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Pediatric Medication Administration Errors and Workflow Following Implementation of a Bar Code Medication Administration System Anna Hardmeier, Candy Tsourounis, Mary Moore, Wendy E. Abbott, and B. Joseph Guglielmo Abstract: Direct observation was used to detect medication errors and Bar Code Medication Administration (BCMA) workarounds on two pediatric units and one neonatal unit at UCSF Benioff Children’s Hospital. The study (1) measured the frequency of nursing medication administration-related errors, (2) characterized the types of medication errors, (3) assessed compliance with the institution’s six medication administration safety processes, and (4) identified observed workarounds following BCMA implementation. The results of the direct observation were compared to medication administration-related incident reports (IRs) for the same period. The frequency of medication errors was 5% for the three units. Compliance with the process measures was achieved 86% of the time (range 23–100%). Seven medication administration-related IRs were submitted during the same observation period. Three BCMA workarounds were identified; (1) failure to visually confirm patient’s identification, (2) failure to compare the medication to the electronic medication administration record at least twice before administration, and (3) charting administration of medication before actual administration. The direct observation methodology identified a low frequency of medication administration errors (MAEs) consistent with post-BCMA implementation. The incident reporting system identified different MAEs than direct observation suggesting that both methods should be used to better characterize the scope of MAEs.
Keywords error/adverse event/incident classification systems/near misses/error medication safety performance improvement/quality improvement performance improvement models reporting
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Background Bar Code Medication Administration (BCMA) is increasingly used in hospitals across the United States. In 2009, 28% of hospitals were using the technology with link to the electronic medication administration record (eMAR), eliminating paper documentation and handwritten dose verification (Pedersen & Gumpper, 2007; Pedersen, Schneider, & Scheckelhoff, 2010). BCMA involves the use of medication bar codes and the patient’s wristband (Coyle & Heinen, 2005). The bar codes are scanned during the medication administration process to verify the patient’s identity and the medication. BCMA was developed to reduce medication administration errors (MAEs) by improving accuracy indicators such as patient identity verification and improved charting after administration (Helmons, Wargel, & Daniels, 2009). Despite the potential bene-
fit of BCMA, barriers and workarounds have been reported (Koppel, Wetterneck, Telles, & Karsh, 2008; Patterson, Cook, & Render, 2002; Patterson, Rogers, Chapman, & Render, 2006). Workarounds occur when the intended process for administering medications is intentionally not followed, potentially increasing the risk for adverse drug events (ADEs). A workaround commonly identified in the literature is when the patient’s identification bar code is affixed to something other than the patient’s wrist so that medications can be scanned for that patient as needed. Among hospitalized patients in the acute care setting, three times as many ADEs are reported in children when compared with adults, primarily due to MAE (Kaushal et al., 2001). Reasons for this finding include the need for weight-based dosing regimens in pediatrics, the lack of alternative drug formulations, and requirements for small drug volumes and routine use of medications that have not been studied in the pediatric population or the neonatal population (Smith, 2004; Wiles, Vinks, & Akinbi, 2013). The frequency of MAE in the pediatric inpatient setting (including, but not limited to intensive care) is between 11.7% and 31.2% (Buckley, Erstad, Kopp, Theodorou, & Priestley, 2007; Chua, Chua, & Omar, 2010; Prot et al., 2005; Schneider, Cotting, & Pannatier, 1998).
The Medication Administration Accuracy Project The Medication Administration Accuracy Project (MAAP) is a quality improvement project, developed at the University of California, San Francisco Medical Center (UCSFMC), whose purpose is to improve the accuracy of nursing medication administration (Kliger, Blegen, Gootee, & O’Neil, 2009). In accordance with Collaboration Alliance for Nursing Outcomes (CALNOC), six safety processes were identified as critical to the medication administration process. MAAP has enabled nurses to
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achieve and sustain medication administration accuracy via direct observation methodology. Direct observation is also considered a more accurate and efficient method in detecting MAE compared to reviewing medical records or voluntary medication error reporting programs (Barker, Flynn, Pepper, Bates, & Mikeal, 2002). There are currently no published direct observation studies investigating medication administration workflow after BCMA and eMAR implementation in pediatrics. The objective of this pilot study was to (1) measure and characterize the frequency of MAE associated with pediatric and neonatal medication administration, (2) characterize compliance with the six safety processes following BCMA implementation, (3) compare those MAE identified through direct observation versus that identified via the voluntary medication error reporting system (referred to as an incident report [IR]), and (4) identify observed barriers or workarounds to BCMA that may have contributed to MAE.
Study Design and Methods Setting This pilot study was conducted on three units at the UCSF Benioff Children’s Hospital in San Francisco, California (UCSF BCH); two acute care units (ACU), ACU 1 and ACU 2 with 36 and 29 beds, respectively, and one intensive care unit (ICU) for neonates with 51 beds. On each unit, 100 direct observations were performed in concordance with the MAAP criteria. Based on the literature, 300 observations in total would be adequate to detect a 20% frequency of MAE. The observations began 1 month after the implementation of BCMA and eMAR. Five pharmacy student observers completed all of the observations. The observers were trained in the MAAP methodology, BCMA, and eMAR. This training consisted of 2 hr of theory and at least 6 hr of practical experience in the adult and children’s hospital. During the observation period, the observers met at least once weekly regarding the data collection process and discussed data collection consistency. When an MAE discrepancy was identified, the observers (nurses and pharmacists) discussed and reconciled the discrepancy by consensus.
Study Design This pilot study was designed as a quality improvement project and therefore did not require Investigational Review Board approval. Consistent with previous direct observation studies in the literature, the naive-observer technique was used during the medication administration process (Flynn, Barker, Pepper, Bates, & Mikeal, 2002). The naive observer technique is when the observer is unaware of the patient’s medications and medication schedule so as to minimize bias during the observation period. Following the direct observation period, a complete medication review is performed (by reviewing the medication administration record) to compare what was ordered to what was given and therefore determine whether an MAE occurred. All activities during the medication administration process were recorded and documented using a data collection tool adapted from an observation code sheet developed by CALNOC (2010a, 2010b). The data collection tool was updated during the observations to include pediatric-specific and BCMA appropriate parameters. Documentation included the following outcome measures: 1. 2. 3. 4. 5. 6. 7. 8.
The right drug (identity of the drug). The right dose. The right route. The right formulation (including right technique). The right date and time of administration. Extra dose. Omission. Unauthorized/unavailable dose.
Data on the following six safety process measures were collected: 1. Compare medication to the medication administration record (eMAR) at least twice. 2. Minimize distractions and interruptions. 3. Keep medication labeled throughout the process. 4. Check two patient identifiers (IDs). 5. Explain drug to patient (as appropriate). 6. Chart immediately after administration. The first nurse who entered the medication room and agreed to be observed, was followed by the observer for the entire medication
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administration process. For the ACU 1 and ACU 2 locations, the observations were performed during the most intensive medication administration hours (between 8:00–10:00 a.m. and 8:00–10:00 p.m.), corresponding to the times previously used by MAAP. Consistent with the CALNOC methodology, the observation process continued until 100 medication administrations had been observed on each unit. In the ICN, neonates typically receive very few medications compared to pediatric patients and adults; therefore the unit observations were spread over the course of the day in order to capture 100 medication administration observations. Patients admitted for more than 3 weeks to any of the three units ACU 1, ACU 2, and ICN were excluded in order to minimize the need for extensive chart review. Data were excluded if the data collection tool was not completed properly (e.g., questions were not answered and information was missing) or if the observations were incomplete (e.g., the nurse stopped the administration or the observer did not observe the entire procedure from beginning to end). The observation period began when the medication was prepared or removed from medication storage (e.g., automated dispensing machine or the patient’s cassette). The observation period ended when the patient received the medication(s) and medication charting had taken place. An MAE was defined as any discrepancy between the prescriber’s order and what was administered to the patient (see list of outcome measures). Consistent with other direct observation studies, an MAE was defined as the number of errors divided by the total number of doses administered expressed as a percentage (Barker et al., 2002; Haw, Stubbs, & Dickens, 2007).
