Telemedicine readiness and its associated factors among healthcare professionals in primary hospitals of Southern Ethiopia

Abstract

Telemedicine and other digital health technologies are being rapidly adopted, which could enhance healthcare delivery, especially in environments with limited resources. Health professionals’ telemedicine readiness is crucial for the successful deployment of telemedicine. Yet, the level of telemedicine readiness and its associated factors among healthcare professionals working in primary hospitals in this setting remain underexplored. This study aimed to assess readiness for telemedicine and to identify factors associated with telemedicine readiness among healthcare professionals working in primary hospitals of the Wolaita Zone, Southern Ethiopia. A facility-based descriptive cross-sectional design was employed from November 2025 to December 2025. A systematic random sampling technique was used with 413 health professionals. The data were coded and entered into Epidata 4.2.0.0, and analysis was done using SPSS version 25. Bivariable and multivariable logistic analyses were used to assess associations, and the odds ratio with a 95% confidence interval was used to declare a significant association. inferences. Telemedicine readiness was found to be 69.5% (95% CI 65.0–74.0%). Digital training (AOR = 2.56, CI (1.54–4.26)), organizational support (AOR = 2.14, CI (1.29–3.56)), and higher digital health literacy (AOR = 2.21, CI (1.29–3.78)), having computer access (AOR: 4.39 (2.45–7.24)), and smartphone access (AOR: 5.21 (2.78–9.63)) were associated with telemedicine readiness. Telemedicine readiness was moderate, primarily affected by organizational and material issues. To enhance telemedicine preparedness, policymakers and healthcare organizations should give top priority to training on digital health tools and supportive supervision.

Introduction

In the current dynamic and digital age, where the health sector is facing significant challenges, digital technologies have transformed global healthcare delivery¹. In resource-constrained settings, digital health transformation is indeed a critical strategic priority for achieving Universal Health Coverage (UHC)^2,3. The evolution of digital health solutions included SMS platforms and comprehensive mobile apps^4–6. One of the common and accessible digital technologies is telemedicine⁷. Telemedicine improves healthcare outcomes by enhancing access to medical care and information, and thereby reduces resource wastage like healthcare costs⁸.

Telemedicine refers to the delivery of healthcare services through information and communication technologies that enable remote consultation, diagnosis, monitoring, and treatment. It encompasses a wide range of services, including video consultations, mobile health platforms, electronic medical records integration, and remote patient monitoring⁹. Telemedicine improves access to care, reduces geographic barriers, and enhances continuity of services, particularly in underserved areas. Telenursing, telepharmacy, telerehabilitation, teleradiology, telepsychiatry, and other specialties are all included in the broad category of telemedicine services¹⁰.

Globally, the World Health Organization (WHO) recognizes telemedicine as a key pillar of digital health transformation and has emphasized its importance for strengthening health systems, especially following the COVID-19 pandemic¹¹. According to the WHO Global Strategy on Digital Health 2020–2025, telemedicine plays a critical role in improving continuity of care, addressing workforce shortages, and expanding access in underserved regions¹². Worldwide, it has been reported that about 70% of countries use telemedicine, with higher adoption in Europe and Western countries¹³.

However, compared to low-income countries, richer nations are better at implementing telemedicine; in the United States, 76% of health institutions fully utilise the technology¹⁴. Only 10% of healthcare facilities in low-income nations offer telemedicine services, compared to 75% of Norway’s healthcare facilities¹⁵. Digital health and telemedicine are innovations in Africa, specifically in sub-Saharan Africa. 41 countries have a national digital health strategy, which indicates progress in formal frameworks and infrastructure development¹⁶. Low-income nations continue to lag in the adoption of telemedicine despite these developments¹⁰.

According to a study conducted in Lebanon on health professionals’ readiness for electronic health, most study participants were ready to use it¹⁷. According to a Ugandan survey, 64.7% of medical professionals were prepared to use telemedicine¹⁸. According to a different survey done in Mauritius, 64.5% of medical professionals were prepared to use the telemedicine system¹⁹. A study conducted in Ethiopia found that about 70% of health professionals are ready to implement telemedicine²⁰.

