Missing, n = 1 (pre-intervention), n = 1 (post-intervention). bMiscellaneous: inhalers, patches, eye drops/ointments, intestinal gel. cMissing, n = 61 (pre-intervention), n = 128 (post-intervention). dMissing, n = 7 (post-intervention). eMissing, n = 1 (pre-intervention).
Prevalence, type and potential severity of MAEs
One or more MAEs were identified in 291 of 1490 administrations (19.5%) pre-intervention and in 258 of 1630 administrations (15.8%) post-intervention (ORadj 0.70, 95% CI 0.51–0.96; Table 3). A total of 316 MAEs and 272 MAEs occurred, respectively, before and after implementation of the intervention.
Table 3.
Effect of central automated unit dose dispensing and barcode-assisted medication administration on medication administration errors (MAEs)
| Mixed-effects logistic regression analysisa | ||||
|---|---|---|---|---|
| MAE prevalence | Univariable analysis N = 3097 | Multivariable analysisb N = 3095 | Multivariable analysis with nurse characteristicsc N = 1561 | |
| Measurement period | n/N (%) | OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) |
| Pre-intervention | 291/1490 (19.5) | Reference | Reference | Reference |
| Post-intervention | 258/1630 (15.8) | 0.76 (0.55–1.04) | 0.70 (0.51–0.96)* | 0.57 (0.37–0.88)* |
CI, confidence interval; OR, odds ratio.
Mixed-effects logistic regression analysis was used to account for within-subject correlations due to repeated measurements by staff member and patient.
ORs have been adjusted for pharmaceutical form, time window and clinical ward type.
ORs have been adjusted for nurse educational level, nurse degree type, pharmaceutical form, time window and clinical ward type.
Statistically significant (P < 0.05).
The type and potential severity of identified MAEs are shown in Table 4. After the intervention, the rate of omission fell from 4.6% to 2.0% and of wrong dose from 3.8% to 2.1%, while rates of other MAE types were similar. The prevalence of potentially harmful MAEs fell from 3.0% (n = 44) to 0.3% (n = 5). Examples of the potential severity of MAEs are described in Supplementary File 1.
Table 4.
Type and potential severity of medication administration errors (MAEs) before and after implementation of central automated unit dose dispensing and barcode-assisted medication administration
| Pre-intervention | Post-intervention | |
|---|---|---|
| Included medication administrations, n | 1490 | 1630 |
| MAEs, n | 316 | 272 |
| Type of MAE, n (% of administrations) | ||
| Wrong administration technique | 78 | 99 |
| Too fast administration | 51 (3.4) | 83 (5.1) |
| Incompatibility of parenteral medication | 21 (1.4) | 3 (0.2) |
| Other | 6 (0.4) | 13 (0.8) |
| Wrong medication handling | 57 (3.8) | 59 (3.6) |
| Omission | 68 (4.6) | 33 (2.0) |
| Wrong dose | 57 (3.8) | 35 (2.1) |
| Unordered drug | 25 (1.7) | 26 (1.6) |
| Wrong dosage form | 25 (1.7) | 20 (1.2) |
| Wrong route of administration | 5 (0.3) | 0 |
| Expired medication | 0 | 0 |
| Other | 1 (0.1) | 0 |
| Potential severity of MAEsa, n (% of administrations) | ||
| Error, no harm | ||
| C | 173 (11.6) | 209 (12.8) |
| D | 99 (6.6) | 58 (3.6) |
| Error, harm | ||
| E | 35 (2.3) | 5 (0.3) |
| F | 7 (0.5) | 0 |
| H | 2 (0.1) | 0 |
NCC MERP classification [24]: no error (category A); error, no harm (category B to D); error, harm (category E to H); and error, death (category I). C: an error occurred that reached the patient but did not cause patient harm; D: an error occurred that reached the patient and required monitoring to confirm that it resulted in no harm to the patient and/or required intervention to preclude harm; E: an error occurred that may have contributed to or resulted in temporary harm to the patient and required intervention; F: an error occurred that may have contributed to or resulted in temporary harm to the patient and required initial or prolonged hospitalization; H: an error occurred that required intervention necessary to sustain life.
Compliance with patient identification and signing of administered medication
Table 5 shows the rates of compliance with procedures regarding patient identification and electronic signing of administered medication. Barcode scanning for patient identification was performed in 124 of 1490 administrations (8.3%) pre-intervention and in 221 of 1630 administrations (13.6%) post-intervention. Electronic signing of administered medication was performed in 1418 administrations (95.2%) pre-intervention and in 1575 administrations (96.6%) post-intervention. Post-intervention, medication barcodes were scanned in 911 administrations (55.9%).
Table 5.
