Offline Digital Education for Medical Students: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration.

BACKGROUND: Medical schools in low- and middle-income countries are facing a shortage of staff, limited infrastructure, and restricted access to fast and reliable internet. Offline digital education may be an alternative solution for these issues, allowing medical students to learn at their own time and pace, without the need for a network connection. OBJECTIVE: The primary objective of this systematic review was to assess the effectiveness of offline digital education compared with traditional learning or a different form of offline digital education such as CD-ROM or PowerPoint presentations in improving knowledge, skills, attitudes, and satisfaction of medical students. The secondary objective was to assess the cost-effectiveness of offline digital education, changes in its accessibility or availability, and its unintended/adverse effects on students. METHODS: We carried out a systematic review of the literature by following the Cochrane methodology. We searched seven major electronic databases from January 1990 to August 2017 for randomized controlled trials (RCTs) or cluster RCTs. Two authors independently screened studies, extracted data, and assessed the risk of bias. We assessed the quality of evidence using the Grading of Recommendations, Assessment, Development, and Evaluations criteria. RESULTS: We included 36 studies with 3325 medical students, of which 33 were RCTs and three were cluster RCTs. The interventions consisted of software programs, CD-ROMs, PowerPoint presentations, computer-based videos, and other computer-based interventions. The pooled estimate of 19 studies (1717 participants) showed no significant difference between offline digital education and traditional learning groups in terms of students' postintervention knowledge scores (standardized mean difference=0.11, 95% CI -0.11 to 0.32; small effect size; low-quality evidence). Meta-analysis of four studies found that, compared with traditional learning, offline digital education improved medical students' postintervention skills (standardized mean difference=1.05, 95% CI 0.15-1.95; large effect size; low-quality evidence). We are uncertain about the effects of offline digital education on students' attitudes and satisfaction due to missing or incomplete outcome data. Only four studies estimated the costs of offline digital education, and none reported changes in accessibility or availability of such education or in the adverse effects. The risk of bias was predominantly high in more than half of the included studies. The overall quality of the evidence was low (for knowledge, skills, attitudes, and satisfaction) due to the study limitations and inconsistency across the studies. CONCLUSIONS: Our findings suggest that offline digital education is as effective as traditional learning in terms of medical students' knowledge and may be more effective than traditional learning in terms of medical students' skills. However, there is a need to further investigate students' attitudes and satisfaction with offline digital education as well as its cost-effectiveness, changes in its accessibility or availability, and any resulting unintended/adverse effects.

A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education.

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators' decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach.