undergraduate medical education in developing countries ... Amongst medical students, high levels of computer ownership and ICT use are reported .... Bandwidth bottlenecks at the University of Botswana. Library Hi. Tech. 2005;23(1):102-17.
Computers, the Internet and Medical Education in Africa
ABSTRACT Objective: To explore the use of Information and Communications Technology (ICT) in undergraduate medical education in developing countries Design: Postal questionnaire Setting: English-speaking countries across Africa Participants: Educators (Deans and Heads of Medical Education) Interventions: 3 postal mailings at 3 monthly intervals, plus an electronic mailing and telephone contact with non-respondents. Main outcome measures: Cross-sectional data about availability of computers, specifications, internet connection speeds, use of ICT by students and teaching of ICT and computerised research skills, presented by country/region. Results: Mean computer/student ratio 0.123. Internet speeds were rated ‘slow’/’very slow’ on a five point Likert scale by 25.0% of respondents overall, but by 58.3% in Eastern Africa and 33.3% in Western/Middle Africa. Mean estimates showed that campus computers more commonly supported CD-ROM (91.4%) and sound (87.3%) than DVD-ROM (48.1%) and internet (72.5%). Teaching of ICT and computerised research skills, and use of computers by medical students for research/assignments and personal projects were common. Conclusions: It is clear that ICT infrastructure in Africa lags behind other regions. Poor download speeds limit the potential of internet resources (especially videos, sound and other large downloads) to benefit students, particularly in Eastern, Western and Middle Africa. CD-ROM capability is more widely available but has not previously gained momentum as a means of distributing materials. Despite infrastructure limitations, ICT is already being used and there is enthusiasm for developing this further. Priority should
be given to developing partnerships to improve ICT infrastructure and maximise the potential of existing technology. Word count = 245
INTRODUCTION Information and Communications Technology (ICT) has become integral to medical education in developed countries(1) and has been championed as a means of improving medical education worldwide(2). However, there are concerns that developing countries lack the relevant infrastructure and skill base(3). In 2006, 58% of people in developed countries used the internet, compared with 11% in developing countries and 3% in sub-Saharan Africa. This gap has widened since the millennium(4). Amongst medical students, high levels of computer ownership and ICT use are reported in Europe and North America(5,6). In India, ICT is gaining popularity although computer ownership lags behind more developed countries and is commoner in private institutions(7,8). In China, ICT is reported to be increasingly used for medical education(9). However, studies from medical schools in Sri-Lanka(10), Mongolia(11) and African countries(12,13) reflect concerns about infrastructure and students’ ICT skills in poorer regions of the word. There is a lack of cross-sectional data about the current state of ICT at medical schools in developing countries, particularly in Africa, where the digital divide is most apparent. This report aims to address this issue. M ETHODS We obtained a list of medical schools from the online list maintained by the Institute for International Medical Education(14). We sent questionnaires to Deans and Heads of Medical Education in countries where English was the official language or widely spoken. (Details of responses by country are available online). The project was discussed with a member of the local National Research Ethics Service committee and was not deemed to require formal ethical approval. A letter of explanation was sent with each questionnaire.
Non-respondents received an electronic mailing and two further postal mailings at three monthly intervals. After this, they were contacted by telephone plus a further mailing if contact was made. The questionnaire covered the size and structure of the medical school, ICT capacity, formal teaching of ICT and computerised research, use of computers by students and access to ICT away from medical school. The questionnaire was piloted with a sample of medical schools before the first mailing. Data are presented by region(16). Cameroon, the only country included from Middle Africa, is grouped with Western Africa. Response rates, frequencies and mean (with standard deviation) are presented as appropriate. Computer/student ratio was calculated separately for each institution and then averaged. RESULTS 53/78 medical schools (69.8%) responded. Responses stratified by region are shown in table 1. Four institutions were involved in a pilot study but then failed to respond to the main study. Their responses to the pilot study are included for questions which were unchanged after piloting. All institutions reported having computing facilities available to students. The mean computer/student ratio was 0.123 but with regional variations (table 1). Means of respondents’ estimates of the proportion of their computers with particular hardware capabilities were:- CD-ROM 91.4% (SD 17.9); DVD-ROM 48.1% (SD 35.9); ability to play sound 87.3% (SD 22.9); internet 72.5% (SD 30.2). Overall, internet speeds were rated ‘fast’ or ’very fast’ by 19.2%; ‘average’ by 55.8%; ‘slow’/’very slow’ by 25.0%. No respondents in Northern and Southern Africa reported speeds worse than ‘average’ but they were rated ‘slow’/’very slow’ by 58.3% in Eastern Africa and 33.3% in Western/Middle Africa. Computers were more widely used and integrated into the curriculum in South Africa, with higher computer ownership, higher mean estimates of proportions of students using computers for producing assignments, research and personal purposes. Respondents were generally enthusiastic about the role of computers in the medical curriculum. All respondents agreed/strongly agreed with the statement ‘I would like to improve my students’ access to IT’.
