Application of the electronic book to promote self-directed learning in medical technologist continuing education: a cross-sectional study.

BACKGROUND: Continuing education (CE) is essential for health professionals to improve competence in clinical practice, yet many medical technologists still experience barriers to learning in complex clinical settings. To better manage CE and address medical technologists' learning needs, we developed a learner-centred electronic book (e-book) to promote self-directed learning for medical technologists. METHODS: A cross-sectional study was conducted to explore the acceptability and learning impacts of the e-book as CE material for medical technologists in two medical centres in Taiwan. We designed the learner-centred context in the e-book based on medical technologists' practice requirements and learning needs. Moreover, we adopted The New World Kirkpatrick Model with four levels (reactions, learning, behaviours and results) to evaluate the e-book's learning impacts on medical technologists. A total of 280 medical technologists were invited to complete a questionnaire and a post-test, providing learning patterns as well as their satisfaction with the e-book and their learning outcomes after using it. RESULTS: Most readers had positive learning experiences and better learning outcomes, including knowledge acquisition and behavioural change, after reading the e-book. The e-book became a new CE activity and reached medical technologists in various types of laboratories. CONCLUSIONS: The low-cost and learner-centred e-book effectively overcame CE learning barriers for medical technologists. The interactivity and flexibility of e-learning particularly helped learners to engage in clinical scenarios in laboratory medicine. This study could pave the way for medical educators to build a high-quality e-learning model in CE.

Oil-in-water emulsion adjuvants for pediatric influenza vaccines: a systematic review and meta-analysis.

Standard inactivated influenza vaccines are poorly immunogenic in immunologically naive healthy young children, who are particularly vulnerable to complications from influenza. For them, there is an unmet need for better influenza vaccines. Oil-in-water emulsion-adjuvanted influenza vaccines are promising candidates, but clinical trials yielded inconsistent results. Here, we meta-analyze randomized controlled trials with efficacy data (3 trials, n = 15,310) and immunogenicity data (17 trials, n = 9062). Compared with non-adjuvanted counterparts, adjuvanted influenza vaccines provide a significantly better protection (weighted estimate for risk ratio of RT-PCR-confirmed influenza: 0.26) and are significantly more immunogenic (weighted estimates for seroprotection rate ratio: 4.6 to 7.9) in healthy immunologically naive young children. Nevertheless, in immunologically non-naive children, adjuvanted and non-adjuvanted vaccines provide similar protection and are similarly immunogenic. These results indicate that oil-in-water emulsion adjuvant improves the efficacy of inactivated influenza vaccines in healthy young children at the first-time seasonal influenza vaccination.

Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies.

BACKGROUND: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer outcomes may be prognosticated on the basis of surface markers of tumor cells and serum tests. However, evaluation of a combination of clinicopathological features may offer a more comprehensive overview for breast cancer prognosis. MATERIALS AND METHODS: We evaluated serum human epidermal growth factor receptor 2 (sHER2) as part of a combination of clinicopathological features used to predict breast cancer metastasis using machine learning algorithms, namely random forest, support vector machine, logistic regression, and Bayesian classification algorithms. The sample cohort comprised 302 patients who were diagnosed with and treated for breast cancer and received at least one sHER2 test at Chang Gung Memorial Hospital at Linkou between 2003 and 2016. RESULTS: The random-forest-based model was determined to be the optimal model to predict breast cancer metastasis at least 3 months in advance; the correspondingarea under the receiver operating characteristic curve value was 0. 75 (p < 0. 001). CONCLUSION: The random-forest-based model presented in this study may be helpful as part of a follow-up intervention decision support system and may lead to early detection of recurrence, early treatment, and more favorable outcomes.

Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers.

BACKGROUND: Analytic measurement of serum tumour markers is one of commonly used methods for cancer risk management in certain areas of the world (e.g. Taiwan). Recently, cancer screening based on multiple serum tumour markers has been frequently discussed. However, the risk-benefit outcomes appear to be unfavourable for patients because of the low sensitivity and specificity. In this study, cancer screening models based on multiple serum tumour markers were designed using machine learning methods, namely support vector machine (SVM), k-nearest neighbour (KNN), and logistic regression, to improve the screening performance for multiple cancers in a large asymptomatic population. METHODS: AFP, CEA, CA19-9, CYFRA21-1, and SCC were determined for 20 696 eligible individuals. PSA was measured in men and CA15-3 and CA125 in women. A variable selection process was applied to select robust variables from these serum tumour markers to design cancer detection models. The sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the curve, and Youden index of the models based on single tumour markers, combined test, and machine learning methods were compared. Moreover, relative risk reduction, absolute risk reduction (ARR), and absolute risk increase (ARI) were evaluated. RESULTS: To design cancer detection models using machine learning methods, CYFRA21-1 and SCC were selected for women, and all tumour markers were selected for men. SVM and KNN models significantly outperformed the single tumour markers and the combined test for men. All 3 studied machine learning methods outperformed single tumour markers and the combined test for women. For either men or women, the ARRs were between 0.003-0.008; the ARIs were between 0.119-0.306. CONCLUSION: Machine learning methods outperformed the combined test in analysing multiple tumour markers for cancer detection. However, cancer screening based solely on the application of multiple tumour markers remains unfavourable because of the inadequate PPV, ARR, and ARI, even when machine learning methods were incorporated into the analysis.

Reduction in Hospital-Wide Clinical Laboratory Specimen Identification Errors following Process Interventions: A 10-Year Retrospective Observational Study.

BACKGROUND: Accurate patient identification and specimen labeling at the time of collection are crucial steps in the prevention of medical errors, thereby improving patient safety. METHODS: All patient specimen identification errors that occurred in the outpatient department (OPD), emergency department (ED), and inpatient department (IPD) of a 3,800-bed academic medical center in Taiwan were documented and analyzed retrospectively from 2005 to 2014. To reduce such errors, the following series of strategies were implemented: a restrictive specimen acceptance policy for the ED and IPD in 2006; a computer-assisted barcode positive patient identification system for the ED and IPD in 2007 and 2010, and automated sample labeling combined with electronic identification systems introduced to the OPD in 2009. RESULTS: Of the 2000345 specimens collected in 2005, 1023 (0.0511%) were identified as having patient identification errors, compared with 58 errors (0.0015%) among 3761238 specimens collected in 2014, after serial interventions; this represents a 97% relative reduction. The total number (rate) of institutional identification errors contributed from the ED, IPD, and OPD over a 10-year period were 423 (0.1058%), 556 (0.0587%), and 44 (0.0067%) errors before the interventions, and 3 (0.0007%), 52 (0.0045%) and 3 (0.0001%) after interventions, representing relative 99%, 92% and 98% reductions, respectively. CONCLUSIONS: Accurate patient identification is a challenge of patient safety in different health settings. The data collected in our study indicate that a restrictive specimen acceptance policy, computer-generated positive identification systems, and interdisciplinary cooperation can significantly reduce patient identification errors.