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The potential use of digital health technologies in the African context: a systematic review of evidence from Ethiopia

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The World Health Organization (WHO) recently put forth a Global Strategy on Digital Health 2020–2025 with several countries having already achieved key milestones. We aimed to understand whether and how digital health technologies (DHTs) are absorbed in Africa, tracking Ethiopia as a key node. We conducted a systematic review, searching PubMed-MEDLINE, Embase, ScienceDirect, African Journals Online, Cochrane Central Registry of Controlled Trials, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform databases from inception to 02 February 2021 for studies of any design that investigated the potential of DHTs in clinical or public health practices in Ethiopia. This review was registered with PROSPERO ( CRD42021240645 ) and it was designed to inform our ongoing DHT-enabled randomized controlled trial (RCT) (ClinicalTrials.gov ID: NCT04216420 ). We found 27,493 potentially relevant citations, among which 52 studies met the inclusion criteria, comprising a total of 596,128 patients, healthy individuals, and healthcare professionals. The studies involved six DHTs: mHealth (29 studies, 574,649 participants); electronic health records (13 studies, 4534 participants); telemedicine (4 studies, 465 participants); cloud-based application (2 studies, 2382 participants); information communication technology (3 studies, 681 participants), and artificial intelligence (1 study, 13,417 participants). The studies targeted six health conditions: maternal and child health (15), infectious diseases (14), non-communicable diseases (3), dermatitis (1), surgery (4), and general health conditions (15). The outcomes of interest were feasibility, usability, willingness or readiness, effectiveness, quality improvement, and knowledge or attitude toward DHTs. Five studies involved RCTs. The analysis showed that although DHTs are a relatively recent phenomenon in Ethiopia, their potential harnessing clinical and public health practices are highly visible. Their adoption and implementation in full capacity require more training, access to better devices such as smartphones, and infrastructure. DHTs hold much promise tackling major clinical and public health backlogs and strengthening the healthcare ecosystem in Ethiopia. More RCTs are needed on emerging DHTs including artificial intelligence, big data, cloud, cybersecurity, telemedicine, and wearable devices to provide robust evidence of their potential use in such settings and to materialize the WHO’s Global Strategy on Digital Health.
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The potential use of digital health technologies in the African
context: a systematic review of evidence from Ethiopia
Tsegahun Manyazewal
1
, Yimtubezinash Woldeamanuel
1
, Henry M. Blumberg
2
, Abebaw Fekadu
1
and Vincent C. Marconi
2
The World Health Organization (WHO) recently put forth a Global Strategy on Digital Health 20202025 with several countries
having already achieved key milestones. We aimed to understand whether and how digital health technologies (DHTs) are
absorbed in Africa, tracking Ethiopia as a key node. We conducted a systematic review, searching PubMed-MEDLINE, Embase,
ScienceDirect, African Journals Online, Cochrane Central Registry of Controlled Trials, ClinicalTrials.gov, and the WHO International
Clinical Trials Registry Platform databases from inception to 02 February 2021 for studies of any design that investigated the
potential of DHTs in clinical or public health practices in Ethiopia. This review was registered with PROSPERO (CRD42021240645)
and it was designed to inform our ongoing DHT-enabled randomized controlled trial (RCT) (ClinicalTrials.gov ID: NCT04216420). We
found 27,493 potentially relevant citations, among which 52 studies met the inclusion criteria, comprising a total of 596,128
patients, healthy individuals, and healthcare professionals. The studies involved six DHTs: mHealth (29 studies, 574,649 participants);
electronic health records (13 studies, 4534 participants); telemedicine (4 studies, 465 participants); cloud-based application
(2 studies, 2382 participants); information communication technology (3 studies, 681 participants), and articial intelligence
(1 study, 13,417 participants). The studies targeted six health conditions: maternal and child health (15), infectious diseases (14),
non-communicable diseases (3), dermatitis (1), surgery (4), and general health conditions (15). The outcomes of interest were
feasibility, usability, willingness or readiness, effectiveness, quality improvement, and knowledge or attitude toward DHTs. Five
studies involved RCTs. The analysis showed that although DHTs are a relatively recent phenomenon in Ethiopia, their potential
harnessing clinical and public health practices are highly visible. Their adoption and implementation in full capacity require more
training, access to better devices such as smartphones, and infrastructure. DHTs hold much promise tackling major clinical and
public health backlogs and strengthening the healthcare ecosystem in Ethiopia. More RCTs are needed on emerging DHTs
including articial intelligence, big data, cloud, cybersecurity, telemedicine, and wearable devices to provide robust evidence of
their potential use in such settings and to materialize the WHOs Global Strategy on Digital Health.
npj Digital Medicine (2021) 4:125 ; https://doi.org/10.1038/s41746-021-00487-4
INTRODUCTION
Health technology innovations are transforming the discovery,
development, and delivery of health products and services
14
and
signicantly changing the way health conditions are diagnosed,
treated, and prevented
5,6
. These innovations are building a
sustainable foundation for affordable, accessible, and high-quality
medicines, vaccines, medical devices, and system innovations,
pursuing novel solutions, entrepreneurial ventures, and public
sector efforts to the most challenging health problems
7,8
. Digital
health technologies (DHTs)
921
, pharmacoginomics
22,23
, and pro-
cess innovations
2427
are rapidly emerging as promising health
interventions. More innovations are expected to emerge as
healthcare demand and spending rise
28,29
. Nevertheless, many of
these breakthroughs have not reached the healthcare providers
and the people most in need to tackle the rising burden of
diseases
3032
. People living in low-income countries, such as many
countries in Africa, are at high risk of many health conditions
compared to those living in other regions, while having the most
limited access to health innovations
30,33
. Africa has the greatest
healthcare challenges in the world: life expectancy is 60 years,
substantially lower than the global average of 72 years; maternal
mortality ratio is 547 per 100,000, but 13 in high-income countries
and 216 globally; under 5 mortality is 76 per 1000, but 5 in high-
income countries and 39 globally
34,35
. While there were 1098
researchers per million inhabitants globally, the corresponding
gure for Africa was 87.9 per million
35
. Africa lags in the capacities
for health technology innovations, while it bears 23% of the global
disease burden and 16% of the world population, with the
continent expected to double its population by 2050, from 1 billion
to nearly 2.4 billion
3640
.