Incident Reports The second component of the pilot consisted of a review of reported MAE submitted as IRs. All medication administration-related IRs received during the direct observation study period were reviewed. Each IR was characterized and reviewed according to the six safety practices by one of the study investigators. In an effort to assess whether the IRs were related to BCMA, a comprehensive review of all IRs was performed for this time period, specifically looking for BCMA or bar coding as a contrib-
utor; all free text fields were further searched and reviewed for these terms.
Results Outcome Measures Of the 300 observed doses administered, 285 doses were classified as correct medication administrations (95%), Figure 1. Of the 15 MAEs identified, the most common MAE type was wrong route (10), followed by wrong technique (2), and medication not available/wrong time (2) and error of omission (1). Wrong route MAE involved medication orders written to be given orally (per os), for patients with a nasogastric/gastrostomy tube in place. Wrong technique MAE occurred when the medication dose was added to the entire bottle of baby food as opposed to a small consumable amount of baby food. The remaining MAE involved delays in accessing or acquiring the medication (medication not available) and one error of omission.
Process Measures Compliance with the six safety processes is described in Figure 2. In general, the six safety process measures were achieved 86% of the time (ranging from 23% to 100%). The compliance varied substantially between the different units and between the different safety processes. Labeling of medications and explaining the medication(s) to the patient or their guardian were the two processes with the highest compliance for all three units. In the ICU, comparison of the medication to the eMAR twice was achieved only 32% of the time. In these situations, the observer noted that the nurse compared the medications to the eMAR only one time. There were a total of 298 distractions observed during the medication administration process across all three units. MAAP provides guidance that each distraction should be accompanied by an attempt to minimize the distraction per dose administered. For example, if a co-worker interrupts the nurse during medication administration, the nurse informs the co-worker to discontinue speaking until the nurse is finished and has thereby minimized the distraction. For each distraction identified during this observation period, attempts to minimize the distraction occurred 68% of the time (204/298). In the ICU, compliance with checking two forms of patient identification was achieved
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Figure 1. Error-Free Dose Administrations at the ICU and the Two ACUs ICU, intensive care unit; ACU, acute care unit 1; ACU 2, acute care unit 2
only 31% of the time as compared to 70% in ACU1 and 91% in ACU2. The most common contributor was failure to visually confirm the patient’s identity by checking the patient’s wristband. Lastly, in approximately 10% of the observations in the ICU it was difficult to determine whether visual confirmation of the patient’s wristband ID had occurred (e.g., difficult to witness the nurse directly reading and confirming the patient’s wristband).
Incident Reports Seven nursing-related medication administration IRs were reported for the ICU, ACU 1, and ACU 2, during the same time period. The types of MAE reported included wrong dose (n = 3), wrong time (n = 1), omission (n = 1), other; forgot to scan patient (n = 1); and extra dose (n = 1). One of the IRs reported an MAE where the patient’s wristband was never scanned to confirm the patient’s identity. All other IRs could not be definitively linked to a failure in BCMA.
Discussion Outcome Measures To our knowledge, this is the first direct observation study that has evaluated MAE in
pediatrics post-BCMA implementation. Using the direct observation methodology we identified a low MAE rate (5%) in children. This MAE rate is lower than what has been described in the pediatric literature using similar direct observation methodology (12– 31%; Buckley et al., 2007; Chua et al., 2010; Prot et al., 2005; Schneider et al., 1998). The low rate of MAE in our study may be due, in part, to BCMA implementation, however a direct cause–effect relationship is difficult to establish. Importantly, none of the published pediatric studies involving direct observation had BCMA in place at the time of evaluation. Our study demonstrated a lower pediatric MAE rate as compared to the adult literature using the same direct observation methodology, 5% vs. 8–28%, respectively (Keers, Williams, Cooke, & Ashcroft, 2013). Reasons for this difference include the heterogeneity of the adult literature, variances in the types of medications selected for observation, timing and duration of the observations, training of the observers, and whether medication timing errors were included as errors in the analysis. A notable trend in the adult literature suggests that when the direct observation process is repeated at least yearly on the same patient care units, the MAE
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Figure 2. Compliance with the Six Safety Processes for Each Administered Dose N = 300 administrations, 100 administrations on each unit *More than one distraction may have occurred for each dose administered
rate decreases over time (4.3% in 2010 and 1.2% in 2011; Kliger et al., 2009). More research is needed to determine the reasons for the decrease and whether this corresponds to an improved culture of safety. Several studies have attempted to assess the impact of BCMA on MAE rates with varied results. For example, implementation of BCMA has not been shown to eliminate MAE rather mixed results have been described with decreases, increases, and no effect being reported (Young, Slebodnik, & Sands, 2010). One of the largest studies conducted using direct observation methodology evaluated MAEs before and after BCMA implementation in adults at a tertiary care medical center and reported a decrease in MAE from 11.5% to 6.8% (Poon et al., 2010). Although our study did not evaluate pre-BCMA MAE, our post-BCMA MAE rate is consistent with this study. The most common errors identified in previous MAAP evaluations on adult units included wrong dose (0.5%), wrong technique (0.3%), omission, and wrong time (0.2%). These results are similar to the outcome measures observed in this study. Wrong route (4%) was the most common MAE identified, which is not
surprising as pediatric medication administration may require the use of alternate routes of medication administration besides the oral route especially for infants and very young children. Importantly, there were no instances of wrong dose and the use of BCMA may have contributed to this finding (Coyle & Heinen, 2005).
Process Measures Compliance with all of the six safety process measures as an aggregate was achieved 86% of the time (ranging from 23% to 100%). This is consistent with that observed during the first year of MAAP in adults (85.6% in 2008). The similar compliance rate is noteworthy considering that the UCSF BCH had not fully instituted the same educational efforts that were in place in the adult hospital regarding the six safety processes. Furthermore, this was the first time that the UCSF BCH was evaluated using the MAAP methodology. There was a greater variation within the six safety processes between the adult MAAP and the pediatric observations with compliance ranging between 23% and 100% in UCSF BCH compared to 73.7–99.8% in adults
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(Kliger et al., 2009). The ICU was the main contributor to the variation, which can be explained by the different workflow. For example, in the ICU, scanning the patient’s armband is sufficient when identifying the patient. A visual check is not required since the patients in the intensive care nursery are not ambulating and remain in isolettes. This difference in workflow was unanticipated. This variation has led to important quality and safety discussions on ways to strengthen the patient identification process as well as changes that should be considered for future direct observation interventions in the intensive care nursery. Finally, the ability to confirm that a visual check of the patient’s wristband was accomplished was difficult to assess in 10% of the doses administered in the ICU. In many of these instances, the visual check happened quickly, often with a glance of the wristband making it a challenge to confirm that there was positive identification. A visual and subsequent verbal confirmation of the patient’s name by the nurse administering the medication may be one way to address this issue. In a minority of instances, medications were charted as being administered before the medication was actually given. The reason for this practice is unknown; however, it is possible that inexperience and confusion with BMCA implementation and subsequent changes to the workflow may have contributed to this finding. It was encouraging to see that, on all three units, attempts were made to minimize distractions the majority of the time. Minimizing distractions during the medication administration process is critical to preventing MAE since the number of interruptions during the administrating medication process has been clearly associated with MAE (Westbrook, Woods, Rob, Dunsmuir, & Day, 2010). This study also identified instances, according to protocol, when the medication was not compared to the eMAR at least twice. In the ICU this occurred at a frequency of 32%, which could be explained by the different workflow. For example, in the ICU, the first comparison occurs when the medications arrive on the floor, long before they are administered. Under this workflow, this comparison would fall outside of the medication administration process and therefore would not be included in the observation.
