
==== Front
Digit Health
Digit Health
DHJ
spdhj
Digital Health
2055-2076
SAGE Publications Sage UK: London, England

10.1177/20552076241261930
10.1177_20552076241261930
Original Research Article
Acceptance of mobile application-based clinical guidelines among health professionals in Northwestern Ethiopia: A mixed-methods study
https://orcid.org/0009-0008-9403-6915
Baykemagn Nebebe Demis 1
Nigatu Araya Mesfin 1
Fikadie Berhanu 1
Tilahun Binyam 1
1 Department of Health Informatics, College of Medicine and Health Sciences, Institute of Public Health, 128166 University of Gondar , Gondar, Ethiopia
Nebebe Demis Baykemagn, Department of Health Informatics, College of Medicine and Health Sciences, Institute of Public Health, University of Gondar, Gondar, Ethiopia. Email: nebebe2@gmail.com
21 7 2024
Jan-Dec 2024
10 2055207624126193026 12 2023
29 5 2024
© The Author(s) 2024
2024
SAGE Publications Ltd, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits any use, reproduction and distribution of the work as published without adaptation or alteration, provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Background

Globally, healthcare providers have faced significant difficulties in adhering to clinical guidelines. Applying mobile health systems is a crucial strategy for enhancing the dissemination and accessibility of clinical guidelines. This study aimed to assess the acceptance of mobile app-based primary healthcare clinical guidelines and associated factors among health professionals in central Gondar health centers.

Methods

A cross-sectional study supplemented with qualitative data was conducted on 403 health workers. Data were collected using a pre-test structured printed questionnaire and entered into EpiData version 4.6. Analysis was conducted using Stata version 14, which included bivariable and multivariable logistic regression analyses. For qualitative data, thematic analysis was conducted using Open Code v.4.2.

Results

Approximately 28% (95% confidence interval (CI): 23%–32%) of health professionals had utilized mobile app-based clinical guidelines. The availability of IT support (adjusted odds ratio (AOR)  = 3.51, 95% CI: 1.82–6.78), good knowledge (AOR = 3.46, 95% CI: 1.5–6.78), perceived usefulness (AOR = 2.21, 95% CI: 1.00–4.99), m-Health app exposure (AOR = 2.34, 95% CI: 1.2–4.50), and ease of use (AOR = 5.77, 95% CI: 2.50–13.32) were significantly associated with the acceptance of the mobile app-based clinical guideline. In qualitative data, lack of training and supervision and access to smartphones were barriers to acceptance of the mobile app-based clinical guideline.

Conclusion

In summary, acceptance of the app is currently low. However, it can be increased by improving the availability of IT support in the workplace, offering training and supervision, and enhancing access to smartphones.

Acceptance
Ethiopia
mobile app-based clinical guidelines
digital health
m-Health
typesetterts19
cover-dateJanuary-December 2024
==== Body
pmcBackground

Globally, healthcare providers have faced significant difficulties in adhering to clinical practice guidelines1,2 primarily due to limited training, restricted access, and lengthiness of the guidelines. 3 Applying digital health, particularly mobile health systems, is a crucial strategy for enhancing the dissemination and accessibility of clinical guidelines.4,5 Clinical guidelines (CPGs) are “concise statements intended to guide physicians and patients in making informed decisions for specific clinical conditions, ensuring appropriate and high-quality care.”6,7

The quality of patient care depends on the source of evidence used. 8 Updated and standardized clinical guidelines serve as the foundation for delivering quality healthcare services. 9 Developing countries face challenges in this regard as their primary source of evidence is paper-based clinical guidelines, which are difficult to update and disseminate promptly.10–12 Mobile-based clinical guidelines improve patient care by empowering healthcare professionals to make timely and well-informed decisions for their patients. 13 A mobile app-based clinical guideline is a digital tool. It provides healthcare professionals with access to evidence-based guidelines for clinical decision-making.” 14

Developing countries’ healthcare systems do not deliver high-quality, comprehensive, standard, and integrated care that is easy to access. This is because healthcare providers do not follow clinical guidelines well.15,16 This leads to unnecessary medical interventions and hospitalizations, as well as higher rates of illness and death. 17

To fix this gap, in 2017, the Ethiopian Ministry of Health adapted and mobile app-based primary healthcare clinical guidelines (EPHCG) from South Africa to fit the local context. Then, they implemented them at primary levels of care.18,19

These mobile-based clinical guidelines play a crucial role in achieving the Ethiopian Health Sector Transformation Agenda (HSTA). As part of the information revolution (IR), digitization serves as a key pillar within the HSTA framework. 20 It covers many medical symptoms and procedures. These include maternal and child health, infectious diseases, non-communicable diseases, and emergency care. 18

The Ethiopian Core Team and Ministry of Health policymakers conducted a phase-based evaluation of the EPHCG system, assessing 400 healthcare facilities in the first phase. They conducted the second phase to review in detail. The goal was to find health system bottlenecks during the EPHCG implementation. 19

Using the mobile app-based clinical guidelines is an effective solution. It bridges the gap in evidence-based clinical decision-making13,21 by increasing accessibility, providing standardized care, and finally improving current evidence-based clinical decision-making.22,23

The mobile app-based clinical guidelines play a crucial role in the healthcare system for care providers and patients by reducing medical errors,24,25 decreasing direct and indirect medical costs,13,26,27 enabling easy updating of medical knowledge, 28 and facilitating communication among healthcare professionals.29,30

Mobile technology adoption in sub-Saharan Africa has risen. It increased from 32% in 2019 to 43% in 2023.31,32 However, healthcare providers demonstrate limited adoption of digital clinical guidelines, including mobile health apps, primarily due to their reluctance.16,33 The sustainable use of health technology for patient care is impossible without the acceptance and support of health professionals. 34 Acceptance of digital technologies among healthcare professionals is impacted by various facilitators and barriers. 35

Facilitators, such as adequate training, user-friendly interfaces, technical support, organizational support, and app content quality, continuous support and updates, incentives and recognition, interoperability, patient engagement, trust and security, integration with other wearable devices, social support and gamification, localization, and cultural adaptation, influence the acceptance of m-Health app systems by healthcare providers.36–41

Barriers include lack of infrastructure, insufficient training, system reliability issues, limited awareness, resistance to change, inadequate technical support, lack of organizational support, lack of internet connectivity, lack of established policies, and a shortage of skilled human resources, particularly in primary healthcare settings.36,40,42–49

Moreover, literature indicated that in developing countries like Ethiopia, challenges to the sustainable adoption of m-Health apps include poor quality of training and supervision, limited digital literacy, simultaneous use of manual and electronic systems, heavy workloads, lack of institutionalization and ownership, insufficient peer collaboration, absence of clear policies and regulations, and insufficient iterative digital system evaluation.50,51

Providers who do not use mobile app clinical guidelines may have inefficiencies and errors in patient care. They may have to rely on outdated or hard-to-access printed guidelines. This reliance can lead to human errors and slow the healthcare processes. 52

Also, evidence has shown that using a mobile health (m-Health) app for clinical guidelines decreases medical errors and costs. It also improves standardized diagnosis and shortens hospital stays for medical care 14 .