Despite the progress in adopting the strategies of telemedicine, Africa, including Ethiopia, faces challenges due to inadequate infrastructure and a lack of political support, despite potential benefits in resource-limited settings²¹. The other important determinant of TR is digital health literacy level (DHL)²². The other personal factors that are associated with TR include prior digital training and a positive attitude toward telemedicine²³. Organizational factors, including leadership support, availability of technical resources, and clear digital health policies, also play a crucial role in shaping TR²⁴. Furthermore, infrastructural elements such as internet connectivity, access to digital devices, and a stable power supply are indispensable for implementing telemedicine, particularly in resource-limited settings^15,25.

The Ethiopian Ministry of Health recommends digital health strategies aimed at strengthening electronic medical records, health information systems, and digital service delivery²⁶. The national Health Information System framework emphasizes improving healthcare efficiency, expanding access in rural areas, and supporting the performance of the health workforce²⁷. Despite these efforts, implementation remains uneven, especially in primary hospitals where infrastructure limitations and workforce capacity gaps persist^28,29. Ethiopia developed a National eHealth Strategy that includes telemedicine as part of its objectives to improve access, quality, and efficiency of health services through digital applications such as telemedicine, mHealth, electronic records, and interoperable systems. The strategy outlines implementation support for telemedicine within the broader eHealth agenda. Additionally, a Digital Health Blueprint (2021–2030) guides the implementation of digital health technologies including telemedicine as part of a long-term transformation plan toward a connected and efficient health ecosystem³⁰.

Adoption and use of telemedicine systems at healthcare facilities depend heavily on readiness assessment, which is essential to the effective implementation of a telemedicine system³¹. Telemedicine Readiness (TR) includes domains such as core readiness, engagement readiness, and structural readiness. Core readiness represents awareness of existing challenges in healthcare delivery and a recognized need for alternative solutions such as telemedicine. Engagement readiness indicates a professional’s motivation and willingness to participate in telemedicine training and implementation activities. Structural readiness reflects the availability of infrastructure, technical resources, and skills necessary to support telemedicine practice²⁰.

Despite the increasing use of digital health tools in many parts of the world, there is still limited evidence on how prepared healthcare professionals are to use telemedicine in Ethiopia, especially within primary health care settings. Most existing studies are often conducted in larger or specialized facilities, which means their findings may not reflect the realities of primary hospitals in Southern Ethiopia. Important factors such as individual capabilities, organizational support, and the availability of essential infrastructure have not been thoroughly examined in this context.

Although several studies in Ethiopia have examined telemedicine readiness, most were conducted in either public tertiary/specialized hospitals or private hospitals^31–33. Evidence from primary hospitals remains limited. Primary hospitals represent the foundation of rural healthcare delivery and operate under distinct infrastructural and workforce constraints. Understanding readiness in these settings provides critical insights for equitable digital health expansion. For this reason, the present study aimed to assess the level of telemedicine readiness and to identify the factors associated with it among healthcare professionals working in the primary hospitals of the Wolaita Zone in Southern Ethiopia.

Methods

Study design and period

An institution-based, descriptive cross-sectional design was conducted in primary hospitals of Wolaita Zone, Southern Ethiopia, from November 2025 to December 2025.

Study area and population

The study was conducted in public primary hospitals in the Wolaita Zone of Southern Ethiopia. Wolaita Zone is situated 380 km from Addis Ababa, the capital city of Ethiopia. During the study period, there were 8 public primary hospitals in the Wolaita zone of Southern Ethiopia; these are Bodity Primary Hospital (BodPH), Bitena Primary Hospital (BitPH), Bele Primary Hospital (BelPH, Bombe Primary Hospital (BomPH), Gesuba Primary Hospital (GPH), Humbo Primary Hospital (HumPH), Halale Primary Hospital (HalPH), and Badesa Primary Hospital (BadPH). Of the 8 primary hospitals in the Wolaita zone, 4 were randomly selected: Bodity, Gasuba, Bitena, and Bombe.

This study focused on primary hospitals because they serve as critical entry points for digital health implementation in resource-limited settings and represent the frontline of hospital-based care. Assessing telemedicine readiness at this level provides practical insights into the health system’s early adoption capacity.

The source population consisted of permanently employed healthcare professionals working in public primary hospitals of the Wolaita Zone, Southern Ethiopia. The study population included healthcare professionals from the selected hospitals who met the eligibility criteria and were recruited during the data collection period. The healthcare professionals include physicians, nurses, midwives, laboratory professionals, and public health officers.

Eligibility criteria

Healthcare professionals who have been working in the primary hospitals of the Wolaita Zone in Southern Ethiopia for at least 6 months participated. Eligible healthcare professionals who were seriously ill during the data collection period were excluded from the study.

