| Literature DB >> 31399450 |
Yannis K Valtis1, Julie D Rosenberg1,2,3, Keri Wachter3,4, Rodrick Kisenge5, Fredirick Mashili5, Rehema Chande Mallya5, Timothy David Walker6, J Damascene Kabakambira6,7, Abahuje Egide6, Blaise Ntacyabukura6, Rebecca Weintraub2,4.
Abstract
OBJECTIVE: Evidence-based clinical resources (EBCRs) have the potential to improve diagnostic and therapeutic accuracy. The majority of US teaching medical institutions have incorporated them into clinical training. Many EBCRs are subscription based, and their cost is prohibitive for most clinicians and trainees in low-income and middle-income countries. We sought to determine the utility of EBCRs in an East African medical school.Entities:
Keywords: EBCR, global medical education; general medicine (see internal medicine); global health; information technology
Mesh:
Year: 2019 PMID: 31399450 PMCID: PMC6701685 DOI: 10.1136/bmjopen-2018-026947
Source DB: PubMed Journal: BMJ Open ISSN: 2044-6055 Impact factor: 2.692
Figure 1Baseline survey of Rwandan medical students. (A) The percentage of students at UR reporting that they own a particular internet capable device or use it in medical education. ‘Any device’ refers to any of the following: tablet, smartphone, laptop and desktop. (B) The percentage of students indicating that they use a specific resource to study for coursework or prepare for examinations. n=547 UR students UR, University of Rwanda.
Multivariate linear regression with UpToDate usage as the dependent variable
| Variable | Coefficients | P value |
| Cohort | −0.04 | 0.21 |
| Year at enrolment | 0.12 |
|
| Own any device | 0.07 | 0.29 |
| Own smartphone | −0.03 | 0.49 |
| Hours devoted to school | 0.00 | 0.91 |
| Google use frequency | −0.02 | 0.30 |
| UpToDate use frequency | 0.00 | 0.70 |
The table shows a multivariable linear regression with average daily topic viewing frequency (natural logarithm transform) as the dependent variable. The dependent variable was calculated as ‘number of UpToDate topics viewed’ / ‘days with an active subscription’ for each user. It was set to zero for users who did not log on to UpToDate. The dependent variable was transformed with the equation Y’=ln(Y+1) to approximate normality. The independent variables were set as follows: ‘Cohort’ was set to one for students enrolling in 2015–2016 and 2 for student enrolling in 2016–2017. ‘Year at enrolment’: PCL1 was set to 1, PLC2 was set to 2, Doc1 was set to 3, Doc3 was set to 5 and Doc4 was set to 6. ‘Own any device’ and ‘Own smartphone’ were set to 0 if the student did not report ownership and to 1 if they did. ‘Hours devoted to school’ is a sum of student reported hours spent in the classroom, in clinical activities, and on studying. ‘Google use frequency’ and ‘UpToDate use frequency’ were set based on student responses at the time of enrolment (before UpToDate subscriptions were given to them). They were set to 4 if student replied ‘almost every day’, 3 if ‘a few times per week’, 2 if ‘a few times per month’, 1 if ‘a few times per year’ and 0 if ‘never’ or ‘i don’t know this resource'. P value bolded if <0.05.
Figure 2Predictor of UpToDate usage. The average daily usage during the study period by UR students broken down by class year at enrolment. Students who did not log onto their accounts after enrolling into the study were assigned a daily topic viewing frequency of zero. n=547 UR students. UR, University of Rwanda.
Figure 3Changes in the usage of electronic resources. Figures show the percentage of respondents who reported using a particular resource ‘almost every day’ at enrolment and 1 year later at the time of annual evaluation. Shading added to highlight responses for UpToDate. (A) Responses of UR users who had graduated at the time of annual evaluation and were practising physicians. (B) Responses of UR clinical students (Doc1 at the time of enrolment) who were still in school at the time of annual evaluation. n=62 UR graduates, and 66 UR students. UR, University of Rwanda.
Figure 4Impact of EBCR provision on class exam performance at UR. (A) The average grades of graduating Doc4 students over time. Each dot represents one student and shows the average of their grades in the following eight exams: written and clinical exams in internal medicine, paediatrics, obstetrics and gynaecology and surgery. (B) The average grades of students pre-UpToDate (UTD) (2012–2015) and post-UpToDate (2016–2017). The p value represents a two-sided heteroscedastic t-test. The error bars represent standard error of the mean. EBCR, evidence-based clinical resource.
Two-way ANOVA with average Doc4 grade as dependent variable
| Independent variable | Partial SS | P value |
| UpToDate offered * | 4197 | <10−4 |
| Year of exam | 6196 | <10−4 |
The table shows a two-way ANOVA test with average Doc4 grade as dependent variable and year of exam and UpToDate provision as independent variables. n=599 Doc4 students over 6 years.
*UpToDate offered was set to 0 for 2012–2015 and 1 for 2016–2017.
ANOVA, analysis of variance; SS, sum of squares.
Figure 5EBCR utilisation over time by UR students and faculty. Lines show the average number of topics viewed per user per month by each user group. Only users who enrolled before 01 March 16 are included in this analysis (n=185 faculty, 70 Doc4 students). Call-outs are added to describe events in the careers of Doc4 students.