Moreover, the acceptance of a mobile application for guidelines shows the growing integration of technology in healthcare provision, enabling rapid updates and revisions as new evidence and best practices become available.13,31

This study is important for policymakers, healthcare managers, mobile system developers, and patients because it provides useful knowledge for formulating policies. The findings have the potential to facilitate the integration of these guidelines in secondary and tertiary healthcare settings, ultimately improving evidence-based patient care.

To the best of our knowledge, there are no existing studies on the use of mobile app-based clinical guidelines in Ethiopia. Hence, the objective of this study is to assess the acceptance of mobile-app-based primary healthcare clinical guidelines and the factors related to them among healthcare professionals in health centers in central Gondar, Northwest Ethiopia.

Methods

Study period

A study was conducted from 19 May to 21 June 2022 in central Gondar health centers in northwestern Ethiopia.

Study design and setting

An institution-based cross-sectional study supplemented with qualitative data was conducted to determine the proportion of acceptance of the mobile app-based primary healthcare clinical guidelines among healthcare professionals in central Gondar health centers in Northwest Ethiopia from 19 May to 21 June 2022. Central Gondar is located 738 km from Addis Ababa, the capital city of Ethiopia, and 168 km from Bahir Dar, the capital city of the Amara regional state. It has 18 Woreda, 9 hospitals, and 76 health centers with an estimated total population of 2,307,773 people, 51% of whom are female. 53

Eligibility criteria

The study population comprised health professionals who had been using the mobile-based clinical guidelines for at least 6 months, worked in the selected health centers, had smartphones, and were available during data collection time.

Health professionals who had less than 6 months of working experience on the mobile-based clinical guidelines were excluded from the study.

Sample size determination and sampling procedures

For a quantitative study, we used the single population proportion formula to calculate the sample size, with the variables n representing the required sample size, Z representing the standard normal distribution value corresponding to α/2 (1.96), p representing the proportion of health professionals using the MBPHCG, q representing the proportion of health professionals not using the MBPHCG, and d representing the assumed precision of 0.05. Since no previous studies had been conducted in Ethiopia on the use of the mobile application-based primary healthcare clinical guideline, we assumed p to be 0.05. This calculation resulted in a required sample size of 384, and after accounting for a 10% non-response rate, 423 healthcare professionals were enrolled in the study.

The quantitative data were collected from study participants selected using the thumb rule of (20–30), taking into account feasibility issues and geographic dispersion. Of the initial 76 health centers, 17 were ultimately chosen in northwest Ethiopia. These included Ambageorgis, Atsedemariam, Aymba, Bira, Delegi, Dengelber, Dinkularba, Enfranze, Gedebye, Graregie, Kolladiba, Maksegnt, Minzero, Musybanb, Sanja, Serako, and Trigosgie. The number of healthcare providers selected from these health centers using the single proportion formula was chosen through simple random sampling.

For the qualitative part, seven key informant interviews (KIIs) were conducted considering the point of saturation. A purposeful sampling technique was employed to select key informants from the heads of health centers and primary healthcare clinical guideline focal persons.

Study variables and operational definitions

The outcome variable was the acceptance of the mobile app-based primary healthcare clinical guidelines and the independent variables included m-Health app-related factors, behavioral factors, and organizational and technology-related factors.

To measure the level of healthcare provider acceptance of the mobile-app-based clinical guideline, we used the following five general questions: Was EPHCG utilized for patients’ history taking and confirming symptoms? Was it utilized for national disease classification (NCOD)? Was it utilized to access medical/drug information? Was it utilized for referral reasons? Was it utilized to identify indications and contraindications in the past 6 months? These questions were adapted from studies, such as “Utilization, of Medical Records in Ethiopia,” “Utilization of a Mobile Phone Application for Reproductive Health,” “Smartphone Medical App Use by Physicians in North Ethiopia,” and the “Ethiopian Primary Health Care Clinical Guideline Implementation Manual.”18,44,54,55

These questions were designed with yes or no answer options. The acceptance scale ranged from 0 to 5, where a score of “5” indicated acceptance of the five functions in the last 6  months, and a score of “0” indicated no acceptance of any of the five functions.

Upon evaluating the distribution of the data, it was found skewed. As a result, median values were used for analysis. The median was calculated using Stata version 14. 44

Mobile app-based primary healthcare clinical guidelines

The primary healthcare clinical guidelines are delivered through a mobile app software application. 56

Perceived usefulness (PU) is the degree to which users’ perceptions of the health-related services offered by mobile app-based clinical guidelines will benefit them. Study participants who scored the median or higher on the 5-point Likert scale were categorized as thinking the mobile app PHCG was useful for their job. At or above the median and those who scored below the median were categorized as thinking the mobile app PHCG was not useful for their job. 57

Perceived ease of use (PEOU), according to Davis 60 , is defined as the degree to which a health professional believes that the use of a mobile app clinical guidelines would require no effort. 58 For the perceived ease of use questions, study participants who scored above the median values on the 5-point Likert scale were categorized as thinking that the mobile app PHCG was easy to use. At or above the median and those who scored below the median value were categorized as thinking that the EPHCG was not easy to use.

Knowledge: For the knowledge questions, study participants who scored at or above the median value were categorized as having good knowledge, and those who scored below the median value were categorized as having poor knowledge. 59

Data collection tools and procedures

For the quantitative part of this study, self-administered structured printed questionnaires were used. These questionnaires were adapted from Davis' study and another related study.44,59–65 The questionnaire was first prepared in English, and then translated into Amharic for data collection purposes. Later, it was translated back from Amharic to English for analysis purposes. The study included 32 questions covering the following domains: socio-demographic characteristics (eight questions), perceived usefulness (four questions), perceived ease of use (five questions), knowledge (five questions), and mobile-based primary healthcare acceptance (outcome variables five questions).