Rates of compliance with patient identification and electronic signing of administered medication before and after the implementation of central automated unit dose dispensing and barcode-assisted medication administration
| Pre-intervention N = 1490 | Post-intervention N = 1630 | |
|---|---|---|
| Procedures | Rate, n (% of N) | Rate, n (% of N) |
| Patient identification | ||
| By barcode scanning | ||
| Yes | 124 (8.3) | 221 (13.6) |
| No | 1251 (84.0) | 1300 (79.8) |
| Unknown | 115 (7.7) | 109 (6.7) |
| Signing of administered medication | ||
| Signed in eMAR | ||
| Yes | 1418 (95.2) | 1575 (96.6) |
| No | 71 (4.8) | 55 (3.4) |
| Unknown | 1 (0.1) | 0 |
| By barcode scanning | ||
| Yes | Not applicable | 911 (55.9) |
| No | Not applicable | 664 (40.7) |
eMAR, electronic medication administration record.
Post hoc analysis: scanned versus non-scanned medication (post-intervention)
A post hoc analysis showed that MAE rates were 13.0% (n/N = 118/911) for scanned medication and 19.5% (n/N = 140/719) for non-scanned medication. The rates of the following MAE types were lower for scanned medication compared to non-scanned medication: omission (n = 3 versus n = 30; 0.3% versus 4.2%), unordered drug (n = 6 versus n = 20; 0.7% versus 2.8%), wrong dosage form (n = 3 versus n = 17; 0.3% versus 2.4%) and wrong dose (n = 14 versus n = 21; 1.5% versus 2.9%). The rates of the following MAE types were higher or similar for scanned medication compared to non-scanned medication: wrong administration technique (n = 64 versus n = 35; 7.0% versus 4.9%) and wrong medication handling (n = 32 versus n = 27; 3.5% versus 3.8%). All potentially harmful MAEs (n = 5) occurred with non-scanned medication.
Nursing staff satisfaction with the medication administration system
The median overall satisfaction score with the medication administration system on a 100-point scale was 70 (interquartile range 63–75, n = 193) pre-intervention and 70 (interquartile range 60–78, n = 145) post-intervention (P = 0.626). Median satisfaction scores on a 6-point scale with regard to 15 statements were moderate (score 3) to high (score 5) (Supplementary File 2). Nurses were moderately satisfied with topics regarding safety related to MAEs, timeliness of acute medication availability, facilitation of communication, information in case of adverse reactions, information on medication actions and adverse effects (post-intervention) and the necessity to hoard medication (pre-intervention). Before intervention, remarks were particularly related to optimizing dispensing times. After intervention, remarks were primarily related to technical issues with scanning (e.g. slow response time) and shortfalls of the system to check the right dose (e.g. half tablets).
Discussion
Statement of principal findings
The implementation of central ADD with BCMA reduced the probability of medication errors during administration from 19.5% to 15.8% and of potentially harmful errors from 3.0% to 0.3%. Procedures for patient identification and signing of administered medication by scanning were not fully adhered to. In the post-implementation period, error rates were lower for scanned medication compared to non-scanned medication (13.0% versus 19.5%) and all potentially harmful errors occurred with non-scanned medication. Compared to non-scanned medication, scanned medication had lower rates of all error types, i.e. omission, unordered drug, wrong dosage form and wrong dose, except for wrong administration technique and wrong medication handling. Overall, nursing staff satisfaction with the medication administration system was moderate and did not change after implementing the intervention.
Interpretation within the context of the wider literature
The MAE frequency reduction found in this study is in line with previous studies examining the effect of central ADD, BCMA and/or closed-loop systems [4, 6, 9, 10, 12–17, 25]. Findings of studies on closed-loop systems are difficult to compare because of heterogeneity with regard to studied interventions (e.g. solely BCMA), baseline setting characteristics (e.g. paper-based systems versus electronic systems) and patient populations. To our knowledge, only two Danish studies have examined the effect of the combined intervention with EMR and CPOE systems already in place [16, 17]. These controlled before-and-after studies have shown a higher reduction of MAEs (odds ratio 0.38 [16] and 0.06 [17]). However, it is difficult to compare these studies with our study because they were performed in acute medical wards or haematological wards, using non-disguised observational methods and focusing solely on oral medication [16, 17]. Other studies on central ADD have shown reductions in MAE rates of approximately 50–60% [14, 15], while most studies on BCMA have shown a reduction of 30–60% after its implementation [9, 12]. However, the absolute MAE reduction found in this study (3.7%) is quite comparable to that in previous studies on cADD or BCMA [9, 12, 14, 15].
The rate of omission reduced from 4.6% to 2.0% and of wrong dose from 3.8% to 2.1%, while rates of other MAE types were unaffected. Comparing scanned versus non-scanned medication in the post-implementation period also showed varying effects on different error types. This supports the claim that the examined intervention only has the ability to reduce specific types of errors [9, 10, 12, 13].
