Nurse Education Today 34 (2014) 912–917
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Nurse Education Today journal homepage: www.elsevier.com/nedt
Delirium knowledge and recognition: A randomized controlled trial of a web-based educational intervention for acute care nurses Judy McCrow a,⁎, Karen A. Sullivan b, Elizabeth R. Beattie a a b
School of Nursing and Midwifery, Queensland University of Technology, Queensland 4059, Australia School of Psychology and Counseling, Queensland University of Technology, Queensland 4059, Australia
a r t i c l e
i n f o
Article history: Accepted 18 December 2013 Keywords: Delirium Delirium superimposed on dementia Nursing Web-based learning
s u m m a r y Delirium is a significant problem for older hospitalized people and is associated with poor outcomes. It is poorly recognized and evidence suggests that a major reason is lack of education. Nurses, who are educated about delirium, can play a significant role in improving delirium recognition. This study evaluated the impact of a delirium specific educational website. A cluster randomized controlled trial, with a pretest/post-test time series design, was conducted to measure delirium knowledge (DK) and delirium recognition (DR) over three time-points. Statistically significant differences were found between the intervention and non-intervention group. The intervention groups' DK scores were higher and the change over time results were statistically significant [T3 and T1 (t = 3.78 p = b 0.001) and T2 and T1 baseline (t = 5.83 p = b0.001)]. Statistically significant improvements were also seen for DR when comparing T2 and T1 results (t = 2.56 p = 0.011) between both groups but not for changes in DR scores between T3 and T1 (t = 1.80 p = 0.074). Participants rated the website highly on the visual, functional and content elements. This study supports the concept that web-based delirium learning is an effective and satisfying method of information delivery for registered nurses. Future research is required to investigate clinical outcomes as a result of this web-based education. © 2013 Elsevier Ltd. All rights reserved.
Introduction Delirium is a serious, potentially reversible disorder that affects up to 37% of general hospital admissions, with an incident rate as high as 67% reported in surgical populations (Eeles et al., 2010; Lundstrom et al., 2007). It is associated with poor outcomes such as a decreases in functional capacity, increased risk of future dementia or increased rates of cognitive decline in people with pre-existing dementia, relocation to residential care and even death (Adamis et al., 2006; Witlox et al., 2010). Delirium is distressing to patients and families and contributes to additional health care costs (Inouye, 2006; Leslie et al., 2008). For instance, a recent study estimated that health care costs attributable to delirium in the United States were between $38 billion and $152 billion, rivalling the costs of falls and diabetes mellitus (Leslie et al., 2008). Despite the evidence that delirium is associated with poor outcomes, less than half of the cases of delirium in older hospitalized people are recognized by clinicians (Rice et al., 2011; Spronk et al., 2009). Nurses are seen as playing a key role in the early recognition of delirium because they spend substantial time at the bedside and have frequent opportunities to determine the subtle changes in a patient's behavior that assist in early recognition (Dahlke and Phinney, 2008; Fick et al., 2007). Early recognition enables prompt diagnosis and ⁎ Corresponding author. Tel.: + 61 7 3882 4585; fax: + 61 7 3138 5941. E-mail addresses: [email protected] (J. McCrow), [email protected] (K.A. Sullivan), [email protected] (E.R. Beattie). 0260-6917/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.nedt.2013.12.006
management, including rapid implementation of targeted interventions (McCusker et al., 2011). A reduction in duration and severity of delirium is crucial as it helps to improve patient outcomes. However, nurses' lack of knowledge and ability to recognize delirium has been previously demonstrated in a study of nurses working in a US hospital. It was noted that nurses had difficulty recognizing delirium, with only 21% able to accurately identify hypoactive delirium superimposed on a pre-existing dementia (Fick et al., 2007). A more recent study identified that nurses at all levels of experience and education had knowledge deficits in relation to medication use in delirium and predisposing and precipitating delirium risk factors (Meako et al., 2011). Similarly an Australia study investigating nurses' knowledge of delirium demonstrated that they had inadequate knowledge (Hare et al., 2008). Moreover, nurses are aware of their knowledge deficits in this area and report that they lack the knowledge to effectively care for someone with delirium (Dahlke and Phinney, 2008). Studies have identified that clinical staff education on the detection and treatment of delirium can improve patient outcomes (Inouye et al., 2001; Milisen et al., 2005; Naughton et al., 2005). There is evidence that delirium education interventions, including formal educational packages, development and implementation of practice guidelines, structured courses and extensive multifaceted programs including geriatric specialist input have been noted to effectively increase knowledge associated with delirium (Bergmann et al., 2005; Lundstrom et al., 2005; Tabet et al., 2005; Webster et al., 1999). However, these types of programs may be difficult to implement in a variety of health
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care settings due to such things as: access to programs; lack of geriatric specialists; lack of time for educational endeavors; and the lack of local resources to develop educational programs (Brown et al., 2009; Meyer et al., 2007). Web-based learning provides an alternative approach that helps to overcome the challenges to more traditional educational approaches (Bollinger et al., 2011; Paavilainen and Salminen-Tuomaala, 2010). It provides opportunities for nurses to access current information and practice initiatives at a time and place to suit their needs (McCord and McCord, 2010). Moreover, students in academic programs using webbased learning performed better than those learning through traditional face-to-face instruction (Means et al., 2010). For example, hand hygiene knowledge was found to be significantly higher following web-based learning (Alemagno et al., 2010). Similarly, web-based learning was found to enhance emergency nurses' triage accuracy and reduce medication errors (Rankin et al., 2013; Straight, 2008). In addition to learning new information, nurses report high levels of satisfaction with webbased learning in terms of meeting their knowledge and practice requirements (Cottrell and Donaldson, 2013; Smith, 2010). This study builds on previous web-based nursing education by using an innovative educational website that was specifically developed to enable access to delirium information. Unique features of this website included realistic video-vignettes, self-assessment questions, downloadable flow charts, and links to external sites to extend learning opportunities. Additionally, it was not developed in a modular form as many online courses are structured. The web-site was developed using cognitive constructivist principles to allow an individualized approach to web-site navigation. The primary aim of this study was to determine the effectiveness of this educational website, as a means of improving delirium knowledge and recognition of delirium in the clinical setting. A secondary aim was to assess the level of nurse satisfaction with this resource. Specifically we hypothesized that acute care registered nurses who used the educational website would have better delirium knowledge and delirium recognition skills than nurses who did not use the website. Additionally, we expected that nurse users of the website would be satisfied with the functional and design aspects of the site. Methods Design A pretest/post-test time series cluster randomized controlled trial was used to investigate the effect of the delirium website on nurses' knowledge of, and ability to recognize, delirium. Cluster randomization, at a clinical level within each hospital, was performed by the researcher and another independent professional, using “GraphPad Software, Quick calcs” (Software Inc., 2005). Sample Registered Nurses (RNs) employed full or part time in four high-risk delirium areas (either critical care, orthopedic, medical or surgical wards) at three similar size hospitals (250–300 beds) in S. E. Queensland, Australia were potentially eligible. The sample frame consisted of twelve clusters of RNs across the three hospitals. Casual float employees and RNs scoring less than three on an Online Readiness: Self-Assessment Questionnaire (Watkins et al., 2004) were excluded. Ethical approval was granted by the respective hospital and the affiliated university Human Research Ethics Committees. Written informed consent was obtained from each participant. Procedures Participants were recruited at each site using information sessions with the researcher. Prior to randomization, all participants completed