Incident Reports The IR system identified different MAE compared to what was detected by direct observations. Consequently, both methods should be used to better characterize the scope of MAE. The IR system does not require the user to indicate which of the six safety process measures contributed to the MAE. Therefore it was not possible to determine which of the six safety processes were not followed and which safety process was a contributing factor. In addition, the IR reports did not contain enough detail to determine whether BCMA contributed to the error. As a result of these findings, the IR system software is being reevaluated to help UCSF BCH address these issues. The success of the direct observation process was based on strong support from UCSF BCH nursing administrators, nurse managers, nursing performance improvement, and a very strong collaboration between pharmacy and nursing. All groups worked together to accommodate the observations while ensuring a seamless, thorough, and collaborative process without interruptions to patient care.
Limitations All observers who participated in the direct observation process attempted to limit observation bias by being objective, nonjudgmental, and discrete (Barker et al., 2002). The pharmacist observers had no prior experience working in the UCSF BCH and therefore did not have any established relationships with staff. In addition, since direct observation had not occurred in the UCSF BCH previously, the observers had no predetermined assumptions as to the expected outcome of the observations. At the time of the study, we offered each nurse the option to participate in the direct observation process. Although this may have introduced bias, we felt it was important to do so as to not adversely impact patient care. Observation bias is a possibility in our study, meaning that nurses may have changed their behavior while being observed (Hawthorne effect). Interestingly, the Hawthorne effect does not seem to play a role in medication administration as described in previous direct observation literature. According to one study, the MAE rate did not differ between nurses who were observed and those that were not observed administering medications (Dean & Barber, 2001). Further studies
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are needed to assess the impact of the observation duration for each individual nurse while administering medications (1–3 hr). An effort was made to limit any unforeseen changes in medication administration behavior while the nurses were being observed (also known as the Hawthorne effect; Barker, 1980). Observers made every effort to observe the same nurse for at least 1–2 hr to maximize nurse comfort with direct observation. In some instances, the nurses had many activities leading up to and between each medication administration, therefore it is difficult to exclude the Hawthorne effect as a possible contributor. Interestingly, the adult MAAP methodology recommends observing different nurses in order to get a wider perspective over the situational setting. One of the more important findings from the adult MAAP project was the frequency of MAE declined with each subsequent year of direct observation. This finding suggests that the direct observation methodology results in a greater awareness of medication administration safety and facilitates a culture of safety. As a result of these data, the UCSF BCH intends to participate in the yearly MAAP observation study. An updated version of the adult MAAP data collection tool has been developed. The updated version included minor changes adapted to the UCSF BCH, and did not differ significantly from the previous version and attempts were made to minimize the impact of the data collection tool changes. When applying the MAAP methodology it is important to emphasize that one event has the ability to impact all subsequent observations. For example, if the nurse is distracted in the beginning of the administration process, it will become a distraction for the entire medication administration process and therefore all subsequent doses can be impacted. This process was very difficult to capture and therefore address in the data collection tool. Another limitation with the methodology is that it only identifies MAE that occurs in relation to the observation and does not include those independently identified by the nurse (outside of the direct observation process). For example, if a nurse has noticed that the previous dose was not given (omitted), that error is not included in the results as it was not observed. Importantly, when MAE was detected by the observers who were not related to the direct
observation process, they were always communicated to the nurse manager for evaluation and follow-up.
Conclusions This pilot study identified a low rate (5%) of MAE and fair compliance with the six safety processes on two pediatric units and one neonatal unit following BCMA implementation. An additional finding was that the IR system identified different MAE compared to that identified via the direct observation methodology. BCMA implementation did not eliminate MAE and relatively few BCMA workarounds were identified. This pilot study serves as a foundation for future observations and provides important insight into reducing MAE in children.
References Barker, K. N. (1980). Data collection techniques: Observation. American Journal of Hospital Pharmacists, 37, 1235– 1243. Barker, K. N., Flynn, E. A., Pepper, G. A., Bates, D. W., & Mikeal, R. L. (2002). Medication errors observed in 36 health care facilities. Archives of Internal Medicine, 162, 1897–1903. Buckley, M. S., Erstad, B. L., Kopp, B. J., Theodorou, A. A., & Priestley, G. (2007). Direct observation approach for detecting medication errors and adverse drug events in a pediatric intensive care unit. Pediatric Critical Care Medicine, 8, 145–152. Chua, S. S., Chua, H. M., & Omar, A. (2010). Drug administration errors in paediatric wards: A direct observation approach. European Journal of Pediatrics, 169, 603–611. doi:10.1007/s00431-009-1084-z Collaborative Alliance for Nursing Outcomes (CALNOC). (2010a). Codebook Part I. Coordinating and using CALNOC data in the hospital setting. Acute Care Version. Sacramento, CA: Author. Collaborative Alliance for Nursing Outcomes (CALNOC). (2010b). Codebook Part II. Data capture and submission. Acute Care Version. Sacramento, CA: Author. Coyle, G. A., & Heinen, M. (2005). Evolution of BCMA within the Department of Veterans Affairs. Nursing Administration Quarterly, 29, 32–38. Dean, B., & Barber, N. (2001). Validity and reliability of observational methods for studying medication administration errors. American Journal of Health-System Pharmacy, 58, 54–59. Flynn, E. A., Barker, K. N., Pepper, G. A., Bates, D. W., & Mikeal, R. L. (2002). Comparison of methods for detecting medication errors in 36 hospitals and skilled-nursing facilities. American Journal of Health System Pharmacy, 59, 436–446. Haw, C., Stubbs, J., & Dickens, G. (2007). An observational study of medication administration errors in old-age psychiatric inpatients. International Journal for Quality in Health Care, 19, 210–216. Helmons, P. J., Wargel, L. N., & Daniels, C. E. (2009). Effect of bar-code-assisted medication administration on medication administration errors and accuracy in multiple patient care areas. American Journal of Health System Pharmacy, 66, 1202–1210. doi:10.2146/ajhp080357
