Socio-demographic characteristics of physicians and nurses
A total of 406 participants responded to the 423 survey questionnaires distributed. Therefore, a response rate of 95.9 % (406/423) was achieved in this study. In this survey, the majority of respondents were males, 249(61.3%). The mean age was 30.61+_6SD years and the majority of respondents were below the age of 30 years. In terms of educational level, the majority of participants were bachelor 57.4% and medical degree 20.2% holders with a total contribution of 77.6% participants. Most of the participants, 59.4 % had working experience between 0-5 years, and only 11.8% have a working experience above 10 years (Table 1).
Access to basic technologies among physicians and nurses
Table 1 shows that 95.1% of physicians and 53.4% of nurses own a personal computer. However, only 46.2% from the 95.1% physicians and 47% from 53.4% of nurses indicated their personal computer had internet capabilities. Regarding smartphones, more than 95.1% of physicians and 73.5% of nurses own smartphones. Furthermore, from the findings, barely 16% of the total respondents indicated they did not have a social media account.
Table 1: Socio-demographic Characteristics and access to basic technologies at teaching hospitals in the Amhara region, Ethiopia, 2020.
|
Variables
|
Categories
|
Frequency (%)
|
Own computer
|
Computer with internet
|
Own smartphone
|
Smartphone with internet
|
Social-media account
|
Gender
|
Male
|
249
|
70.7
|
44.3
|
81.5
|
94.1
|
85.9
|
Female
|
157
|
58.6
|
51.1
|
77.7
|
94.3
|
80.9
|
Age
|
<30
|
234
|
69.2
|
51.9
|
84.6
|
95.5
|
86.8
|
>=30
|
172
|
61.6
|
38.7
|
73.8
|
92.2
|
80.2
|
Educational level
|
Medical doctor+
|
40
|
100.0
|
35.0
|
100.0
|
100.0
|
87.5
|
Medical degree
|
82
|
93.9
|
51.9
|
93.9
|
98.7
|
97.6
|
Master’s degree
|
15
|
53.3
|
37.5
|
60.0
|
70.0
|
80.0
|
Bachelor
|
233
|
53.6
|
46.4
|
72.1
|
92.5
|
79.8
|
Diploma
|
36
|
50.0
|
55.6
|
86.1
|
94.2
|
77.8
|
Work experience
|
0-5
|
145
|
72.2
|
50.0
|
84.6
|
97.1
|
88.8
|
6-10
|
128
|
53.8
|
46.0
|
76.9
|
91.1
|
82.1
|
>10
|
131
|
64.6
|
29.0
|
64.6
|
83.9
|
64.6
|
Profession
|
Physician
|
123
|
95.1
|
46.2
|
95.1
|
99.1
|
93.5
|
Nurse
|
283
|
53.4
|
47.0
|
73.5
|
91.4
|
79.9
|
Participant awareness for Tele-monitoring
Regarding the awareness of physicians and nurses about TM, participants have shown low awareness in general. Only 38.7 %( 157/406) reported they had heard about telemonitoring. Even though there are slightly few respondents who are aware of TM technology, the majority 83.5 %( 339/406) of respondents are aware of the availability of self-management tools for diabetes patients. More than 88.5% (300/339) from 83.5% of respondents who are aware of self-management tools, indicated that they recommend their patients to use different self-management tools.
As can be seen in figure 1, Regarding the specific self-management tools that are recommended, respondents reported that the most commonly recommended self-management tools were glucometer, 97.3%(292/300), blood pressure measurement 78.3%(235/300), thermometer 39%(117/300), and only 17.3%(52/300) of them recommend mobile health applications.
The practice of using Information technologies among physicians and nurses
A slim majority of 52.7 %( 214/406) of respondents are communicating with patients through either of the information technologies, phone calls, SMS, social media, email, and videoconference. The results also revealed that the highly used intercommunication method was voice calls, 96.7 %( 207/214) while SMS, 59.8 %( 128/214) (Table 2).
Table 2: Frequency of using information technologies to support or consult patients among participants at teaching hospitals in Amhara region 2020.
|
Tools
|
Physician n (%)
|
Nurse n (%)
|
Total
|
Mobile phone(voice calls)
|
81(39.1)
|
126(60.9)
|
207(96.7)
|
SMS(text messaging)
|
54(42.2)
|
74(57.8)
|
128(59.8)
|
Email
|
3(12)
|
22(88)
|
25(11.6)
|
Social media
|
32(49.2)
|
33(50.8)
|
65(30.3)
|
Videoconferencing
|
4(57.1)
|
3(42.9)
|
7(3.27)
|
*multiple response set, totals may sum up to more than 100%
|
Participant readiness for Tele-monitoring
Out of total participants, only 9.4% CI: [6.7-12.3] of them have high readiness towards TM, 25.1%CI: [20.1-29.6] of participants showed moderate or average readiness while a majority of participants 65.5% CI: [60.8-70.4] shows low readiness level in this study.
Factors associated with physicians' and nurses' awareness of TM technology.
Table 3 shows the details of bivariate and multivariate logistic regression, the results of logistic regression analysis examined the association between awareness of TM and the independent variables (i.e., own a personal computer, Computer-related training, work experience, frequency of uploading and downloading the information through internet and experience in communicating with patients using information technology tools).
In the crude analysis, participants who owned a personal computer were about 2.46 times more likely to be aware of TM (OR=2.46, 95% CI=1.56-3.87) as compared to those who did not own a personal computer. Likewise, participants who download/upload information daily were 2.4 times more likely to be aware of TM (OR=2.4, 95% CI=1.93-6.258) as compared to those who never download or upload.
Furthermore, participants who had experience in supporting/communicating patients using information technology tools were about 1.75 times more likely to be aware towards (OR=1.747, 95% CI=1.164-2.62) as compared to participants who had no experience to support/communicate patients through information technology tools
On the other hand, after adjusting the individual effect of the above confounders, participants who had computer-related training (AOR=1.808, 95% CI=1.032-3.167) were more likely to be aware of TM as compared to participants who had no computer-related training. Similarly, participants who use computers daily were 2.84 times more aware likely to be aware of TM (AOR=2.84, 95% CI=1.129-7.121) than those who did not use computers daily.
Table 3: Bivariate and multivariate logistic regression factors associated with awareness of TM technologies among physicians and nurses at teaching hospitals in the Amhara region 2020.
|
Variable
|
Category
|
Awareness TM
Yes n (%) No n (%)
|
Crude OR(95%CI)
|
AOR(95%CI)
|
Having a computer
|
Yes
NO
|
122(77.7) 146(58.6)
35(22.3) 103(41.4)
|
2.5[1.6-3.7]*
1
|
1.8[.9-3.4]
1
|
Computer training
|
Yes
No
|
70(44.6) 69(27.7)
87(55.4) 180(72.3)
|
2.09[1.4-3.2]*
1
|
1.8[1.0-3.2]*
|
Computer use
|
Daily
Weekly
Never
|
83(52.9) 101(40.6)
61(38.9) 91(36.5)
13(8.3) 57(22.9)
|
3.6[1.9-7.0]*
2.9[1.5-5.8]*
1
|
2.9[1.2-7.1]**
2.0[.8-5.0]
|
Experience in supporting using ICT tools
|
Yes
No
|
96(61.1) 118(47.4)
61(38.9) 131(52.6)
|
1.7[1.2-2.6]*
1
|
1.7[1.0-2.8]*
1
|
Downloading/uploading through internet
|
Daily
Weekly
Never
|
65(41.4) 99(39.8)
86(54.8) 128(51.4)
6(3.8) 22(8.8)
|
2.4[1.9-6.3]*
2.5[.9-6.4]
1
|
1.1[.3-3.9]
1.8[.5-6.3]
1
|
Work experience
|
<5 years
>10 years
|
109(69.4) 132(53.0)
11(7.0) 37(14.9)
|
2.8[1.4-5.7]*
1
|
1.8[.5-6.5]
1
|
Note: TM, Tele-monitoring *p-value<0.05 for bivariable analysis
** P-value <0.01 and *** P-value <0.001 for multivariable analysis, 1=reference category
|
Factors associated with physicians' and nurses' readiness for TM technology using the ordinal logistic regression model.
In this survey, ordinal logistic regression was conducted to examine the effect of predictor variables, such as owning computer, owned smartphone, computer-related training, IT support, internet access, awareness, attitude towards ICT tools, perception towards data security of TM technologies, and frequency of computer use on the readiness of participants. Table 4 shows the results of the ordinal logistic regression model. Even though five of the considered variables in the POM (proportional odds model) are found significant and the data satisfy the overall proportional odds assumption, the overall goodness of fit of the model shows a low p-value.
Therefore, to fulfill the assumption of proportional odds, Brant test was employed, after conducting the Brant test, p-values of 0.01 were found for the owned smartphone and computer-related training variables, indicating the two variables were found to violate the proportional odds assumption. The results of Brant test are shown in the last column of Table 4. This reveals that all variables except having a smartphone and computer-related training were found insignificant.
As a result, a partial proportional odds model was fitted. As can be seen in Table 5, the Partial proportional odds model (PPOM) with logit function was fitted with variables that are changing across equations, while other variables were imposed to have their effects meet Parallel-line assumption and the global Wald test for the final model indicates that the final model does not violate the proportional odds assumption.