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baseline (Time One; T1) questionnaires and delirium knowledge and recognition testing. Six to eight weeks later, immediately after closure of the website educational intervention, Time Two (T2) post-intervention data were collected. At T2 the intervention group participants also provided website evaluation information. At Time Three (T3), all participants completed a final set of delirium knowledge and delirium recognition tests to assess the longer term effects of the intervention. Participants completed the questionnaire and tests either with research staff or at a time convenient to them and under indirect supervision by another person (usually Nurse Unit Manager or Clinical Nurse Teacher). Following completion of T1 data collection, cluster randomization at a clinical area level was undertaken within each health care facility. That is, within each of the three facilities, RN participants in two clinical areas were randomized to a non-intervention group and RN participants from the other two clinical areas randomized to an intervention group. A total of 72 RNs were randomized to the non-intervention group and 75 RNs randomized to the intervention group. Intervention group participants were given five weeks access to the delirium educational website while non-intervention group participants did not receive access. Both groups continued to work as usual in their clinical areas. Intervention The intervention consisted of a purposely developed educational website called learnaboutdelirum. The website included delirium facts (e.g., definition, types, prevalence), delirium management strategies and information about how to recognize delirium using the internationally accepted Confusion Assessment Method (CAM) (Inouye et al., 1990). The website also included videotaped vignettes of people (actors) with various clinical presentations (e.g., hyperactive delirium, hypoactive delirium, delirium superimposed on dementia, and dementia), with attached narrative captions, questions and answers, and links to other educational websites. Current literature and best practice guidelines for delirium assessment and management, including the Australian delirium clinical practice guidelines (Australian Health Ministers' Advisory Council (AHMAC), 2006) underpinned all website information. The website was developed using constructivist learning principles to enhance user engagement. These principles included, providing processes for participants to build on prior knowledge and experience and support self-guided use of the website. A discussion forum was incorporated to enable social learning opportunities. After a five week period access to the educational website was closed and all participants (non-intervention and intervention groups) were asked to complete the Time Two (T2) questionnaires and six to eight weeks later the Time Three (T3) questionnaires. To reduce attrition we used ongoing communication via electronic mail and researcher contact with the participants and the Nurse Unit Managers of each clinical area. Data collected from participants who were lost to follow-up or who withdrew from the study, was included in the final analyses. Measures A modified version of the Online Learner Readiness: Self-Assessment questionnaire was used as an inclusion criterion for this study to ensure that RN participants had at least basic computer literacy skills (Watkins et al., 2004). The original questionnaire comprised 27 items, rated by respondents on a 5-point Likert scale and had good internal validity (Cronbach's Alpha for each element = 0.74–0.95). Following approval from the original author, six items were removed from the measure as they were specific to online courses rather than an online learning website. This left a total of 21 items. The Nurses' Knowledge of Delirium questionnaire (range 0–34) was developed by a group of Australian academics (Hare et al., 2008) and used to measure delirium knowledge in a group of nurses working
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Fig. 1. Flow of participants through the study.
within one acute health care facility. No reliability or validity data is currently available for this measure. Delirium recognition (DR) was measured using two sets of standardized vignettes, each with five different case scenarios, illustrating older persons with delirium +/− dementia. Participants were asked to read the five validated case vignettes and then select from five choices; a) dementia, b) delirium, c) delirium superimposed on dementia, d) normal aging and e) other, the condition they thought the vignette portrayed. Each vignette had a single correct answer. Both sets of vignettes had undergone an expert validation processes prior to use (Fick et al., 2007; McCrow et al., 2013). Different sets of vignettes were used at different time points. An Evaluation of Online Learning: Self-Assessment questionnaire was developed for this project as a participant evaluation of the website, and included information about content as well as visual, communication and technical aspects of the website. Initially the questionnaire was reviewed and questions refined by three educational web design experts and then pilot tested in a group of 15 Registered Nurses. The final questionnaire comprised 27 items, which were scored on a 5point Likert scale (1 = completely disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree and 5 = completely agree) (Cronbach's alpha = 0.94).
Data Analysis Descriptive data analyses were undertaken to examine demographic variables. A repeated measures multivariable analysis by means of linear mixed modeling (LMM) was undertaken to examine the changes in outcome variables between the intervention and non-intervention groups over time, and associations between the outcome variables and multiple independent variables. Study bias as a result of participant attrition and noncompliance was limited by using Linear mixedmodeling (West, 2009). To capture the between-group variation, a random effect associated with the intercept for both groups was included in the model. Including random intercepts and random slopes allowed correlations between the repeated measures to change over time. Additionally, a random effect of clinical area and clinical area*time was included in the model to account for clustering. To assess the impact of baseline characteristics and the primary outcomes backward elimination of the weakest predicator was used until a parsimonious solution was established. Data was assessed using an intention-to-treat analysis. A p-value of less than 0.05 was considered to be statistically significant. Analyses were performed using the Predictive Analytics SoftWare (PASW®) version 18.0. Results
Table 1 Baseline comparisons of mean Delirium Knowledge and Delirium Recognition scores. Outcome variable
Group
n
Mean
SD
Delirium knowledge (range 0–34) Delirium recognition (range 0–5)
Non-intervention Intervention Non-intervention Intervention
72 75 72 75
20.03 19.69 02.89 02.94
3.98 3.93 1.37 1.28
t-Test
df
p
0.51
145
0.61
−0.26
145
0.79
A total of 205 registered nurses from three different hospitals, with similar case-mixes of patients, were approached to participate in this study. The facilities were part of Queensland's public healthcare network (Queensland Health). Of these RNs, 175 agreed to participate (85%). Prior to completion of baseline questionnaires 28 (14%) participants withdrew and one participant was excluded because this person was not a RN, leaving a sample of 147 (86%) participants who
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Table 2 Delirium Knowledge (range 0–34), selected estimates of fixed effects (N = 147). Parameter
Non-intervention group Intervention group T2 T3 T2 * Intervention group T3 * Intervention group
Estimate
20.05 19.67 1.81 2.96 3.95 2.98
Std. Error
0.45 0.46 0.45 0.56 0.68 0.79
df
t
85.96 79.74 54.92 84.84 145.92 163.20
44.71 42.66 4.08 5.35 5.83 3.78
Sig.