Without urgent technological, industrial, intellectual, and
research-oriented health interventions, Africa cannot tackle the
needs and demands of its population. If health technology
innovations are needed to transform health system gaps in Africa,
it is important to generate country-specic evidence to identify
challenges and opportunities in the region as potential resources
for further interventions. The World Health Organization (WHO)
embraced a more proactive stance in this regard. In 2020, the
WHO developed a global strategy on digital health for
20202025
41
. The vision of the strategy was to improve health
for everyone, everywhere by accelerating the development and
adoption of appropriate, accessible, affordable, scalable, and
sustainable person-centric digital health solutions to prevent,
detect and respond to epidemics and pandemics, developing
infrastructure and applications that enable countries to use data
to promote health and wellbeing, and to achieve the health-
related United Nationss Sustainable Development Goals (SDGs).
Through its Africa ofce, the WHO Regional Ofce for Africa
1
Addis Ababa University, College of Health Sciences, Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), Addis Ababa, Ethiopia.
2
Emory
University School of Medicine and Rollins School of Public Health, Atlanta, GA, USA. email: tsegahunm@gmail.com
www.nature.com/npjdigitalmed
Published in partnership with Seoul National University Bundang Hospital
1234567890():,;
(WHO-AFRO), the WHO designed the health technologies and
innovations program to guide the assessments, development,
ethics, use, and monitoring of national health technology
strategies, with a broader aim of improving access, quality, and
rational use of health innovations, including medicines, medical
products, and technologies
42
. Similarly, in 2019, the WHO
developed a guideline that established recommendations on
DHTs for health systems.
This study is in support of the WHOs DHT initiatives. We
focused on Ethiopia, the fastest growing economy in Africa per
the 2019 World Bank report
43
, and the second most populated
country, with more than 117 million people in 2021. Ethiopia aims
to reach lower-middle-income status by 2025, with strong
commitment and dedication to achieve the SDGs by 2030. The
Ethiopian Ministry of Health (MOH) recently, on 06 August 2020,
launched a Digital Health Innovation and Learning Center, the rst
of its kind, where experts can design and validate digital health
tools, synthesize and promote best practices, and scale-up
innovations
44
. As of 30 April 2021, there were 54.7 million total
telecommunication subscribers in the country
45
. Mobile voice
subscribers reached 52.8 million, data and internet users 25
million, xed broadband subscribers 349,000 and xed service
subscribers 923,000. The telecom population and geographic
coverage reached 95% and 85.4%, respectively and the density
reached 50%
45
. On 22 May 2021, the Ethiopian government
awarded a new nationwide telecom license to the Safaricom-led
consortium that includes its parent rms Vodafone and Vodacom,
British development nance agency CDC Group and Japans
Sumitomo Corporation after submitting a nancial bid offering US
$850 million
46
. The Consortia is expected to invest over $8 billion
and create jobs for US$1.5 million citizens. The Ethiopian Health
Sector Transformation Plan recognizes the need for improving
digital health infrastructure to facilitate equitable access to quality
healthcare for all Ethiopians
47
. A systematic review of digital
health technology-enabled research in Ethiopia has not been
synthesized to inform debates and decisions.
Thus, we aimed to investigate whether and how digital health
technologies (DHTs) are absorbed in Africa, tracking Ethiopia as a
key node, through a systematic review of available studies.
RESULTS
Characteristics of included studies
Study selection: From the 27,493 articles screened, 2397
duplicates were removed and 24,863 were excluded based on
the title or abstract. The rest of 233 full-text articles were screened
for eligibility, of which 181 were excluded for being irrelevant to
the main subject (144) or focused on non-health conditions (37).
27,493
Total records
25,096
Aer duplicates removed
25,096
Records screened against tle/abstract
24,863 Excluded
233
Full-text screened for eligibility
181 Excluded
144 Irrelevant to
the main subject
37 DHTs studied for
non-health
condions
Studies included
52
18
Cochrane
CENTRAL
355
WHO
ICTRP
20
ClinicalTri
als.gov
20,400
AJOL
277
Science
Direct
3,578
Embase
2,819
PubMed/
MEDLINE
Fig. 1 PRISMA ow diagram of the study. PRISMA (preferred reporting items for systematic reviews and meta-analyses) ow diagram of
included studies.
T. Manyazewal et al.
2
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Fifty-two (52) studies were identied that met the inclusion
criteria. Figure 1summarizes the PRISMA owchart of the study.
Participants: The 52 included studies had a total of 596,128 study
participants. The health conditions studied were maternal and
child health, including antenatal care, postnatal care, infant
feeding, contraceptives, and delivery (n=15), infectious diseases
including TB, HIV, malaria, lymphatic lariasis, and onchocerciasis
(n=14), non-communicable diseases including diabetes and
cancer (n=3), dermatitis (n=1), surgery (n=4), and the
remaining 15 had general health conditions.
Interventions: The included studies were involved six digital
health domains: 29 on mobile health (mHealth) (574,649
participants); 13 on EHR (4534 participants); four on telemedicine
(465 participants); two on Cloud usage (2382 participants); three
on information and communication technology (ICT, 681 partici-
pants), and one on AI (13,417 participants). Our search did not nd
articles relevant to wearable devices, software as a medical device,
computing sciences in big data, cybersecurity, wireless medical
devices, and robotics.
Outcomes: The primary outcome variables were feasibility
(n=12), usability (n=9), willingness or readiness to use
(n=10), effectiveness (n=10), quality improvement (n=7), and
knowledge or attitude about the DHT (n=4). Some of the studies
employed two or more of these outcomes.
Study type: The study designs were cross-sectional study
(n=41), RCT or non-randomized experimental (6), and cohort (5).
Study setting: All the studies were conducted in Ethiopia. Some
of the studies were multi-country with a signicant number of
participants included from Ethiopia.
mHealth
There were 29 publications identied in the mHealth eld
4876
,
involving a total of 574,649 study participants: 402,542 patients,
171,295 healthy individuals, and 812 healthcare professionals. The
most common citations were on the potential use of mHealth for
maternal, child, and reproductive health, covering 52% (15/29) of
the citations, followed by infectious diseases, 48% (14/29). The
publications were mainly cross-sectional, 69% (20/29), followed by
RCT, 17% (5/29). The outcomes of interest were effectiveness (n=
9), usability (n=5), feasibility (n=6), quality (n=4), willingness
(n=4), and knowledge (n=1). Table 1summarizes the character-
istics of the included mHealth studies.
