| Literature DB >> 36174016 |
Sisay Maru Wubante1, Araya Mesfin Nigatu1, Adamu Takele Jemere1.
Abstract
INTRODUCTION: In resource-limited settings incorporating the Telemedicine system into the healthcare system enhances exchanging valid health information for practicing evidence-based medicine for the diagnosis, treatment, and prevention of diseases. Despite its great importance, the adoption of telemedicine in low-income country settings, like Ethiopia, was lagging and increasingly failed. Assessing the readiness of health professionals before the actual adoption of telemedicine is considered the prominent solution to tackle the problem. However, little is known about Health professionals' telemedicine readiness in this study setting.Entities:
Mesh:
Year: 2022 PMID: 36174016 PMCID: PMC9522275 DOI: 10.1371/journal.pone.0275133
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.752
Fig 1Sampling procedure for selecting study participants at private hospitals in Amhara region, Ethiopia 2021.
Socio-demographic characteristics of health professionals working in all private hospitals Amhara region 2021 (N = 410).
| Variables | Categories | Frequency (N) | Percentage (%) |
|---|---|---|---|
| Sex | Male | 226 | 55.1 |
| Female | 184 | 44.9 | |
| Age | 20–24 | 29 | 7.1 |
| 25–29 | 183 | 44.6 | |
| 30–34 | 128 | 31.2 | |
| > = 35 | 70 | 17.1 | |
| Professions | Medical Doctor | 90 | 22.0 |
| Nurse | 165 | 40.2 | |
| midwifery | 40 | 9.8 | |
| pharmacy | 46 | 11.2 | |
| Medical laboratory | 57 | 13.9 | |
| Other | 12 | 2.9 | |
| Work experiences | <2years | 66 | 16.1 |
| 2–3 years | 58 | 14.1 | |
| 4–5 years | 67 | 16.3 | |
| >5 years | 219 | 53.4 | |
| Educational status | Diploma | 82 | 20.0 |
| Degree | 202 | 49.3 | |
| Masters and above | 126 | 30.7 |
Technical factors towards telemedicine readiness among health professionals at private hospitals in the Amhara region 2021.
| Variables | Category | Frequency (N) | Percent (%) |
|---|---|---|---|
| Computer literate | Yes | 240 | 58.5 |
| No | 170 | 41.5 | |
| Computer skill | Yes | 245 | 59.8 |
| No | 165 | 40.2 |
Organizational factors for health professionals’ readiness towards telemedicine in private hospitals Amhara region 2021 (N = 410).
| Variables | Categories | Frequency (N) | Percent (%) |
|---|---|---|---|
| Computer access at the office | Yes | 245 | 59.8 |
| No | 165 | 40.2 | |
| Internet access at the office | Yes | 251 | 61.2 |
| No | 159 | 38.8 | |
| Available IT support | Yes | 252 | 61.5 |
| No | 158 | 38.5 | |
| Computer Training | Yes | 168 | 41.0 |
| No | 242 | 59.0 | |
| Backup power generator | Yes | 334 | 81.5 |
| No | 76 | 18.5 |
Fig 2Knowledge and attitude towards telemedicine among health professionals working at private hospitals in Amhara region, Ethiopia, 2021.
Fig 3Core and engagement and overall readiness of health professionals’ towards telemedicine at private hospitals in Amhara region Ethiopia, 2021.
Bi-variate and multivariate analysis on factors associated with readiness of health professionals for telemedicine system in private hospitals, Amhara region, 2021.
| Variables | Readiness | Crude OR (95% CI) | Adjusted OR (95% CI) | p-value | |
|---|---|---|---|---|---|
| Ready | Not ready | ||||
| Knowledge | |||||
| Good | 221 (78.6%) | 60 (21.4%) | 6.43 (4.1, 10.2) | 2.5 (1.4, 4.6)* | 0.002 |
| Poor | 47 (36.4%) | 82 (63.6%) | 1.0 | ||
| Attitude | |||||
| Favorable | 186 (85.7%) | 31 (14.3%) | 8.1 (5.1, 13.1) | 3.2 (1.6, 6.2)* | 0.001 |
| Unfavorable | 82 (42.5%) | 111 (57.5%) | 1.0 | ||
| Personal computer | |||||
| Yes | 223 (71.5%) | 89 (28.5%) | 2.96 (1.85, 4.71) | 3.0 (1.5, 5.9)* | 0.002 |
| No | 45 (45.9%) | 53 (54.1%) | 1.0 | ||
| Computer skill | |||||
| Adequate | 188 (76.7%) | 57 (23.3%) | 3.5 (2.3, 5.4) | 1.9 (1.1, 3.4)* | 0.025 |
| Not adequate | 80 (48.5%) | 85 (51.5%) | 1.0 | ||
| Computer literacy | |||||
| Adequate | 186 (77.5%) | 54 (22.5%) | 3.7 (2.4, 5.7) | 2.2 (1.3, 3.9)* | 0.007 |
| Not adequate | 82 (48.2) | 88 (51.8%) | 1.00 | ||
| Computer Training | |||||
| Yes | 140 (83.3%) | 28 (16.7%) | 4.5 (2.8, 7.2) | 2.1 (1.1, 4.1)* | 0.022 |
| No | 128 (52.9%) | 114 (47.1%) | 1.0 | ||
| Work experience | |||||
| <2 years | 35 (53%) | 31 (47%) | 1.0 | ||
| 2–3 years | 32 (55.2%) | 26 (44.8%) | 1.1 (0.5, 2.2) | 0.9 (0.4, 2.4) | |
| 4–5 years | 32 (47.8%) | 35 (52.2%) | 0.8 (0.4, 1.6) | 1.1 (0.4, 2.7) | |
| >5 years | 169 (77.2%) | 50 (22.8%) | 3.0 (1.9, 5.3) | 3.1 (1.4, 6.7)* | 0.004 |
| Internet access | |||||
| Yes | 200 (79.7%) | 51 (20.3%) | 5.3 (3.4, 8.2) | 2.8 (1.6, 5.1)* | 0.001 |
| No | 68 (42.8%) | 91 (57.2%) | 1.0 | ||
| Computer access | |||||
| Yes | 186 (75.9%) | 59 (24.1%) | 3.2 (2.1, 4.9) | 2.1 (1.1, 3.7)* | 0.017 |
| No | 82 (49.7%) | 83 (50.3%) | 1.0 | ||
| Sex | |||||
| Male | 171 (75.7%) | 55 (24.3%) | 2.8 (1.8, 4.2) | 1.5 (0.74, 2.9) | |
| Female | 97 (52.7%) | 87 (47.3%) | 1.0 | ||
| Educational status | |||||
| Diploma | 39 (47.6%) | 43 (52.4%) | 1.0 | ||
| Degree | 124 (61.4%) | 78 (38.6%) | 1.7 (1.0, 2.9) | 1.3 (0.58, 2.8) | |
| Master and above | 105 (83.3%) | 21 (16.7%) | 5.5 (2.9, 10.4) | 1.6 (0.7, 3.9) | |