0.000 0.000 0.000 0.000 0.000 0.000
95% CI Lower bound
Upper bound
19.15 18.74 0.92 1.86 2.61 1.42
20.95 20.58 2.71 4.07 5.29 4.54
Note: All values rounded: Reference groups = Non-intervention group and Time 1 (baseline).
completed the baseline questionnaires. Reasons for study withdrawal included extended sick leave or health problems, time limitations, bereavement leave and change in work position/job status including job transfers. The flow of participants through the study is summarized in Fig. 1. As shown in Fig. 1 a total of 72 RNs (6 clusters) were randomized to the non-intervention group and 75 RNs randomized to intervention group (six clusters). A total of 50 (34%) RNs were recruited from facility one, 49 (33%) from facility two and 48 (32%) from facility three. The majority of participants were female (87%; n = 130) and the mean (standard deviation) age was 39 years (12). Most participants reported a bachelor degree as their highest qualification (68%; n = 100) with the remainder having hospital-based nurse training (16%; n = 23), graduate certificates (5%; n = 7), graduate diplomas (7%; n = 11) or a Master degree (3%; n = 4). Two participants did not supply information about their highest qualification. Approximately half of the sample had less than ten years experience since completing undergraduate nursing studies. Another 33 (22%) had between ten and twenty years of nursing experience and the remaining 41 (28%) had greater than twenty years experience. The mean (standard deviation) number of years since completion of undergraduate studies was 13 years (11.47). At baseline the mean number (standard deviation) of correctly identified diagnoses for the five vignettes was 2.89 (1.3) out of a possible score of 5. For the 34 delirium knowledge questions the average number of correct answers was 19.77 (3.9). The percentage of participants who correctly identified the vignettes was highest for the dementia vignette (78%; n = 115), followed by, in decreasing numerical order, hyperactive delirium (67%; n = 98), hyperactive delirium superimposed on dementia (58%: n = 85); hypoactive delirium (50%; n = 74); and hypoactive delirium superimposed on dementia (37%; n = 54). As displayed in Table 1, there were no statistically significant differences in Delirium Knowledge (DK) or Delirium Recognition (DR) between the non-intervention and intervention group at baseline (DK, t = 0.51 p = 0.61; DR, t = −0.26, p = 0.79). Intervention Effects on Delirium Knowledge and Delirium Recognition Results from the mixed models demonstrated that the intervention significantly influenced DK and DR scores over time. RN participants in the intervention group had statistically significant higher DK scores
over time [T3 and T1 (t = 3.78 p = b0.001) and T2 and T1 baseline (t = 5.83 p = b0.001)] than those in the non-intervention group (see Table 2). Calculations using the coefficients from the estimates of fixed-effects suggested that, compared to T1, at T2 participants in the intervention group scored, on average, about 3.6 (10%) more questions correct than participants in the non-intervention group. The difference between T1 and T3 DK scores for the two groups showed an advantage to the intervention group (they answered approximately 2.6 (7%) more questions, correctly than participants in the non-intervention group (see Table 4)). There were statistically significant differences in the mean DR change scores over time from T2 and T1 (t = 2.56 p = 0.011) but not T3 and T1 (t = 1.80 p = 0.074) between the intervention and nonintervention groups (see Table 3). Calculations using the coefficients from the estimates of fixed-effects identified that, compared to T1, on average participants in the intervention group scored an estimated 0.7(14%) more diagnoses (using delirium recognition vignettes) correct at T2 than non-intervention group participants (see Table 4). Educational Website Evaluation Approximately 80% (n = 49) of the 61 participants in the intervention group who provided responses to the Online Learning: Self-Assessment questionnaire rated the educational website as being visually appealing and easy to read and 84% (n = 51) liked the navigational features and page formats. The majority (78%) self-reported that the educational website had improved their delirium knowledge with the remainder of the sample neither agreeing nor disagreeing with this statement. Over 80% (n = 50) of participants in the intervention group found the educational website to be flexible, relevant to clinical practice and interesting. Discussion The findings from this study support the positive effects of a webbased delirium intervention on acute care RNs' knowledge of, and ability to, recognize delirium when portrayed in a vignette. Specifically, those exposed to the learnaboutdelirium website performed significantly better than the non-intervention group in terms of both DK and DR. Additionally, there was a relatively high level of satisfaction with the website by RNs in the intervention group.
Table 3 Delirium Recognition (range 0–5), selected estimates of fixed effects (N = 147). Parameter
Non-intervention group Intervention group T2 T3 T2 * Intervention group T3 * Intervention group
Estimate
2.85 2.92 0.43 −0.18 0.64 0.51
Std. Error
0.15 0.14 0.18 0.20 0.25 0.29
df
71.82 81.45 96.38 94.37 163.74 160.67
Note: All values rounded: Reference groups = Non-intervention group and Time-point 1 (baseline).
t
18.63 20.56 2.44 −0.88 2.56 1.80
Sig.