The studies had emerging insights into the potential use of
mHealth to transform healthcare and improve access to services
by addressing nancial, social, or geographic factors, though the
ndings were not consistent. In a study that compared the cost-
effectiveness of facility-based, stand-alone, and mobile-based HIV
voluntary counseling and testing, the results revealed a cost-
effective and improved VCT service with the use of mobile phones
when compared with the two arms
48
. A study that assessed the
potential of telephone calls to identify and follow-up post-surgery
infections and total complications reported the telephone calls
were feasible and valuable
50
. A similar study that assessed the
potential of cell phones to follow-up patients after short-term
surgical missions found the tool as cost-effective and reliable
52
.
The use of SMS-based education was found to be a good option
for improving the knowledge and awareness of parents regarding
infant feeding
49
.
Several studies assessed the potential of using mHealth for
family planning and maternal and child health services. One study
reported that a high proportion of pregnant women in an
antenatal care clinic had a mobile phone and were willing to
receive an SMS text message-based mHealth intervention, though
two-third of the participants lacked smartphones to upload some
application software
62
. Locally customized mHealth applications
during antenatal care signicantly improved delivery and post-
natal care service utilization through positively inuencing the
behavior of health workers and their clients
71
. Mobile phone
reminders were effective in terms of enhancing adherence to
postnatal care appointments, with the potential improving
postnatal appointment adherence
69
. On the contrary, a retro-
spective analysis of demographic and health survey data reported
that mobile phone ownership or receiving family planning
information via SMS had no signicant effect on improving
contraceptive uptake
51
.
A study that assessed the potential of mHealth on antiretroviral
(ART) services for people with HIV reported that the willingness of
patients to receive SMS in support of medication reminders was
not consistent, with 49% unwilling to receive the reminders
73
.
Age, educational status, and previous experience using the
internet had a signicant association with their willingness
73
.A
cross-sectional study of patients with diabetes showed that a high
proportion of the patients had access to mobile phones and were
willing to use them for medication reminders
58
.
Ten of the 29 included studies on mHealth had healthcare
professionalsas their target participants. Several of these studies
revealed that mHealth had the potential to improve skills and
competency of healthcare providers toward safe birth
57
, data
quality and ow
63
, patient follow-up
63
, community-based tuber-
culosis and maternal health service delivery
64
, quality and cost-
effective reporting of lymphatic lariasis, and podoconiosis data
65
,
and timely and complete reporting of maternal health
data
67,70,74,76
. Some healthcare providers were able to appro-
priately use mHealth technologies for patient assessment and
routine data collection with minimal training and supervision.
However, there were major preconditions needed for the
healthcare providers to effectively apply such technologies,
including in-service application training
54
, strong connectivity
and electric power supply, especially in rural areas of the
country
63
, and uninterrupted mobile network airtime
74
. One study
reported on genomics data
61
. A psychiatric genomics consortium
exchanged data between research groups for genome-wide
association studies on neurogenetics of schizophrenia, with tens
of thousands of patients and controls included. The consortium
developed and used an mHealth application, MiGene Family
History App (MFHA), to assist clinicians with the collection and
analysis of patient genetic data over six months and assessed its
feasibility through a survey of 47 clinicians. The results showed the
potential expansion of medical genetics services into low and
middle-income countries (LMICs) and the feasibility and benetof
the MFHA for the services
61
.
Electronic health records
Of the nal set of 52 studies, 13 were on EHR
7789
, involving
4534 study participants: 4232 health professionals, 250 patients,
and 52 healthy individuals. The studies used health information
technology, health management information system (HMIS),
tablet-based electronic data capture (EDC), electronic information
source (EIS), health smart card (HSC), and Android-based data
collection system as their EHR of interest. Five studies focus on
infectious diseases, one on non-communicable diseases (dia-
betes), and the remaining seven had a broader health system
scope. The studies were all cross-sectional, 92% (12/13), except for
one RCT. The outcomes of interest were willingness (n=5),
usability (n=3), quality (n=3), and feasibility (n=2). Table 2
summarizes the characteristics of the included EHR studies.
According to the studies reviewed, EHRs have the potential to
exchange real-time patient-related data for better clinical
decision-making and to capture and share electronic health
information efciently. However, the studies also reported
potential gaps and drawbacks associated with EHRs. A study that
compared EHR with paper-based records for ART data reported a
higher incomplete data with the use of EHR for various reasons
including difculties implementing EHR in high patient load
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Table 1. Characteristics of included mHealth studies (n=29).
Reference mHealth Condition Participants type Participants # Study design Outcome measure Finding
Yigezu et al.
48
Mobile-based VCT HIV VCT VCT attendants 144,267 Cross-sectional -
cost-effectiveness
Effectivenesscost-
effectiveness
Mobile-based VCT costs less than
both facility-based and stand-
alone VCTs
Gebremariam
et al.
49
SMS Infant feeding Parents of child-
bearing age
41 Cross-sectional Feasibility, acceptability Feasible and acceptable option
for knowledge sharing and
awareness
Starr et al.
50
mHealth Post-surgery follow-
up
Patients on post-
surgery follow-up
701 Cohort, prospective Feasibility Telephone follow-up after surgery
is feasible and valuable
Jadhav et al.
51
Own mobile phone Contraceptive Women of
reproductive age
15,683 Cross-sectional,
retrospective
Effectiveness
Contraceptive uptake
No association between mobile
phone ownership and
contraceptive uptake
Bradley et al.
52
Smartphone Post-surgery follow-
up
Patients on post-
surgery follow-up
24 Cohort Feasibility smartphones were low-cost,
reliable method to follow-up
patient after surgery
Nesemann
et al.
53
Smartphone-CellScope
device for conjunctival
photograph
Trachomatous
inammation -
follicular
Children aged 19 yrs 412 Cross-sectional Effectiveness 84.1% sensitive 97.6% specic
Tadesse et al.
54
mHealth- based
e-Partograph
Obstetric care Healthcare
professionals
466 Cross-sectional Willingness 46% willing to use mobile-phone
for e-Partograph
Kassa et al.
55
Own mobile phone Postnatal care Women in
postnatal care
370 Cross-sectional Knowledge, attitude 3x higher odds of positive
attitude to preconception in
women who own phone
Kebede et al.
56
SMS or voice call reminder Postnatal
appointment
Women in
postnatal care
700 RCT EffectivenessPostnatal
compliance
3x higher odds of postnatal
compliance in women who
received a reminder
Thomsen et al.
57
mHealth-based Safe
Delivery App
Delivery Healthcare
professionals
56 Cross-sectional Usabilityuser experience The App improved providers
delivery knowledge and skills
Jemere et al.
58
mHealth-based health
services
Diabetes Patients with diabetes 423 Cross-sectional Willingness, access, 78% had a phone; 71% willing to
receive mHealth-based diabetes
services
Habtamu et al.
59
Smartphone-based
Contrast Sensitivity Test
((PeekCS)
Contrast
Sensitivity (CS)
Adults with
trachomatous
trichiasis
147 RCT Effectiveness It is repeatable, rapid, accessible
and easy to perform CS testing.
Endebu et al.
60
SMS to support medication
adherence
HIV/AIDS people living
with HIV/AIDS
receiving
antiretroviral
treatment
420 Cross-sectional Feasibility, acceptability High (90.9%) acceptability of SMS
on adherence to antiretroviral
therapy
Quinonez et al.
61
MiGene Family History App Medical genetics
services
Healthcare
professionals
47 Cross-sectional Feasibility The App was useful for the
collection and analysis of
genetics data.
Endehabtu
et al.
62
SMS-based intervention Antenatal care Women in
antenatal care
416 Cross-sectional Willingness access, 36% had smartphones; 71%
willing to receive SMS-based
antenatal care intervention
Mengesha
et al.
63
mHealth-based HMIS Data use Health extension
workers
62 Cross-sectional Data quality, user
experience
mHealth-based HMIS improved
data quality, data ow, patient
follow-up.
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npj Digital Medicine (2021) 125 Published in partnership with Seoul National University Bundang Hospital
Table 1 continued
Reference mHealth Condition Participants type Participants # Study design Outcome measure Finding
Steege et al.
64
mHealth-based data and
reminder
TB Health extension
workers
19 Cross-sectional Qualityhealthcare
delivery
Improved community TB and
maternal health service delivery
Martindale
et al.
65
MeasureSMS- morbidity
reporting tool
lymphatic lariasis,
podoconiosis
Healthcare
professionals
59 Cross-sectional,
comparative
Effectiveness, cost, time MeasureSMS tool was more
effective, 13.7% less costly than
paper-based reporting
Abate et al.
66
Telepathology
Acquiring microscopic
images using a
smartphone camera
blood cell count,
malaria lab diagnosis
Healthcare
professionals
2 Cross-sectional Usability, accuracy It was fast, cost-effective, and
accurate in low resource setting.
Shiferaw et al.
67
mHealth-based data
collection
Maternal health
service
Healthcare
professionals
15 Experimental/
Implementation
Effectiveness Timely and complete maternal
health data
Atnafu et al.
68
SMS-based data
exchange Ap.
Antenatal care Women on
antenatal care
3240 RCT EffectivenessMCH
outcomes
9% increased deliveries attended
by skilled health workers
Mableson et al.
69
MeasureSMS-Morbidity
reporting tool
Lymphatic lariasis
(LF) case estimate
People with LF clinical
manifestations
400,000 Cross-sectional Usability as a reporting tool The tool improved survey and
reporting of clinical burden of LF
Medhanyie
et al.
70
Smartphones for collecting
patient data
Maternal health
records
Healthcare
professional
25 Cross-sectional Usability 8% improved data completeness
compered with paper records
Shiferaw et al.
71
Locally customized
mHealth App.
Delivery and
postnatal care
Women on ANC 2261 Cohort QualityANC services
utilization
The App improved delivery in
health centers, but not ANC visits
Lund et al.
72
mHealth
safe delivery App (SDA)
Perinatal and
neonatal survival
Women in
active labor,
provider
3777 RCT QualityPerinatal
mortality
The SDA nonsignicantly lowered
perinatal mortality compared with
standard
Kebede et al.
73
SMS medication reminders HIV HIV patients on ART 415 Cross-sectional Willingness, access 76% owned cellphone
50.9% willing to receive SMS
medication reminder
Medhanyie
et al.
74
Smartphone-based data
records
Maternal health Healthcare
professionals
24 Cross-sectional Usability The records were useful for day-
to-day maternal healthcare
services delivery
Desta et al.
75
mVedio for
behavior change
Maternal and
newborn health
Community members 540 Cross-sectional. Effectiveness
Community
behavior change
mViedo changed community
behavior change on maternal and
newborn health in rural Ethiopia
Little et al.
76
Smartphone open-source
health App.
maternal health Healthcare
professionals
37 Cohort Feasibility
Technical needs
Ownership and empowerment
are prerequisites for a successful
mHealth program
ANC antenatal care, HMIS health management information systems, LF lymphatic lariasis, MCH maternal and child health, RCT randomized controlled trial, SDA safe delivery app., SMS short message ser vice, VCT
voluntary counseling and testing.
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Table 2. Characteristics of included EHR studies (n=13).
Reference EMR Condition Participants type Participants # Study design Outcome measure Finding
Seboka et al.
77
Information system for
managing diabetes
Diabetes Healthcare professionals 406 Cross-sectional Willingness, attitude, 64% had a favorable attitude to remotely
monitor diabetes patients,74% willing to
use voice calls.
Berihun et al.
78
EMR in health facilities HIV Healthcare professionals 616 Cross-sectional Willingness 86% willing to use EMR
Ahmed et al.
79
EMR in health facilities Healthcare professionals 420 Cross-sectional Willingness iIntention 40% intention to use EMR
Kebede et al.
80
HMIS in health facilities Healthcare professionals 332 Cross-sectional Quality 48% accuracy and 82% completeness of
data; below national standards
Awol et al.
81
EMR in health facilities Healthcare professionals 414 Cross-sectional Willingnessreadiness 62% ready to use EMR system
Zeleke et al.
82
Electronic data capture
(EDC)- tablet
Interviewers 12 RCT Quality of data Better data quality and efciency with
EDC than standard paper-based data
Abiy et al.
83
EMR at ART clinic HIV Patients on HIV care 250 Cross-sectional,
comparative
Qualitycompleteness,
reliability
Slightly lower (76%) data completeness
in EMR, than paper-based (78%)
Bramo et al.
