METHOD: A systematic Boolean search in PubMed, EMBase and EBSCOhost research databases was performed. Keyword search and citation analysis were also conducted. Empirical studies reporting ICT based interventions, and their implications on relative effectiveness in reducing unnecessary diagnostic tests (pathology tests or medical imaging) were evaluated independently by two reviewers based on a rigorously developed coding protocol.
RESULTS: 92 research articles from peer-reviewed journals were identified as eligible. 47 studies involved a single-method intervention and 45 involved multi-method interventions. Regardless of the number of interventions involved in the studies, ICT-based interventions were utilized by 71 studies and 59 of them were shown to be effective in reducing unnecessary testing. A clinical decision support (CDS) tool appeared to be the most adopted ICT approach, with 46 out of 71 studies using CDS tools. The CDS tool showed effectiveness in reducing test volume in 38 studies and reducing cost in 24 studies.
CONCLUSIONS: This review investigated five frequently utilized intervention methods, ICT-based, education, introduction of guidelines or protocols, audit and feedback, and reward and punishment. It provides in-depth analysis of the efficacy of different types of interventions and sheds insights about the benefits of ICT based interventions, especially those utilising CDS tools, to reduce unnecessary diagnostic testing. The replicability of the studies is limited due to the heterogeneity of the studies in terms of context, study design, and targeted types of tests.
METHODS: A cross-sectional anonymous web-based survey was conducted between 10th September 2020 and 30th November 2020. The survey was open to Malaysian aged 18 years and older via various social media platforms. The questionnaire consists of sociodemographic, experience of utilising healthcare facilities, and views on clinic appointment structure.
RESULTS: A total of 1,144 complete responses were received. The mean age was 41.4 ± 12.4 years and more than half of the respondents had a preference for public healthcare. Among them, 77.1% reported to have a clinical appointment scheduled in the past. Less than a quarter experienced off-office hour appointments, mostly given by private healthcare. 70.2% answered they would arrive earlier if they were given a specific appointment slot at a public healthcare facility, as parking availability was the utmost concern. Majority hold positive views for after office hour clinical appointments, with 68.9% and 63.2% agreed for weekend and weekday evening appointment, respectively. The top reason of agreement was working commitment during office hours, while family commitment and personal resting time were the main reasons for disagreeing with off-office hour appointments.
CONCLUSION: We found that majority of our respondents chose to come early instead of arriving on time which disrupts the staggered appointment system and causes over crowdedness. Our findings also show that the majority of our respondents accept off-office hour appointments. This positive response suggests that off-office hour appointments may have a high uptake amongst the public and thus be a possible solution to distribute the patient load. Therefore, this information may help policy makers to initiate future plans to resolve congestions within public health care facilities which in turn eases physical distancing during the pandemic.
METHODS: We adopted the Joanna Briggs Institute's scoping review protocol and followed the Cochrane Rapid Review method to accelerate the review process, using the Implementation and Operation of Mobile Health projects framework and The Extended Technology Acceptance Model of Mobile Telephony to categorise the results. We conducted the review in four stages: (1) establishing value, (2) identifying digital health policy, (3) searching for evidence of infrastructure, design, and end-user adoption, (4) local input to interpret relevance and adoption factors. We used open-source national/international statistics such as the World Health Organization, International Telecommunication Union, Groupe Speciale Mobile, and local news/articles/government statistics to scope the current status, and systematically searched five databases for locally relevant exemplars.
RESULTS: We found 118 studies (2015-2021) and 114 supplementary online news articles and national statistics. Digital health policy was available in all countries, but scarce skilled labour, lack of legislation/interoperability support, and interrupted electricity and internet services were limitations. Older patients, women and those living in rural areas were least likely to have access to ICT infrastructure. Renewable energy has potential in enabling digital health care. Low usage mobile data and voice service packages are relatively affordable options for mHealth in the five countries.
CONCLUSIONS: Effective implementation of digital health technologies requires a supportive policy, stable electricity infrastructures, affordable mobile internet service, and good understanding of the socio-economic context in order to tailor the intervention such that it functional, accessible, feasible, user-friendly and trusted by the target users. We suggest a checklist of contextual factors that developers of digital health initiatives in LMICs should consider at an early stage in the development process.
OBJECTIVES: This study aimed to critically evaluate and determine the effectiveness of educational interventions in improving pharmacogenomics knowledge and practice.
METHODS: Four electronic databases were searched: MEDLINE, EMBASE, CENTRAL, and PsycINFO. Studies on pharmacogenomics educational interventions for health care professionals and students with pre- and post-intervention assessments and results were included. No restrictions were placed on time, language, or educational contexts. The educational outcomes measured include both objective and subjective outcomes. The pharmacogenomics competency domains used to judge educational interventions are based on the competency domains listed by the American Association of Colleges of Pharmacies (AACP). The National Heart, Lung, and Blood Institute of the National Institutes of Health was used for the quality assessment of pre-post studies with no control group and the controlled intervention studies. No meta-analysis was conducted; the data were synthesized qualitatively. The systematic review was reported in accordance with the PRISMA statement.
RESULTS: Fifty studies were included in this review. All included studies integrated the AACP pharmacogenomics competency domains into their educational interventions. Most of the studies had educational interventions that integrated clinical cases (n = 44; 88%). Knowledge was the most frequently evaluated outcome (n = 34; 68%) and demonstrated significant improvement after the educational intervention that integrated AACP pharmacogenomics competency domains and employed active learning with clinical case inclusion.
CONCLUSION: This review provided evidence of the effectiveness of educational interventions in improving pharmacogenomics knowledge and practice. Incorporating pharmacogenomics competency domains into education and training, with patient cases for healthcare professionals and students, dramatically improved their pharmacogenomics knowledge, attitudes, and confidence in practice.