Table 4: Result of the proportional odds model for TM readiness among physicians and nurses at teaching hospitals, 2020.
|
Variable
|
coefficient Standard error pvalue
|
Odds ratio 95% CI
|
Brant test
p-value
|
Intercept 1
|
3.748 0.644 0.000
|
- - - -
|
|
Intercept 2
|
5.72 0.686 0.000
|
-- -
|
|
Having a computer [No as Reference ]
|
|
Yes
|
0.462 0.4297 0.087
|
1.588 0.934-2.699
|
0.14
|
Use of computer at work[never as Reference ]
|
0.94
|
Weekly
|
-0.585 .2162 0.132
|
.5568 .2601-1.191
|
|
Daily
|
0.130 .4381 0.735
|
1.139 05360-2.420
|
|
Having a smartphone[No as Reference ]
|
|
Yes
|
0.259 .4122 0.415
|
1.295 .6946-2.417
|
0.01*
|
Computer related training [No as Reference ]
|
|
Yes
|
0.072 .26602 0.768
|
1.075 .6623-1.746
|
0.01*
|
IT-support [No as Reference ]
|
|
Yes
|
0.462 .42546 0.084
|
1.588 .9397-2.685
|
0.27
|
Internet access[No as Reference ]
|
|
Yes
|
0.299 .3497 0.248
|
1.349 .8116-2.242
|
0.23
|
Heard about Tele-monitoring[No as Reference ]
|
|
Yes
|
0.2499 .3038 0.291
|
1.283 .8074-2.0418
|
0.91
|
Attitude about ICT in current health care [bad as Reference ]
|
|
Good
|
0.7911 .5951 0.003
|
2.205 1.300-3.7431
|
0.75
|
Attitude about ICT in future health care [bad as Reference ]
|
|
Good
|
0.7937 .8325 0.035
|
2.211 1.0575-4.625
|
0.14
|
Attitude about ICT for remote monitoring [bad as Reference ]
|
|
Good
|
1.189 1.036 0.000
|
3.285 1.769-6.098
|
0.78
|
Self-perceived innovativeness[not innovative as Reference ]
|
|
Innovative
|
1.249 1.059 0.000
|
3.488 1.9228-6.327
|
0.73
|
Note: TM, Tele-monitoring POM, partial proportional odds model
*p-value <0.05 and **p-value<0.01 shows violation of proportional odd assumption
|
Factors associated with physicians' and nurses' readiness for TM technology using a partial proportional odds model.
In this survey, variables like owning smartphone, attitude towards ICT tools in healthcare, attitude towards remote monitoring, and use of computers were positively associated with the readiness towards TM (Table 5).
The result of PPOM revealed that participants who had a favorable attitude towards remote monitoring were about 3.5 times more likely to have high readiness for TM as compared to those participants with an unfavorable attitude. Similarly, participants who had a favorable attitude to healthcare ICT tools were about 2.4 times more likely to have high readiness than those participants with an unfavorable attitude.
In addition, when high readiness and average readiness compared with low readiness level, participants who used computers daily and weekly had 1.628 and 1.55 times greater odds of having average or high readiness respectively compared with participants who never used computers. Correspondingly, the odds of having high readiness for TM were 1.65 times higher for participants who perceived themselves as innovative as compared with those who did not perceive themselves as innovative.
Furthermore, the odds of having high readiness for TM were 1.65 times higher for the participants who owned personal computers as compared with those who did not own a personal computer.
Table 5: Result of partial proportional odds model for Tele-monitoring readiness among physicians and nurses at teaching hospitals, 2020.
|
|
Comparisons
|
Variable
|
Low readiness Vs.(average and high readiness for TM )
|
Low readiness & average vs. high readiness for TM
|
|
B1 OR1 p-value
|
B2 OR2 p-value
|
Coefficient
|
-3.9498 - 0.000
|
-4.731 - 0.000
|
Having a computer [No as Reference ]
|
Yes
|
0.44887 1.64975 0.024
|
0.44887 1.64975 0.964
|
Use of computer at work[never as Reference ]
|
Weekly
|
0.2160 1.5517 0.019
|
0.2160 1.5517 0.019
|
Daily
|
0.4515 1.628 0.032
|
0.4515 1.628 0.032
|
Having a smartphone[No as Reference ]
|
Yes
|
0.40962 1.2702 0.860
|
0.4096 1.2702 0.034
|
Computer related training [No as Reference ]
|
Yes
|
0.3099 1.1658 0.543
|
0.15857 0.42433 0.102
|
IT-support [No as Reference ]
|
Yes
|
0.4404 1.61857 0.062
|
0.4404 1.618557 0.383
|
Internet access[No as Reference ]
|
Yes
|
0.34933 1.32788 0.413
|
0.34933 1.32788 0.296
|
Heard about Tele-monitoring[No as Reference ]
|
Yes
|
0.3006 1.2575 0.338
|
0.3006 1.25754 0.49
|
Attitude about ICT in current health care [bad as Reference ]
|
Good
|
0.6060 2.2276 0.003
|
1.7108 3.0627 0.045
|
Attitude about ICT in future health care [bad as Reference ]
|
Good
|
0.90752 2.36617 0.025
|
0.9075 2.3661 0.025
|
Attitude about ICT for remote monitoring [bad as Reference ]
|
Good
|
1.1261 3.4959 0.000
|
1.261 3.4959 0.000
|
Self-perceived innovativeness[not innovative as Reference ]
|
Innovative
|
1.1609 3.8048 0.000
|
0.8041 1.7103 0.254
|