0.000 0.000 0.017 0.382 0.011 0.074
95% CI Lower bound
Upper bound
2.54 2.64 0.08 −0.57 0.15 −0.05
3.16 3.21 0.79 0.22 1.13 1.08
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Table 4 Estimates of mean Delirium Knowledge and Delirium Recognition scores at each timepoint for both groups. Measure
Delirium Knowledge (range 0–34) Delirium Recognition (range 0–5)
Time-point
One Two Three One Two Three
Group
nursing education interventions and they should be part of future research. Conclusion
Intervention (n = 75)
Non-intervention (n = 72)
19.67 25.43 25.62 2.93 4.00 3.26
20.05 21.86 23.02 2.85 3.28 2.67
As shown in prior research (Fick et al., 2007; Hare et al., 2008), the nurses in this study had some knowledge of delirium, and some ability to recognize delirium, at baseline (T1). Specifically, current study participants had a mean score of 20 (57%) correct delirium knowledge questions correct compared to 53% in the previous study (Hare et al., 2008). In contrast, a slightly higher percentage of participants in the current study were able to correctly recognize delirium compared to participants, using the same vignettes, in a previous study (Fick et al., 2007). For example, 37% of the current participants were able to correctly identify hypoactive delirium when superimposed on dementia compared to 21% in the previous study. Similar differences were found for the other vignettes, where 50% of the current participants versus 41% of previous study participants were able to correctly identify hypoactive delirium alone; 67% (vs. 59%) correctly identified hyperactive delirium superimposed on dementia and 58% (vs. 52%) hyperactive delirium. There were relatively high percentages of participants in both studies who could correctly identify dementia in the dementia vignette but had the most trouble correctly identifying hypoactive delirium and hypoactive delirium superimposed on dementia in the corresponding vignette. These findings support previous research that indicate these forms of delirium are the most frequently missed in clinical environments. People with the hypoactive form of delirium exhibit quiet, non-disruptive behaviors and are often not identified as having delirium or misdiagnosed as having depression, dementia or simply being “old” (Farrell and Ganzini, 1995; Forrest et al., 2007). This study found that over time there was an improvement in DK for both groups. However, this change was significantly higher in the intervention group when compared to the control group in all comparisons. For DR there was a statistically significant difference in T1 and T2 mean change scores with both groups improving but much more so in the intervention group. However, there was not a statistically significant difference in mean DR change scores over time between T1 and T3. Overall, there was a relatively high level of satisfaction with the educational website. The majority of participants indicated that they found the educational website visually appealing, the navigation around the educational website easy and the pages readable. Additionally, the RN users deemed the information on the educational website appropriate and easy to understand. Limitations of this study were that blinding was not possible and there were numerous variables that were not able to be controlled for including motivation of the participants, individual expectations and the physical and emotional environment. However, ward level randomization aimed to reduce the effect of potential contamination of data and the real-life effect of educational interventions has been demonstrated as the prior mentioned variables are common to life experiences. It may be argued that delirium knowledge and recognition of delirium using vignettes are not appropriate measures in nursing education. Some may consider evaluations of clinical practice and patient outcomes more substantial outcome measures. However, these outcome measurements were outside the scope of this study. We acknowledge that practice change and patient outcomes are important endpoints of
The results of this study suggest that the learnaboutdelirium educational website effectively and significantly improved RNs' knowledge about delirium as well as their ability to recognize delirium and delirium superimposed on dementia using vignettes, compared to the nonintervention group. However, the statistically significant improvement between the non-intervention and the intervention groups for delirium recognition was not found at the delay test time-point (T3), supporting the concept that education should be ongoing or that ways to improve retention of this content be explored. Although statistically significant increases in knowledge and recognition were identified in this study, the clinical value of this intervention is its potential to improve patient outcomes through nurse education. Further research is required to assess the impact of this deliriumfocused web-based educational intervention on clinical practice. This study has shown how this change could be achieved using a potentially highly accessible resource for nurses employed in acute care facilities. Funding This work was partially supported by The Queensland Nurses Council (QNC), Australia through a novice research grant. Acknowledgments The authors would like to acknowledge the Registered Nurses who voluntarily participated in this research and for research assistance from Ms Rachel McCrow, Mr William Dore and Mr Daniel Lee. We are grateful for the support offered from the Caboolture, Redcliffe and the Queen Elizabeth 11 Jubilee hospitals, Brisbane, Australia. We acknowledge the support from the University of Maryland Online Dissemination and Implementation Institute funded by the University of Maryland and the John A. Hartford Foundation for assistance in preparation of this manuscript. Prior presentations of this data include: The Sigma Theta Tau International Honor Society of Nursing conference in Brisbane, Australia, on July 30 to August 3, 2012, The Australian Association of Gerontology conference in Brisbane, Australia, on November 20–23, 2012; and The American Delirium Society annual meeting in Indianapolis, USA on June 2–4, 2013. References Adamis, D., Treloar, A., Martin, F.C., Macdonald, A.J.D., 2006. Recovery and outcome of delirium in elderly medical inpatients. Arch. Gerontol. Geriatr. 43 (2), 289–298. http://dx.doi.org/10.1016/j.archger.2005.11.005. Alemagno, S., Guten, S., Warthman, S., Young, E., Mackay, D., 2010. Online learning to improve hand hygiene knowledge and compliance among health care workers. J. Contin. Educ. Nurs. 41 (10), 463–471. http://dx.doi.org/10.3928/0022012420100610-06. Australian Health Ministers' Advisory Council (AHMAC), 2006. Clinical Practice Guidelines for the Management of Delirium in Older People. Victorian Government Department of Human Services, Melbourne (Retrieved from www.health.vic.gov.au). Bergmann, M., Murphy, K., Kiely, D., Jones, R., Marcantonio, E., 2005. A model for management of delirious postacute care patients. J. Am. Geriatr. Soc. 53 (10), 1817. http://dx.doi.org/10.1111/j.1532-5415.2005.53519.x. Bollinger, R.C., McKenzie-White, J., Gupta, A., 2011. Building a global health education network for clinical care and research: the benefits and challenges of distance learning tools. Infect. Dis. Clin. North Am. 25 (2), 385. http://dx.doi.org/ 10.1016/j.idc.2011.02.006. Brown, S., Kirkpatrick, M., Greer, A., Matthias, A., Swanson, M., 2009. The use of innovative pedagogies in nursing education: an international perspective. Nurs. Educ. Perspect. 30 (3), 153–158. http://dx.doi.org/10.1043/1536-5026-030.003.0153. Cottrell, S., Donaldson, J.H., 2013. Exploring the opinions of registered nurses working in a clinical transfusion environment on the contribution of e-learning to personal learning and clinical practice: results of a small scale educational research study. Nurse Educ. Pract. 13 (3), 221–227. http://dx.doi.org/10.1016/j.nepr.2013.01.014.
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Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K., 2010. Evaluation of Evidence-based Practices in Online Learning: A Meta-analysis and Review of Online Learning Studies. Retrieved 20/07/2012 http://eprints.cpkn.ca/7/. Meyer, E., Lees, A., Humphris, D., Connell, N., 2007. Opportunities and barriers to successful learning transfer: impact of critical care skills training. J. Adv. Nurs. 60 (3), 308–316. http://dx.doi.org/10.1111/j.1365-2648.2007.04422.x. Milisen, K., Lemiengre, J., Braes, T., Foreman, M.D., 2005. Multicomponent intervention strategies for managing delirium in hospitalized older people: systematic review. J. Adv. Nurs. 52 (1), 79–90. http://dx.doi.org/10.1111/j.1365-2648.2005.03557.x. Naughton, B.J., Saltzman, S., Ramadan, F., Chadha, N., Priore, R., Mylotte, J.M., 2005. A multifactorial intervention to reduce prevalence of delirium and shorten hospital length of stay. J. Am. Geriatr. Soc. 53 (1), 18–23. http://dx.doi.org/10.1111/j.15325415.2005.53005.x. Paavilainen, E., Salminen-Tuomaala, M., 2010. Web-based learning for continuing nursing education of emergency unit staff. J. Nurses Staff Dev. 26 (4), 159–163. http://dx.doi.org/10.1097/NND.0b013e31819945ae. Rankin, J.A., Then, K.L., Atack, L., 2013. Can emergency nurses' triage skills be improved by online learning? Results of an experiment. J. Emerg. Nurs. 39 (1), 20–26. http://dx.doi.org/10.1016/j.jen.2011.07.004. Rice, K.L., Bennett, M., Gomez, M., Theall, K.P., Knight, M., Foreman, M.D., 2011. Nurses' recognition of delirium in the hospitalized older adult. Clin. Nurse Spec. 25 (6), 299–311. http://dx.doi.org/10.1097/NUR.0b013e318234897b. Smith, A., 2010. Learning styles of registered nurses enrolled in an online nursing program. J. Prof. Nurs. 26 (1), 49–53 (doi: S8755-7223(09)00069-6). Software Inc., GraphPad, 2005. GraphPad Software: Quick Calcs Retrieved 10th October, 2010. http://www.graphpad.com/quickcalcs/randomize1.cfm. Spronk, P.E., Riekerk, B., Hofhuis, J., Rommes, J.H., 2009. Occurrence of delirium is severely underestimated in the ICU during daily care. Intensive Care Med. 35 (7), 1276–1280. http://dx.doi.org/10.1007/s00134-009-1466-8. Straight, M., 2008. One strategy to reduce medication errors: the effect of an online continuing education module on nurses' use of the Lexi-Comp Feature of the Pyxis MedStation 2000. Comput. Inform. Nurs. 26, 23–30. http://dx.doi.org/10.1097/ 01.NCN.0000304761.56671.ad. Tabet, N., Hudson, S., Sweeney, V., Sauer, J., Bryant, C., MacDonald, A., Howard, R., 2005. An educational intervention can prevent delirium on acute medical wards. Age Ageing 34 (2), 152–156. http://dx.doi.org/10.1093/ageing/afi031. Watkins, R., Leigh, D., Triner, D., 2004. Assessing readiness for E-learning. Perform. Improv. Q. 17 (4), 66–79. http://dx.doi.org/10.1111/j.1937-8327.2004.tb00321.x. Webster, J., Chew, R., Mailliard, L., Moran, M., 1999. Improving clinical and cost outcomes in delirium: use of practice guidelines and a delirium care team. Ann. Long Term Care 7 (4), 128–134. West, B.T., 2009. Analyzing longitudinal data with the linear mixed models procedure in SPSS. Eval. Health Prof. 32 (3), 207. Witlox, J., Eurelings, L., de Jonghe, J., Kalisvaart, K., Eikelenboom, P., van Gool, W., 2010. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. JAMA 304 (4), 443–451. http://dx.doi.org/ 10.1001/jama.2010.1013.