84
Electronic information
sourse (EIS)
HIV/AIDS Care and
Treatment
Healthcare professionals 352 Cross-sectional Usabilityutilization 67% not used EIS for not having training,
prefer print resource
Dusabe-
Richards et al.
85
HMIS TB Healthcare professionals 90 Cross-sectional Feasibility HMIS is usable, but with gaps in quality,
accuracy, reliability, timeliness of data
Samuel et al.
86
Electronic Information
Sources (EIS)
Healthcare professionals 590 Cross-sectional Usability, access 42% used EIS, affected by computer
literacy, access to internet
Tilahun et al.
87
SmartCard Healthcare professionals 406 Cross-sectional Usabilityuser
satisfaction,
61% dissatised with the EMR; 64%
believed EMR had less quality impact
Biruk et al.
80
EMR Healthcare professionals 606 Cross-sectional Willingnessreadiness 54% ready to use EMR
King et al.
89
Android-based data
collection
Neglected tropical
diseases
Community members
(households)
40 cross-sectional,
comparative
Feasibility, effectiveness Suitable, accurate, and save time over
standard paper-based survey
questionnaires
EDC electronic data capture, EIS electronic information source, EMR electronic medical records, HMIS health management information systems, RCT randomized controlled trial.
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conditions and the frequent need for capturing dual electronic
and paper-based data for individual patients
83
.
Studies that assessed the current HMIS practice in healthcare
facilities reported several gaps in the accuracy, completeness, and
timeliness of data for reasons including poor support from facility
management, lack of accountability for data errors, poor
supportive supervision, and absence of a dedicated Information
Management unit responsible for EHR functions
80,85
.
A signicant number of healthcare professionals were either not
ready
81,84,88
or not willing
78,87
to use EHR. Some healthcare
professionals still preferred paper-based records to EHR in their
daily work
82,85,8789
while other preferred EHR
82
. Lack of access to
EHR training, computer skills, and performance expectancy were
the major barriers to their willingness or intention to use
EHR
7781,83,84,87,88
.
Telemedicine
Of the 52 studies considered, four reported on telemedicine,
involving 465 participants who were healthcare professionals
9093
.
The studies investigated what level of knowledge and attitude
healthcare professionals have toward telemedicine
90
, why health-
care providers resist using telemedicine
91
, and how telemedicine
and teledermatology systems can be improved in a given
program
92,93
. All were cross-sectional and their outcomes of
interest were knowledge and attitude, willingness, usability, and
feasibility (Table 3).
The studies highlighted that telemedicine has the potential
improving healthcare; however, healthcare providers had less
knowledge and information about it. One study reported that of
the 312 healthcare professionals included, 62% lacked good
knowledge and 36% lacked a good attitude about telemedicine
90
.
Healthcare professionals resisted the use of telemedicine in their
clinical practices mainly due to their perceived threat and
controllability, with reduced autonomy, anxiety, and costs
indirectly aggravating the resistance
91
. Telemedicine implementa-
tion in Ethiopia is inuenced by technological dynamics,
e-government preparedness, enabling policy environment, multi-
stakeholder engagement, and capacity building
93
.
Cloud-based applications
Two studies reported on Cloud-based interventions, involving
2382 participants: 1748 surgical cases
94
and 634 healthy women
on cervical cancer screening
95
. The aims of the studies were on
the feasibility of a multicentre Cloud-based peri-operative registry
for surgical care
94
and the feasibility of a cloud-based electronic
data system for human papillomavirus (HPV) cervical cancer
screening
95
. Table 4summarizes the characteristics of the
included Cloud studies.
A Network for Peri-operative Critical care (N4PCc) developed
and evaluated a multicentre Cloud-based peri-operative registry in
Ethiopia
94
. The authors reported on 1748 consecutive surgical
cases for key performance indicators including compliance with
the World Health Organizations Surgical Safety Checklist, adverse
events during anesthesia, and surgical site infections. With these,
the authors reported a successful multicentre digital surgical
registry that can enable the measurement of key performance
indicators for surgery and evaluation of perioperative
outcomes
95
.
One study conducted home-based human papillomavirus (HPV)
self-sampling assisted by a Cloud-based electronic data system.
The study used an electronic app-based data system with an
ofine mode function for tablet computers, based on a Cloud
solution. The app-based data system showed robust technical
functionality, stability, comfort, data accuracy, and ease-of-use by
health workers, with no data loss observed. The ofine data
collection, uploading, and synchronization system were safe and
error-free
95
.
Table 3. Characteristics of included telemedicine studies (n=4).
Reference Telemedicine Condition Participants type Participants # Study design Outcome measure Finding
Biruk et al.
90
Telemedicine Healthcare
professionals
312 Cross-sectional Knowledge, attitude 62% lack good knowledge, 36% lack a good attitude toward
telemedicine
Xue et al.
91
Telemedicine Healthcare
professionals
107 Cross-sectional Willingness -reasons for
resistance to telemedicine
Reduced autonomy, anxiety, and costs increased resistance
Delaigue
et al.
92
Teledermatology Dermatitis Healthcare
professionals
26 Cross-sectional Usability Teledermatology delivered a useful service, system gap for
case follow-up
Shiferaw
et a.
93
Telemedicine Healthcare
professionals
20 Cross-sectional Feasibility Telemedicine is in a premature phase and its success needs
technology, e-governance, an enabling policy, and multi-
sectorial involvement
T. Manyazewal et al.
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Articial intelligence
One study evaluated the precision of AI in differentiating between
target and implanted intraocular lens (IOL) power in cataract
outreach campaigns in Ethiopia
96
. The study applied machine
learning (ML) to optimize the IOL inventory and minimize
avoidable refractive error in patients from the cataract campaigns
(n=13,417). The result indicated good precision, with the ML
optimized the implanted intraocular lens inventory and minimized
avoidable refractive error (Table 5).
Information communication technology (ICT)
Three studies reported on ICT, involving a total of 681 participants:
551 healthcare professionals
97,98
and 130 patients
99
(Table 6). One
study
97
assessed the digital competency of healthcare providers in
seven public health centers and found low-level competency, with
factors such as sex, educational status, profession type, monthly
income, and years of experience were statistically signicant
predictors. The second study
98
assessed health professionals
behavioral intention to adopt eHealth systems and revealed that
among the different eHealth constructs, healthcare professionals
attitude toward eHealth had the strongest effect on the intention
to use eHealth systems. The third study evaluated the accuracy of
a live-streamed video conference over a third-generation (3G)
network for consultation of ultrasound scans from a remote
location for a variety of pediatric indications and found the
method accurate and feasible
99
.
