(How) do we learn from errors? A prospective study of the link between the ward's learning practices and medication administration errors

International Journal of Nursing Studies 51 (2014) 448–457

Contents lists available at SciVerse ScienceDirect

International Journal of Nursing Studies journal homepage: www.elsevier.com/ijns

(How) do we learn from errors? A prospective study of the link between the ward’s learning practices and medication administration errors A. Drach-Zahavy a,*, A. Somech b, H. Admi c, I. Peterfreund d, H. Peker e, O. Priente f a

Department of Nursing, University of Haifa, Haifa, Israel Department of Leadership & Policy in Education, University of Haifa, Haifa, Israel Rambam Medical Center, Haifa, Israel d Bnei Zion Medical Center, Haifa, Israel e Carmel Medical Center, Haifa, Israel f Naharia Medical Center, Naharia, Israel b c

A R T I C L E I N F O

A B S T R A C T

Article history: Received 9 January 2013 Received in revised form 17 April 2013 Accepted 13 June 2013

Background: Attention in the ward should shift from preventing medication administration errors to managing them. Nevertheless, little is known in regard with the practices nursing wards apply to learn from medication administration errors as a means of limiting them. Aims: To test the effectiveness of four types of learning practices, namely, non-integrated, integrated, supervisory and patchy learning practices in limiting medication administration errors. Methods: Data were collected from a convenient sample of 4 hospitals in Israel by multiple methods (observations and self-report questionnaires) at two time points. The sample included 76 wards (360 nurses). Medication administration error was defined as any deviation from prescribed medication processes and measured by a validated structured observation sheet. Wards’ use of medication administration technologies, location of the medication station, and workload were observed; learning practices and demographics were measured by validated questionnaires. Findings: Results of the mixed linear model analysis indicated that the use of technology and quiet location of the medication cabinet were significantly associated with reduced medication administration errors (estimate = .03, p < .05 and estimate = .17, p < .01 correspondently), while workload was significantly linked to inflated medication administration errors (estimate = .04, p < .05). Of the learning practices, supervisory learning was the only practice significantly linked to reduced medication administration errors (estimate = .04, p < .05). Integrated and patchy learning were significantly linked to higher levels of medication administration errors (estimate = .03, p < .05 and estimate = .04, p < .01 correspondently). Non-integrated learning was not associated with it (p > .05). Conclusions: How wards manage errors might have implications for medication administration errors beyond the effects of typical individual, organizational and technology risk factors. Head nurse can facilitate learning from errors by ‘‘management by walking around’’ and monitoring nurses’ medication administration behaviors. ß 2013 Elsevier Ltd. All rights reserved.

Keywords: Learning Management Medication administration Medication administration errors Nurses

* Corresponding author at: Department of Nursing, Faculty of Social Welfare and Health Sciences, University of Haifa, Mount Carmel, 31905, Israel. Tel.: +972 4 8288007; fax: +972 4 8288017. E-mail address: [email protected] (A. Drach-Zahavy). 0020-7489/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijnurstu.2013.06.010

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

What is already known about the topic?  Medication safety continues to be a major challenge for healthcare institutions, nurses and patients.  Nurses’ personal attributions, organizational characteristics, and the use of technological solution can inflate or limit medication administration errors.  Wards with a positive learning climate have the competence to draw the appropriate conclusions from their safety information systems, leading to reduced rates of medication administration errors. What this paper adds  Supervisory learning – where the head nurse took responsibility for the learning at the ward – was the only practice linked to reduced medication administration errors.  Integrated learning, where all team members collectively engaged with learning from errors and patchy learning, where no systematic learning occurred were linked to higher levels of medication administration errors.  Non-integrated learning, where learning was centralized in the hospital’s risk-management unit – was not associated with medication administration errors.  While computerized medication administration was safer in the steps of preparing the medication and identifying the patient, it was less safe in the steps of taking patients’ relevant measures and administering the medicine. 1. (How) do we learn from errors? A prospective study in hospitals of the link between the ward’s learning practices and medication administration errors Since the publication of the influential report To err is human (Kohn et al., 2000), a tremendous amount of research has been devoted to identifying factors that promote safety medication administration in healthcare organizations (Chang and Mark, 2011; Wimpenny and Kirkpatrick, 2010). Nevertheless, further quality reports, one in 2007 (Aspden et al., 2007) and another in 2010 (the Robert Wood Johnson Committee, 2011), concluded that healthcare has not necessarily grown safer, implying that the past several years might represent a ‘‘lost decade’’ in patient safety (Wynia and Classen, 2011). In fact, the 2007 report estimated that a hospitalized patient is subject to one medication administration error per day, implying that at least 1.5 million preventable drug events occur annually in the USA alone (Aspden et al., 2007); most research heretofore has focused on identifying personal and organizational predictors of errors in an attempt to prevent them from occurring in the first place (Chang and Mark, 2011). Unfortunately there has been less emphasis on error management, namely how to deal with errors when they already occur (Chang and Mark, 2011). In this vein, research has identified that organizations with a positive learning climate1 have the competence to draw

1 Although learning climate has been considered similar to, or incorporated into safety climate, learning climate is focused on learning from errors, while safety climate signals the importance of safety.