Contents lists available at ScienceDirect
Nurse Education Today journal homepage: www.elsevier.com/nedt
Delirium knowledge and recognition: A randomized controlled trial of a web-based educational intervention for acute care nurses Judy McCrow a,⁎, Karen A. Sullivan b, Elizabeth R. Beattie a a b
School of Nursing and Midwifery, Queensland University of Technology, Queensland 4059, Australia School of Psychology and Counseling, Queensland University of Technology, Queensland 4059, Australia
a r t i c l e
i n f o
Article history: Accepted 18 December 2013 Keywords: Delirium Delirium superimposed on dementia Nursing Web-based learning
s u m m a r y Delirium is a significant problem for older hospitalized people and is associated with poor outcomes. It is poorly recognized and evidence suggests that a major reason is lack of education. Nurses, who are educated about delirium, can play a significant role in improving delirium recognition. This study evaluated the impact of a delirium specific educational website. A cluster randomized controlled trial, with a pretest/post-test time series design, was conducted to measure delirium knowledge (DK) and delirium recognition (DR) over three time-points. Statistically significant differences were found between the intervention and non-intervention group. The intervention groups' DK scores were higher and the change over time results were statistically significant [T3 and T1 (t = 3.78 p = b 0.001) and T2 and T1 baseline (t = 5.83 p = b0.001)]. Statistically significant improvements were also seen for DR when comparing T2 and T1 results (t = 2.56 p = 0.011) between both groups but not for changes in DR scores between T3 and T1 (t = 1.80 p = 0.074). Participants rated the website highly on the visual, functional and content elements. This study supports the concept that web-based delirium learning is an effective and satisfying method of information delivery for registered nurses. Future research is required to investigate clinical outcomes as a result of this web-based education. © 2013 Elsevier Ltd. All rights reserved.
Introduction Delirium is a serious, potentially reversible disorder that affects up to 37% of general hospital admissions, with an incident rate as high as 67% reported in surgical populations (Eeles et al., 2010; Lundstrom et al., 2007). It is associated with poor outcomes such as a decreases in functional capacity, increased risk of future dementia or increased rates of cognitive decline in people with pre-existing dementia, relocation to residential care and even death (Adamis et al., 2006; Witlox et al., 2010). Delirium is distressing to patients and families and contributes to additional health care costs (Inouye, 2006; Leslie et al., 2008). For instance, a recent study estimated that health care costs attributable to delirium in the United States were between $38 billion and $152 billion, rivalling the costs of falls and diabetes mellitus (Leslie et al., 2008). Despite the evidence that delirium is associated with poor outcomes, less than half of the cases of delirium in older hospitalized people are recognized by clinicians (Rice et al., 2011; Spronk et al., 2009). Nurses are seen as playing a key role in the early recognition of delirium because they spend substantial time at the bedside and have frequent opportunities to determine the subtle changes in a patient's behavior that assist in early recognition (Dahlke and Phinney, 2008; Fick et al., 2007). Early recognition enables prompt diagnosis and ⁎ Corresponding author. Tel.: + 61 7 3882 4585; fax: + 61 7 3138 5941. E-mail addresses: [email protected] (J. McCrow), [email protected] (K.A. Sullivan), [email protected] (E.R. Beattie). 0260-6917/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.nedt.2013.12.006
management, including rapid implementation of targeted interventions (McCusker et al., 2011). A reduction in duration and severity of delirium is crucial as it helps to improve patient outcomes. However, nurses' lack of knowledge and ability to recognize delirium has been previously demonstrated in a study of nurses working in a US hospital. It was noted that nurses had difficulty recognizing delirium, with only 21% able to accurately identify hypoactive delirium superimposed on a pre-existing dementia (Fick et al., 2007). A more recent study identified that nurses at all levels of experience and education had knowledge deficits in relation to medication use in delirium and predisposing and precipitating delirium risk factors (Meako et al., 2011). Similarly an Australia study investigating nurses' knowledge of delirium demonstrated that they had inadequate knowledge (Hare et al., 2008). Moreover, nurses are aware of their knowledge deficits in this area and report that they lack the knowledge to effectively care for someone with delirium (Dahlke and Phinney, 2008). Studies have identified that clinical staff education on the detection and treatment of delirium can improve patient outcomes (Inouye et al., 2001; Milisen et al., 2005; Naughton et al., 2005). There is evidence that delirium education interventions, including formal educational packages, development and implementation of practice guidelines, structured courses and extensive multifaceted programs including geriatric specialist input have been noted to effectively increase knowledge associated with delirium (Bergmann et al., 2005; Lundstrom et al., 2005; Tabet et al., 2005; Webster et al., 1999). However, these types of programs may be difficult to implement in a variety of health
J. McCrow et al. / Nurse Education Today 34 (2014) 912–917
care settings due to such things as: access to programs; lack of geriatric specialists; lack of time for educational endeavors; and the lack of local resources to develop educational programs (Brown et al., 2009; Meyer et al., 2007). Web-based learning provides an alternative approach that helps to overcome the challenges to more traditional educational approaches (Bollinger et al., 2011; Paavilainen and Salminen-Tuomaala, 2010). It provides opportunities for nurses to access current information and practice initiatives at a time and place to suit their needs (McCord and McCord, 2010). Moreover, students in academic programs using webbased learning performed better than those learning through traditional face-to-face instruction (Means et al., 2010). For example, hand hygiene knowledge was found to be significantly higher following web-based learning (Alemagno et al., 2010). Similarly, web-based learning was found to enhance emergency nurses' triage accuracy and reduce medication errors (Rankin et al., 2013; Straight, 2008). In addition to learning new information, nurses report high levels of satisfaction with webbased learning in terms of meeting their knowledge and practice requirements (Cottrell and Donaldson, 2013; Smith, 2010). This study builds on previous web-based nursing education by using an innovative educational website that was specifically developed to enable access to delirium information. Unique features of this website included realistic video-vignettes, self-assessment questions, downloadable flow charts, and links to external sites to extend learning opportunities. Additionally, it was not developed in a modular form as many online courses are structured. The web-site was developed using cognitive constructivist principles to allow an