98.1% agreed/strongly agreed with the statements ‘I feel that computers allow my students better access to information’ and ‘I see funding computing as a priority area’.
DISCUSSION ICT capacity at medical schools in Africa clearly lags behind more developed regions. Computer ownership by a minority of students is reported and is unsurprisingly more common in more prosperous regions. The use of shared facilities (such as internet cafes) appears common. Particularly in Eastern, Western and Middle Africa, internet connection speeds are a significant barrier to downloading medical information, especially bulky files containing video, audio, large documents and pictures. The authors’ experience in Uganda and Tanzania reflects this, with typical download times of 3-6 minutes for short Adobe PDF documents from European and North American websites. Given the growth of open access journals and other online initiatives to improve access to health information, slow download times are concerning. It has been hoped that new intercontinental cables will improve this, but research from Botswana suggests that delays may persist as the most significant bottlenecks occur within countries and institutional networks(17). We urge that internet resources should be designed to minimise download times with low graphic/text-only versions, avoiding plug-ins (downloadable programs required to access files). Feedback should be encouraged and resources tested from computers in developing countries. Whilst CD-ROM appears to be more widely available, low levels of uptake by health workers have been previously reported, possibly related to difficulties distributing sufficient quantities and updating materials(13). Further exploration of CD-ROMs to reach students without reliable internet facilities should be considered. In practice, it may be necessary to provide resources on mixed platforms - CD-ROM, internet, DVD - to maximise delivery and overcome infrastructure limitations. Many institutions are providing literature searching and computing skills training. Given concerns about a lack of ICT skills, this is encouraging. Furthermore, our data suggest that
students already use ICT for literature searching, preparing assignments and personal tasks such as e-mail, suggesting both a degree of applied ICT skill and willingness to use technology. Educators were positive about using and funding ICT. The main limitations of this study are our reliance on reports from educators which may be inaccurate, and the speed with which data will become outdated. Also, our findings may not be reflected in non-English speaking countries. Internet speeds were subjectively assessed and further work is needed to quantify download times and computer specifications. These medical schools were all in major cities. For health workers in more rural areas, access is likely to be even more limited. However, our data show that ICT is being used in the undergraduate medical curriculum in Africa, despite limited numbers of mostly older computers with limited internet access. Two challenges emerge: (i) how to improve ICT infrastructure, and (ii) how to respond to growing enthusiasm for ICT using the limited infrastructure currently available. Interventions should be developed in partnership and carefully evaluated. We are currently assessing the use of different multimedia platforms to deliver multimedia training, in medical schools, hospitals and rural sites in Africa. Total = 1092 References
1. Greenhalgh T. Computer assisted learning in undergraduate medical education. BMJ. 2001 Jan 6;322(7277):40-4. 2. Edejer TT. Disseminating health information in developing countries: The role of the internet. BMJ. 2000 Sep 30;321(7264):797-800. 3. Chandrasekhar CP, Ghosh J. Information and communication technologies and health in low income countries: The potential and the constraints. Bull World Health Organ. 2001;79(9):850-5. 4. United Nations Department of Economic and Social Affairs. The millenium development goals report 2008. . 2008. 5. Romanov K, Aarnio M. A survey of the use of electronic scientific information resources among medical and dental students. BMC Medical Education. 2006;6:28.