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Kaushal, R., Bates, D. W., Landrigan, C., McKenna, K. J., Clap, M. D., Federico, F., & Goldmann, D. A. (2001). Medication errors and adverse drug events in pediatric inpatients. Journal of the American Medical Association, 285, 2114–2120. doi:10.1001/jama.285.16.2114 Keers, R. N., Williams, S. D., Cooke, J., & Ashcroft, D. M. (2013). Prevalence and nature of medication administration errors in health care settings: A systematic review of direct observational evidence. Annals of Pharmacotherapy, 47, 237–256. doi:10.1345/aph.1R147 Kliger, J., Blegen, M. A., Gootee, D., & O’Neil, E. (2009). Empowering frontline nurses: A structured intervention enables nurses to improve medication administration accuracy. Joint Commission Journal on Quality and Patient Safety, 35, 604–612. doi:10.1377/hlthaff.2011.0687 Koppel, R., Wetterneck, T., Telles, J. L., & Karsh, B. T. (2008). Workarounds to barcode medication administration systems: Their occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics Association, 15, 408–423. Patterson, E. S., Cook, R. I., & Render, M. L. (2002). Improving patient safety by identifying side effects from introducing bar coding in medication administration. Journal of the American Medical Informatics Association, 9, 540–553. doi:10.1197/jamia.M1061 Patterson, E. S., Rogers, M. L., Chapman, R. J., & Render, M. L. (2006). Compliance with intended use of Bar Code Medication Administration in acute and longterm care: An observational study. Human Factors, 48, 15–22. Pedersen, C. A., & Gumpper, K. F. (2007). ASHP national survey on informatics: Assessment of the adoption and use of pharmacy informatics in U.S. hospitals—2007. American Journal of Health-System Pharmacy, 65, 2244– 2264. doi:10.2146/ajhp080488 Pedersen, C. A., Schneider, P. J., & Scheckelhoff, D. J. (2010). ASHP national survey of pharmacy practice in hospital settings: Monitoring and patient education— 2009. American Journal of Health-System Pharmacy, 67, 542– 558. doi:10.2146/ajhp090596 Poon, E. G., Keohane, C. A., Yoon, C. S., Ditmore, M., Bane, A., Levtzion-Korach, O., et al. (2010). Effect of barcode technology on the safety of medication administration. New England Journal of Medicine, 362, 1698–1707. doi:10.1056/NEJMsa0907115 Prot, S., Fontan, J. E., Alberti, C., Bourdon, O., Farnoux, C., Macher, M. A., et al. (2005). Drug administration errors and their determinants in pediatric in-patients. International Journal for Quality in Health Care, 17, 381– 389. Schneider, M. P., Cotting, J., & Pannatier, A. (1998). Evaluation of nurses’ errors associated in the preparation and administration of medication in a pediatric intensive care unit. Pharmacy World and Science, 20, 178–182. Smith, D. J. (2004). Building a safer NHS for patients: Improving medication safety. Pan American Health Organization, World Health Organization. Retrieved April 11, 2013, from http://www.bvsde.paho. org/bvsacd/cd65/medicationsafety.pdf Westbrook, J. I., Woods, A., Rob, M. I., Dunsmuir, W. T., & Day, R. O. (2010). Association of interruptions with an increased risk and severity of medication administration errors. Archives of Internal Medicine, 170, 683–690. doi:10.1001/archinternmed.2010.65 Wiles, J. R., Vinks, A. A., & Akinbi H. (2013). Federal legislation and the advancement of neonatal drug studies. Journal of Pediatrics, 162, 12–15. doi:10.1016/j.jpeds.2012.08.034. Young, J. Y., Slebodnik, M., & Sands, L. (2010). Bar code technology and medication administra-
tion error. Journal of Patient Safety, 6, 115–120. doi:10.1097/PTS.0b013e3181de35f7
Authors’ Biographies Anna Hardmeier, MSc in Pharmacy, at the time she was a visiting master’s student from Uppsala University in Sweden. Dr. Candy Tsourounis is Professor of Clinical Pharmacy in the Medication Outcomes Center in the Department of Clinical Pharmacy at the UCSF School of Pharmacy. Mary Moore, RN, MS, CPHQ, is responsible for Nursing Performance Improvement at UCSF Medical Center and has been involved in coordinating the Medication Administration Accuracy Project in the adult hospital. Wendy E. Abbott is a senior analyst responsible for Nursing Performance Improvement at UCSF Medical Center and has been involved in managing the Medication Administration Accuracy Project in the adult hospital. B. Joseph Guglielmo, PharmD, was the Chair of the Department of Clinical Pharmacy; currently he serves as Dean of the UCSF School of Pharmacy. For more information on this article, contact Candy Tsourounis at [email protected].
Journal for Healthcare Quality is pleased to offer the opportunity to earn continuing education (CE) credit to those who read this article and take the online posttest at http://www. nahq.org/education/content/jhq-ce.html. This continuing education offering, JHQ 249, will provide 1 contact hour to those who complete it appropriately.
Core CPHQ Examination Content Area IV. Patient Safety
Pediatric Medication Administration Errors and Workflow Following Implementation of a Bar Code Medication Administration System Objectives r Identify two advantages and two disadvantages associated with using direct observation to assess medication administration errors r List the six medication safety process measures r Characterize the types of medication administration errors that may not be captured using the direct observation methodology
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Please select the single best answer. 1. The na¨ıve observer technique is when the observer: a. is na¨ıve to the direct observation process b. is unaware of the patients medications and diagnosis c. is unsure about the appropriateness of each medication d. is not able to scan the patient’s arm band for identification 2. The outcome measures that relate to medication administration safety include which of the following? a. Administering the right drug b. Checking two patient identifiers c. Comparing the medication to the medication administration record d. Keeping the medication labeled throughout the administration process 3. Which of the following process measures was problematic when using bar coding medication administration? a. Checking two patient identifiers b. Explaining the medication to the patient c. Minimizing distractions and interruptions d. Keeping the medication labeled throughout the administration process 4. Which of the following statements regarding the direct observation method for detecting medication administration errors is TRUE? a. Implementation of bar code medication administration has been shown to eliminate medication administration errors b. “Wrong technique” is the most common type of medication administration error identified in pediatrics c. Pediatric medication administration error rates are higher than those observed in adults d. Direct observation is one of several methods that can help identify different types of medication administration errors 5. Which of the following is a limitation to using the direct observation method? a. Repeating the observations yearly on the same patient care areas
6.
7.
8.
9.
b. Including observers who are unfamiliar in the care of the patients observed c. Using a multidisciplinary team to observe the medication administration process d. The duration of time that the individual administering medications is observed Which of the following steps of the direct observation process is FALSE? a. Patients with extended length of stay (> 3 weeks) were excluded from observation b. Records that had incomplete documentation of the observation process were included c. Observations were recorded during the most intensive medication administration hours d. The observation process continued until 100 medication administrations were observed on each unit Distractions during the start of the medication administration process: a. Have the potential to impact all subsequent medications administered b. Can be minimized if bar code medication administration is used c. Result in bar code medication administration workarounds d. Account for the majority of omitted medications Which of the following workarounds were identified during the direct observation process? a. Forgetting to scan the patient’s wrist band b. Using more than one form of patient identification c. Medications charted before they were actually given d. Printing multiple patient wrist bands affixed to something other than the patients wrist band for scanning of medications Implementing a successful direct observation program is dependent upon which of the following? a. Including specialists trained in medication error detection on the multidisciplinary team b. Ensuring bar code medication administration technology be instituted before observations begin
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c.