The data were collected by six health information technicians with diplomas and experience in data collection, and supervision was provided by three BSC health informatics professionals.

For the qualitative part, data were collected by the principal investigator through the use of open-ended questions. Several probes were used to obtain deep insight into participants’ beliefs, and the guide was pretested to ensure relevance and appropriateness.

Data quality control

Two days of training were given to the data collectors and supervisors based on the objective of the study, data collection procedures, data collection tools, respondents’ approach, data confidentiality, and respondent rights before the data collection date. The completeness of the questionnaires was checked every day by the supervisors before the actual data were collected. We consulted with three PhD health informaticians and senior researchers to ascertain the validity of the questionnaire. They were invited to review the tool, focusing on content validity, and make necessary revisions. The questionnaire's reliability was calculated to be α = 0.96 for the outcome variable and α = 0.94 or higher for the independent variables.

For the qualitative part, after examining pertinent literature on the barriers and facilitators of utilizing digital clinical guidelines, a comprehensive interview guide was developed for conducting key informant interviews. The responses were audio recorded, and notes were taken by the principal investigator and data collectors. Separate and quiet rooms were used to conduct the interviews.

To ensure trustworthiness and eliminate sources of bias, data were collected from different sources, including health center heads, clinical guideline focals, and healthcare providers, to ensure credibility. Additionally, the audio data were collected by an investigator, and short notes were taken by data collectors. Subsequently, both sets of data were cross-validated. Furthermore, member checking was conducted after translating the text back to the key informant respondents (health center heads and EPHCG focals) for feedback. Refinement was then done accordingly. Data were rechecked repeatedly during transcription, translation, and analysis to ensure conformability.

Statistical analysis

After carefully checking each completed questionnaire for any mistakes and completing the data cleaning process, the cleaned data were entered into EpiData 4.6. Subsequently, the data were exported to Stata version 14 for statistical analysis.

Descriptive statistics were used to describe the data, including summary statistics, frequency and percentage distributions, as well as visual representations, such as graphs and tables.

A bivariable and multivariable logistic regression model was employed to assess the factors associated with the acceptance of the app. Variables with a p-value less than or equal to 0.2 were included in the multivariable logistic regression analysis.

The presence of multicollinearity among the independent variables was checked using a variance inflation factor (VIF). Among them, the maximum VIF value was 1.95, indicating that there was no multicollinearity between the independent variables. In addition, the Hosmer–Lemeshow goodness-of-fit test was performed to assess the model fit. The obtained p-value was 0.42, which is greater than the significance level of 0.05. This indicates that the model fits the data well according to the standard p-value criterion for model fit assessment. For the qualitative analysis, Open Code version 4.2 was used for thematic analysis.

Results

Socio-demographic characteristics

A total of 403 healthcare providers participated in the study, with 223 males and 180 females, resulting in a response rate of 93%. The respondents had a median age of 31 years, with an interquartile range (IQR) of 28–35. The majority of the participants 375 (93.0%) were orthodox religious followers. More than half of the participants were married. Among the participants, 195 (48.3%) were nurses, and 190 (47.1%) had more than 6 years of clinical experience. Regarding the sources of information for evidence-based patient management, 356 (88.3%) mentioned medical books, while 145 (35.9%) mentioned mobile health apps. A total of 7 KII respondents actively participated in the qualitative study (Table 1).

Table 1. Socio-demographic factors for HPs to utilize EPHCG in northwest Ethiopia in 2022.

Variable	Frequency	Percentage	
Age (years)			
 20–29	162	40.2	
 30–39	218	54.0	
 40–56	23	5.7	
Sex			
 Male	223	55.3	
 Female	180	44.6	
Marital status			
 Single	173	42.9	
 Married	220	54.5	
 Divorced	10	2.4	
Educational status			
 Diploma	215	53.3	
 Degree	188	46.6	
Religion			
 Orthodox	375	93.0	
 Muslim	25	6.2	
 Protestant	3	0.7	
Profession			
 Doctor	5	1.24	
 Nurse	195	48.3	
 Lab. tech.	39	9.6	
 Pharm. tech	52	12.9	
 Midwifery	67	16.6	
 HO	45	11.1	
Work experience (years)			
 1–3	69	17.1	
 4–6	144	35.7	
 >6	190	47.1	
References for clinical decisions			
Other health professionals	72	17.8	
 Medical books	356	88.3	
 Internet site	140	34.7	
 Library	27	6.7	
 Mobile health app	145	35.9	

Mobile app-related variables

Perceived usefulness

The perceived usefulness of the mobile health clinical guidelines was recognized by 195 (48.3%) healthcare professionals as beneficial. The study results indicated that 155 (38.4%) healthcare professionals confirmed that the use of EPHCG enhances their clinical effectiveness. Additionally, 160 (39.7%) healthcare professionals agreed that EPHCG empowers them to effectively manage their patients’ health (Table 2).

Table 2. Perceived usefulness factors for health professionals’ acceptance of EPHCG.

Perceived usefulness variable	SD	DA	N	A	SA	
EPHCG useful for my healthcare practice	22 (5.4)	133 (33.0)	30 (7.4)	168 (41.6)	50 (12.1)	
EPHCG enables obtaining information about Dx	20 (4.9)	116 (28.7)	60 (14.8)	157 (38.9)	50 (12.4)	
EPHCG helped me manage my patients’ health	18 (4.4)	134 (33.2)	44 (10.9)	160 (39.7)	47 (11.6)	
Using EPHCG improves my clinical performance	24 (5.9)	111 (27.5)	62 (15.3)	155 (38.4)	51 (12.6)	
SD: strongly disagree; D: disagree; N: neutral; A: agree; SA: strongly agree.

EPHCG: mobile app-based primary healthcare clinical guidelines.

Perceived ease of use

According to the data presented in Table 3, 146 (36.2%) healthcare professionals (HPs) found the user interface of the EPHCG to be complex. Additionally, 157 (38.9%) HPs reported that the content within the EPHCG was disorganized, making it difficult for them to locate the necessary information (Table 3).