individualized approach to web-site navigation. The primary aim of this study was to determine the effectiveness of this educational website, as a means of improving delirium knowledge and recognition of delirium in the clinical setting. A secondary aim was to assess the level of nurse satisfaction with this resource. Specifically we hypothesized that acute care registered nurses who used the educational website would have better delirium knowledge and delirium recognition skills than nurses who did not use the website. Additionally, we expected that nurse users of the website would be satisfied with the functional and design aspects of the site. Methods Design A pretest/post-test time series cluster randomized controlled trial was used to investigate the effect of the delirium website on nurses' knowledge of, and ability to recognize, delirium. Cluster randomization, at a clinical level within each hospital, was performed by the researcher and another independent professional, using “GraphPad Software, Quick calcs” (Software Inc., 2005). Sample Registered Nurses (RNs) employed full or part time in four high-risk delirium areas (either critical care, orthopedic, medical or surgical wards) at three similar size hospitals (250–300 beds) in S. E. Queensland, Australia were potentially eligible. The sample frame consisted of twelve clusters of RNs across the three hospitals. Casual float employees and RNs scoring less than three on an Online Readiness: Self-Assessment Questionnaire (Watkins et al., 2004) were excluded. Ethical approval was granted by the respective hospital and the affiliated university Human Research Ethics Committees. Written informed consent was obtained from each participant. Procedures Participants were recruited at each site using information sessions with the researcher. Prior to randomization, all participants completed
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baseline (Time One; T1) questionnaires and delirium knowledge and recognition testing. Six to eight weeks later, immediately after closure of the website educational intervention, Time Two (T2) post-intervention data were collected. At T2 the intervention group participants also provided website evaluation information. At Time Three (T3), all participants completed a final set of delirium knowledge and delirium recognition tests to assess the longer term effects of the intervention. Participants completed the questionnaire and tests either with research staff or at a time convenient to them and under indirect supervision by another person (usually Nurse Unit Manager or Clinical Nurse Teacher). Following completion of T1 data collection, cluster randomization at a clinical area level was undertaken within each health care facility. That is, within each of the three facilities, RN participants in two clinical areas were randomized to a non-intervention group and RN participants from the other two clinical areas randomized to an intervention group. A total of 72 RNs were randomized to the non-intervention group and 75 RNs randomized to the intervention group. Intervention group participants were given five weeks access to the delirium educational website while non-intervention group participants did not receive access. Both groups continued to work as usual in their clinical areas. Intervention The intervention consisted of a purposely developed educational website called learnaboutdelirum. The website included delirium facts (e.g., definition, types, prevalence), delirium management strategies and information about how to recognize delirium using the internationally accepted Confusion Assessment Method (CAM) (Inouye et al., 1990). The website also included videotaped vignettes of people (actors) with various clinical presentations (e.g., hyperactive delirium, hypoactive delirium, delirium superimposed on dementia, and dementia), with attached narrative captions, questions and answers, and links to other educational websites. Current literature and best practice guidelines for delirium assessment and management, including the Australian delirium clinical practice guidelines (Australian Health Ministers' Advisory Council (AHMAC), 2006) underpinned all website information. The website was developed using constructivist learning principles to enhance user engagement. These principles included, providing processes for participants to build on prior knowledge and experience and support self-guided use of the website. A discussion forum was incorporated to enable social learning opportunities. After a five week period access to the educational website was closed and all participants (non-intervention and intervention groups) were asked to complete the Time Two (T2) questionnaires and six to eight weeks later the Time Three (T3) questionnaires. To reduce attrition we used ongoing communication via electronic mail and researcher contact with the participants and the Nurse Unit Managers of each clinical area. Data collected from participants who were lost to follow-up or who withdrew from the study, was included in the final analyses. Measures A modified version of the Online Learner Readiness: Self-Assessment questionnaire was used as an inclusion criterion for this study to ensure that RN participants had at least basic computer literacy skills (Watkins et al., 2004). The original questionnaire comprised 27 items, rated by respondents on a 5-point Likert scale and had good internal validity (Cronbach's Alpha for each element = 0.74–0.95). Following approval from the original author, six items were removed from the measure as they were specific to online courses rather than an online learning website. This left a total of 21 items. The Nurses' Knowledge of Delirium questionnaire (range 0–34) was developed by a group of Australian academics (Hare et al., 2008) and used to measure delirium knowledge in a group of nurses working
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Fig. 1. Flow of participants through the study.
within one acute health care facility. No reliability or validity data is currently available for this measure. Delirium recognition (DR) was measured using two sets of standardized vignettes, each with five different case scenarios, illustrating older persons with delirium +/− dementia. Participants were asked to read the five validated case vignettes and then select from five choices; a) dementia, b) delirium, c) delirium superimposed on dementia, d) normal aging and e) other, the condition they thought the vignette portrayed. Each vignette had a single correct answer. Both sets of vignettes had undergone an expert validation processes prior to use (Fick et al., 2007; McCrow et al., 2013). Different sets of vignettes were used at different time points. An Evaluation of Online Learning: Self-Assessment questionnaire was developed for this project as a participant evaluation of the website, and included information about content as well as visual, communication and technical aspects of the website. Initially the questionnaire was reviewed and questions refined by three educational web design experts and then pilot tested in a group of 15 Registered Nurses. The final questionnaire comprised 27 items, which were scored on a 5point Likert scale (1 = completely disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree and 5 = completely agree) (Cronbach's alpha = 0.94).