Getting support from administrators and managers before initiating the program d. Educating nursing staff on the consequences of not contributing to the program 10. This study identified the following: a. A high rate of medication administration errors among pediatric and neonatal units
b. The incident reporting system was inferior to the direct observation process in identifying medication errors c. Bar code medication administration did not eliminate pediatric medication administration errors d. Non-compliance with the six safety processes
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Pediatric Medication Administration Errors and Workflow Following Implementation of a Bar Code Medication Administration System Anna Hardmeier, Candy Tsourounis, Mary Moore, Wendy E. Abbott, and B. Joseph Guglielmo Abstract: Direct observation was used to detect medication errors and Bar Code Medication Administration (BCMA) workarounds on two pediatric units and one neonatal unit at UCSF Benioff Children’s Hospital. The study (1) measured the frequency of nursing medication administration-related errors, (2) characterized the types of medication errors, (3) assessed compliance with the institution’s six medication administration safety processes, and (4) identified observed workarounds following BCMA implementation. The results of the direct observation were compared to medication administration-related incident reports (IRs) for the same period. The frequency of medication errors was 5% for the three units. Compliance with the process measures was achieved 86% of the time (range 23–100%). Seven medication administration-related IRs were submitted during the same observation period. Three BCMA workarounds were identified; (1) failure to visually confirm patient’s identification, (2) failure to compare the medication to the electronic medication administration record at least twice before administration, and (3) charting administration of medication before actual administration. The direct observation methodology identified a low frequency of medication administration errors (MAEs) consistent with post-BCMA implementation. The incident reporting system identified different MAEs than direct observation suggesting that both methods should be used to better characterize the scope of MAEs.
Keywords error/adverse event/incident classification systems/near misses/error medication safety performance improvement/quality improvement performance improvement models reporting
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Background Bar Code Medication Administration (BCMA) is increasingly used in hospitals across the United States. In 2009, 28% of hospitals were using the technology with link to the electronic medication administration record (eMAR), eliminating paper documentation and handwritten dose verification (Pedersen & Gumpper, 2007; Pedersen, Schneider, & Scheckelhoff, 2010). BCMA involves the use of medication bar codes and the patient’s wristband (Coyle & Heinen, 2005). The bar codes are scanned during the medication administration process to verify the patient’s identity and the medication. BCMA was developed to reduce medication administration errors (MAEs) by improving accuracy indicators such as patient identity verification and improved charting after administration (Helmons, Wargel, & Daniels, 2009). Despite the potential bene-
fit of BCMA, barriers and workarounds have been reported (Koppel, Wetterneck, Telles, & Karsh, 2008; Patterson, Cook, & Render, 2002; Patterson, Rogers, Chapman, & Render, 2006). Workarounds occur when the intended process for administering medications is intentionally not followed, potentially increasing the risk for adverse drug events (ADEs). A workaround commonly identified in the literature is when the patient’s identification bar code is affixed to something other than the patient’s wrist so that medications can be scanned for that patient as needed. Among hospitalized patients in the acute care setting, three times as many ADEs are reported in children when compared with adults, primarily due to MAE (Kaushal et al., 2001). Reasons for this finding include the need for weight-based dosing regimens in pediatrics, the lack of alternative drug formulations, and requirements for small drug volumes and routine use of medications that have not been studied in the pediatric population or the neonatal population (Smith, 2004; Wiles, Vinks, & Akinbi, 2013). The frequency of MAE in the pediatric inpatient setting (including, but not limited to intensive care) is between 11.7% and 31.2% (Buckley, Erstad, Kopp, Theodorou, & Priestley, 2007; Chua, Chua, & Omar, 2010; Prot et al., 2005; Schneider, Cotting, & Pannatier, 1998).
The Medication Administration Accuracy Project The Medication Administration Accuracy Project (MAAP) is a quality improvement project, developed at the University of California, San Francisco Medical Center (UCSFMC), whose purpose is to improve the accuracy of nursing medication administration (Kliger, Blegen, Gootee, & O’Neil, 2009). In accordance with Collaboration Alliance for Nursing Outcomes (CALNOC), six safety processes were identified as critical to the medication administration process. MAAP has enabled nurses to
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achieve and sustain medication administration accuracy via direct observation methodology. Direct observation is also considered a more accurate and efficient method in detecting MAE compared to reviewing medical records or voluntary medication error reporting programs (Barker, Flynn, Pepper, Bates, & Mikeal, 2002). There are currently no published direct observation studies investigating medication administration workflow after BCMA and eMAR implementation in pediatrics. The objective of this pilot study was to (1) measure and characterize the frequency of MAE associated with pediatric and neonatal medication administration, (2) characterize compliance with the six safety processes following BCMA implementation, (3) compare those MAE identified through direct observation versus that identified via the voluntary medication error reporting system (referred to as an incident report [IR]), and (4) identify observed barriers or workarounds to BCMA that may have contributed to MAE.
Study Design and Methods Setting This pilot study was conducted on three units at the UCSF Benioff Children’s Hospital in San Francisco, California (UCSF BCH); two acute care units (ACU), ACU 1 and ACU 2 with 36 and 29 beds, respectively, and one intensive care unit (ICU) for neonates with 51 beds. On each unit, 100 direct observations were performed in concordance with the MAAP criteria. Based on the literature, 300 observations in total would be adequate to detect a 20% frequency of MAE. The observations began 1 month after the implementation of BCMA and eMAR. Five pharmacy student observers completed all of the observations. The observers were trained in the MAAP methodology, BCMA, and eMAR. This training consisted of 2 hr of theory and at least 6 hr of practical experience in the adult and children’s hospital. During the observation period, the observers met at least once weekly regarding the data collection process and discussed data collection consistency. When an MAE discrepancy was identified, the observers (nurses and pharmacists) discussed and reconciled the discrepancy by consensus.
Study Design This pilot study was designed as a quality improvement project and therefore did not require Investigational Review Board approval. Consistent with previous direct observation studies in the literature, the naive-observer technique was used during the medication administration process (Flynn, Barker, Pepper, Bates, & Mikeal, 2002). The naive observer technique is when the observer is unaware of the patient’s medications and medication schedule so as to minimize bias during the observation period. Following the direct observation period, a complete medication review is performed (by reviewing the medication administration record) to compare what was ordered to what was given and therefore determine whether an MAE occurred. All activities during the medication administration process were recorded and documented using a data collection tool adapted from an observation code sheet developed by CALNOC (2010a, 2010b). The data collection tool was updated during the observations to include pediatric-specific and BCMA appropriate parameters. Documentation included the following outcome measures: 1. 2. 3. 4. 5. 6. 7. 8.
The right drug (identity of the drug). The right dose. The right route. The right formulation (including right technique). The right date and time of administration. Extra dose. Omission. Unauthorized/unavailable dose.
Data on the following six safety process measures were collected: 1. Compare medication to the medication administration record (eMAR) at least twice. 2. Minimize distractions and interruptions. 3. Keep medication labeled throughout the process. 4. Check two patient identifiers (IDs). 5. Explain drug to patient (as appropriate). 6. Chart immediately after administration. The first nurse who entered the medication room and agreed to be observed, was followed by the observer for the entire medication
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administration process. For the ACU 1 and ACU 2 locations, the observations were performed during the most intensive medication administration hours (between 8:00–10:00 a.m. and 8:00–10:00 p.m.), corresponding to the times previously used by MAAP. Consistent with the CALNOC methodology, the observation process continued until 100 medication administrations had been observed on each unit. In the ICN, neonates typically receive very few medications compared to pediatric patients and adults; therefore the unit observations were spread over the course of the day in order to capture 100 medication administration observations. Patients admitted for more than 3 weeks to any of the three units ACU 1, ACU 2, and ICN were excluded in order to minimize the need for extensive chart review. Data were excluded if the data collection tool was not completed properly (e.g., questions were not answered and information was missing) or if the observations were incomplete (e.g., the nurse stopped the administration or the observer did not observe the entire procedure from beginning to end). The observation period began when the medication was prepared or removed from medication storage (e.g., automated dispensing machine or the patient’s cassette). The observation period ended when the patient received the medication(s) and medication charting had taken place. An MAE was defined as any discrepancy between the prescriber’s order and what was administered to the patient (see list of outcome measures). Consistent with other direct observation studies, an MAE was defined as the number of errors divided by the total number of doses administered expressed as a percentage (Barker et al., 2002; Haw, Stubbs, & Dickens, 2007).