Sample size calculation

The required sample size was determined using the single population proportion formula for cross-sectional studies. A 50% proportion was used to ensure the maximum possible sample size because no prior study provided directly transferable estimates specific to the same study context and population. Although previous telemedicine readiness studies exist in Ethiopia, differences in setting, hospital level, and study population limited their applicability for precise estimation. Therefore, we used p = 0.5 to obtain the maximum sample size: thus,

where; n = required sample size, Z = standard normal value for 95% confidence = 1.96, p = estimated proportion = 0.5, d = margin of error = 0.05.

Putting the above numbers in the sample size formula yields the final sample size as follows:

To account for potential non-response or incomplete questionnaires, an additional 10% was added to the sample size. The final sample size recruited was 423 participants.

Sampling technique

A systematic random sampling technique was selected because the sampling frame consisted of an ordered list of eligible healthcare professionals obtained from hospital records, making systematic selection operationally efficient while maintaining randomness. A random starting point was applied before selecting every k-th participant to preserve representativeness and minimize selection bias.

At the time of the study, the total number of healthcare professionals working in the primary hospitals of the Wolaita Zone in Southern Ethiopia was 2,200. The hospitals include BodPH = 275, BitPH = 321, BelPH = 286, BomPH = 332, GPH = 298, HumPH = 241, HalPH = 229, and BadPH = 218. A total sample size of 423 participants was proportionally allocated based on the number of eligible HCPs in the randomly selected 4 facilities using the following formula: -

where  = is allocated sample size for hospital i, Ni = staff in hospital i, ∑Ni = total staff in the selected hospitals, n = 423.

Accordingly, the proportional allocation to every 4 hospitals is:—BodPH (275) → 95, BitPH (321) → 111, BomPH (332) → 115, and GPH (298) → 103.

Thus, allocation was based on the number of eligible healthcare providers in each facility, ensuring that hospitals with larger staff sizes contributed proportionally more participants to the overall sample. Within each hospital, a systematic random sampling method was applied. Lists of eligible HCPs were obtained from human resources, and participants were recruited randomly until the needed sample size for each hospital was reached. To find the sampling interval (k) for systematic sampling, we used the following formula:

A proportional allocation was performed to distribute the total sample (n = 423) across the four selected primary hospitals (BodPH, BitPH, BomPH, GPH). Within each hospital, a systematic random sampling technique was used. For each hospital i, we listed all available staff in a fixed order (staff ID), computed the sampling interval ki = Ni/ni (rounded to 3), selected a random start r between 1 and ki, and recruited every k^i-th person (r, r + ki, r + 2ki, …) until the hospital quota ni was reached. If a selected participant was temporarily unavailable, up to three revisit attempts were made. If the participant remained unavailable after repeated visits, they were replaced by the next eligible healthcare professional in the sampling interval to maintain the required sample size. Non-response was defined only as refusal to participate or incomplete questionnaire responses.

Data collection tools

Data were collected using validated tools designed to assess digital health literacy and TR through interviewer-administered questionnaires. The questionnaire was created in English because all hospital professionals learn in English in Ethiopia, and the treatment, diagnosis, and all other service-related activities were documented and analyzed in English. The questionnaire comprised questions to gather information on sociodemographic, access to digital devices like smartphones, tablets, and computers, as well as the reliability of internet connectivity at their workplace. The questionnaire also explored prior experience with digital health training and participants’ attitudes toward technology, using a five-point Likert scale from “strongly disagree” to “strongly agree”.

We used a digital health literacy tool, which was developed to assess individuals’ perceived skills at finding, evaluating, and applying electronic health information to health problems. The tool consists of eight items, each rated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The total score ranges from 8 to 40, with higher scores indicating a higher level of eHealth literacy and lower scores representing poorer eHealth literacy³⁴. In the original validation study, the scale showed a Cronbach’s alpha of 0.88, indicating excellent internal consistency. In the current study, the tool was retested and demonstrated a Cronbach’s alpha of 0.847, confirming its reliability for use in the local context.