449

the appropriate conclusions from their safety information systems, leading to reduced rates of error (Edmondson, 2004; Katz-Navon et al., 2009). These studies typically advocate the development of organizational practices related to communicating about errors, sharing error knowledge, helping in error situations, and quickly detecting and handling errors, all of which are attributes of learning climate (Chang and Mark, 2011). Certainly, these findings benefit theoreticians and practitioners aiming at limiting medication administration errors. Yet they simultaneously raise questions for future research as these recommendations frequently seem somewhat unclear for practitioners aiming to absorb them into the ward’s routine work (Vashdi et al., 2007). How do errors become transparent, discussed and reflected upon? What specific practices/routines can enhance ‘‘talking about errors’’, ‘‘sharing knowledge’’ or ‘‘quickly handling errors’’? Who should be responsible for the development and endorsement of these practices: specialists in risk management? The unit’s staff? The unit’s leader? And most importantly, are some practices more effective in limiting errors than others? To fill these voids, this study examines the learning practices followed by a medical ward to handle errors. Learning practices are defined as institutionalized structural and procedural arrangements, and informal systematic practices, which allow the ward systematically to collect, analyze, store, disseminate and use information relevant to its performance and its members (Popper and Lipshitz, 2000). Our study thus makes a number of contributions to the medication administration safety literature in hospitals. First, while recent research has demonstrated that learning from errors might have significant effects on handling errors as well as on preventing them in the future, little is known about the practices underlying the learning process, perhaps because authors assumed that learning develops naturally among team members, and thus cannot be nurtured (Vashdi et al., 2007). Furthermore, there is some primary evidence showing that in the absence of appropriate practices, the learning potential of a team may be lost (Popper and Lipshitz, 2000). Moreover, in this study, we explore the unique contributions of learning practices beyond personal and organizational predictors commonly identified in prior research. We do so in a prospective study, which also controls the effects of baseline errors in the ward. 2. Conceptual framework 2.1. Medication errors Medication errors can occur at any time along the continuum of the medication system, from prescribing to administration. Lisby et al. (2005) found that most errors occurred in the transcription stage (56%), followed by the nurse administration (41%) and doctor prescribing (39%) stages, with a much lower error rate at the pharmacy dispensing stage (4%). This study focuses on the complex and demanding medication administration stage. According to the best practice for medication administration, the nurse must ensure the ‘‘5 rights’’ of drug administration:

450

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

identifying the right patient, the right drug, the right time, the right route, and the right dose. Best practice for medication administration also advocates that nurses should be responsible for preparing and checking medications, updating their own knowledge of medications, monitoring the effectiveness of treatment, reporting adverse reactions and teaching patients about their drugs (Fogarty and McKeon, 2006). Definitions of medication administration error vary across the literature (Brady et al., 2009). Here it defined as any deviation from procedures, policies, and/or best practices for medication administration. Our definition emphasizes that although these deviations in themselves do not necessarily produce adverse consequences (i.e., patient’s harm), they create conditions that make such consequences more likely to occur. The vast majority of adverse consequences for patients were attributable to deviations from standard procedures of medication administration (Armitage and Knapman, 2003). In fact, procedural violation is such an influential factor in accident causation that many researchers (e.g., Reason, 1990; Fogarty and McKeon, 2006) have suggested that it be treated as a safety outcome variable in its own right, rather than just one of the predictors of error. Traditionally, research on medication administration errors adopted an individual perceptive by identifying personal characteristics such as nurses’ knowledge, motivation, or demographic characteristics relevant to medication administration errors (Chang and Mark, 2011). Examples are: nurse’s mathematical skills (Armitage and Knapman, 2003), knowledge of medications (Brady et al., 2009; Fogarty and McKeon, 2006); length of nursing experience (O’Shea, 1999). However, the context in which practitioners work also contributes to errors. Specifically, robust research has emphasized that nurses’ workplace characterized by high workload, distraction and interruptions contributes to medication administration errors (Tucker et al., 2002). This led to designing technology to reduce distraction and interruptions which might lead to medication administration errors (Wulff et al., 2011). Quiet, locked medication rooms/cabinets have been designed so as to limit interruptions during medication preparation (Connard et al., 2010). Medication administration technologies have been developed. Examples are barcode point of care, which allows tracking and recording of medication administration (Larrabee and Brown, 2003); automated medication dispensing machines which control access to medications by signaling dosing times and supplying medication doses in ready-to-administer form (Barker, 1995); electronic medication administration records which replace handwritten records to reduce transcription errors (Pedersen et al., 2006). Some studies have documented the beneficial effects of such technologies for limiting medication administration errors (Meadows, 2003); others have shown mixed effects (Oren et al., 2003; Wulff et al., 2011). To sum up, research has demonstrated the important role of personal and context characteristics, as well as technology solutions in preventing medication administration errors. This study suggests shifting attention from preventing medication administration errors in the ward to managing them, by addressing

the ward’s learning practices employed, and testing their effectiveness. 2.2. The cumulative benefit of learning practices A central premise of the error management approach is that human errors cannot be prevented completely; it emphasizes how errors may be managed when they occur and how lessons may be learned from them (Keith and Frese, 2008). Without the transformation of gathered data on errors into useful knowledge, in-depth understanding of most common error types, patterns and characteristics, as well as of individual or healthcare system deficiencies as error-contributing factors, it is obviously impossible (Chang and Mark, 2011; Kiekkas, 2011). Based on an observational study of 33 wards, Drach-Zahavy and Pud (2010) recently proposed four patterns of learning practices endorsed in wards to handle medication administration errors. (a) Non-integrated learning: practices for data collection, data analysis and change implementation were all centralized in the hospital’s risk-management unit, outside the ward. This unit collected data in order to monitor and document variations in the medication administration process by means of planned or surprise observations of nurses administering medications in their unit; alternatively, the risk management unit could collect data from reports of error and irregular cases. Data analysis was through debriefings of manifested or latent errors or near misses and was aimed at pinpointing the root cause of errors. Conclusions were drawn by risk management professionals. Nurses were then charged with implementing the lessons learned in their daily routine. (b) Supervisory learning: practices of data collection, analysis and change implementation were structured in a manner that define the head nurse’s responsibility for operating the entire learning cycle. The head nurse took active steps to collect error data at the unit, by directly observing the nurses who administer medications or reviewing nurses’ documents. When she discovered deviations, she activated several mechanisms to prevent further errors: enhanced training, staff meetings and limited punishments. (c) Integrated (team) learning: learning practices were performed mainly by collaborative efforts of team nurses: data collection was frequently based on systematic investigation of handwritten/electronic medication administration records for data collection; formal and informal analyzing tools were operated at staff meetings. Change might involve collective implementation of a design or plan arising from a discussion, or conducting a test or trial as a means of obtaining better performance of an action understood not to be perfect. (d) Patchy learning: learning practices seemed less structured. Mechanisms for data collection, data analysis and change implementation were loosely structured in the ward, resulting in limited shared knowledge, collective insight or change. Error data were collected primarily through the willingness of the erring nurse, another staff member, or a patient’s report of an error. Data analysis was primarily through discussing the specific error per se, usually with the erring nurse. At times sanctions were