Limited compliance with patient and medication identification by scanning may have diluted the positive effect of the intervention in our study. Potential causes of non-compliance may include technical difficulties, time constraints and incomplete integration in the standard workflow [26]. Shortcomings in implementation, design and workflow integration are known triggers of workarounds [27], which subsequently may lead to reduced safety effects [10, 27–29]. Workarounds observed in our study include affixing patient identification barcodes to beds and carrying several patients’ pre-scanned medication on carts. Other issues observed include difficulties while using the scanner (e.g. slow response), insufficient number of scanners or medication carts and non-barcoded medication.
Strengths and limitations
Strength of this study is that we included a large number of representative medication administrations performed by many staff members of different clinical wards, supporting the generalizability of our results to similar settings. Another strength is that we used a robust method to identify and assess MAEs in daily clinical practice. This study also has some limitations. First, observer bias may have occurred. We tried to limit this by using the disguised method [19–21] and obligatory extensive training programmes for observers. Second, a potential limitation of before-and-after studies is that other changes in the medication process (e.g. other patient safety initiatives) may have influenced the results. However, to our knowledge, no additional substantial changes related to the medication administration process were made. Finally, the monocentre setting may limit generalizability.
Implications for policy, practice and research
The findings of this study support the implementation of central ADD with BCMA. However, this study also emphasizes the need for comprehensive implementation strategies and ongoing evaluation strategies (e.g. by using the Plan-Do-Study-Act method [30]). Scanning procedures were not fully adhered to, although extensive resources were expended for the implementation in our institution. Exploration of facilitators and barriers for implementation of such interventions seems crucial because not using patient-safety technology as intended may compromise the efficacy of such interventions [10, 26–28]. Also, such interventions should be co-developed with all stakeholders, especially the target audience, to tailor the technology to the needs of the people that will be using it.
Conclusions
Central ADD with BCMA was associated with a reduced frequency of MAEs, including potentially harmful errors, but compliance with scanning patient and medication barcodes needs improvement. In conclusion, this study shows that this intervention contributes to the improvement of medication safety in hospitals.
Supplementary Material
Acknowledgements
We thank L.S.C. Nanda (pharmacy student at time of the study) for assistance with data collection.
Contributor Information
Janique Gabriëlle Jessurun, Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, CA 3000, The Netherlands.
Nicole Geertruida Maria Hunfeld, Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, CA 3000, The Netherlands; Department of Intensive Care, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, CA 3000, The Netherlands.
Joost Van Rosmalen, Department of Biostatistics, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, CA 3000, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, CA 3000, The Netherlands.
Monique Van Dijk, Department of Internal Medicine, Section of Nursing Science, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, CA 3000, The Netherlands.
Patricia Maria Lucia Adriana Van Den Bemt, Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, CA 3000, The Netherlands; Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, P.O. Box 30.001, Groningen, RB 9700, The Netherlands.
Supplementary material
Supplementary material is available at International Journal for Quality in Health Care online.
Funding
No external funding received.
Contributorship
J.J., N.H., M.D. and P.B. contributed to the conception, data collection, data analysis and writing of the manuscript. J.M. contributed to the conception, data analysis and writing of the manuscript. All authors have reviewed the manuscript and agreed to the publication of the manuscript.
Ethics and other permissions
The Medical Ethics Review Committee of Erasmus MC (University Medical Center Rotterdam) waived approval for this study (reference number MEC-2018-1532) in accordance with the Dutch Medical Research involving human subjects Act.
Data availability statement
The data underlying this article will be shared on reasonable request to the corresponding author.