The tool to collect TR among HCPs is adapted from previous studies^35,36. After adapting the original items to fit the operational realities of primary hospitals in Wolaita, we asked health information system professionals and senior clinicians to review the revised items, focusing on clarity, relevance, and appropriateness for the local context. Their comments helped refine a few statements to improve comprehension and cultural fit. We also examined the internal consistency of the TR domains, which showed acceptable Cronbach’s alpha values of 0.81, indicating that the items measured readiness reliably. Feedback from the pretest further guided minor adjustments in wording and flow, strengthening both the accuracy and practicality of the tool without altering its original structure. Overall, TR was computed from three domains: core, engagement, and structural readiness. Each domain was measured using multiple Likert-scale items. Mean scores were computed for each domain, and respondents scoring at or above the mean were categorized as having high readiness in that domain.

Organizational support was assessed using Likert-scale items measuring perceived leadership support, availability of technical assistance, and institutional encouragement for telemedicine use. Each item was rated on a five-point scale (1 = strongly disagree to 5 = strongly agree). A composite organizational support score was calculated by summing item responses.

Data collection procedures

Four trained data collectors and two supervisors were recruited for data collection. Before starting, data collectors explained the study’s purpose, ensured the confidentiality of responses, and obtained written informed consent. A face-to-face interview was conducted in private rooms within the hospital compounds to maintain confidentiality and minimize distractions.

Study variables

The outcome variable for this study was telemedicine readiness (TR), operationalized as a binary variable (high vs low readiness). The classification threshold was determined based on the distributional characteristics of the readiness scores using an appropriate summary measure. The independent variables include: Sociodemographic characteristics, including age, sex, educational background, profession, and years of clinical experience. Access to technology-related variables includes ownership or regular access to smartphones, the quality and consistency of internet services at participants’ workplaces, and whether they had supportive digital infrastructure like electronic medical record systems. Behavioral factors like knowledge and attitude related to telemedicine. The other independent variables included prior participation in digital health or telemedicine training programs and experience with digital platforms.

Operational Definitions

Digital health literacy: DHL was categorized as DHL if respondents scored at or above the mean and low DHL if respondents scored below the mean³⁷.

Core readiness: represents awareness of existing challenges in healthcare delivery and a recognized need for alternative solutions such as telemedicine³⁸.

Engagement readiness: indicates a professional’s motivation and willingness to participate in telemedicine training and implementation activities³⁸.

Structural readiness: reflects the availability of infrastructure, technical resources, and skills necessary to support telemedicine practice³⁸.

Overall TR: Overall readiness was determined by computing the mean total score across all readiness items (Core Readiness-3 items, Engagement Readiness—4 items, and Structural Readiness—4 items). Each item was scored on a five-point Likert scale (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree). The internal consistency (reliability) of the overall scale in this study was confirmed with a Cronbach’s alpha of 0.81. The overall readiness score was calculated by summing the scores of all 11 items (potential range: 11 to 55) and calculating the mean. Participants were categorized into two levels: Ready (High Readiness): Respondents with a total score equal to or greater than the sample mean. Not Ready (Low Readiness): Respondents with a total score below the sample mean. This categorization method was adopted directly from the validated approach used in the previously published peer-reviewed study^31,38, which applied the same telemedicine readiness tool and used a mean-based threshold to classify overall readiness.

Access to digital devices: means having and using technologies like smartphones, computers, or tablets that allow interaction with digital health systems or online health information. We classified access based on participant feedback about owning or regularly using these devices.

Attitudes toward telemedicine: reflect how healthcare workers view the usefulness, ease of use, and value of telemedicine in healthcare. We measured these attitudes using a five-point Likert scale; individuals who scored below 50% were categorized as having unfavorable attitudes, while those with scores above 50% were regarded as having favorable attitudes^38,39.

Digital health training refers to any formal or informal education related to digital health systems, electronic medical records, telemedicine tools, or similar technologies. This variable tracked whether respondents had received such training, acknowledging its importance in developing digital skills.

Knowledge about Telemedicine: refers to the level of understanding of telemedicine as assessed by the knowledge section of the questionnaire, which comprised 13 items. Participants who correctly answered 50% or more of the items were classified as having adequate knowledge, whereas those scoring below 50% were considered to have inadequate knowledge³⁸.

Organizational support: respondents scoring at or above the mean composite score were classified as having adequate organizational support.

Quality assurance

We took several steps during the research process to ensure the data we collected was accurate, reliable, and credible. Before collecting data, all data collectors participated in a training session focused on the study objectives, ethical standards, informed consent procedures, and practical guidelines for administering the questionnaire respectfully and non-intrusively. The training was given to data collectors and supervisors for two consecutive days before the actual data collection task. All completed data collection forms were examined for completeness and consistency during data management, storage, cleaning, and analysis.