Table 3. Perceived ease of use for HP's mobile app-based primary healthcare clinical guidelines.

Perceived ease of use variable	SD	DA	N	A	SA	
The information in the EPHCG was well organized.	18 (4.47)	157 (38.9)	54 (13.4)	143 (35.48)	31 (7.69)	
I like the interface of the EPHCG.	14(3.4)	146 (36.2)	86 (21.3)	112 (27.7)	45(11.1)	
I can easily use mobile-based PHCG	14 (3.4)	150 (37.2)	63 (15.6)	126 (31.2)	50 (12.4)	
The use of EPHCG requires little mental effort	11(2.7)	153(37.9)	67(16.6)	120(29.7)	52(12.9)	
I made a mistake and recovered easily and quickly.	16(3.9)	165(40.9)	62(15.3)	111(27.5)	49(12.1)	

Knowledge of the acceptance of the mobile app-based clinical guidelines

More than half 227 (56.3%) of the healthcare providers had poor knowledge of EPHCG. Moreover, 200 (49.5%) HPs were found to be aware of the definition of EPHCG. However, more than half, that is, 221 (54.8%) HPs, claimed that using EPHCG for patient care did not reduce pointless referrals or transportation expenses (Table 4).

Table 4. Knowledge of the acceptance of mobile app-based primary healthcare clinical guidelines.

Knowledge-related variable	Yes	No	
Heard about the mobile-based PHCG?	251 (62.2)	152 (37.7)	
Are you aware of the availability of EPHCG for smartphones	231 (57.3)	172 (42.6)	
definition of EPHCG	200 (49.6)	203 (50.3)	
Using mobile-based PHCG can reduce medical errors	198 (49.1)	205 (50.8)	
EPHCG reduces unnecessary referrals and transportation costs	182 (45.16)	221 (54.8)	

Organizational variables of the acceptance of the mobile app-based clinical guidelines

Only 98 (24%) of the participants had access to a computer in their working area. The majority of the 382 (94.7%) HPs had not received formal training in the mobile-based guidelines. According to 351 (87.1%) respondents, EPHCG was not part of the health centers’ plan. Additionally, more than half of the health professionals, that is 298 (73.9%), were unable to access the internet. Furthermore, 93% of healthcare professionals report that no focal person had been assigned to oversee EPHCG-related activities (Table 5).

Table 5. Organization- and technological-related variables on the acceptance of mobile app-based clinical guidelines.

Organization- and technological-related variables	Yes	No	
Computer access in the working area	98 (24.3)	305 (75.6)	
Internet access at the workplace	105 (26.0)	298 (73.9)	
Had any formal training in ICTs?	63 (15.6)	340 (84.3)	
Had any formal training in mobile-based PHCG	21 (5.2)	382 (94.7)	
Is the PHCG part of the plan of the health center?	52 (12)	351 (87.1)	
Has a focal person been assigned to overlook PHCG-related activities	39 (9.6)	364 (90.3)	
Is the availability of IT support staff?	141 (34.9)	262 (65.0)	
Is the PHCG part of the health education session of the health center?	34 (8.4)	369 (91.5)	
Raise PHCG issues during the meeting?	47 (11.6)	356 (88.3)	
Are morning sessions conducted based on the PHCG?	31 (7.6)	372 (92.3)	

The overall percentage of perceived usefulness, perceived ease of use, and knowledge of health professions on the acceptance of the mobile app-based primary healthcare clinical guidelines in 2022 is shown in Figure 1.

Figure 1. Percentage of perceived usefulness, perceived ease of use, and knowledge of health professions.

Acceptance of the mobile app-based primary healthcare clinical guidelines

Among all healthcare professionals, 28% (95% CI 23–32%) had good acceptance of the mobile app-based primary healthcare clinical guidelines, while 72% had poor acceptance of them, as shown in Figure 2.

Figure 2. Acceptance of mobile app-based primary healthcare clinical guidelines in central Gondar health centers, 2022 (n = 403).

Factors associated with the acceptance of the mobile app-based primary healthcare clinical guidelines

The study evaluated the factors influencing the acceptance of healthcare providers for the mobile app-based primary healthcare clinical guidelines through bi-variable and multivariable analyses. In the bi-variable analysis, significant associations were observed between the acceptance of the mobile app-based primary healthcare clinical guidelines and factors, such as IT support availability, knowledge, previous use of the m-Health app, perceived usefulness of EPHCG, and perceived ease of EPHCG (Table 6).

Table 6. Multi-variable analysis of factors associated with the acceptance of MBPHCG.

Variable	EPHCG acceptance total (%)	COR 95% CI	AOR 95% CI	
Good	Poor	
Availability of IT Support	Yes	81 (20.10)	60 (14.89)	9.703 (5.89–15.96)	3.51 (1.82–6.78)	
No	32 (7.94)	230 (57.07)	1	1	
Knowledge	Good	98 (24.32)	78 (19.35)	17.757 (9.72–32.42)	3.46 (1.56–7.68)	
Poor	15 (3.72)	212 (52.61)	1	1	
m-Health app use	Yes	74 (18.36)	71 (17.62)	5.852 (3.653–9.375)	2.34 (1.22–4.50)	
No	39 (9.68)	219 (54.34)	1	1	
Internet availability	Yes	55 (13.65)	50 (12.41)	4.551 (2.820–7.344)	1.57 (.74–3.30)	
No	58 (14.39)	240 (59.55)	1	1	
Ease of use	Yes	103 (25.56)	84 (20.84)	25.259 (12.581–50.714)	5.77 (2.50–13.32)	
No	10 (2.48)	206 (51.12)	1	1	
Perceived usefulness	Yes	97 (24.07)	98 (24.32)	11.877 (6.635–21.259)	2.21 (1.00–4.89)	
No	16 (3.97)	192 (47.64)	1	1	
Computer availability	Yes	50 (12.41)	48 (11.91)	4.001 (2.467–6.488)	1.89 (.90–3.98)	
No	63 (15.63)	242 (60.05)	1	1	
Education	degree	71 (17.62)	117 (29.03)	2.499 (1.597–3.911)	1.54 (.80–2.96)	
Diploma	42 (10.42)	173 (42.93)	1	1	
Focal person	Yes	24 (5.96)	15 (3.72)	4.943 (2.484–9.836)	2.52 (.93–6.80)	
No	89 (22.08)	275 (68.24)	1	1	
AOR: adjusted odds ratio; COR: crude odds ratio.