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

imposed on the erring nurse, such as preventing her from administering medication until she revised her medication knowledge. The head nurse tended to close the case within the unit, which limited insights for nurses to improve further medication administration. These patterns of learning practices vary according to three main assumptions in learning theory. First, learning is a cyclical process involving three steps: data collection, data analysis, and drawing conclusions for implementing change (Edmondson, 2004). Hence practices that emphasize change process, based on systematic collection and analysis of error data, are more effective than nonsystematic practices. Hence we expect patchy learning, the least systematic and structured learning practice, to be positively associated with medication administration errors. Secondly, learning practices can be classified as integrated or non-integrated (Popper and Lipshitz, 2000). A learning mechanism is integrated if its ‘operators’ and ‘clients’ (i.e., organization members who respectively are responsible for generating and applying the lessons learned) are the same people. Team meetings to analyze certain flaws in formal regulations of medication administration exemplify an integrated learning mechanism for analyzing data. By contrast, a learning mechanism is nonintegrated if operators and clients are not the same people (Popper and Lipshitz, 2000). An example is the activity of the risk-management unit in collecting and analyzing data, as well as preparing reports for the unit’s management, which then must be implemented by nurses in ward’s daily routine. We suggest that the application of integrated learning mechanisms, namely those operated by the nurses who administer the medication, can limit error rate. Nurses are frequently in the best position to identify operational problems in the medication administration process, as they often have direct access to data on the causes or consequences of errors, and it is their work routines that are disrupted by problems (Brady et al., 2009; Chang and Mark, 2011). Integrated learning mechanisms also enjoy wide acceptance and minor resistance by team members because they themselves participate in the decision making (Edmondson, 2004). Drach-Zahavy and Pud (2010) found that team learning practices constituted the only effective pattern associated with lower medication administration error rates. Non-integrated learning, which takes place through risk-management units, might be advantageous because it is undertaken by specialists trained and engaged in analyzing errors and implementing conclusions drawn. They therefore benefit from a comprehensive understanding of the hospital’s safety issues. These specialists are also less involved in wards’ internal politics, hence might be more objective in their analysis. However, when learning is not integrated, recommendations by the riskmanagement unit might not be implemented due to nurses’ resistance to change; they might perceive the recommendations as not relevant to their ward’s daily routine. Similarly, if the resultant conclusions are not directly transferred to the clinicians they cannot be used to institute effective changes in clinical practice (Kiekkas, 2011). In sum, we expect that non-integrated learning

451

will not be associated with medication administration errors. Thirdly, practices can also vary by the degree of involvement of the direct supervisor, namely the head nurse. Managerial involvement in learning practices signals to employees the extent to which their supervisor is committed to safety. Supervisors set the tone and tempo for safety, for example, by emphasizing specific safety behaviors while overlooking others (Leroy et al., 2012). Conversely, if nurses perceive that the head nurse undermines learning or sends a message that safety could be ignored without consequences, medication administration error ratios might be inflated. Therefore we expect supervisory learning to be associated with lower medication administration errors. Hypotheses: Learning practices will be associated with medication administration errors above and beyond personal characteristics, organizational overload and technological solutions; such that (a) Supervisory learning and integrated learning practices will be negatively associated with medication administration errors. (b) Patchy learning will be positively associated with medication administration errors. (c) Non-integrated learning will not be associated with medication administration errors. 3. Methods 3.1. Sample Participants were nurses at four large urban hospitals working on 80 nursing wards altogether. Four of the 80 units had a policy of not participating in research, so 76 nursing units, representing medical, surgical, and internal care, participated in the study (response rate was 95%). Unit size ranged from six to 20 nursing staff, with an average of 15 nurses (SD = 8.23). In each unit, all registered nurses who worked at least three times a week on morning shifts (on average seven nurses) participated in the study (N = 360; mean response rate 68%, range 55–90%). Nurses on morning shifts were chosen to control for the effects of different rosters on safety compliance (Armitage and Knapman, 2003). The sample was mainly women (83.1%). Average unit tenure was 10.51 years (SD = 7.54) and average job tenure was 14.31 years (SD = 9.20). In education, most nurses – 53.9% – had a Bachelor’s degree, 36.9% a college degree and 9.2% a Master’s degree. Possible differences among hospitals and/or units were calculated in the statistical analyses. 3.2. Design and procedure The study employed a prospective multi-methods (survey, observations and administrative archive data) design. The first wave of data collection served for collecting baseline measures of medication administration errors and assessing the control and independent variables. Four months later, we re-assessed medication administration errors. Several steps were taken to assure the hospitals’ commitment to the study. Firstly, approval