We conducted a pretest with 5% of the total sample (n = 21) of HCPs who worked in a similar context, Shinshicho Primary Hospital. Depending on the result of the pretest, corrections and modifications were made to the questionnaire before applied to the study population. Internal consistency reliability was evaluated using Cronbach’s alpha based on the pre-test data, and the coefficient (Cronbach’s alpha = 0.81), indicating acceptable internal consistency before the main data collection.

Data analysis

The data collected were entered into EpiData Manager version 3.1, verified, coded, and then exported to SPSS version 25 for analysis. Descriptive statistics summarized the characteristics of the study population. This included means, medians, and standard deviations for continuous variables, along with frequencies and percentages for categorical variables. The Hosmer–Lemeshow goodness-of-fit test was applied to assess model fitness, and the test (p = 0.3) indicated a good model fit for logistic regression.

To identify factors associated with telemedicine readiness, a two-staged modeling strategy was employed. First, bivariable logistic regression was performed; variables with a p-value ≤ 0.25 were considered candidates for the multivariable model. Subsequently, a multivariable logistic regression model was constructed using the forced-entry (Enter) method. This method was chosen to allow for the simultaneous inclusion of candidate variables and core theoretical predictors (such as age, sex, and prior digital training), ensuring that the adjusted odds ratios (AOR) accounted for potential confounding regardless of the individual variables’ statistical significance.

Multicollinearity was evaluated by examining variance inflation factors (VIF). The significance was checked using a p-value of 0.05 and a 95% confidence interval. The strength of association was interpreted by using an adjusted odds ratio.

Results

Out of 423 recruited HCPs, 413 responded survey and which gives a response rate of 97.4%. The mean age of participants was 35.6 years (SD = 10.8), and the average years of clinical experience was 7.4 (SD = 6.5). From the all participants, 69.5% of respondents were classified as ready for telemedicine (Table 1).

Table 1.

Sociodemographic and Professional Characteristics of HCPs in Primary Hospitals of Wolaita Zone, Southern Ethiopia, 2025 (n = 413).

Variable	Category	Frequency (n)	Percent (%)
Sex	Female	220	53.3
	Male	193	46.7
Age group (years)	< 30	83	20.1
	30–39	112	27.1
	40–49	124	30.0
	≥ 50	94	22.8
Profession	Nurse	110	26.6
	Physician	95	23.0
	Midwife	80	19.4
	Public health officer	75	18.2
	Laboratory technician	53	12.8
Education level	Diploma	123	29.8
	BSc	155	37.5
	MSc & other	135	32.7
Years of experience	0–5	85	20.6
	6–10	115	27.8
	11–15	108	26.1
	> 15	105	25.4
Smartphone access	Yes	380	94.4
	No	33	5.6
Computer access	Yes	268	64.9
	No	145	35.1
Internet quality	Poor	73	17.7
	Moderate	195	47.2
	Good	145	35.1
Digital training received	Yes	334	80.8
	No	79	19.2
Digital health literacy	Low	175	42.4
	High	238	57.6
Organizational support	Yes	255	61.7
	No	158	38.3
Prior telemedicine experience	Yes	108	26.2
	No	305	73.8

Level of readiness for telemedicine to implement telemedicine services

Health professionals’ readiness to implement telemedicine systems was assessed across three domains: core, engagement, and structural readiness. The findings revealed that 303 (73.4%) of participants demonstrated high core readiness, 298 (72.2%) showed high engagement readiness, and 293 (70.9%) had high structural readiness. Overall, 287 (69.5%) of respondents were classified as ready for telemedicine implementation (95% CI: 65.0–74.0%) (Table 2).

Table 2.

Participants’ Readiness for Telemedicine Implementation among HCPs in Primary Hospitals, Wolaita Zone, Southern Ethiopia, 2025 (n = 413).