Data Analysis Descriptive data analyses were undertaken to examine demographic variables. A repeated measures multivariable analysis by means of linear mixed modeling (LMM) was undertaken to examine the changes in outcome variables between the intervention and non-intervention groups over time, and associations between the outcome variables and multiple independent variables. Study bias as a result of participant attrition and noncompliance was limited by using Linear mixedmodeling (West, 2009). To capture the between-group variation, a random effect associated with the intercept for both groups was included in the model. Including random intercepts and random slopes allowed correlations between the repeated measures to change over time. Additionally, a random effect of clinical area and clinical area*time was included in the model to account for clustering. To assess the impact of baseline characteristics and the primary outcomes backward elimination of the weakest predicator was used until a parsimonious solution was established. Data was assessed using an intention-to-treat analysis. A p-value of less than 0.05 was considered to be statistically significant. Analyses were performed using the Predictive Analytics SoftWare (PASW®) version 18.0. Results
Table 1 Baseline comparisons of mean Delirium Knowledge and Delirium Recognition scores. Outcome variable
Group
n
Mean
SD
Delirium knowledge (range 0–34) Delirium recognition (range 0–5)
Non-intervention Intervention Non-intervention Intervention
72 75 72 75
20.03 19.69 02.89 02.94
3.98 3.93 1.37 1.28
t-Test
df
p
0.51
145
0.61
−0.26
145
0.79
A total of 205 registered nurses from three different hospitals, with similar case-mixes of patients, were approached to participate in this study. The facilities were part of Queensland's public healthcare network (Queensland Health). Of these RNs, 175 agreed to participate (85%). Prior to completion of baseline questionnaires 28 (14%) participants withdrew and one participant was excluded because this person was not a RN, leaving a sample of 147 (86%) participants who
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Table 2 Delirium Knowledge (range 0–34), selected estimates of fixed effects (N = 147). Parameter
Non-intervention group Intervention group T2 T3 T2 * Intervention group T3 * Intervention group
Estimate
20.05 19.67 1.81 2.96 3.95 2.98
Std. Error
0.45 0.46 0.45 0.56 0.68 0.79
df
t
85.96 79.74 54.92 84.84 145.92 163.20
44.71 42.66 4.08 5.35 5.83 3.78
Sig.
0.000 0.000 0.000 0.000 0.000 0.000
95% CI Lower bound
Upper bound
19.15 18.74 0.92 1.86 2.61 1.42
20.95 20.58 2.71 4.07 5.29 4.54
Note: All values rounded: Reference groups = Non-intervention group and Time 1 (baseline).
completed the baseline questionnaires. Reasons for study withdrawal included extended sick leave or health problems, time limitations, bereavement leave and change in work position/job status including job transfers. The flow of participants through the study is summarized in Fig. 1. As shown in Fig. 1 a total of 72 RNs (6 clusters) were randomized to the non-intervention group and 75 RNs randomized to intervention group (six clusters). A total of 50 (34%) RNs were recruited from facility one, 49 (33%) from facility two and 48 (32%) from facility three. The majority of participants were female (87%; n = 130) and the mean (standard deviation) age was 39 years (12). Most participants reported a bachelor degree as their highest qualification (68%; n = 100) with the remainder having hospital-based nurse training (16%; n = 23), graduate certificates (5%; n = 7), graduate diplomas (7%; n = 11) or a Master degree (3%; n = 4). Two participants did not supply information about their highest qualification. Approximately half of the sample had less than ten years experience since completing undergraduate nursing studies. Another 33 (22%) had between ten and twenty years of nursing experience and the remaining 41 (28%) had greater than twenty years experience. The mean (standard deviation) number of years since completion of undergraduate studies was 13 years (11.47). At baseline the mean number (standard deviation) of correctly identified diagnoses for the five vignettes was 2.89 (1.3) out of a possible score of 5. For the 34 delirium knowledge questions the average number of correct answers was 19.77 (3.9). The percentage of participants who correctly identified the vignettes was highest for the dementia vignette (78%; n = 115), followed by, in decreasing numerical order, hyperactive delirium (67%; n = 98), hyperactive delirium superimposed on dementia (58%: n = 85); hypoactive delirium (50%; n = 74); and hypoactive delirium superimposed on dementia (37%; n = 54). As displayed in Table 1, there were no statistically significant differences in Delirium Knowledge (DK) or Delirium Recognition (DR) between the non-intervention and intervention group at baseline (DK, t = 0.51 p = 0.61; DR, t = −0.26, p = 0.79). Intervention Effects on Delirium Knowledge and Delirium Recognition Results from the mixed models demonstrated that the intervention significantly influenced DK and DR scores over time. RN participants in the intervention group had statistically significant higher DK scores
over time [T3 and T1 (t = 3.78 p = b0.001) and T2 and T1 baseline (t = 5.83 p = b0.001)] than those in the non-intervention group (see Table 2). Calculations using the coefficients from the estimates of fixed-effects suggested that, compared to T1, at T2 participants in the intervention group scored, on average, about 3.6 (10%) more questions correct than participants in the non-intervention group. The difference between T1 and T3 DK scores for the two groups showed an advantage to the intervention group (they answered approximately 2.6 (7%) more questions, correctly than participants in the non-intervention group (see Table 4)). There were statistically significant differences in the mean DR change scores over time from T2 and T1 (t = 2.56 p = 0.011) but not T3 and T1 (t = 1.80 p = 0.074) between the intervention and nonintervention groups (see Table 3). Calculations using the coefficients from the estimates of fixed-effects identified that, compared to T1, on average participants in the intervention group scored an estimated 0.7(14%) more diagnoses (using delirium recognition vignettes) correct at T2 than non-intervention group participants (see Table 4). Educational Website Evaluation Approximately 80% (n = 49) of the 61 participants in the intervention group who provided responses to the Online Learning: Self-Assessment questionnaire rated the educational website as being visually appealing and easy to read and 84% (n = 51) liked the navigational features and page formats. The majority (78%) self-reported that the educational website had improved their delirium knowledge with the remainder of the sample neither agreeing nor disagreeing with this statement. Over 80% (n = 50) of participants in the intervention group found the educational website to be flexible, relevant to clinical practice and interesting. Discussion The findings from this study support the positive effects of a webbased delirium intervention on acute care RNs' knowledge of, and ability to, recognize delirium when portrayed in a vignette. Specifically, those exposed to the learnaboutdelirium website performed significantly better than the non-intervention group in terms of both DK and DR. Additionally, there was a relatively high level of satisfaction with the website by RNs in the intervention group.
Table 3 Delirium Recognition (range 0–5), selected estimates of fixed effects (N = 147). Parameter
Non-intervention group Intervention group T2 T3 T2 * Intervention group T3 * Intervention group
Estimate
2.85 2.92 0.43 −0.18 0.64 0.51
Std. Error
0.15 0.14 0.18 0.20 0.25 0.29
df
71.82 81.45 96.38 94.37 163.74 160.67
Note: All values rounded: Reference groups = Non-intervention group and Time-point 1 (baseline).
t
18.63 20.56 2.44 −0.88 2.56 1.80
Sig.
