Abstract:

Background:
Pre-registration pharmacists’ (PRP) training programs often involve traditional group discussions (TGD) using pen and paper. Studies have shown that these are time-consuming, inconvenient and adopt a one-size-fits-all educational model that is unable to cater towards individuals’ own learning pace nor achieve optimal learning outcomes. Newer technologies has enabled better educational approaches with improved efficiency and effectiveness. A new learning approach, known as adaptive e-learning (AEL), offers benefits that can overcome these disadvantages, thereby personalizing learning and optimizing learning outcomes for the learner. The objective of this study was to develop a clinical algorithmic software based on AEL and test its effectiveness in comparison to TGD.

Methods:
Three case studies comprising of multiple choice questions (MCQs) were developed and mapped into algorithmic flowcharts and entered into a software. The MCQs were categorized into simple, challenging (required deeper knowledge and understanding) and defining questions (patient’s clinical outcome would be affected). Clinical algorithms were created such that learners had to answer the questions correctly before proceeding. Learners would also get a unique clinical outcome at the end of each case study depending on the answers chosen. Appropriate feedback, compliments and hints would be provided at the end of each case study. A comparison study was conducted between 2 groups of PRP (TGD versus AEL). A short quiz was given as a pre-test and post-test before and after the intervention. The difference in learners’ pre-test and post-test scores was used as a measure of learning outcomes. A self-administered questionnaire also obtained participants’ demographics, usability of the AEL software and their perceptions on how AEL compared to TGD. Descriptive statistics, t-test and Wilcoxon signed rank test were used to analyze the results.

Results:
Fifteen PRP participated. Both TGD and AEL groups showed similar improvement in post-test scores (2.5% versus 5%, p=0.810). The time taken to complete one AEL session was much shorter than one TGD session (26.8 versus 102.3 min, p=0.001). In fact, 12 PRP (80.0%) felt that TGD was too time-consuming, while 10 (66.7%) agreed that AEL was more time efficient. Those who took longer than 60 minutes to complete the case studies felt that TGD was more inconvenient and that the pace of discussion in TGD was too fast (60.0% versus 0%, p=0.022 each) compared to those who took less than 30 minutes to complete. Nine PRP (60.0%) agreed that AEL allowed them to learn at their own pace, and 6 of them (40.0%) felt that AEL was more convenient. In contrast, all of them felt that there was a lack of human interaction in AEL. Twelve (80.0%) agreed that the MCQ choices were too limited and 9 (60%) felt that the content was lacking. Those who preferred AEL agreed that the software made learning more fun and interesting compared to those who preferred TGD (100% versus 27.3%, p=0.026).

Conclusion:
A clinical algorithmic software based on AEL was developed. It has advantages of being more time efficient, convenient and able to adapt to the individual’s learning pace, and thus can be used to supplement current methods of pre-registration training. The inclusion of more comprehensive case studies with clinical algorithms of higher complexity can potentially benefit the learning outcomes of larger cohorts of PRP.

Keywords:

Pre-registration training, Clinical algorithm, Adaptive e-learning, Pharmacy training program, Technology-enhanced learning.