6. Peterson MW, Rowat J, Kreiter C, Mandel J. Medical students' use of information resources: Is the digital age dawning?. Academic Medicine. 2004 Jan;79(1):89-95. 7. Sharma R, Verma U, Sawhney V, Arora S, Kapoor V. Trend of internet use among medical students. JK Science. 2006;8(2):101-2. 8. Unnikrishnan B, Kulshrestha V, Saraj A, Agrahari AC, Prakash S, Samantaray L, et al. Pattern of computer and internet use amons medical students in coastal south india. South East Asian Journal of Medical Education. 2008;2(2):18-25. 9. Lam TP, Wan XH, Ip MS. Current perspectives on medical education in china. Med Educ. 2006 Oct;40(10):940-9. 10. Kommalage M, Gunawardena S. Feasibility of introducing information technology-based activities into medical curricula in developing countries. Med Educ. 2008 Jan;42(1):113. 11. Callen JL, Buyankhishig B, McIntosh JH. Clinical information sources used by hospital doctors in mongolia. Int J Med Inf. 2008 Apr;77(4):249-55. 12. Samuel M, Coombes JC, Miranda JJ, Melvin R, Young EJ, Azarmina P. Assessing computer skills in tanzanian medical students: An elective experience. BMC Public Health. 2004 Aug 12;4:37. 13. Smith H, Bukirwa H, Mukasa O, Snell P, Adeh-Nsoh S, Mbuyita S, et al. Access to electronic health knowledge in five countries in africa: A descriptive study. BMC Health Services Research. 2007;7:72. 14. Institute for International Medical Education. IIME database of medical schools (updated july 2006). 15. The World Medical Association. Declaration of helsinki: Ethical principles for medical research involving human subjects. (DoH/2008). 16. United Nations Statistics Division. Composition of macro geographical (continental) regions, geographical sub-regions, and selected economic and other groupings. . 2008. 17. Gerhan DR, Matula SM. Bandwidth bottlenecks at the University of Botswana. Library Hi Tech. 2005;23(1):102-17.
Table 1: Data stratified by region
Region/Country
Response
Number of
Computer per
In undergraduate medical
(Number of
Rate (%)
Students per
student ratio
curriculum, % of institutions
medical schools
institution
identified)
Mean (SD) Mean (SD)
Eastern Africa
teaching
teaching
literature
computing
searching
skills
Estimated % of students owning
using public
using
using
using
computers
computers
computers for
computers for
computers for
assignments
research
personal use
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
12 (66.7)
557 (251)
.157 (.112)
81.8
83.3
15.0 (10.0)
49.4 (33.1)
63.3 (33.9)
60.8 (31.8)
72.1 (26.9)
15 (50.0)
2041 (2602.9)
.044 (.039)
46.7
80.0
35.0 (20.6)
49.4 (29.7)
61.2 (30.6)
47.2 (31.3)
68.9 (26.2)
7 (87.5)
1006 (277.3)
.205 (.100)
100
100
45.8 (30.4)
28.0 (20.5)
97.9 (3.9)
78.6 (35.8)
90.0 (12.7)
19 (86.4)
934 (347.8)
.124 (.193)
57.9
47.4
17.9 (9.6)
79.0 (16.0)
40.6 (31.0)
53.8 (32.0)
73.7 (21.3)
53 (68.0)
1150.5 (1456.6)
.123 (0.137)
65.4
71.7
27.1 (20.7)
58.4 (30.0)
60.2 (34.1)
58.1 (32.8)
74.5 (23.1)
(18) Northern Africa (30) Southern Africa (8) Western +Middle Africa (22) TOTAL (78)
SD = Standard Deviation
W+M = Western and Middle