Incident Reports The second component of the pilot consisted of a review of reported MAE submitted as IRs. All medication administration-related IRs received during the direct observation study period were reviewed. Each IR was characterized and reviewed according to the six safety practices by one of the study investigators. In an effort to assess whether the IRs were related to BCMA, a comprehensive review of all IRs was performed for this time period, specifically looking for BCMA or bar coding as a contrib-
utor; all free text fields were further searched and reviewed for these terms.
Results Outcome Measures Of the 300 observed doses administered, 285 doses were classified as correct medication administrations (95%), Figure 1. Of the 15 MAEs identified, the most common MAE type was wrong route (10), followed by wrong technique (2), and medication not available/wrong time (2) and error of omission (1). Wrong route MAE involved medication orders written to be given orally (per os), for patients with a nasogastric/gastrostomy tube in place. Wrong technique MAE occurred when the medication dose was added to the entire bottle of baby food as opposed to a small consumable amount of baby food. The remaining MAE involved delays in accessing or acquiring the medication (medication not available) and one error of omission.
Process Measures Compliance with the six safety processes is described in Figure 2. In general, the six safety process measures were achieved 86% of the time (ranging from 23% to 100%). The compliance varied substantially between the different units and between the different safety processes. Labeling of medications and explaining the medication(s) to the patient or their guardian were the two processes with the highest compliance for all three units. In the ICU, comparison of the medication to the eMAR twice was achieved only 32% of the time. In these situations, the observer noted that the nurse compared the medications to the eMAR only one time. There were a total of 298 distractions observed during the medication administration process across all three units. MAAP provides guidance that each distraction should be accompanied by an attempt to minimize the distraction per dose administered. For example, if a co-worker interrupts the nurse during medication administration, the nurse informs the co-worker to discontinue speaking until the nurse is finished and has thereby minimized the distraction. For each distraction identified during this observation period, attempts to minimize the distraction occurred 68% of the time (204/298). In the ICU, compliance with checking two forms of patient identification was achieved
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Figure 1. Error-Free Dose Administrations at the ICU and the Two ACUs ICU, intensive care unit; ACU, acute care unit 1; ACU 2, acute care unit 2
only 31% of the time as compared to 70% in ACU1 and 91% in ACU2. The most common contributor was failure to visually confirm the patient’s identity by checking the patient’s wristband. Lastly, in approximately 10% of the observations in the ICU it was difficult to determine whether visual confirmation of the patient’s wristband ID had occurred (e.g., difficult to witness the nurse directly reading and confirming the patient’s wristband).
Incident Reports Seven nursing-related medication administration IRs were reported for the ICU, ACU 1, and ACU 2, during the same time period. The types of MAE reported included wrong dose (n = 3), wrong time (n = 1), omission (n = 1), other; forgot to scan patient (n = 1); and extra dose (n = 1). One of the IRs reported an MAE where the patient’s wristband was never scanned to confirm the patient’s identity. All other IRs could not be definitively linked to a failure in BCMA.
Discussion Outcome Measures To our knowledge, this is the first direct observation study that has evaluated MAE in
pediatrics post-BCMA implementation. Using the direct observation methodology we identified a low MAE rate (5%) in children. This MAE rate is lower than what has been described in the pediatric literature using similar direct observation methodology (12– 31%; Buckley et al., 2007; Chua et al., 2010; Prot et al., 2005; Schneider et al., 1998). The low rate of MAE in our study may be due, in part, to BCMA implementation, however a direct cause–effect relationship is difficult to establish. Importantly, none of the published pediatric studies involving direct observation had BCMA in place at the time of evaluation. Our study demonstrated a lower pediatric MAE rate as compared to the adult literature using the same direct observation methodology, 5% vs. 8–28%, respectively (Keers, Williams, Cooke, & Ashcroft, 2013). Reasons for this difference include the heterogeneity of the adult literature, variances in the types of medications selected for observation, timing and duration of the observations, training of the observers, and whether medication timing errors were included as errors in the analysis. A notable trend in the adult literature suggests that when the direct observation process is repeated at least yearly on the same patient care units, the MAE
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Figure 2. Compliance with the Six Safety Processes for Each Administered Dose N = 300 administrations, 100 administrations on each unit *More than one distraction may have occurred for each dose administered
rate decreases over time (4.3% in 2010 and 1.2% in 2011; Kliger et al., 2009). More research is needed to determine the reasons for the decrease and whether this corresponds to an improved culture of safety. Several studies have attempted to assess the impact of BCMA on MAE rates with varied results. For example, implementation of BCMA has not been shown to eliminate MAE rather mixed results have been described with decreases, increases, and no effect being reported (Young, Slebodnik, & Sands, 2010). One of the largest studies conducted using direct observation methodology evaluated MAEs before and after BCMA implementation in adults at a tertiary care medical center and reported a decrease in MAE from 11.5% to 6.8% (Poon et al., 2010). Although our study did not evaluate pre-BCMA MAE, our post-BCMA MAE rate is consistent with this study. The most common errors identified in previous MAAP evaluations on adult units included wrong dose (0.5%), wrong technique (0.3%), omission, and wrong time (0.2%). These results are similar to the outcome measures observed in this study. Wrong route (4%) was the most common MAE identified, which is not
surprising as pediatric medication administration may require the use of alternate routes of medication administration besides the oral route especially for infants and very young children. Importantly, there were no instances of wrong dose and the use of BCMA may have contributed to this finding (Coyle & Heinen, 2005).
Process Measures Compliance with all of the six safety process measures as an aggregate was achieved 86% of the time (ranging from 23% to 100%). This is consistent with that observed during the first year of MAAP in adults (85.6% in 2008). The similar compliance rate is noteworthy considering that the UCSF BCH had not fully instituted the same educational efforts that were in place in the adult hospital regarding the six safety processes. Furthermore, this was the first time that the UCSF BCH was evaluated using the MAAP methodology. There was a greater variation within the six safety processes between the adult MAAP and the pediatric observations with compliance ranging between 23% and 100% in UCSF BCH compared to 73.7–99.8% in adults
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(Kliger et al., 2009). The ICU was the main contributor to the variation, which can be explained by the different workflow. For example, in the ICU, scanning the patient’s armband is sufficient when identifying the patient. A visual check is not required since the patients in the intensive care nursery are not ambulating and remain in isolettes. This difference in workflow was unanticipated. This variation has led to important quality and safety discussions on ways to strengthen the patient identification process as well as changes that should be considered for future direct observation interventions in the intensive care nursery. Finally, the ability to confirm that a visual check of the patient’s wristband was accomplished was difficult to assess in 10% of the doses administered in the ICU. In many of these instances, the visual check happened quickly, often with a glance of the wristband making it a challenge to confirm that there was positive identification. A visual and subsequent verbal confirmation of the patient’s name by the nurse administering the medication may be one way to address this issue. In a minority of instances, medications were charted as being administered before the medication was actually given. The reason for this practice is unknown; however, it is possible that inexperience and confusion with BMCA implementation and subsequent changes to the workflow may have contributed to this finding. It was encouraging to see that, on all three units, attempts were made to minimize distractions the majority of the time. Minimizing distractions during the medication administration process is critical to preventing MAE since the number of interruptions during the administrating medication process has been clearly associated with MAE (Westbrook, Woods, Rob, Dunsmuir, & Day, 2010). This study also identified instances, according to protocol, when the medication was not compared to the eMAR at least twice. In the ICU this occurred at a frequency of 32%, which could be explained by the different workflow. For example, in the ICU, the first comparison occurs when the medications arrive on the floor, long before they are administered. Under this workflow, this comparison would fall outside of the medication administration process and therefore would not be included in the observation.
