| Literature DB >> 28732028 |
Birama Apho Ly1,2, Ronald Labonté1,3, Ivy Lynn Bourgeault1,4, Mbayang Ndiaye Niang5.
Abstract
Telemedicine is considered to be an effective strategy to aid in the recruitment and retention of physicians in underserved areas and, in doing so, improve access to healthcare. Telemedicine's use, however, depends on individual and contextual factors. Using a mixed methods design, we studied these factors in Senegal based on a micro, meso and macro framework. A quantitative questionnaire administered to 165 physicians working in public hospitals and 151 physicians working in district health centres was used to identify individual (micro) factors. This was augmented with qualitative descriptive data involving individual interviews with 30 physicians working in public hospitals, 36 physicians working in district health centres and 10 telemedicine project managers to identify contextual (meso and macro) factors. Physicians were selected using purposeful random sampling; managers through snowball sampling. Quantitative data were analyzed descriptively using SPSS 23 and qualitative data thematically using NVivo 10. At the micro level, we found that 72.1% of the physicians working in public hospitals and 82.1% of the physicians working in district health centres were likely to use telemedicine in their professional activities. At the meso level, we identified several technical, organizational and ethical factors, while at the macro level the study revealed a number of financial, political, legal, socioeconomic and cultural factors. We conclude that better awareness of the interplay between factors can assist health authorities to develop telemedicine in ways that will attract use by physicians, thus improving physicians' recruitment and retention in underserved areas.Entities:
Mesh:
Year: 2017 PMID: 28732028 PMCID: PMC5521789 DOI: 10.1371/journal.pone.0181070
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1The micro, meso and macro framework of the use of telemedicine.
The use of telemedicine is influenced by micro, meso and macro levels factors.
Socio-demographic and professional characteristics of the participants involved in the quantitative study (micro-factors).
| Characteristics | Physicians working in public hospitals | Physicians working in district health centres | |||
|---|---|---|---|---|---|
| N | % | N | % | ||
| Male | 112 | 67.88 | 97 | 64.24 | |
| Female | 53 | 32.12 | 54 | 35.76 | |
| ≤ 30 | 10 | 6.10 | 16 | 10.59 | |
| 31–35 | 39 | 23.64 | 38 | 25.16 | |
| 36–40 | 46 | 27.88 | 45 | 29.80 | |
| 41–45 | 26 | 15.76 | 20 | 13.24 | |
| 46–50 | 22 | 13.33 | 18 | 11.92 | |
| > 50 | 22 | 13.33 | 14 | 9.27 | |
| General practitioners | 30 | 18.18 | 64 | 42.38 | |
| Specialist physicians | 135 | 81.82 | 74 | 49.01 | |
| Trainees physicians | 0 | 0.00 | 13 | 8.61 | |
| Dakar | 125 | 75.76 | 100 | 66.23 | |
| Out of Dakar | 40 | 24.24 | 51 | 33.77 | |
Socio-demographic and professional characteristics of the participants involved in the qualitative study (meso and macro-factors).
| Characteristics | Physicians/ public hospitals | Physicians/ district health centres | Telemedicine project managers | ||||
|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | ||
| Male | 24 | 80.00 | 34 | 94.44 | 10 | 100.00 | |
| Female | 6 | 20.00 | 2 | 5.56 | 0 | 0.00 | |
| ≤ 30 | 2 | 6.67 | 0 | 0 | 1 | 10.00 | |
| 31–35 | 3 | 10.00 | 10 | 27.77 | 1 | 10.00 | |
| 36–40 | 4 | 13.33 | 13 | 36.11 | 0 | 0.00 | |
| 41–45 | 7 | 23.33 | 7 | 19.44 | 0 | 0.00 | |
| 46–50 | 6 | 20.00 | 5 | 16.66 | 2 | 20.00 | |
| 51–55 | 7 | 23.33 | 1 | 0.00 | 2 | 20.00 | |
| 56–60 | 0 | 0.00 | 0 | 0.00 | 3 | 30.00 | |
| > 60 | 1 | 3.33 | 0 | 0.00 | 1 | 10.00 | |
| General practitioner | 0 | 0.00 | 13 | 36.11 | 0 | 0.00 | |
| Specialist physician | 30 | 100 | 23 | 63.89 | 7 | 70.00 | |
| Not a physician | 0 | 0.00 | 0 | 0.00 | 3 | 30.00 | |
| Dakar | 24 | 80.00 | 2 | 5.56 | 8 | 80.00 | |
| Outside Dakar | 6 | 20.00 | 34 | 94.44 | 2 | 20.00 | |
Intention of the physicians working in public hospitals according to their region of practice.
| Intention | Dakar | Outside Dakar | Total |
|---|---|---|---|
| Highly unlikely | 16 (12.80%) | 2 (5.00%) | 18 (10.91%) |
| Quite unlikely | 7 (5.60%) | 3 (7.50%) | 10 (6.06%) |
| Slightly unlikely | 2 (1.60%) | 1 (2.50%) | 3 (1.82%) |
| Neither unlikely nor likely | 14 (11.20%) | 1 (2.50%) | 15 (9.09%) |
| Slightly likely | 25 (20.00%) | 8 (20.00%) | 33 (20.00%) |
| Quite likely | 40 (32.00%) | 10 (25.00%) | 50 (30.30%) |
| Highly likely | 21 (16.80%) | 15 (37.50%) | 36 (21.82%) |
Meso- and macro-level factors identified by participants.
| Level | Factors identified by key informant |
|---|---|
| Technical | Lack of telemedicine equipment Lack of equipment maintenance Lack or poor quality of internet connection Lack or poor quality of electricity supply Lack of training |
| Organizational | Lack of information on telemedicine Undersupply of human resources |
| Ethical | Lack of an ethical framework for telemedicine |
| Legal | Lack of a legal framework for telemedicine |
| Political | Dysfunction of the Telemedicine National Steering Committee Non-translation of political will into concrete actions Lack of consideration of telemedicine as a political priority Lack of a national telemedicine strategy |
| Financial | High investment, operating, training and maintenance costs Scarcity of funding sources No physician compensation for telemedicine use |
| Socioeconomic and Cultural | Religious and socio-cultural beliefs about telemedicine Healthcare worker strikes Social conflicts (Casamance) Patient poverty |