Bold value indicates significant at p-value<0.05.

According to our multi-variable logistic regression analysis, the availability of IT support (adjusted odds ratio (AOR) = 3.51, 95% confidence interval (CI): 1.82–6.78), knowledge (AOR = 3.46, 95% CI: 1.5–6.78), perceived usefulness of EPHCG (AOR = 2.21, 95% CI: 1.00–4.89), previous use of m-Health apps (AOR = 2.34, 95% CI: 1.2–4.50), and perceived ease of use of EPHCG (AOR = 5.77, 95% CI: 2.50–13.32) were found to be significantly associated with the acceptance of the mobile app-based primary healthcare clinical guideline.

Qualitative results on using the mobile app-based clinical guidelines

A total of seven KII respondents participated in the qualitative study. Regarding sex and age, the majority of the study participants were male and aged >30 years old.

Barriers to using the mobile app-based guidelines in clinical practice

According to the qualitative data, the main barriers to using mobile apps include long patient wait times, a lack of access to smartphones, insufficient training and supervision, and a shortage of healthcare professionals.

Patient wait time-related barriers

The majority of key informant respondents reported that one barrier to using EPHCG is extended patient waiting times.Three of our healthcare professionals have trained on the mobile app primary healthcare clinical guidelines, but do not use it due to extended patient waiting time. (33-year-old PHCG focal)

Limited access to smartphones

Key informant respondents expressed that utilizing the mobile app clinical guidelines had been challenging because not all health workers had smartphones.The problem is that some healthcare professionals at our health center do not use the app because they do not have access to them. For example, three of them do not have smartphones. (36-year-old OPD coordinator)

Lack of formal training and supervision on the digital clinical guidelines

According to qualitative findings, the majority of healthcare providers reported a lack of formal training and supervision on digital clinical guidelines, which was a barrier to utilizing the app.We have implemented the manual-based guideline, but we have not fully adopted mobile app clinical guidelines because healthcare providers have not received formal training. (39-year-old health center head)

Another common issue with using the mobile app-based clinical guidelines was the absence of supervision from higher authorities.

There are a lot of apps on our phone that are easy to use, and I believe this one is as well but lack monitoring from the zonal/regional health office. (35-year-old OPD coordinator)

Facilitating factors for using the mobile app-based guidelines in clinical practice

mHealth app exposure

Respondents revealed that previous m-Health exposure was one motivator to use apps like the NCOD.…Most of our healthcare professionals are familiar with and use the mobile app clinical guideline because they have experience using the National classification of disease app. (33-year-old health center head)

EPHCG training

According to some health professionals’ feedback, those who received training used the app consistently.…We have received training on mobile clinical guidelines, comprehended their benefits, and actively utilized them in our decision-making processes. (41-year-old health center head)

Discussion

One of the biggest problems in healthcare is non-adherence to clinical guidelines. To address these challenges, in 2017, the Ethiopian Federal Ministry of Health implemented mobile application-based primary healthcare clinical guidelines.

This study aimed to assess the acceptance of mobile app-based primary healthcare clinical guidelines and associated factors among health professionals in central Gondar health centers in northwestern Ethiopia. To the best of our knowledge, this is the first study on the use of mobile app-based primary healthcare clinical guidelines.

This research has the potential to offer important information for policymakers and the Ministry of Health, guide the preparation of training programs for healthcare professionals to effectively use these tools, and play a critical role in shaping the future of primary healthcare delivery and enhancing access to quality care through the use of mobile technology.

The findings of our study showed that 28.04% (95% CI: 23–32%) of health professionals accepted mobile app-based primary healthcare clinical guidelines. This is lower than studies conducted in Gondar, Ethiopia (63.3%), 44 Saudi Arabia (42.3%), 66 Uganda (81%) 55 , Iran (40%), 67 and Canada (65% 68 ). This is supported by qualitative data. The lack of formal training and supervision on the app and limited access to smartphones were barriers to using the mobile-based clinical guidelines.We have implemented the manual-based guideline, but we have not fully adopted mobile app clinical guidelines because healthcare providers have not received formal training. (39-year-old health center head)

Another potential explanation for these differences is the variation between the study area and study populations. Our study was specifically conducted in rural health centers.

Participants who had access to IT support at health centers were 3.45 times more likely to use EPHCG compared to those without IT support. These findings are in line with previous studies.44,66,69 This is a fact: having IT support available near healthcare professionals is essential for the successful acceptance of digital health. It ensures that users receive necessary technical assistance, maintenance, updates, training, and issue resolution, ultimately fostering a higher likelihood of successful adoption.

Health professionals who had good knowledge were 3.03 times more likely to use EPHCGs than those with poor knowledge. The findings of this study corroborate those from South Africa, 70 Finland, and Lithuania. 71

This is due to the fact that healthcare professionals who are knowledgeable about technology can easily understand digital health concepts and navigate the features and functionalities of digital health tools. 72

Individuals who perceived EPHCGs to be beneficial for their job were 2.21 times more likely to use them than their colleagues. This study is in line with previous studies..44,47,73–75 Healthcare professionals are more likely to utilize the mobile app-based clinical guidelines if they perceive them as enhancing their decision-making process, improving patient care, saving time, improving workflow efficiency, providing accessibility and convenience, and supporting professional development. 76

Health professionals who had experience using mobile health apps were found to be 2.28 times more likely to use mobile app-based primary healthcare clinical guidelines than those who had not used such apps before. This finding is in line with those of studies conducted in Iran 77 and Germany. 78 This result is supported by a qualitative study.…Most of our healthcare professionals are familiar with and use the mobile app clinical guideline because they have experience using the National Classification of Disease app (NCOD). (33-year-old health center head)

Due to their familiarity with app features, increased confidence in using technology, and awareness of best practices gained through experience with mobile health apps, healthcare professionals can effectively utilize mobile app-based clinical guidelines. 79

Those who found mobile app-based primary healthcare clinical guidelines easy to use were 5.7 times more likely to use them than their peers. This study is in line with previous studies.75,80–82 Some studies similarly demonstrated that ease of use plays a crucial role in the acceptance of mobile apps.