Incident Reports The IR system identified different MAE compared to what was detected by direct observations. Consequently, both methods should be used to better characterize the scope of MAE. The IR system does not require the user to indicate which of the six safety process measures contributed to the MAE. Therefore it was not possible to determine which of the six safety processes were not followed and which safety process was a contributing factor. In addition, the IR reports did not contain enough detail to determine whether BCMA contributed to the error. As a result of these findings, the IR system software is being reevaluated to help UCSF BCH address these issues. The success of the direct observation process was based on strong support from UCSF BCH nursing administrators, nurse managers, nursing performance improvement, and a very strong collaboration between pharmacy and nursing. All groups worked together to accommodate the observations while ensuring a seamless, thorough, and collaborative process without interruptions to patient care.
Limitations All observers who participated in the direct observation process attempted to limit observation bias by being objective, nonjudgmental, and discrete (Barker et al., 2002). The pharmacist observers had no prior experience working in the UCSF BCH and therefore did not have any established relationships with staff. In addition, since direct observation had not occurred in the UCSF BCH previously, the observers had no predetermined assumptions as to the expected outcome of the observations. At the time of the study, we offered each nurse the option to participate in the direct observation process. Although this may have introduced bias, we felt it was important to do so as to not adversely impact patient care. Observation bias is a possibility in our study, meaning that nurses may have changed their behavior while being observed (Hawthorne effect). Interestingly, the Hawthorne effect does not seem to play a role in medication administration as described in previous direct observation literature. According to one study, the MAE rate did not differ between nurses who were observed and those that were not observed administering medications (Dean & Barber, 2001). Further studies
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are needed to assess the impact of the observation duration for each individual nurse while administering medications (1–3 hr). An effort was made to limit any unforeseen changes in medication administration behavior while the nurses were being observed (also known as the Hawthorne effect; Barker, 1980). Observers made every effort to observe the same nurse for at least 1–2 hr to maximize nurse comfort with direct observation. In some instances, the nurses had many activities leading up to and between each medication administration, therefore it is difficult to exclude the Hawthorne effect as a possible contributor. Interestingly, the adult MAAP methodology recommends observing different nurses in order to get a wider perspective over the situational setting. One of the more important findings from the adult MAAP project was the frequency of MAE declined with each subsequent year of direct observation. This finding suggests that the direct observation methodology results in a greater awareness of medication administration safety and facilitates a culture of safety. As a result of these data, the UCSF BCH intends to participate in the yearly MAAP observation study. An updated version of the adult MAAP data collection tool has been developed. The updated version included minor changes adapted to the UCSF BCH, and did not differ significantly from the previous version and attempts were made to minimize the impact of the data collection tool changes. When applying the MAAP methodology it is important to emphasize that one event has the ability to impact all subsequent observations. For example, if the nurse is distracted in the beginning of the administration process, it will become a distraction for the entire medication administration process and therefore all subsequent doses can be impacted. This process was very difficult to capture and therefore address in the data collection tool. Another limitation with the methodology is that it only identifies MAE that occurs in relation to the observation and does not include those independently identified by the nurse (outside of the direct observation process). For example, if a nurse has noticed that the previous dose was not given (omitted), that error is not included in the results as it was not observed. Importantly, when MAE was detected by the observers who were not related to the direct
observation process, they were always communicated to the nurse manager for evaluation and follow-up.
Conclusions This pilot study identified a low rate (5%) of MAE and fair compliance with the six safety processes on two pediatric units and one neonatal unit following BCMA implementation. An additional finding was that the IR system identified different MAE compared to that identified via the direct observation methodology. BCMA implementation did not eliminate MAE and relatively few BCMA workarounds were identified. This pilot study serves as a foundation for future observations and provides important insight into reducing MAE in children.
References Barker, K. N. (1980). Data collection techniques: Observation. American Journal of Hospital Pharmacists, 37, 1235– 1243. Barker, K. N., Flynn, E. A., Pepper, G. A., Bates, D. W., & Mikeal, R. L. (2002). Medication errors observed in 36 health care facilities. Archives of Internal Medicine, 162, 1897–1903. Buckley, M. S., Erstad, B. L., Kopp, B. J., Theodorou, A. A., & Priestley, G. (2007). Direct observation approach for detecting medication errors and adverse drug events in a pediatric intensive care unit. Pediatric Critical Care Medicine, 8, 145–152. Chua, S. S., Chua, H. M., & Omar, A. (2010). Drug administration errors in paediatric wards: A direct observation approach. European Journal of Pediatrics, 169, 603–611. doi:10.1007/s00431-009-1084-z Collaborative Alliance for Nursing Outcomes (CALNOC). (2010a). Codebook Part I. Coordinating and using CALNOC data in the hospital setting. Acute Care Version. Sacramento, CA: Author. Collaborative Alliance for Nursing Outcomes (CALNOC). (2010b). Codebook Part II. Data capture and submission. Acute Care Version. Sacramento, CA: Author. Coyle, G. A., & Heinen, M. (2005). Evolution of BCMA within the Department of Veterans Affairs. Nursing Administration Quarterly, 29, 32–38. Dean, B., & Barber, N. (2001). Validity and reliability of observational methods for studying medication administration errors. American Journal of Health-System Pharmacy, 58, 54–59. Flynn, E. A., Barker, K. N., Pepper, G. A., Bates, D. W., & Mikeal, R. L. (2002). Comparison of methods for detecting medication errors in 36 hospitals and skilled-nursing facilities. American Journal of Health System Pharmacy, 59, 436–446. Haw, C., Stubbs, J., & Dickens, G. (2007). An observational study of medication administration errors in old-age psychiatric inpatients. International Journal for Quality in Health Care, 19, 210–216. Helmons, P. J., Wargel, L. N., & Daniels, C. E. (2009). Effect of bar-code-assisted medication administration on medication administration errors and accuracy in multiple patient care areas. American Journal of Health System Pharmacy, 66, 1202–1210. doi:10.2146/ajhp080357
