Effectiveness of Online Team-based Learning for Pharmacists on How to Conduct Clinical Medication Reviews for Old Patients in Japan: A Randomized Controlled Trial.

BACKGROUND/AIM: This study aimed to determine the effectiveness of online team-based learning (TBL) and the factors influencing dropouts from online TBL for pharmacists on how to conduct clinical medication reviews for older adults. PARTICIPANTS AND METHODS: All participants were randomly assigned to the TBL or non-TBL group by using a random number sequence table matched by their years of experience working as a pharmacist. The primary outcome was whether the score on the team readiness assurance test (TRAT) in the TBL group differed from that on the second individual readiness assurance test (IRAT) in the non-TBL group. The secondary outcome was to identify factors contributing to dropouts from the online TBL program. RESULTS: The TRAT score in the TBL group was significantly higher than the second IRAT score in the non-TBL group during the first session (p=0.010). There were no differences in TRAT and IRAT scores between groups in two subsequent sessions. Logistic regression analysis revealed that less than 10 years of pharmacy experience was a contributor to dropouts (p=0.039), whereas experience in home-based care prevented dropouts (p=0.026) in our online TBL program. CONCLUSION: This study revealed the short-term usefulness of online TBL on medication reviews for older adults and elucidated the factors related to dropouts. Although instructors should provide positive feedback to participants with insufficient experience in pharmacy practice and home-based care, online TBL has the potential to improve educational effectiveness for community pharmacists during the COVID-19 pandemic.

Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients.

Objective: To compare the performance of the diagnostic model for fall risk based on the short physical performance battery (SPPB) developed using commercial machine learning software (MLS) and binomial logistic regression analysis (BLRA). Methods: We enrolled 797 out of 850 outpatients who visited the clinic between March 2016 and November 2021. Patients were categorized into the development (n = 642) and validation (n = 155) datasets. Age, sex, number of comorbidities, number of medications, body mass index (BMI), calf circumference (left-right average), handgrip strength (left-right average), total SPPB score, and history of falls were determined. We defined fall risk by an SPPB score of ≤6 in men and ≤9 in women. The main metrics used for evaluating the machine learning model and BLRA were the area under the curve (AUC), accuracy, precision, recall (sensitivity), specificity, and F-measure. The commercial MLS automatically calculates the parameter range of the highest contribution. Results: The participants included 797 outpatients (mean age, 76.3 years; interquartile range, 73.0-81.0; 288 men). The metrics of the current diagnostic model in the commercial MLS were as follows: AUC = 0.78, accuracy = 0.74, precision = 0.46, recall (sensitivity) = 0.81, specificity = 0.71, F-measure = 0.59. The metrics of the current diagnostic model in the BLRA were as follows: AUC = 0.77, accuracy = 0.75, precision = 0.47, recall (sensitivity) = 0.67, specificity = 0.77, F-measure = 0.55. The risk factors for falls in older adult outpatients were handgrip strength, female sex, experience of falls, BMI, and calf circumference in the commercial MLS. Conclusions: The diagnostic model for fall risk based on SPPB scores constructed using commercial MLS is noninferior to BLRA.