Ease of use plays a crucial role in the adoption and utilization of mobile app-based clinical guidelines by healthcare professionals. Therefore, developers and designers should prioritize these aspects during the development and implementation of such apps to ensure their effectiveness and impact on clinical practice. 83

Implications for practice and research

The use of mobile app clinical guidelines has significant implications for practice, research, and policymakers. Healthcare professionals can improve the quality and efficiency of healthcare delivery by accessing up-to-date clinical guidelines on their mobile devices.

This can lead to more informed decision-making and better patient outcomes. Researchers can also benefit from studying the effectiveness and impact of mobile app clinical guidelines on healthcare delivery, which can further improve their effectiveness.

Policymakers may need to consider how to regulate and standardize the use of these tools to ensure quality and consistency in healthcare delivery. Overall, stakeholders should consider these implications to effectively integrate and maximize the benefits of mobile app clinical guidelines in healthcare delivery.

Strengths and limitations

This is the first study in Ethiopia supported by a qualitative design and study of rural areas at health centers because the majority of the population receives primary healthcare at health centers.

There are a few limitations to this research; establishing temporal relationships may be difficult because the study used a cross-sectional methodology. The other limitation of the study is that it was conducted at the primary healthcare level, specifically in health centers within the Ethiopian context. The majority of participants recruited were other healthcare professionals, such as public health workers and nurses. This could make it difficult to generalize and transfer the findings given the limited participation of physicians. The self-administered questionnaire could result in the possibility of response bias.

Conclusion

Based on the quantitative findings, knowledge, perceived usefulness, previous use of the m-Health app, and perceived ease of use were found to be major contributors to the low acceptance of the mobile app-based primary healthcare clinical guidelines.

Based on qualitative findings, barriers to the use of the mobile app-based primary healthcare clinical guidelines include long patient wait times, a lack of access to smartphones, insufficient training and supervision, and a shortage of healthcare professionals.

In summary, the acceptance of the app is currently low. However, it can be increased by improving the availability of IT support in the workplace, offering training and supervision, and enhancing access to smartphones. Future research should focus on studying comparative research on private and public healthcare facilities.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076241261930 - Supplemental material for Acceptance of mobile application-based clinical guidelines among health professionals in Northwestern Ethiopia: A mixed-methods study

Supplemental material, sj-docx-1-dhj-10.1177_20552076241261930 for Acceptance of mobile application-based clinical guidelines among health professionals in Northwestern Ethiopia: A mixed-methods study by Nebebe Demis Baykemagn, Araya Mesfin Nigatu, Berhanu Fikadie and Binyam Tilahun in DIGITAL HEALTH

Acknowledgments

We would like to acknowledge the University of Gondar for enabling us to carry out this study. We also like to thank the central Gondar zonal health department and health centers for providing all necessary information and support. We are grateful to the individuals who participated in the study, as well as those who collected and supervised the data collection. Finally, this work would not be possible without the financial support of the Doris Duke Charitable Foundation under grant number 2017187. The mission of the Doris Duke Charitable Foundation is to improve the quality of people's lives through grants supporting the performing arts, environmental conservation, medical research, and child well-being and through preservation of the cultural and environmental legacy of Doris Duke's properties.

Availability of data and materials: The datasets for this study will be available upon reasonable request from the corresponding author.

Consent for publication: Not applicable.

Contributorship: All the authors made significant contributions to the work reported, whether in the conception, study design, execution, acquisition of data, analysis, and interpretation, or each author played a role in drafting, editing, and critical review of the article and agreed on full responsibility for every aspect of the work.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval and consent to participate: Ethical approval was obtained from the Institutional Review Board (IRB) of the University of Gondar College of Medicine and Health Sciences, Institute of Public Health (Ref No. /IPH2124/2014). Communication with the different official administrators was made through a formal letter obtained from the University of Gondar. Written consent was obtained from each study participant after they were informed of the objective of the study. The individuals involved were informed about the advantages and potential drawbacks of the research. The data collection procedure was anonymous; their privacy was also maintained; and the study was performed according to the Declaration of Helsinki.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

Guarantor: NDB

ORCID iDs: Nebebe Demis Baykemagn https://orcid.org/0009-0008-9403-6915

Supplemental material: Supplemental material for this article is available online.
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References