DISCUSSION
We conducted a systematic review of the available literature to
provide strong evidence on the potential impact of DHTs on clinical
and public health practices in the context of a resource-constrained
sub-Saharan African country, Ethiopia. The review identied
52 studies across different areas of DHTs, including mHealth, EMR,
telemedicine, cloud-based technology, ICT, and AI. Of the 52
included studies, emerging DHTs had a very small share at 13%: AI
2%, Cloud-based technology 4%, and telemedicine 8%, while the
major 81% share was for mHealth (56%), EHR (25%), and ICT 6%).
This analysis demonstrated that only 10% (5/52) of the studies were
tested in RCTs to provide robust and more credible evidence of the
potential of the DHTs. Digital health solutions have substantial
benets and considerable potential to transform the healthcare
system and societal wellbeing in Ethiopia. However, their adoption
and implementation in full capacity face challenges in terms of
infrastructure, training, access to better devices such as smart-
phones, and some hesitancies from patients and providers. Such
challenges have been reported in studies from other African
countries including Uganda
100,101
,Kenya
102105
, and Tanzania
106,107
.
A meta-analysis was not conducted due to the heterogeneous
nature of the compiled studies.
The mHealth solutions identied in this systematic review
mainly aimed to improve maternal and child healthcare and
services. This review found that mHealth interventions, either a
phone call or SMS, were feasible and acceptable for improving
contraceptive uptake, maternal healthcare, and tuberculosis
medication adherence among the Ethiopian population. The
evidence also showed the potential utility of mHealth for HIV
counseling and testing, outpatient follow-up, post-surgery follow-
up, child-immunization follow-up, pregnant women antenatal and
postnatal follow-ups, and in improving knowledge and awareness
of parents regarding infant feeding, while its potential for
contraceptive uptake was not signicant. The included studies
revealed that a signicant number of study participants owned
mobile phones and were willing to participate in mHealth-related
clinical or public health interventions. However, the type of mobile
phone that the patients own may not be smartphones to support
an upload of needed software. Such challenges were also reported
Table 4. Characteristics of included Cloud-based studies (n=2).
Reference Cloud Condition Participants type Participants # Study design Outcome
measure
Finding
N4PCc
94
Cloud-based peri-
operative registry
Surgical care Surgical cases 1748 Cohort Feasibility A successful multicentre digital surgical registry for key
surgery performance indicators and evaluation of peri
operative outcomes.
Jede et al.
95
Tablet-based data linked
to cloud-based IT
Cervical cancer Women offered genital self-
sampling
634 Cross-sectional Feasibility Home-based HPV-DNA self-sampling and clinic-based triage
assisted by cloud-based technology was feasible in rural
Ethiopia
DNA deoxyribonucleic acid, HPV human papillomavirus, IT information technology.
T. Manyazewal et al.
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npj Digital Medicine (2021) 125 Published in partnership with Seoul National University Bundang Hospital
Table 5. Characteristics of included articial intelligence study (n=1).
Reference Articial
intelligence
Condition Participants type Participants # Study design Outcome measure Finding
Brant et al.
96
Machine
learning (ML)
Cataract
surgery
Cataract patients with target and
implanted intraocular lens
13,417 Cross-sectional Effectiveness
-precision
ML optimized implanted intraocular lens
inventory, minimized avoidable refractive error
ML machine learning.
Table 6. Characteristics of included ICT studies (n=3).
Reference ICT Condition Participants type Participants # Study design Outcome measure Finding
Shiferaw
et al.
97
ICT competency Healthcare
professionals
167 Cross-sectional Knowledge
competency
Low basic digital competency level of
healthcare providers
Kalayou
et al.
98
eHealth behavior Healthcare
professionals
384 Cross-sectional Attitude
behavioral
intention
Attitude toward eHealth showed the
strongest effect on the intention to use
eHealth systems
Whitney
et al.
99
Live-stream videos
conference using 3G network
for ultrasound interpretation
Ultrasound scans from
trauma, intussusception, hip
effusion
Pediatric emergency
patients
130 Cross-sectional Effectiveness The ICT system is accurate (92%, 81%, and
88%) and feasible for consultation of
ultrasoundscans from a remote location
3G third-generation cellular data technology, ICT information and communication technology.
T. Manyazewal et al.
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elsewhere in Kenya
108110
and Tanzania
111
where there exist
similar socioeconomic disparities in mobile phone ownership in
support of the implementation of mHealth.
Our analysis indicates that patients with HIV may resist having
their ART adherence information followed up using electronic
medication reminders for fear of potential disclosure of their HIV
status. The nding was consistent with a recent study in Tanzania
that fear for potential involuntary disclosure of HIV status
signicantly affects mHealth interventions in such patients
112
.
The development of the next generation of mHealth in such
developing countries requires a broad understanding of the local
social contexts that may affect the successes of DHTs
113
.
Currently, various innovative DHTs are emerging to address the
multifaceted problems associated with tuberculosis diagnosis,
care and prevention. However, available data are limited for
stronger conclusions of their effectiveness in various countries
and settings, including Ethiopia. An RCT is currently ongoing in
Ethiopia
114
to bridge this gap. An initial synthesis of the evidence
on DHTs is thus essential to better understand the overall digital
health ecosystem in the country and successfully implement
DHT-enabled healthcare and research programs.
Our analysis indicates that the use of cloud computing could
help resource-constrained countries like Ethiopia to acquire
advanced data storage, servers, and databases without investing
in new IT infrastructure, though we have identied only two studies
that are less likely to support its potential. There have been
controversies on the potential benets of the Cloud and the issues
surrounding legal and regulatory implications
115
. For countries like
Ethiopia that have not yet established a standardized legal
cybersecurity framework, strategy, and governance at the national
level
116
, adopting appropriate laws and building technical capacity
would reassure the partnership and uptake of Cloud services.
Telemedicine was an emerging technology in the Ethiopian
healthcare system which had its drawbacks on successful
implementations, despite positive energy that healthcare provi-
ders to step up. Building capacity of the healthcare providers
before full-scale implementation could bring real benets out of
telemedicine. In the COVID-19 pandemic that restricted physical
contacts
117119
, telemedicine revealed signicant contributions in
Ethiopia by connecting patients with their healthcare providers to
discuss and follow-up their disease conditions
120
.