0.000 0.000 0.017 0.382 0.011 0.074
95% CI Lower bound
Upper bound
2.54 2.64 0.08 −0.57 0.15 −0.05
3.16 3.21 0.79 0.22 1.13 1.08
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Table 4 Estimates of mean Delirium Knowledge and Delirium Recognition scores at each timepoint for both groups. Measure
Delirium Knowledge (range 0–34) Delirium Recognition (range 0–5)
Time-point
One Two Three One Two Three
Group
nursing education interventions and they should be part of future research. Conclusion
Intervention (n = 75)
Non-intervention (n = 72)
19.67 25.43 25.62 2.93 4.00 3.26
20.05 21.86 23.02 2.85 3.28 2.67
As shown in prior research (Fick et al., 2007; Hare et al., 2008), the nurses in this study had some knowledge of delirium, and some ability to recognize delirium, at baseline (T1). Specifically, current study participants had a mean score of 20 (57%) correct delirium knowledge questions correct compared to 53% in the previous study (Hare et al., 2008). In contrast, a slightly higher percentage of participants in the current study were able to correctly recognize delirium compared to participants, using the same vignettes, in a previous study (Fick et al., 2007). For example, 37% of the current participants were able to correctly identify hypoactive delirium when superimposed on dementia compared to 21% in the previous study. Similar differences were found for the other vignettes, where 50% of the current participants versus 41% of previous study participants were able to correctly identify hypoactive delirium alone; 67% (vs. 59%) correctly identified hyperactive delirium superimposed on dementia and 58% (vs. 52%) hyperactive delirium. There were relatively high percentages of participants in both studies who could correctly identify dementia in the dementia vignette but had the most trouble correctly identifying hypoactive delirium and hypoactive delirium superimposed on dementia in the corresponding vignette. These findings support previous research that indicate these forms of delirium are the most frequently missed in clinical environments. People with the hypoactive form of delirium exhibit quiet, non-disruptive behaviors and are often not identified as having delirium or misdiagnosed as having depression, dementia or simply being “old” (Farrell and Ganzini, 1995; Forrest et al., 2007). This study found that over time there was an improvement in DK for both groups. However, this change was significantly higher in the intervention group when compared to the control group in all comparisons. For DR there was a statistically significant difference in T1 and T2 mean change scores with both groups improving but much more so in the intervention group. However, there was not a statistically significant difference in mean DR change scores over time between T1 and T3. Overall, there was a relatively high level of satisfaction with the educational website. The majority of participants indicated that they found the educational website visually appealing, the navigation around the educational website easy and the pages readable. Additionally, the RN users deemed the information on the educational website appropriate and easy to understand. Limitations of this study were that blinding was not possible and there were numerous variables that were not able to be controlled for including motivation of the participants, individual expectations and the physical and emotional environment. However, ward level randomization aimed to reduce the effect of potential contamination of data and the real-life effect of educational interventions has been demonstrated as the prior mentioned variables are common to life experiences. It may be argued that delirium knowledge and recognition of delirium using vignettes are not appropriate measures in nursing education. Some may consider evaluations of clinical practice and patient outcomes more substantial outcome measures. However, these outcome measurements were outside the scope of this study. We acknowledge that practice change and patient outcomes are important endpoints of
The results of this study suggest that the learnaboutdelirium educational website effectively and significantly improved RNs' knowledge about delirium as well as their ability to recognize delirium and delirium superimposed on dementia using vignettes, compared to the nonintervention group. However, the statistically significant improvement between the non-intervention and the intervention groups for delirium recognition was not found at the delay test time-point (T3), supporting the concept that education should be ongoing or that ways to improve retention of this content be explored. Although statistically significant increases in knowledge and recognition were identified in this study, the clinical value of this intervention is its potential to improve patient outcomes through nurse education. Further research is required to assess the impact of this deliriumfocused web-based educational intervention on clinical practice. This study has shown how this change could be achieved using a potentially highly accessible resource for nurses employed in acute care facilities. Funding This work was partially supported by The Queensland Nurses Council (QNC), Australia through a novice research grant. Acknowledgments The authors would like to acknowledge the Registered Nurses who voluntarily participated in this research and for research assistance from Ms Rachel McCrow, Mr William Dore and Mr Daniel Lee. We are grateful for the support offered from the Caboolture, Redcliffe and the Queen Elizabeth 11 Jubilee hospitals, Brisbane, Australia. We acknowledge the support from the University of Maryland Online Dissemination and Implementation Institute funded by the University of Maryland and the John A. Hartford Foundation for assistance in preparation of this manuscript. Prior presentations of this data include: The Sigma Theta Tau International Honor Society of Nursing conference in Brisbane, Australia, on July 30 to August 3, 2012, The Australian Association of Gerontology conference in Brisbane, Australia, on November 20–23, 2012; and The American Delirium Society annual meeting in Indianapolis, USA on June 2–4, 2013. References Adamis, D., Treloar, A., Martin, F.C., Macdonald, A.J.D., 2006. Recovery and outcome of delirium in elderly medical inpatients. Arch. Gerontol. Geriatr. 43 (2), 289–298. http://dx.doi.org/10.1016/j.archger.2005.11.005. Alemagno, S., Guten, S., Warthman, S., Young, E., Mackay, D., 2010. Online learning to improve hand hygiene knowledge and compliance among health care workers. J. Contin. Educ. Nurs. 41 (10), 463–471. http://dx.doi.org/10.3928/0022012420100610-06. Australian Health Ministers' Advisory Council (AHMAC), 2006. Clinical Practice Guidelines for the Management of Delirium in Older People. Victorian Government Department of Human Services, Melbourne (Retrieved from www.health.vic.gov.au). Bergmann, M., Murphy, K., Kiely, D., Jones, R., Marcantonio, E., 2005. A model for management of delirious postacute care patients. J. Am. Geriatr. Soc. 53 (10), 1817. http://dx.doi.org/10.1111/j.1532-5415.2005.53519.x. Bollinger, R.C., McKenzie-White, J., Gupta, A., 2011. Building a global health education network for clinical care and research: the benefits and challenges of distance learning tools. Infect. Dis. Clin. North Am. 25 (2), 385. http://dx.doi.org/ 10.1016/j.idc.2011.02.006. Brown, S., Kirkpatrick, M., Greer, A., Matthias, A., Swanson, M., 2009. The use of innovative pedagogies in nursing education: an international perspective. Nurs. Educ. Perspect. 30 (3), 153–158. http://dx.doi.org/10.1043/1536-5026-030.003.0153. Cottrell, S., Donaldson, J.H., 2013. Exploring the opinions of registered nurses working in a clinical transfusion environment on the contribution of e-learning to personal learning and clinical practice: results of a small scale educational research study. Nurse Educ. Pract. 13 (3), 221–227. http://dx.doi.org/10.1016/j.nepr.2013.01.014.
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