1 Zeng L Li Y Zhang L , et al. Guideline use behaviours and needs of primary care practitioners in China: a cross-sectional survey. BMJ Open 2017; 7 : e015379.
2 Matthews S Atkins B Walton N , et al. Development and use of a cardiac clinical guideline mobile app in Australia: acceptability and multi-methods study. JMIR Format Res 2022; 6 : e35599.
3 Al Sada D Husain YI Al Misbah Z . Assessing adherence to clinical practice guidelines among primary care physicians in Bahrain: a cross-sectional study. Oman Med J 2023; 38 : e527.
4 Global strategy on digital health 2020-2025.
5 Stoumpos AI Kitsios F Talias MA . Digital transformation in healthcare: technology acceptance and its applications. Int J Environ Res Public Health 2023; 20 : 3407.36834105
6 WHO EMRO | Training package for clinical practice guidelines | Training package | Evidence and data to policy.
7 Sarrafzadegan N Shahidi S Kholenjani FB . The challenges of developing clinical practice guidelines. Iran J Nurs Midwifery Res 2023; 28 : 31.
8 Lehane E Leahy-Warren P O’Riordan C , et al. Evidence-based practice education for healthcare professions: an expert view. BMJ Evid Based Med 2018; 24 :103–108.
9 Beauchemin M Cohn E Shelton RC . Implementation of clinical practice guidelines in the health care setting: a concept analysis. Adv Nurs Sci 2019; 42 : 307–324.
10 Wang Z Norris SL Bero L . The advantages and limitations of guideline adaptation frameworks. Implement Sci 2018; 13 : 1–13.29301543
11 Pereira VC Silva SN Carvalho VK , et al. Strategies for the implementation of clinical practice guidelines in public health: an overview of systematic reviews. Health Res Policy Syst 2022; 20 : 13.35073897
12 Wang T Tan J-YB Liu X-L , et al. Barriers and enablers to implementing clinical practice guidelines in primary care: an overview of systematic reviews. BMJ Open 2023; 13 : e062158.
13 Meidani Z Atoof F Mobarak Z , et al. Development of clinical-guideline-based mobile application and its effect on head CT scan utilization in neurology and neurosurgery departments. BMC Med Inform Decis Mak 2022; 22 : 106.35443649
14 Pokhrel P Karmacharya R Taylor Salisbury T , et al. Perception of healthcare workers on mobile app-based clinical guideline for the detection and treatment of mental health problems in primary care: a qualitative study in Nepal. BMC Med Inform Decis Mak 2021; 21 : 21.33468120
15 Yang J Zhong Q Liao Z , et al. Socioeconomic deprivation, medical services accessibility, and income-related health inequality among older Chinese adults: evidence from a national longitudinal survey from 2011 to 2018. Fam Pract 2023; 40 : 671–681.36928561
16 Chen Z Liang N Zhang H , et al. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart 2023; 10 : e002432.
17 Kith G Lisker S Sarkar U , et al. Defining and measuring adherence in observational studies assessing outcomes of real-world active surveillance for prostate cancer: a systematic review. Eur Urol Oncol 2021; 4 : 192–201.31288992
18 Ethiopian Primary Health Care Clinical Guideline Implementation Manual.
19 Feyissa YM Hanlon C Emyu S , et al. Using a mentorship model to localise the Practical Approach to Care Kit (PACK): from South Africa to Ethiopia. BMJ Glob Health 2019; 3 : e001108.
20 Health Sector Transformation Plan II 2020/21-2024/25 (2013 EFY - 2017 EFY).
21 Sutton RT Pincock D Baumgart DC , et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Dig Med 2020; 3 : 17.
22 McLean KA Knight SR Diehl TM , et al. Readiness for implementation of novel digital health interventions for postoperative monitoring: a systematic review and clinical innovation network analysis. Lancet Digl Health 2023; 5 : e295–e315.
23 Mittermaier M Venkatesh KP Kvedar JC . Digital health technology in clinical trials. NPJ Dig Med 2023; 6 : 88.
24 Siebert JN Bloudeau L Combescure C , et al. Effect of a mobile app on prehospital medication errors during simulated pediatric resuscitation: a randomized clinical trial. JAMA Netw Open 2021; 4 : e2123007-e.
25 Siebert JN Gosetto L Sauvage M , et al. Usability testing and technology acceptance of an mHealth app at the point of care during simulated pediatric in-and out-of-hospital cardiopulmonary resuscitations: study nested within 2 multicenter randomized controlled trials. JMIR Hum Fact 2022; 9 : e35399.
26 Bene BA O’Connor S Mastellos N , et al. Impact of mobile health applications on self-management in patients with type 2 diabetes mellitus: protocol of a systematic review. BMJ Open 2019; 9 : e025714.
27 Gordon WJ Landman A Zhang H , et al. Beyond validation: getting health apps into clinical practice. NPJ Dig Med 2020; 3 : 14.
28 Rowland SP Fitzgerald JE Holme T , et al. What is the clinical value of mHealth for patients? NPJ Dig Med 2020; 3 : 4.
29 Guidelines. ACoCC. 2021.
30 Qudah B Luetsch K . The influence of mobile health applications on patient–healthcare provider relationships: a systematic, narrative review. Patient Educ Couns 2019; 102 : 1080–1089.30745178
31 Taylor Salisbury T Kohrt BA Bakolis I , et al. Adaptation of the World Health Organization electronic mental health gap action programme intervention guide app for mobile devices in Nepal and Nigeria: protocol for a feasibility cluster randomized controlled trial. JMIR Res Protoc 2021; 10 : e24115.
32 The mobile economy sub-Saharan Africa 2023. GSM Association. 2023.
33 Manzano-Monfort G Paluzie G Diaz-Gegundez M , et al. Usability of a mobile application for health professionals in home care services: a user-centered approach. Sci Rep 2023; 13 : 2607.36788261
34 Walle AD Demsash AW Adem JB , et al. Exploring facilitators and barriers of the sustainable acceptance of e-health system solutions in Ethiopia: a systematic review. PLoS One 2023; 18 : e0287991.
35 Borges do Nascimento IJ Abdulazeem H Vasanthan LT , et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. NPJ Dig Med 2023; 6 : 161.
36 Alaiad A Alsharo M Alnsour Y . The determinants of m-Health adoption in developing countries: an empirical investigation. Appl Clin Inform 2019; 10 : 820–840.31667819
37 De P Pradhan MR . Effectiveness of mobile technology and utilization of maternal and neonatal healthcare in low and middle-income countries (LMICs): a systematic review. BMC Womens Health 2023; 23 : 664.38082424
38 Rashid NSA Chen XW Mohamad Marzuki MF , et al. Development and usability assessment of a mobile app (Dementia KITA) to support dementia caregivers in Malaysia: a study protocol. Int J Environ Res Public Health 2022; 19 : 11880.36231181
39 Yang M Al Mamun A Gao J , et al. Predicting m-Health acceptance from the perspective of unified theory of acceptance and use of technology. Sci Rep 2024; 14 : 339.38172184
40 Zhou L Bao J Watzlaf V , et al. Barriers to and facilitators of the use of mobile health apps from a security perspective: mixed-methods study. JMIR mHealth uHealth 2019; 7 : e11223.
41 Amiri P Nadri H Bahaadinbeigy K . Facilitators and barriers of mHealth interventions during the COVID-19 pandemic: systematic review. BMC Health Serv Res 2023; 23 : 1176.37898755