References
- 1.Panagioti M, Khan K, Keers RN. et al. Prevalence, severity and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. BMJ 2019;366:l4185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Keers RN, Williams SD, Cooke J. et al. Prevalence and nature of medication administration errors in health care settings: a systematic review of direct observational evidence. Ann Pharmacother 2013;47:237–56. [DOI] [PubMed] [Google Scholar]
- 3.Berdot S, Gillaizeau F, Caruba T. et al. Drug administration errors in hospital inpatients: a systematic review. PLoS One 2013;8:e68856. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Berdot S, Roudot M, Schramm C. et al. Interventions to reduce nurses’ medication administration errors in inpatient settings: a systematic review and meta-analysis. Int J Nurs Stud 2016;53:342–50. [DOI] [PubMed] [Google Scholar]
- 5.Manias E, Kusljic S, Wu A. Interventions to reduce medication errors in adult medical and surgical settings: a systematic review. Ther Adv Drug Saf 2020;11:1–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Keers RN, Williams SD, Cooke J. et al. Impact of interventions designed to reduce medication administration errors in hospitals: a systematic review. Drug Saf 2014;37:317–32. [DOI] [PubMed] [Google Scholar]
- 7.Kuitunen SK, Niittynen I, Airaksinen M. et al. Systemic defenses to prevent intravenous medication errors in hospitals: a systematic review. J Patient Saf 2020. 10.1097/PTS.0000000000000688. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Westbrook JI, Sunderland NS, Woods A. et al. Changes in medication administration error rates associated with the introduction of electronic medication systems in hospitals: a multisite controlled before and after study. BMJ Health Care Inform 2020;27:e100170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hutton K, Ding Q, Wellman G. The effects of bar-coding technology on medication errors: a systematic literature review. J Patient Saf 2021;17:e192–206. [DOI] [PubMed] [Google Scholar]
- 10.Shah K, Lo C, Babich M. et al. Bar code medication administration technology: a systematic review of impact on patient safety when used with computerized prescriber order entry and automated dispensing devices. Can J Hosp Pharm 2016;69:394–402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tsao NW, Lo C, Babich M. et al. Decentralized automated dispensing devices: systematic review of clinical and economic impacts in hospitals. Can J Hosp Pharm 2014;67:138–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hassink J, Jansen M, Helmons P. Effects of bar code-assisted medication administration (BCMA) on frequency, type and severity of medication administration errors: a review of the literature. Eur J Hosp Pharm 2012;19:489–94. [Google Scholar]
- 13.Ahtiainen HK, Kallio MM, Airaksinen M. et al. Safety, time and cost evaluation of automated and semi-automated drug distribution systems in hospitals: a systematic review. Eur J Hosp Pharm 2020;27:253–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Viprey M, Leon-Djian CB, Dode X. et al. Effect of unit dose drug dispensing system on medication administration errors. Int J Clin Pharm 2012;34:174. [Google Scholar]
- 15.Cousein E, Mareville J, Lerooy A. et al. Effect of automated drug distribution systems on medication error rates in a short-stay geriatric unit. J Eval Clin Pract 2014;20:678–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Risor BW, Lisby M, Sorensen J. Complex automated medication systems reduce medication administration errors in a Danish acute medical unit. Int J Qual Health Care 2018;30:457–65. [DOI] [PubMed] [Google Scholar]
- 17.Risor BW, Lisby M, Sorensen J. An automated medication system reduces errors in the medication administration process: results from a Danish hospital study. Eur J Hosp Pharm 2016;23:189–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.von Elm E, Altman DG, Egger M. et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 2007;335:806–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Allan EL, Barker KN. Fundamentals of medication error research. Am J Hosp Pharm 1990;47:555–71. [PubMed] [Google Scholar]
- 20.Dean B, Barber N. Validity and reliability of observational methods for studying medication administration errors. Am J Health Syst Pharm 2001;58:54–9. [DOI] [PubMed] [Google Scholar]
- 21.Flynn EA, Barker KN, Pepper GA. et al. Comparison of methods for detecting medication errors in 36 hospitals and skilled-nursing facilities. Am J Health Syst Pharm 2002;59:436–46. [DOI] [PubMed] [Google Scholar]
- 22.Hurley AC, Lancaster D, Hayes J. et al. The Medication Administration System—Nurses Assessment of Satisfaction (MAS-NAS) scale. J Nurs Scholarsh 2006;38:298–300. [DOI] [PubMed] [Google Scholar]
- 23.van den Bemt PM, Egberts ACG. Drug related problems: definitions and classification. EJHP Practice 2007;13:62–4. [Google Scholar]
- 24.Snyder RA, Abarca J, Meza JL. et al. Reliability evaluation of the adapted national coordinating council medication error reporting and prevention (NCC MERP) index. Pharmacoepidemiol Drug Saf 2007;16:1006–13. [DOI] [PubMed] [Google Scholar]
- 25.Franklin BD, O’Grady K, Donyai P. et al. The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study. Qual Saf Health Care 2007;16:279–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Mulac A, Mathiesen L, Taxis K. et al. Barcode medication administration technology use in hospital practice: a mixed-methods observational study of policy deviations. BMJ Qual Saf 2021. 10.1136/bmjqs-2021-013223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Koppel R, Wetterneck T, Telles JL. et al. Workarounds to barcode medication administration systems: their occurrences, causes and threats to patient safety. J Am Med Inform Assoc 2008;15:408–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.van der Veen W, van den Bemt P, Wouters H. et al. Association between workarounds and medication administration errors in bar-code-assisted medication administration in hospitals. J Am Med Inform Assoc 2018;25:385–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Adams KT, Pruitt Z, Kazi S. et al. Identifying health information technology usability issues contributing to medication errors across medication process stages. J Patient Saf 2021. 10.1097/PTS.0000000000000868. [DOI] [PubMed] [Google Scholar]
- 30.Taylor MJ, McNicholas C, Nicolay C. et al. Systematic review of the application of the plan-do-study-act method to improve quality in healthcare. BMJ Qual Saf 2014;23:290–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.