In recent years, capacities for research, development, and trade
on DHTs are rising sharply, while more work is needed to
delineate the mechanisms of how the gains could be shared out
with resource-constrained countries and global digital health
strategy met. Our analysis demonstrated the feasibility and
potential demands of DHTs, with the greatest opportunities in
emerging health technology markets in Ethiopia.
CONCLUSION
DHTs hold much promise tackling major clinical and public health
backlogs and strengthening health systems in Ethiopia. Although
they are a relatively recent phenomenon in Ethiopia, their potential
harnessing clinical and public health practices are highly visible.
More RCTs are needed on emerging DHTs including articial
intelligence, big data, cloud, cybersecurity, telemedicine, and
wearable devices to provide robust evidence of their potential use
in such settings and to materialize the Global Digital Health Strategy.
METHODS
Study design
This study was based on a systematic review of scientic literature
utilizing the Preferred Reporting Items for Systematic Review and
Meta-Analysis Protocols (PRISMA-P) 2015 guidelines for the design
and reporting of the results. The protocol was registered with
PROSPERO (CRD42021240645).
To broaden the scope of DHTs in our review, we combined the
latest descriptions of digital health given by the WHO
41
and the U.
S. Drug and Food Administration (FDA)
121
. With this, the following
technologies were included in the review: mobile health,
telehealth, electronic health records (EHR), telemedicine, health
information technology, wearable devices, software as a medical
device, articial intelligence, machine learning, genomics, com-
puting sciences in big data, cybersecurity, wireless medical
devices, and medical device interoperability.
Search strategy
We searched the PubMed-MEDLINE, Embase, ScienceDirect, African
Journals Online, Cochrane Central Registry of Controlled Trials,
ClinicalTrials.gov, and the WHO International Clinical Trials Registry
Platform databases from inception to the latest 02 February 2021
for studies of any design and in any setting in Ethiopia that
investigated the potential of DHTs in clinical or public health
practices in Ethiopia. We performed manual searches of the WHO
website, Google search engine, and reference lists of included
studies, and contacted authors of original studies to retrieve extra
possible articles or additional data. See the search strategy in the
Supplementary information (Supplementary Note 1).
We tailored search strategies to each database and used
controlled medical subject headings (MeSHs) and search lters
where available, or Boolean search methods and free-text terms,
referring to Ethiopia and Digital OR Mobile OR Smartphone OR
Cell phoneOR Techno* OR short message serviceOR SMS OR
Tele* OR Telemedicine OR Telehealth OR E-health OR eHealth
OR Remote OR Electro* OR Comput* OR cloud OR Software OR
Application OR Robotics OR Blockchain OR Articial intelligence
OR genomics OR big dataOR cybersecurity OR wireless.
Eligibility
Studies were included if they met the following inclusion criteria:
Participants: Eligible participants could be patients, healthcare
professionals, data collectors, or healthy individuals in Ethiopia,
either women or men, and without age restrictions. Thus, the
search was not restricted to participants except that they should
reside in Ethiopia.
Interventions: All DHTs that were included in our denition.
Studies that investigated digital technologies for non-health
conditions were excluded.
Comparisons: Studies with a comparison condition were not
required as a criterion. Thus, studies with or without a comparator
were eligible.
Outcome: Studies assessing the potential efcacy, effectiveness,
feasibility, usability, acceptability, or any related outcomes were
included in the review without specic restrictions.
Study design: All available study designs were included. We
excluded reviews, commentaries, editorials, and proceedings as
these are nonempirical publications.
Study selection
Two independent authors examined the title and abstract of all
screened publications. From the title and abstract of all publica-
tions identied by the database search, those that were
duplicated or did not meet the inclusion criteria were excluded.
The full texts of the remaining publications were further reviewed.
Disagreements were resolved by consensus and, if persisted, were
arbitrated through discussion with a third author.
Data extraction
The identied data were listed, and information was provided
on the type and details of the type of DHT under investigation, the
T. Manyazewal et al.
10
npj Digital Medicine (2021) 125 Published in partnership with Seoul National University Bundang Hospital
disease condition studied, the type of participants, the number of
participants, the study design employed, outcome measures,
major ndings, the surname of the rst author, and year of
publication.
Data management and analysis
The publications were grouped in exhaustive tables based on the
type of DHT investigated. A qualitative content analysis of all
documents and articles was performed. Each article was
summarized, and the data were reported descriptively. Less
emphasis was placed on the assessment of the quality of the
included literature as that was not the major objective of this
review.
This analysis was designed to inform our ongoing DHT-
enabled randomized controlled trial (RCT) on tuberculosis in
Ethiopia (ClinicalTrials.gov, ID: NCT04216420). The trial aims to
evaluate the effectiveness of a digital health technology-enabled
self-administered therapy over standard directly observed
therapy on adherence to TB medication and treatment outcomes
in Ethiopia
114
.
Operational denitions
Mobile health (mHealth): The use of mobile phone devices core
utility of voice or short messaging service (SMS) as well as more
complex functionalities to improve health outcomes and health-
care services.
Electronic health records (EHRs): are patient-centered electronic
records that provide immediate and secure information to
authorized users.
Telemedicine: The practice of medicine at a distance which
involves an interaction between a healthcare provider and a
patient when the two are separated by distance.
Cloud: The practice of storing and computing health data
remotely over the internet, which is managed by external service
providers.
Articial intelligence (AI): The simulation of human intelligence
in a digital computer that is programmed to think or perform
health tasks like humans.
Information and communications technology (ICT): technol-
ogies that provide access to health information through
telecommunications.
DATA AVAILABILITY
All the data included in this study are available within the paper and its
Supplementary Information les.
Received: 4 March 2021; Accepted: 24 June 2021;
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ACKNOWLEDGEMENTS
The work was supported by the Fogarty International Center and National Institute of
Allergy and Infectious Diseases of the US National Institutes of Health (D43TW009127)
and the Emory Center for AIDS Research (P30 AI050409). The content is solely the
responsibility of the authors and does not necessarily represent the ofcial views of
the National Institutes of Health or the Emory Center for AIDS Research.
AUTHOR CONTRIBUTIONS
Study conception, acquisition and synthesis of data, and rst draft: T.M. Acquisition,
analysis, and interpretation of data: V.C.M. and Y.W. interpretation of data and
resource acquisition: H.M.B. and A.F. All authors reviewed and approved the nal
version for publication.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41746-021-00487-4.