452

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

for the study was received from the ethical committee in each hospital. Secondly, we approached the nursing manager of each hospital and presented her with the research aim; when necessary, we also presented the study at the head nurses’ staff meetings. Thirdly, each nurse had to sign a consent form, which was kept separately from the questionnaire and observational data. To elicit greater accuracy of response, each questionnaire carried a cover sheet guaranteeing secrecy of the data. 3.3. Measures Medication administration errors were measured by a structured observation sheet developed and validated in prior research on nurses administering medications (Drach-Zahavy and Pud, 2010). This has been described as one of the better methods for measuring safety compliance, as most studies to date have relied on nurses’ retrospective self-report, thus were subject to underestimation bias (Armitage and Knapman, 2003; Tissot et al., 2003). In addition, the ‘‘good impression bias’’ (Hawthorne effect, Mayo, 1949) stemming from the presence of the observer in patient-provider encounters is deemed minimal, as healthcare providers typically become quickly accustomed to the observer’s presence and tend to exhibit their natural behavior (Roter, 1989). The structured nine-column observation sheet corresponded to the nine distinctive steps in the medication administering process, based on best practices and Health Ministry guidelines: verifying the physician’s prescription of medication, prescription documentation in the cardex (nurses’ reporting sheet), preparation of the medication for a specific patient, bedside patient identification before administration of the drug, taking relevant measurements (e.g., blood pressure), giving information about the medicine, giving the medicine and ascertaining that it has been fully taken, signing the cardex to confirm administration of the medication, and checking for possible side effects. Observers were directed to mark + when the step was executed correctly, and to specify in detail the nature of the deviation when it was not. On each of the two waves of data collection, every nurse was observed administrating medications for a patient on three different occasions, thus medication administration errors were measured as the proportion of deviations from the prescribed process per patient across the three observations. Two graduate students, nurses by profession, participated as observers. The unit’s nurses perceived their presence as natural, which might prevent bias, yet their objectivity could be relied on and they were familiar with the best practices of medication administration. To ensure inter-rater reliability and observation validity the observers received 10 h of extensive training (Kappa = .85–.93). Learning practices were assessed by the Learning Practices Questionnaire (Drach-Zahavy and Somech, 2009), consisting of four subscales: (1) four items on non-integrated learning, for example, ‘‘Risk-management unit conducted planned observations of nurses administering medication’’ (a = .81); (2) four items on integrated learning (team learning), for example, ‘‘The team decides on

improvement processes such as identifying new ways to improve the medication administration process’’ (a = .74); (3) six items on supervisory learning, for example, ‘‘The head nurse observes nurses who administer medication’’ (a = .79); and (4) four items on patchy learning, for example, ‘‘The case is closed at the unit level through conversation with the erring nurse’’ (a = .80). Confirmatory factor analysis on the learning practices scales to test the dimensionality of our four-scale measure showed good fit (x2 = 77.98, df = 51, root mean square error of approximation [RMSEA] = .06, comparative fit index [CFI] = .95, goodness of fit index [GFI] = .90). Control variables. Nurses’ demographic data on their gender and education were controlled for at the individual level because research has shown that they may affect errors in medication administration (Armitage and Knapman, 2003). We also controlled for medication administration errors measured at the first wave of data collection. At the unit level, we controlled for unit’s load, calculated as the mean nurse/patient ratio across the observational session days (higher ratio indicates lower unit’s load: Buchan, 2005). Unit’s load was identified in prior research as a primary predictor of medication errors (e.g., Tucker et al., 2002). Finally, we controlled for technological devices, as previous research has identified their role in limiting medication administration errors (Oren et al., 2003; Wulff et al., 2011). Location of medication dispenser station was assessed by the observers, and dummy coded as 1 if the station was locked, isolated and located in a quiet room; 0 if not. Medication administration technologies were assessed by the observers, and dummy coded 0 if not used and 1 if used. 3.4. Analytic approach We examined individual-level, ward-level, and hospital-level effects on the individual-level outcome (medication administration errors measured at time 2). Because of the multilevel nested structure of the data (360 nurses in 76 different units, located at four different hospitals) we used a data-analysis method of mixed linear models, which takes into account that (a) nurses come from different wards, which are situated in different hospitals; and (b) individuals in one group may be more similar to each other than to individuals in other groups. The dependent variable was medication administration errors measured at time 2. The control variables on the individual level (medication administration errors at time 1, nurse’s gender, and education) and on the unit level (unit’s load, location of medication dispenser station and whether the medication administration process was computerized) were entered in step 1. The main effect terms of the unit-level learning practices were entered in step 2. In the analysis, the unit and the hospital were treated as random effects, and the other independent variables as fixed effects (Singer, 1998). Because learning practices assessed a property of the ward, we aggregated the individual responses to the ward level. Tests of rwg indicated ‘‘good’’ amount of homogeneity of responses at the unit level: the median values were .78, .90, .87 and .86 for non-integrated, team, supervisory and

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

453

patchy learning practices respectively (James et al., 1993). Inter-Class Correlation (ICC), assessing group variance relative to total variance tests had significant results for non-integrated, team, supervisory and patchy learning practices, with values of .12, .16, .14 and .13 respectively, suggesting further support for aggregation (Bliese, 2000). ICC generally ranges from 0 to .50 with a median of .12. 4. Results Table 1 presents medication administration errors means and SDs in the whole sample, and under computerized and manual administration. Of the nine medication administration steps, four were consistently performed by all nurses, with negligible deviations from the procedure: verifying the physician’s prescription of medication, documenting the prescription in the nurses’ reporting sheet, signing on the nurses’ reporting sheet to verify execution of the medication administration, and medicine provision. In 22% of the observations, nurses did not adhere to the guideline to identify the patient by name prior to medication provision. Next, in 31% of the observations, nurses did not prepare the medicine according to the ‘‘triple check’’ principle and in 37% – nurses did not take relevant measures (e.g., blood pressure) during the medication administration, as required. More important, in 62% of the observations, nurses did not provide the patient information about the medicine and almost in all cases (97%) nurses did not check for possible after effects. As for the mode of administration, computerization proved significantly safe in preparing the medication and identifying the patient, significantly less safe in taking patients’ relevant measures and administering the medicine, and non-significant in the other medication administration steps. These findings are illustrated in Fig. 1. Table 2 presents the means, standard deviations and correlation pattern of the study variables. The mean medication administration error ratio was .28 and .29 for time 1 and time 2 (r = .15; p < .01) respectively, indicating that approximately every third patient was exposed to some sort of deviation from procedure whenever he or she received medication. Secondly, among the control variables, while load and medication administration errors time 1 were positively associated with medication