42 Wang C Qi H . Influencing factors of acceptance and use behavior of mobile health application users: systematic review. Healthcare (Basel) 2021; 9 : 357. 33809828
43 Liu P Astudillo K Velez D , et al. Use of mobile health applications in low-income populations: a prospective study of facilitators and barriers. Circul Cardiovasc Qual Outc 2020; 13 : e007031-e.
44 Teferi GH Tilahun BC Guadie HA , et al. Smartphone medical app use and associated factors among physicians at referral hospitals in Amhara region, north Ethiopia, in 2019: cross-sectional study. JMIR mHealth uHealth 2021; 9 : e19310.
45 Gopidasan B Amanullah S Adebowale A . Electronic medical records—a review of cost-effectiveness, efficiency, quality of care, and usability. J Psych Spectr 2022; 1 : 76–79.
46 Odendaal WA Watkins JA Leon N , et al. Health workers’ perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis. Cochrane Database Syst Rev 2020; 3 : CD011942.
47 Zakerabasali S Ayyoubzadeh SM Baniasadi T , et al. Mobile health technology and healthcare providers: systemic barriers to adoption. Healthc Inform Res 2021; 27 : 267.34788907
48 Mayer MA Rodríguez Blanco O Torrejon A . Use of health apps by nurses for professional purposes: web-based survey study. JMIR mHealth uHealth 2019; 7 : e15195.
49 Dahlhausen F Zinner M Bieske L , et al. Physicians’ attitudes toward prescribable mHealth apps and implications for adoption in Germany: mixed methods study. JMIR mHealth uHealth 2021; 9 : e33012.
50 Motiwala F Ezezika O . Barriers to scaling health technologies in sub-Saharan Africa: lessons from Ethiopia, Nigeria, and Rwanda. Afr J Sci Technol Innov Dev 2022; 14 : 1788–1797.
51 Manyazewal T Woldeamanuel Y Blumberg HM , et al. The potential use of digital health technologies in the African context: a systematic review of evidence from Ethiopia. NPJ Dig Med 2021; 4 : 125.
52 Oinas-Kukkonen H Raisanen T Leiviska K , et al. Physicians’ user experiences of mobile pharmacopoeias and evidence-based medical guidelines. Int J Healthc Inf Syst Inform 2009; 4 : 57–68.
53 Central Gondar zonal health office report. 2022.
54 Oumer A Muhye A Dagne I , et al. Utilization, determinants, and prospects of electronic medical records in Ethiopia. BioMed Res Int 2021; 2021 : 1–11.35465048
55 Nalwanga R Nuwamanya E Nuwasiima A , et al. Utilization of a mobile phone application to increase access to sexual and reproductive health information, goods, and services among university students in Uganda. Reprod Health 2021; 18 : 95.34001178
56 Shuren J Patel B Gottlieb S . FDA regulation of mobile medical apps. JAMA 2018; 320 : 337–338.29971339
57 Alloghani M Hussain A Al-Jumeily D Abuelma'atti O , (eds). Technology acceptance model for the use of m-health services among health related users in UAE. 2015 International Conference on Developments of E-Systems Engineering (DeSE): IEEE, 2015.
58 Yee TS Seong LC Chin WS . Patient’s intention to use mobile health app. J Manage Res 2019; 11 : 18–35.
59 Adebara OV Adebara IO Olaide R , et al. Knowledge, attitude and willingness to use mHealth technology among doctors at a semi urban tertiary hospital in Nigeria. J Adv Med Med Res 2017; 22 : 1–10.
60 Davis FD . Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 1989; 13 : 319–339.
61 Schnall R Cho H Liu J . Health information technology usability evaluation scale (health-ITUES) for usability assessment of mobile health technology: validation study. JMIR mHealth uHealth 2018; 6 : e8851.
62 Stoyanov SR Hides L Kavanagh DJ , et al. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR mHealth uHealth 2015; 3 : e3422.
63 Biruk K Abetu E . Knowledge and attitude of health professionals toward telemedicine in resource-limited settings: a cross-sectional study in north west Ethiopia. J Healthc Eng 2018; 2018 .
64 Yehualashet G Asemahagn M Tilahun B . The attitude towards and use of electronic medical record system by health professionals at a referral hospital in northern Ethiopia: cross-sectional study. J Health Inform Afr 2015; 3 .
65 Kalayou MH Endehabtu BF . Physicians’ attitude towards electronic medical record systems: an input for future implementers. Biomed Res Int 2021; 2021 : 5523787.34493979
66 Abolfotouh MA BaniMustafa A Salam M , et al. Use of smartphone and perception towards the usefulness and practicality of its medical applications among healthcare workers in Saudi Arabia. BMC Health Serv Res 2019; 19 : 1–8.30606168
67 Nemati-Anaraki L Mousavi SS AliBeyk M . Medical students knowledge and use of smartphone-based applications. J Health Adm 2022; 24 : 84–94.
68 Dahri K Gong Y Loewen P . A quantitative and qualitative assessment of the utilization of mobile computing devices by clinical pharmacists. Health Policy Technol 2016; 5 : 285–290.
69 Deniz-Garcia A Fabelo H Rodriguez-Almeida AJ , et al. Quality, usability, and effectiveness of mHealth apps and the role of artificial intelligence: current scenario and challenges. J Med Internet Res 2023; 25 : e44030.
70 Marufu C Mabo KA . Utilisation of mobile health by medical doctors in a Zimbabwean health care facility. Health SA Gesondheid 2017; 22 : 228–234.
71 HU Raj . Nurses’ Knowledge and Attitude toward Using Mobile Health Application: A Study in Finland and Lithuania. 2019.
72 Byambasuren O Beller E Glasziou P . Current knowledge and adoption of mobile health apps among Australian general practitioners: survey study. JMIR mHealth uHealth 2019; 7 : e13199.
73 Garavand A Samadbeik M Kafashi M , et al. Acceptance of health information technologies, acceptance of mobile health: a review article. J Biomed Phys Eng 2017; 7 : 403.29445717
74 Aljarboa S Miah SJ (eds). Investigating acceptance factors of clinical decision support systems in a developing country context. 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE): IEEE, 2019.
75 Wang C Qi H (eds). Influencing factors of acceptance and use behavior of mobile health application users: systematic review. Healthcare 2021; 9 : 357.33809828
76 Al-Shorbaji N Al-Shorbaji N . Improving healthcare access through digital health: the use of information and communication technologies. Healthcare Access 2021; 10 .
77 Sheikhtaheri A Kermani F (eds). Use of mobile apps among medical and nursing students in Iran. eHealth 2018; 248 : 33–39.
78 Alhazri WA Bugis BA . Electronic healthcare applications and programs among healthcare workers in Riyadh and conflict management. J Taibah Univ Med Sci 2022; 17 :1658–3612b.
79 Agyei DD Adzobu P . Factors influencing professional nurses’ acceptance and use of mobile medical apps in Ghana. J Inf Technol Manage 2020; 12 : 27–42.
80 Wu P Zhang R Luan J , et al. Factors affecting physicians using mobile health applications: an empirical study. BMC Health Serv Res 2022; 22 : 1–14.34974828
81 Schnall R Higgins T Brown W , et al. Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Stud Health Technol Inform 2015; 216 : 467.26262094
82 Hossain A Quaresma R Rahman H . Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: an empirical study. Int J Inf Manage 2019; 44 : 76–87.
83 Pan M Gao W . Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Serv Res 2021; 21 : 1–11.33388053