Correspondence and requests for materials should be addressed to T.M.
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Published in partnership with Seoul National University Bundang Hospital npj Digital Medicine (2021) 125
... To protect data, researchers have advocated advancing knowledge and providing training on digital health data security [15]. Previous studies have suggested that improving digital health data security status, educational status, health professionals' knowledge, computer training, risk perception, motivation, and attitudes were contributing factors [3,16,17,18,19,20,21,22,23]. ...
... The data were distributed normally by checking the Shapiro-Wilk test (p value > 0.05), skewness, and kurtosis. Therefore, it selects the mean over the median for the outcome variable, and scoring above the mean value of 5.19 ± 2.1 (SD) has good digital data security practice, whereas scoring below the mean has not been practiced [19,20,21]. The motivation to practice digital security refers to an individual's commitment to engage in or strive to practice digital security techniques when health data are used and scoring above the median value of 23 (IQR 20-24) using six-item Likert scale questions that have good motivation, whereas those below the median value are poor motivation [23]. ...
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Introduction Protecting digital health data from unauthorized access, alteration, and destruction is a crucial aspect of healthcare digitalization. Currently, digital security breaches are becoming more common. Healthcare data breaches have compromised over 50 million medical records per year. In Ethiopia, health digitization has grown gradually. However, there is a limitation of study in digital health security. Studying digital health data security helps individuals protect digital data as a baseline and contributes to developing a digital health security policy. Objective To assess the practice of healthcare professionals in digital health data security among specialized teaching referral hospitals in Amhara Region, Ethiopia. Method A cross-sectional study design supplemented by a qualitative purposive sampling method was used to measure the digital data security practices of health professionals. The sample size was determined via single population proportion formula. A simple random sampling technique was used for the study participants. Then, self-administered questionnaires were administered. Multivariable logistic analysis was used to identify associated factors using STATA software. For the qualitative study, key informant interviews were used and analyzed using thematic analysis approach via open-code software. Results Out of the 423 health professionals, 95.0% were involved in the survey. The finding indicates digital health data security practice of health professionals working at specialized teaching hospitals were 45.0%, CI: (40, 50). Health professionals 41–45-year age group (AOR = 0.107), master’s degree (AOR = 2.45), postmaster’s degree (AOR = 3.87), time to visit the internet for more than two hours (AOR = 2.46), basic computer training (AOR = 2.77), training in digital data security (AOR = 2.14), and knowledge (AOR = 1.76) were associated with the practice of digital health data security. For the qualitative study, three teams were prepared. The findings indicate digital health data security can be improved through training, advanced knowledge and working with digital security. Conclusion The practice of digital health data security in specialized teaching hospitals in the Amhara region was inadequate. Therefore, it can be improved through enhancing education status, increasing the time needed to visit the internet, providing computer training, and updating health professionals’ knowledge toward digital health data security.
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Chapter
In the Health 5.0, disease screening and management of pandemics need a mixture of cutting-edge technology with Artificial Intelligence (AI). AI also spreads to the key stages of recognizing threats such as disease, diagnosing with ever - increasing precision and planning actions like response. Healthcare systems can use AI-driven tools to examine large-scale data, detect the early signals of an outbreak and get resources allocated more efficiently. AI also enables pandemic prevention, control and disaster relief by predicting vaccine demand in large-scale vaccination (mass-vaccination), optimizing supplies chains as well identifying populations with greater vulnerability to the virus. While the world battles rising health emergencies, leveraging AI to detect diseases, and manage pandemics is essential for global health security amplifying our response times and reaches while reducing disparities in access to care.
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Disease surveillance and a well-concerted pandemic outbreak plan have taken on greater importance within the broader realm of global health today. Artificial Intelligence (AI) is transforming this through increasingly precise and large-scale screenings, allowing to increase chances of recovery by making best use of resources. Powerful AI-based solutions, such as machine learning algorithms, can process vast amounts of data swiftly to recognize symptoms from patient records and estimate how the disease is going to spread so health authorities take corrective action in time. AI can also predict where infections will be higher this enables you to target vaccine campaigns and get vaccines distributed, ensuring an equitable distribution of said resources. This chapter focuses on the AI solutions can be used to speed up the vaccine research process by knocking out complex biological data faster than conventional methods.
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Background The use of information technologies could help to improve communications between patients and care providers, might improve overall patient management practice. However, the potential for implementing these patient management options in Ethiopia has not been well documented. This institution-based survey aimed to describe the attitude and willingness of care providers towards the use of information technologies for managing diabetes patients, and factors influencing their interest. Methods A cross-sectional quantitative survey was conducted on 423 study participants from February to March 2020 at two teaching hospitals in Northwest Ethiopia, where remote monitoring patients had not been implemented. A pretested self-administered questionnaire was used to collect the required data. Other than descriptive statistics, the binary logistic regression analysis method was used to identify factors associated with attitude. Also, the negative binomial regression method was used to identify factors associated with willingness to use information technologies. Result A total of 406 participants (69.7%, n = 283 nurses and 30.3%, n = 123 physicians) were completed survey. Overall, 64% of respondents had a favorable attitude towards remote monitoring, and the majority of them were willing to use voice call (74.4%), text message (62.1%), video conference (61.3%), e-mail (60.6%), and social media (57.4%) as a source of communication to support patients. The result of regression analysis shows that having a computer (AOR = 2.3, 95% CI: [1.3, 3.8]), innovativeness (AOR = 2.8, 95% CI: [1.8, 4.3]), and practice of supporting patient by electronic technologies (AOR = 1.7, 95% CI: [1.1, 2.6]) were significantly associated with attitude to remote monitoring. Attitude towards remote monitoring (IRR = 2.3, 95% CI: 1.1–4.7), computer use (IRR = 1.3, 95% CI: 1.162–3.023), frequently searching health-related information (IRR = 1.7, 95% CI: 1.459–6.570), gender (IRR = 1.2, 95% CI: 1.0–5.1), awareness towards remote monitoring (IRR = 1.4, 95% CI: 1.1–2.7) were significantly associated with willingness to use information technologies. Conclusion Improving the intention and skill of using computers should be a major point of attention for teaching hospitals who wish to improve their care providers' attitudes to remote monitoring and willingness in using information technologies. Besides, the awareness of professionals is crucial for improving willingness.