Fig. 1. Medication administration error ratios by type of error and mode of administration. Note: 1 – verifying the physician’s prescription; 2 – documenting in nurses’ report sheet; 3 – preparing medication; 4 – identifying the patient; 5 – taking patient’s relevant measures; 6 – providing information on the medication; 7 – administering the medicine to the patient; 8 – confirming medication administration by signing the records; 9 – checking medication’s effect on the patient.

administration errors time 2, computerized medication administration was negatively associated with it. Thirdly, of the four learning practices, the one most frequently applied in the wards was non-integrated learning (M = 3.615; SD = .64); next was supervisory learning (M = 3.58; SD = .42). In third place was the integrated learning practice (M = 3.45; SD = .58) and the least frequent practice was patchy learning (M = 2.96; SD = .56). The correlations among non-integrated, team learning and supervisory learning practices were moderate and significant (ranging from .28 to .31), while the correlations of these practices with patchy learning practice were insignificant. These findings indicate that the four learning practices are distinct dimensions, albeit sometimes correlated. Finally, supervisory learning practices were negatively correlated, patchy learning practices and team learning were positively associated and non-integrated learning practices were not associated with medication administration errors time 2. Table 3 shows the findings of the mixed linear model analysis testing the study’s hypotheses. Of the control variables (Model 1), medication administration errors time

Table 1 Medication administration error ratio by source of error and mode of administration. Source of error

Whole sample Mean (SD)

Computerized medication administration Mean (SD)

Manual medication administration Mean (SD)

Verifying the physician’s prescription Documenting in nurses’ report sheet Preparing medication Identifying the patient Taking patient’s relevant measures Providing information on the medication Administering the medicine to the patient Confirming administration by signing the records Checking medication’s effect on the patient

.01 (.03) .00 (.00) .31 (.43) .22 (.38) .37 (.44) .62 (.20) .03 (.12) .03(.04) .97 (.10)

0 (.00) .00 (.00) .22 (.39) .17 (.34) .44 (.42) .62 (.23) .06 (.18) .06 (.04) .97 (.11)

0.3 (0.4) .00 (.00) .37 (.49) .25 (.39) .32 (.44) .62 (.18) .01 (.05) .01 (.02) .98 (.08)

* p < .05. ** p < .001. *** t was computerized with the Bonferroni correction.

t***

.82 – 3.39** 1.96* 2.54** .24 3.88** 1.42 .86

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

454

Table 2 Means, standard deviations and correlations among study variables.

a

(1) MAE time 1 (2) Gender (3) Education (4) Load (5) MA procedureb (6) Medication stationc (7) Non-integrated learning practices (8) Integrated learning practices (9) Supervisory learning practices (10) Patchy learning practices (11) MAE time 2

M

SD

.28 – – 5.34 – – 3.61 3.44 3.58 2.96 .29

.07 – – 1.48 – – .95 .93 .64 .94 .07

(1) 1.00 .05 .03 .12* .05 .01 .02 .00 12* .12* .15**

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

1.00 .08 .06 .16* .15* .09 .05 .01 .01 .01

1.00 .04 .04 .02 .01 .09 .05 .06 .12*

1.00 .19** .06 .10* .07 .07 .12* .12*

1.00 .31** .04 .05 .08 .04 .14*

1.00 .05 .07 .01 .05 .19*

1.00 .29** .28** .07 .06

1.00 .31* .08 .11*

1.00 .08 .12*

.13*

N = 360. a MAE: medication administration errors. b MA procedure: medication administration procedure: 2 – non-computerized; 1 – computerized. c Medication station: location of medication dispenser station: 1 – locked, isolated and quiet, 0 – not locked, isolated and quiet. * p < .05. ** p < .001.

1 and load were significantly and positively associated with medication administration errors time 2, but computerized MA and isolated, quiet and locked medication dispenser station were negatively associated with it. However, in contrast to our predictions, nurses’ personal characteristics (gender and education) were not associated with medication administration errors time 2. As for learning practices, in line with our hypothesis supervisory learning practice was negatively associated with medication administration errors, non-integrated learning practices were not associated with it, and patchy learning practices were positively associated with it. But contrary to our predictions, integrated learning practices were significantly and positively associated with medication administration errors time 2. Finally, the variance of the department level was significant but the variance of

the hospital level was not; hence, we could conclude that medication administration error scores vary by ward, but not by hospitals. This means that a fixed-effects analysis of scores ignoring the ward effect would violate the assumption of independence of observations (Bliese, 2000). 5. Discussion 5.1. Medication administration errors Our findings show that approximately every third hospital patient is exposed to some sort of deviation from regulations when receiving medication. Our evidence thus augments previous findings, reporting departure from guidelines from 14% (Tissot et al., 2003) to 49% (Taxis and

Table 3 Mixed linear model analysis for predicting medication administration errors. Variables

Medication administration errors time 2 Model 1: controls

MAEa time 1 Gender Education Load MA procedureb Medication stationc Non-integrated learning practices Integrated learning practices Supervisory learning practices Patchy learning practices

Model 2: direct effects

Estimate

SE

Estimate

SE

.09* .01 .01 .04* .03* .17**

.04 .00 .00 .01 .01 .05

.09* .01 .01 .02* .02* .12** .01 .03* .04* .02*

.04 .00 .00 .01 .01 .05 .01 .01 .01 .01

.01* .01 .01*

.00 .04 .00

14.53** .01* .01 .01*

.00 .04 .00

D-Restricted Log Likelihood Variance of the department level Variance of the hospital level Residual

N = 360. a MAE: medication administration error. b MA procedure: medication administration procedure: 2 – non-computerized; 1 – computerized. c Medication station – location of medication dispenser station: 1 – locked, isolated and quiet, 0 – not locked, isolated and quiet. * p < .05. ** p < .001.