Online Table: Data stratified by country and region
Region/Country
Response Rate
Number of
Computer per
In undergraduate medical
Estimated %
Estimated %
Estimated %
Estimated %
Estimated %
(Number of
(%)
Students per
student ratio
curriculum, % of institutions
owning
using public
using
using
using
medical schools
institution
Mean (SD)
identified)
Mean (SD)
E Africa (18)
Ethiopia (3)
12 (66.7)
2 (66.7)
teaching
teaching
computers
computers
computers for
computers for
computers for
literature
computing
Mean (SD)
Mean (SD)
assignments
research
personal use
searching
skills
Mean (SD)
Mean (SD)
Mean (SD)
81.8
83.3
557
.157
(251)
(.112)
556(79.2)
.113(.090)
100
d
49.4
63.3
60.8
72.1
(10.0)
(33.1)
(33.9)
(31.8)
(26.9)
100
25.0(-c)
15(14.1)
12.5(10.6)
27.5(31.8)
33.8(23.0)
d
d
d
Kenya (2)
1 (50.0)
700(- )
-
-
100
-
-
100(- )
60(- )
60(-c)
Malawi (1)
1 (100)
234(-c)
.342(-c)
100
100
10.0(-c)
10.0(-c)
100(-c)
90.0(-c)
50.0(-c)
Moz’bique (1)
1 (100)
930(-c)
.032(-c)
0.0
0.0
-d
-d
50.0(-c)
50.0(-c)
-d
Somalia (2)
0 (0)
-
-
-
-
-
-
-
-
-
Tanzania (4)
3 (75.0)
541(341)
.186(.131)
100
100
25.0(7.1)
85.0(21.2)
96.6(5.8)
90.0(10.0)
93.3(5.8)
Uganda (3)
2 (66.7)
450(212)
.183(.0240)
100
100
5.0(0)
72.5(10.6)
47.5(3.5)
52.5(24.8)
72.5(24.8)
Zambia (1)
1 (100)
350(-c)
-d
0.0
0.0
-d
60(-c)
60(-c)
10(-c)
90(-c)
Zimbabwe (1)
1 (100)
840(-c)
.048(-c)
100
100
10(-c)
30(-c)
40(-c)
90(-c)
100(-c)
15 (50.0)
2041
.044
46.7
80.0
35.0
49.4
61.2
47.2
68.9
(2602.9)
(.039)
(20.6)
(29.7)
(30.6)
(31.3)
(26.2)
N Africa (30)
c
15.0
c
c
Egypt (11)
6 (45.5)a
4865(3401)
.011(.004)
80.0
100
32.0(19.2)
31.3(30.7)
56.3(39.5)
43.3(35.1)
80.0(14.1)
Libya (4)
1 (25.0)
539(-c)
.056(-c)
100
100
50.0(-c)
60.0(-c)
70.0(-c)
100(-c)
80.0(-c)
Sudan (14)
8 (57.1)
818(256.4)
.055(.0410)
25.0
62.5
35.8(25.8)
61.1(28.9)
67.9(28.3)
38.8(27.2)
66.0(33.6)
Tunisia (1)
1 (100)a
-d
-d
0.0
100
30.0(-c)
30.0(-c)
25,0(-c)
40.0(-c)
50.0(-c)
7 (87.5)
1006 (277.3)
.205
100
100
45.8
28.0
97.9
78.6
90.0
(30.4)
(20.5)
(3.9)
(35.8)
(12.7)
45.8(30.4)
28.0(20.5)
97.9(3.9)
78.6(35.8)
90.0(12.7)
S Africa (8)
(.100) South Africa (8)
7 (87.5)
1006(277.3)
.205(.100)
100
100
W+M Africa (22)
19
934
.124
(86.4)
(347.8)
(.193)
Cameroon (1)
1(100)
640(-c)
.025(-c)
0
Gambia (1)
0(0.0)
-
-
Ghana (2)
2 (100)
844(103.9)
.396(.484)
Nigeria (16)
TOTAL (78)
17.9
79.0
40.6
53.8
73.7
(9.6)
(16.0)
(31.0)
(32.0)
(21.3)
0
10.0(-c)
80.0(-c)
10.0(-c)
30.0(-c)
70.0(-c)
-
-
-
-
-
-
-
50.0
50.0
25.0(-c)
90.0(-c)
0
10.0(- )
60.0(- )
25.0(- )
10.0(- )
60.0(-c)
15 (93.8)b
1044(274.8)
.075(.065)
60.0
53.3
18.6(10.0)
79.5(16.7)
47.1(33.3)
61.3(32.0)
76.0(21.5)
0 (0.0)
-
-
-
-
-
-
-
-
-
53 (68.0)
1150.5
0.123
65.4
71.7
27.1
58.4
60.2
58.1
74.5
(1456.6)
(0.137)
(20.7)
(30.0)
(34.1)
(32.8)
(23.1)
b Including two pilot responses; c Single answer to questionnaire item from this group of respondents therefore SD not calculated; d No answer to questionnaire item from this group of respondents, mean not calculated
c
c
65.0(35.4)
100
a Including one pilot response;
c
35(7.1)
.120(- )
SD = Standard Deviation;
c
25(7.1)
83(- )
Sierra Leone (1)
c
47.4
1 (100)
Liberia (1)
c
57.9
