Objective To systematically catalogue review studies on digital health to establish extent of evidence on quality healthcare and illuminate gaps for new understanding, perspectives and insights for evidence-informed policies and practices. Methods We systematically searched PubMed database using sensitive search strings. Two reviewers independently conducted two-phase selection via title and abstract, followed by full-text appraisal. Consensuses were derived for any discrepancies. A standardized data extraction tool was used for reliable data mining. Results A total of 54 reviews from year 2014 to 2021 were included with notable increase in trend of publications. Systematic reviews constituted the majority (61.1%, (37.0% with meta-analyses)) followed by scoping reviews (38.9%). Domains of quality being reviewed include effectiveness (75.9%), accessibility (33.3%), patient safety (31.5%), efficiency (25.9%), patient-centred care (20.4%) and equity (16.7%). Mobile apps and computer-based were the commonest (79.6%) modalities. Strategies for effective intervention via digital health included engineering improved health behaviour (50.0%), better clinical assessment (35.1%), treatment compliance (33.3%) and enhanced coordination of care (24.1%). Psychiatry was the discipline with the most topics being reviewed for digital health (20.3%). Conclusion Digital health reviews reported findings that were skewed towards improving the effectiveness of intervention via mHealth applications, and predominantly related to mental health and behavioural therapies. There were considerable gaps on review of evidence on digital health for cost efficiency, equitable healthcare and patient-centred care. Future empirical and review studies may investigate the association between fields of practice and tendency to adopt and research the use of digital health to improve care.

To become skilled physicians, medical students must master surface anatomy. However, the study of surface anatomy is less emphasized in medical and allied health science curricula, and the time devoted to direct engagement with the human body is limited. This scoping review was designed to answer one research question: "What are the elements and strategies that are effective in teaching surface anatomy?" The review was performed using a five-stage scoping review framework, including research question identification, relevant study identification, study selection, data charting, and result collating and reporting. Three databases were searched using two search terms combined with a Boolean operator: "teaching" and "surface anatomy." The initial pool of 3,294 sources was assessed for duplication, and study eligibility was evaluated using inclusion and exclusion criteria. Data were abstracted from 26 original articles by one researcher and verified by two other researchers. A thematic analysis was performed, and several elements of effective teaching strategies for surface anatomy were identified, namely contextualized teaching, embracing experiential learning, and learning facilitation. This review revealed that a multimodal approach was most commonly used in surface anatomy instruction. Hence, future research should explore the effectiveness of multimodal teaching strategies that adopt the three aforementioned primary elements of effective teaching in an authentic learning environment. It is pertinent to clarify the effectiveness of these teaching strategies by evaluating their impact on student learning, organizational changes, and benefits to other stakeholders. Anat Sci Educ 15: 166-177.

Background: The Anatomy Education Environment Measurement Inventory (AEEMI) evaluates the perception of medical students of educational climates with regard to teaching and learning anatomy. The study aimed to cross-validate the AEEMI, which was previously studied in a public medical school, and proposed a valid universal model of AEEMI across public and private medical schools in Malaysia.Methods: The initial 11-factor and 132-item AEEMI was distributed to 1,930 pre-clinical and clinical year medical students from 11 medical schools in Malaysia. The study examined the construct validity of the AEEMI using exploratory and confirmatory factor analyses. The best-fit model of AEEMI was achieved using five factors and 26 items (ꭓ2 = 3300.71 (df = 1680), P < 0.001, ꭓ2/df = 1.965, RMSEA = 0.018, GFI = 0.929, CFI = 0.962, NFI = 0.927, TLI = 0.956) with Cronbach’s alpha values ranging from 0.621 to 0.927.Results: Findings of the cross-validation across institutions and phases of medical training indicated that the AEEMI measures nearly the same constructs as the previously validated version with several modifications to the item placement within each factor.Conclusions: These results confirmed that variability exists within factors of the anatomy education environment among institutions. Hence, with modifications to the internal structure, the proposed model of the AEEMI can be considered universally applicable in the Malaysian context and thus can be used as one of the tools for auditing and benchmarking the anatomy curriculum.

Background The Anatomy Education Environment Measurement Inventory (AEEMI) evaluates the perception of medical students of educational climates with regard to teaching and learning anatomy. The study aimed to cross-validate the AEEMI, which was previously studied in a public medical school, and proposed a valid universal model of AEEMI across public and private medical schools in Malaysia. Methods The initial 11-factor and 132-item AEEMI was distributed to 1930 pre-clinical and clinical year medical students from 11 medical schools in Malaysia. The study examined the construct validity of the AEEMI using exploratory and confirmatory factor analyses. Results The best-fit model of AEEMI was achieved using 5 factors and 26 items (χ 2 = 3300.71 (df = 1680), P < 0.001, χ 2/df = 1.965, Root Mean Square of Error Approximation (RMSEA) = 0.018, Goodness-of-fit Index (GFI) = 0.929, Comparative Fit Index (CFI) = 0.962, Normed Fit Index (NFI) = 0.927, Tucker–Lewis Index (TLI) = 0.956) with Cronbach’s alpha values ranging from 0.621 to 0.927. Findings of the cross-validation across institutions and phases of medical training indicated that the AEEMI measures nearly the same constructs as the previously validated version with several modifications to the item placement within each factor. Conclusions These results confirmed that variability exists within factors of the anatomy education environment among institutions. Hence, with modifications to the internal structure, the proposed model of the AEEMI can be considered universally applicable in the Malaysian context and thus can be used as one of the tools for auditing and benchmarking the anatomy curriculum.