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

Barber, 2003) depending on definition, type, and assessment of the deviation (Brady et al., 2009). The deviation pattern portrayed in Fig. 1 supports previous findings suggesting that the reasons for non-compliance with medication administration guidelines are mostly cognitive rather than stemming from lack of knowledge or motivation (Brady et al., 2009). Nurses seem to choose primarily to follow guidelines that are more easily supervised by the ward’s management, and to neglect those typically performed at the patient’s bedside, hence are more difficult to monitor (Brady et al., 2009; DrachZahavy and Somech, 2010). Other authors have noted that nurses frequently weighed the risks of not following the exact guideline (e.g., not providing the patient information about the medication) against the benefits of continuing the care for patients, and generally preferred the latter (Zohar and Tenne-Gazit, 2008; Tucker et al., 2002). Another example is the poor adherence to the procedure of checking the possible side effects or providing information about the medication. When approached, nurses explained that because they were familiar with the patient it was not necessary to replicate these procedures at each administering of medication. And because they had already provided the patient with the medication before, with no adverse consequences, there was no need to replicate these procedures. As not clinging to the guidelines generally does not result in serious accidents, many nurses do not acknowledge the fallacy of their choices, and mistakenly perceive their implicit theories as correct, thus contributing to the worsening rates of medication administration errors (Drach-Zahavy and Somech, 2010). 5.2. Factors contributing to limiting medication administration errors Perhaps the most novel finding of this study is that beyond the already known personal, organizational and technological factors, ward-situated learning practices played a crucial role in limiting (or, conversely, inflating) medication administration error ratios. As our findings show, the only learning practice associated with reduced medication administration errors was supervisory learning. This top-down method, charging the middle management level (in this case the head nurse) alone to instill learning in the unit, might limit medication administration errors directly – by monitoring, providing feedback and correcting staff nurses’ medication administration behaviors, and indirectly – by setting the ward’s priorities and assimilating safety norms (Leroy et al., 2012). In this our findings concur with laboratory studies in cognitive psychology and field studies in various healthcare settings showing that regular auditing, supervision and close monitoring are the best strategy to improve compliance with rules and procedures (Brady et al., 2009). Moreover, by monitoring nurses’ adherence to rules, the head nurse also displays behaviors that signal staff nurses that a safe, deviation-free medication administration process is what really counts at the ward. Indeed, safety climate researchers regard the direct supervisor as the dominant figure that can assimilate a safety climate in the workplace (e.g., Zohar

455

and Tenne-Gazit, 2008), thereby limiting medication administration errors. Not surprisingly, we found no link between nonintegrative learning practices and medication administration errors. Using risk-management units to limit medication administration errors has of course several advantages, including appointing experts to deal solely with safety issues, which enables them to see the ‘‘big picture’’ at the organizational level and to provide solutions for safety issues based on their cumulative experience (Drach-Zahavy and Pud, 2010). Nevertheless, this approach has several limitations. First, appointing external entity to deal with safety issues sends a message to unit members that safety is not their responsibility (Edmondson, 2004). And the solution that the riskmanagement unit proposes might be perceived by staff nurses as irrelevant or detached from the ward’s daily practice, and thus give rise to resistance. Finally, the fact that risk management unit is external to the unit can evoke nurses’ reluctance to report errors (Edmondson, 2004). Patchy learning practices proved positively linked to medication administration errors, in line with our hypothesis. In the absence of an explicit intention to learn from errors, and of appropriate practices for information gathering, analysis, drawing conclusions and implementing change, the learning potential may well be lost (Popper and Lipshitz, 2000), with increasing medication administration errors ratios. Surprisingly, integrated learning practices were also associated with increased medication administration error ratios. A possible explanation for this unexpected finding is that true learning might not occur without practitioners taking risks and deviating from standard routines, as well as trying out new work processes, asking questions, seeking feedback, and reflecting on potential results (Katz-Navon et al., 2009). Learners need the opportunity to practice and experiment so that learning retention and transfer can take place (Keith and Frese, 2008). In the specific context of healthcare organizations, Katz-Navon and her colleagues found that learning was positively associated with errors (Katz-Navon et al., 2009). Hence, medication administration errors might be a natural byproduct of team learning (Keith and Frese, 2008), especially because medication administration errors are defined and measured in this study as deviations from standard procedures. In line with previous research, our findings also contributed in highlighting the organizational and system characteristics which can limit or inflate medication administration errors. First, our findings indicated that workload, defined as number of patients per nurse, was associated with medication administration errors (Tissot et al., 2003). This finding is not surprising as workload has been repeatedly described in the literature as fertile ground for ‘‘cutting corners’’ (Drach-Zahavy and Somech, 2010), ‘‘workaround’’ (Halbeslebben et al., 2008) and deviating from prescribed procedures (Zohar and TenneGazit, 2008) as means to provide efficient, incessant patient care. Secondly, in line with the robust findings that interferences cause deviations from prescribed procedures (e.g.,