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Kaushal, R., Bates, D. W., Landrigan, C., McKenna, K. J., Clap, M. D., Federico, F., & Goldmann, D. A. (2001). Medication errors and adverse drug events in pediatric inpatients. Journal of the American Medical Association, 285, 2114–2120. doi:10.1001/jama.285.16.2114 Keers, R. N., Williams, S. D., Cooke, J., & Ashcroft, D. M. (2013). Prevalence and nature of medication administration errors in health care settings: A systematic review of direct observational evidence. Annals of Pharmacotherapy, 47, 237–256. doi:10.1345/aph.1R147 Kliger, J., Blegen, M. A., Gootee, D., & O’Neil, E. (2009). Empowering frontline nurses: A structured intervention enables nurses to improve medication administration accuracy. Joint Commission Journal on Quality and Patient Safety, 35, 604–612. doi:10.1377/hlthaff.2011.0687 Koppel, R., Wetterneck, T., Telles, J. L., & Karsh, B. T. (2008). Workarounds to barcode medication administration systems: Their occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics Association, 15, 408–423. Patterson, E. S., Cook, R. I., & Render, M. L. (2002). Improving patient safety by identifying side effects from introducing bar coding in medication administration. Journal of the American Medical Informatics Association, 9, 540–553. doi:10.1197/jamia.M1061 Patterson, E. S., Rogers, M. L., Chapman, R. J., & Render, M. L. (2006). Compliance with intended use of Bar Code Medication Administration in acute and longterm care: An observational study. Human Factors, 48, 15–22. Pedersen, C. A., & Gumpper, K. F. (2007). ASHP national survey on informatics: Assessment of the adoption and use of pharmacy informatics in U.S. hospitals—2007. American Journal of Health-System Pharmacy, 65, 2244– 2264. doi:10.2146/ajhp080488 Pedersen, C. A., Schneider, P. J., & Scheckelhoff, D. J. (2010). ASHP national survey of pharmacy practice in hospital settings: Monitoring and patient education— 2009. American Journal of Health-System Pharmacy, 67, 542– 558. doi:10.2146/ajhp090596 Poon, E. G., Keohane, C. A., Yoon, C. S., Ditmore, M., Bane, A., Levtzion-Korach, O., et al. (2010). Effect of barcode technology on the safety of medication administration. New England Journal of Medicine, 362, 1698–1707. doi:10.1056/NEJMsa0907115 Prot, S., Fontan, J. E., Alberti, C., Bourdon, O., Farnoux, C., Macher, M. A., et al. (2005). Drug administration errors and their determinants in pediatric in-patients. International Journal for Quality in Health Care, 17, 381– 389. Schneider, M. P., Cotting, J., & Pannatier, A. (1998). Evaluation of nurses’ errors associated in the preparation and administration of medication in a pediatric intensive care unit. Pharmacy World and Science, 20, 178–182. Smith, D. J. (2004). Building a safer NHS for patients: Improving medication safety. Pan American Health Organization, World Health Organization. Retrieved April 11, 2013, from http://www.bvsde.paho. org/bvsacd/cd65/medicationsafety.pdf Westbrook, J. I., Woods, A., Rob, M. I., Dunsmuir, W. T., & Day, R. O. (2010). Association of interruptions with an increased risk and severity of medication administration errors. Archives of Internal Medicine, 170, 683–690. doi:10.1001/archinternmed.2010.65 Wiles, J. R., Vinks, A. A., & Akinbi H. (2013). Federal legislation and the advancement of neonatal drug studies. Journal of Pediatrics, 162, 12–15. doi:10.1016/j.jpeds.2012.08.034. Young, J. Y., Slebodnik, M., & Sands, L. (2010). Bar code technology and medication administra-
tion error. Journal of Patient Safety, 6, 115–120. doi:10.1097/PTS.0b013e3181de35f7
Authors’ Biographies Anna Hardmeier, MSc in Pharmacy, at the time she was a visiting master’s student from Uppsala University in Sweden. Dr. Candy Tsourounis is Professor of Clinical Pharmacy in the Medication Outcomes Center in the Department of Clinical Pharmacy at the UCSF School of Pharmacy. Mary Moore, RN, MS, CPHQ, is responsible for Nursing Performance Improvement at UCSF Medical Center and has been involved in coordinating the Medication Administration Accuracy Project in the adult hospital. Wendy E. Abbott is a senior analyst responsible for Nursing Performance Improvement at UCSF Medical Center and has been involved in managing the Medication Administration Accuracy Project in the adult hospital. B. Joseph Guglielmo, PharmD, was the Chair of the Department of Clinical Pharmacy; currently he serves as Dean of the UCSF School of Pharmacy. For more information on this article, contact Candy Tsourounis at [email protected].
Journal for Healthcare Quality is pleased to offer the opportunity to earn continuing education (CE) credit to those who read this article and take the online posttest at http://www. nahq.org/education/content/jhq-ce.html. This continuing education offering, JHQ 249, will provide 1 contact hour to those who complete it appropriately.
Core CPHQ Examination Content Area IV. Patient Safety
Pediatric Medication Administration Errors and Workflow Following Implementation of a Bar Code Medication Administration System Objectives r Identify two advantages and two disadvantages associated with using direct observation to assess medication administration errors r List the six medication safety process measures r Characterize the types of medication administration errors that may not be captured using the direct observation methodology
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Please select the single best answer. 1. The na¨ıve observer technique is when the observer: a. is na¨ıve to the direct observation process b. is unaware of the patients medications and diagnosis c. is unsure about the appropriateness of each medication d. is not able to scan the patient’s arm band for identification 2. The outcome measures that relate to medication administration safety include which of the following? a. Administering the right drug b. Checking two patient identifiers c. Comparing the medication to the medication administration record d. Keeping the medication labeled throughout the administration process 3. Which of the following process measures was problematic when using bar coding medication administration? a. Checking two patient identifiers b. Explaining the medication to the patient c. Minimizing distractions and interruptions d. Keeping the medication labeled throughout the administration process 4. Which of the following statements regarding the direct observation method for detecting medication administration errors is TRUE? a. Implementation of bar code medication administration has been shown to eliminate medication administration errors b. “Wrong technique” is the most common type of medication administration error identified in pediatrics c. Pediatric medication administration error rates are higher than those observed in adults d. Direct observation is one of several methods that can help identify different types of medication administration errors 5. Which of the following is a limitation to using the direct observation method? a. Repeating the observations yearly on the same patient care areas
6.
7.
8.
9.
b. Including observers who are unfamiliar in the care of the patients observed c. Using a multidisciplinary team to observe the medication administration process d. The duration of time that the individual administering medications is observed Which of the following steps of the direct observation process is FALSE? a. Patients with extended length of stay (> 3 weeks) were excluded from observation b. Records that had incomplete documentation of the observation process were included c. Observations were recorded during the most intensive medication administration hours d. The observation process continued until 100 medication administrations were observed on each unit Distractions during the start of the medication administration process: a. Have the potential to impact all subsequent medications administered b. Can be minimized if bar code medication administration is used c. Result in bar code medication administration workarounds d. Account for the majority of omitted medications Which of the following workarounds were identified during the direct observation process? a. Forgetting to scan the patient’s wrist band b. Using more than one form of patient identification c. Medications charted before they were actually given d. Printing multiple patient wrist bands affixed to something other than the patients wrist band for scanning of medications Implementing a successful direct observation program is dependent upon which of the following? a. Including specialists trained in medication error detection on the multidisciplinary team b. Ensuring bar code medication administration technology be instituted before observations begin
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c.
Getting support from administrators and managers before initiating the program d. Educating nursing staff on the consequences of not contributing to the program 10. This study identified the following: a. A high rate of medication administration errors among pediatric and neonatal units
b. The incident reporting system was inferior to the direct observation process in identifying medication errors c. Bar code medication administration did not eliminate pediatric medication administration errors d. Non-compliance with the six safety processes
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