456

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

Biron et al., 2009; Brady et al., 2009), we found that locating the medication dispenser in a locked isolated and quiet spot was associated with fewer medication administration errors (e.g., Connard et al., 2010). The mechanisms underlying this contribution could depend on the task-performance level, namely skill-based, rule-based and knowledge-based performance (Reason, 1990). Skillbased performance, such as preparing the medication according to the triple check procedure, is mostly automatic, at least for experienced nurses. Even these routine actions require occasional attentional checks to ensure proper task completion; however, work interruptions may interfere with these required checks, causing slips and lapses (Reason, 1990; Biron et al., 2009). At the other end of the spectrum, with knowledge-based task performance nurses must rely on conscious analytical processes and stored knowledge to solve problems (e.g., verifying that the right medication, of the right dose, is given to the right patient). Work interruptions add to the amount of information being processed, and if the demands for cognitive resources are higher than those available, deviations from prescribed procedures and even errors might occur more often (Biron et al., 2009). Hence, as our findings indicated, the human factor solution for work interferences – a locked, isolated and quiet location for the medication dispenser – might safeguard against work interferences, thereby limiting medication administration errors. Thirdly, our findings on the use of medication administration technologies as means to limit medication administration errors accord with previous studies. That is, despite their great potential, the contribution of such technologies to patient safety has not proven very solid: certain types of errors are indeed reduced (e.g., errors due to prescriber’s handwriting), others are not. Even more striking, a new generation of errors came into being when technology interacted with real-life care practice (Pirnejad and Bal, 2011; Wulff et al., 2011). Our findings attest to a generally beneficial impact of technology on medication administration errors. Yet closer inspection shows that while computerized administration was significantly safe in the steps of preparing the medication and identifying the patient, it was less safe in the steps of taking patients’ relevant measures and administering the medicine, and non-significant in all the other medication administration steps. This pattern of findings illustrates that technologies can help in providing the right medication to the right patient, but might be less effective in preventing other, less vigilant deviations, committed at the patients’ bedside. Nevertheless, why nurses comply with some steps and not with others remains unknown and merits more research attention through qualitative, in-depth studies of the implementation sites (e.g., Halbeslebben et al., 2008; Pirnejad and Bal, 2011). 6. Limitations Medication administration errors are defined here in terms of safety compliance, and not in terms of error type or seriousness (its consequences for patient). The question

arises as to whether the four patterns of learning practices will exhibit similar associations with other definitions of medication administration errors. Further, the observational approach to measuring errors could be subject to biases. Nevertheless, Roter (1989) concluded that the ‘‘good impression bias’’ that stems from the presence of the observer is minimal, as nurses typically become quickly accustomed to the observer’s presence and tend to behave naturally. Moreover, empirical evidence demonstrates substantial self-report bias in assessing adherence to guidelines (Adams et al., 1999). Finally, it seems that the four types of learning practices exerted relatively small effect sizes on medication administration errors. Yet, one must keep in mind that these effects were demonstrated above and beyond the effects of various personal (gender and education level), organizational (load) and technology (Wards’ use of medication administration technologies, and location of the medication station) factors. Hence, our findings indicate that there is still room for improvement by employing learning practices. 6.1. Managerial implications First, nurses must be educated in the potential costs of their ‘‘cutting corners’’ in the course of medication administration. Secondly, local leaders can facilitate learning from errors by monitoring nurses’ medication administration behaviors, exhibiting ‘‘management by walking around’’, asking questions, and providing on-thespot feedback. This way they send a clear message to nurses that medication safety is an important, highpriority strategic goal in the ward (Zohar and Tenne-Gazit, 2008). Thirdly, risk management units should aim to devolve learning to the unit level, involving bedside nurses in the learning processes. This collaboration can reduce resistance, improve both top-down and bottom-up information flow, and promote a more prompt response to errors. Finally, although we focused on error management, we do not suggest that error prevention is unimportant. On the contrary, error prevention is required as a first line of defense to ensure patients’ safety. In this study we highlighted the value of a quiet, isolated medication dispenser station and of medication administration technologies. Nevertheless, as a predominantly nursing function, medication administration and its associated technologies should be shaped by nursing knowledge and understanding of the technology (Wulff et al., 2011). Conflict of interest None declared. Funding We received a grant from the Israel National Institute for Health Policy Research. Ethical approval We received approval from ERB of the hospital.

A. Drach-Zahavy et al. / International Journal of Nursing Studies 51 (2014) 448–457

References Adams, A.S., Soumerai, S.B., Lomas, J., Ross-Degnan, D., 1999. Evidence of self-report bias in assessing adherence to guidelines. International Journal for Quality in Health Care 11 (3) 187–192. Armitage, G., Knapman, H., 2003. Adverse events in drug administration: a literature review. Journal of Nursing Management 11 (2) 130–140. Aspden, P., Wolcott, J., Bootman, J.L., Cronewett, L.R., 2007. Preventing medication errors: quality chasm series. National Academy of Sciences Executive Summary 7, 1–7 Retrieved from: http://www.nap.edu (February 7). Barker, K.N., 1995. Ensuring safety in the use of automated medication dispensing systems. American Journal of Health-System Pharmacy 52 (21) 2445–2447. Biron, A.D., Carmen, G.L., Lavoie-Tremblay, M., 2009. Work interruptions and their contribution to medication administration errors: an evidence review. Worldviews on Evidence-Based Nursing 6 (2) 70–86. Bliese, P., 2000. Within-group agreement, non-independence, and reliability: implications for data aggregation and analysis. In: Klein, J.K., Kozlowski, S.W. (Eds.), Multilevel Theory, Research, and Methods in Organizations: Foundations, Extensions, and New Directions. JosseyBass, San Francisco, CA, pp. 349–381. Brady, A.M., Malone, A.M., Fleming, S., 2009. A literature review of the individual and systems factors that contribute to medication errors in nursing practice. Journal of Nursing Management 17 (6) 679–697. Buchan, J., 2005. A certain ratio? The policy implications of minimum staffing ratios in nursing. Journal of Health Services Research & Policy 10 (4) 239–244. Chang, Y.K., Mark, B.A., 2011. Effects of learning climate and registered nurse staffing on medication errors. Nursing Research 60, 32–39. Committee on the Robert Wood Johnson Foundation Initiative on the Future of Nursing at the Institute of Medicine, 2011. The Future of Nursing: Leading Change, Advancing Health. The National Academies Press, Washington, DC. Connard, C., Fields, W., McNamara, T., Cone, M., Atkins, P., 2010. Medication room madness: calming the chaos. Journal of Nursing Care Quality 25 (2) 137–144. Drach-Zahavy, A., Pud, D., 2010. Learning mechanisms to limit medication administration errors. Journal of Advanced Nursing 66, 794–805. Drach-Zahavy, A., Somech, A., 2009. To err is human, to learn from them is mandatory. Final report submitted to the INHP. . Drach-Zahavy, A., Somech, A., 2010. Implicit as compared with explicit safety procedures: the experiences of Israeli nurses. Qualitative Health Research 20 (10) 1406–1417. Edmondson, A.C., 2004. Learning from failure in healthcare: frequent opportunities, pervasive barriers. Quality and Safety in Health Care 13, ii3–ii9, http://dx.doi.org/10.1136/qshc.2003.009597. Fogarty, G.J., McKeon, C.M., 2006. Patient safety during medication administration: the influence of organizational and individual variables on unsafe work practices and medication errors. Ergonomics 49 (5–6) 444–456. Halbeslebben, J.R., Wakefield, D.S., Wakefield, B.J., 2008. Work-arounds in health care settings: literature review and research agenda. Health Care Management Review 33 (1) 2–12. James, L.R., Damaree, R.G., Wolf, G., 1993. rwg: an assessment of withingroup interrater reliability. Journal of Applied Psychology 78 (2) 306– 309. Katz-Navon, T., Naveh, E., Stren, Z., 2009. Active learning: when is more better? The case of resident physicians’ medical errors. Journal of Applied Psychology 94 (5) 1200–1209. Keith, N., Frese, M., 2008. Effectiveness of error management training: a meta-analysis. Journal of Applied Psychology 93 (1) 59–69. Kiekkas, P., 2011. Psychiatric long-term complications of intensive care unit survivors. Critical Care Medicine 39 (7) 1852–1853. Kohn, L.T., Corrigan, J.M., Donaldson, M.S., 2000. To Err is Human: Building a Safer Health System. National Academy Press, Washington, DC.

457

Larrabee, S., Brown, M.M., 2003. Recognizing the institutional benefits of barcode point-of-care technology. Journal on Quality and Patient Safety 29 (7) 345–353. Leroy, H., Dierynck, B., Anseel, F., Simons, T., Halbesleben, J., McCaughey, D., Savage, G.T., Sels, L., 2012. Behavioral integrity for safety, priority of safety, psychological safety, and patient safety: a team-level study. Journal of Applied Psychology 97 (6) 1273–1281. Lisby, M., Nielsen, L.P., Mainz, J., 2005. Errors in the medication process: frequency, type, and potential clinical consequences. International Journal of Quality Health Care 17 (1) 15–22. Mayo, E., 1949. Hawthorne and the Western Electric Company, the Social Problems of an Industrial Civilisation. Routledge. Meadows, M., 2003. Greater access to generic drugs: new FDA initiatives to improve drug reviews and reduce legal loopholes. FDA Consumer 37 (5) 12–17. Oren, E., Shaffer, E.R., Guglielmo, B.J., 2003. Impact of emerging technologies on medication errors and adverse drug events. American Journal of Health-System Pharmacy 60 (14) 1447–1458. O’Shea, E., 1999. Factors contributing to medication errors: a literature review. Journal of Clinical Nursing 8 (5) 496–504. Pedersen, C.A., Schneider, P.J., Scheckelhoff, D.J., 2006. ASHP national survey of pharmacy practice in hospital settings: dispensing and administration. American Journal of Health System Pharmacy 63 (4) 327–345. Pirnejad, H., Bal, R., 2011. The precarious gap between information technology and patient safety: lessons from medication systems. In: Rowley, E., Waring, J. (Eds.), A Socio-cultural Perspective on Patient Safety. Ashgate, England, pp. 115–130. Popper, M., Lipshitz, R., 2000. Organizational learning mechanisms, culture, and feasibility. Management Learning 31 (2) 181–196. Reason, J.T., 1990. Human Error. Cambridge University Press, Cambridge, UK. Roter, D., 1989. Which facets of communication have strong effects on outcome: a meta analysis. In: Stewart, M., Roter, D. (Eds.), Communicating with Medical Patients. Sage, Newbury Park, CA, pp. 183–196. Singer, J.D., 1998. Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics 23 (4) 323–355. Taxis, K., Barber, N., 2003. Causes of intravenous medication errors: an ethnographic study. Quality and Safety in Health Care 12, 343–348. Tissot, E., Cornette, C., Limat, S., Mourand, J.L., Becker, M., Etievent, J.P., Woronoff-Lemsi, M.C., 2003. Observational study of potential risk factors of medication administration errors. Pharmacy World and Science 25 (6) 264–268. Tucker, A.L., Edmondson, A.C., Spear, S., 2002. When problem solving prevents organizational learning. Journal of Organizational Change Management 15 (2) 122–137. Vashdi, D.R., Erez, M., Bamberger, P.A., Weiss-Meilik, A., 2007. Team selfregulation in the operating room: the mediators and moderators of reflexivity–performance relations in organizational action teams. Human Resources Management 46, 115–142. Wimpenny, P., Kirkpatrick, P., 2010. Roles and systems for routine medication administration to prevent medication errors in hospitalbased, acute care settings: a systematic review. JBI Library of Systematic Reviews and Implementation Reports 8 (10) 405–446. Wulff, K., Cummings, G.G., Marck, P., Yurtseven, O., 2011. Medication administration technologies and patient safety: a mixed-method systematic review. Journal of Advanced Nursing 67 (10) 2080– 2095. Wynia, M.K., Classen, D.C., 2011. Improving ambulatory patient safety: learning from the last decade, moving ahead in the next. JAMA 306 (22) 2504–2505. Zohar, D., Tenne-Gazit, O., 2008. Transformational leadership and group interaction as climate antecedents: a social network analysis. Journal of Applied Psychology 93 (4) 744–757.