Readiness items	Completely disagree n (%)	Disagree n (%)	Neutral n (%)	Agree n (%)	Completely agree n (%)
Core readiness
I recognize limitations in our current patient management approach that telemedicine could address	18 (4.4)	39 (9.4)	60 (14.5)	204 (49.4)	92 (22.3)
I believe there is a clear need to integrate telemedicine into daily healthcare services	14 (3.4)	35 (8.5)	62 (15.0)	214 (51.8)	88 (21.3)
Telemedicine could help overcome distance and access barriers faced by patients	19 (4.6)	33 (8.0)	55 (13.3)	210 (50.8)	96 (23.2)
Engagement readiness
I am willing to learn and use telemedicine tools in my daily practice	14 (3.4)	35 (8.5)	68 (16.5)	204 (49.4)	92 (22.3)
I am motivated to promote telemedicine use among colleagues and patients	17 (4.1)	39 (9.4)	62 (15.0)	204 (49.4)	91 (22.0)
I am ready to participate in telemedicine training and pilot programs	21 (5.1)	41 (9.9)	66 (16.0)	199 (48.2)	85 (20.6)
I feel confident that I can manage telemedicine consultations effectively	24 (5.8)	47 (11.4)	65 (15.7)	195 (47.2)	82 (19.9)
Structural readiness
I have regular access to functional digital devices (computer, tablet, or smartphone)	25 (6.8)	55 (13.3)	68 (16.5)	185 (44.8)	77 (18.6)
My workplace has a stable internet connection suitable for telemedicine	27 (6.5)	59 (14.3)	76 (18.4)	176 (42.6)	75 (18.2)
Technical and administrative support is available for digital or telemedicine systems	29 (7.0)	60 (14.5)	78 (18.9)	168 (40.7)	78 (18.9)
Continuous digital health or telemedicine training is provided in my organization	34 (8.2)	66 (16.0)	76 (18.4)	161 (39.0)	76 (18.4)

Attitude of HCPs toward telemedicine

Most of the respondents agreed that telemedicine can improve access, quality, and communication in healthcare. Overall, 139 (33.7%) healthcare professionals had a favorable attitude toward telemedicine, while 274 (66.3%) had an unfavorable attitude. Most of them have positive perceptions towards telemedicine: improve access to healthcare services (75.2% agreed or completely agreed) and enhance the quality of healthcare delivery (69.8% agreed or completely agreed) (Table 3).

Table 3.

Participants’ attitudes toward telemedicine among health care professionals working in primary hospitals in Wolaita zone 2025 (n = 413).

Attitude questions	Completely disagree n (%)	Disagree n (%)	Neutral n (%)	Agree n (%)	Completely agree n (%)
Telemedicine improves access to healthcare services	14 (3.4)	29 (7.0)	59 (14.3)	209 (50.6)	101 (24.5)
Telemedicine enhances the quality of healthcare delivery	17 (4.1)	34 (8.2)	73 (17.7)	204 (49.4)	84 (20.3)
Telemedicine helps HCPs perform their duties efficiently	19 (4.6)	39 (9.4)	68 (16.5)	204 (49.4)	82 (19.9)
Telemedicine facilitates communication between healthcare providers	21 (5.1)	37 (9.0)	87 (21.1)	185 (44.8)	82 (19.9)
Telemedicine increases timely access to information	24 (5.8)	49 (11.9)	80 (19.4)	187 (45.3)	73 (17.7)
Telemedicine enhances clinical decision-making	27 (6.5)	54 (13.1)	95 (23.0)	177 (42.9)	59 (14.3)
Telemedicine improves communication between departments	10 (2.4)	44 (10.7)	87 (21.1)	185 (44.8)	87 (21.1)
Telemedicine reduces patient waiting time and healthcare costs	17 (4.1)	43 (10.4)	74 (17.9)	204 (49.4)	74 (17.9)
Telemedicine reduces workload for HCPs	31 (7.5)	95 (23.0)	126 (30.5)	122 (29.5)	39 (9.4)
Telemedicine threatens patient confidentiality and privacy	43 (10.4)	117 (28.3)	132 (32.0)	95 (23.0)	25 (6.1)

Knowledge related to telemedicine

Among the 413 participants, 253 (61.3%) participants had inadequate knowledge of telemedicine, while 160 (38.7%) had adequate knowledge. Of all participants, 67.5% had heard of telemedicine, primarily through the Internet (50.2%) and mass media (28.0%). Half were familiar with at least one telemedicine application, while 31.0% knew about E-health types. Most recognized telemedicine’s benefits in healthcare delivery, reducing overcrowding, and cutting transportation costs (Table 4).

Table 4.

Knowledge of telemedicine among healthcare professionals working in primary hospitals of Wolaita zone, South Ethiopia, 2025 (n = 413).

Questions	Yes N (%)	No N (%)
Have you ever heard about telemedicine before?	279 (67.6)	134 (32.4)
If yes to the above question, what was your source of information? (N = 279)	Options	N (%)
	School	11 (3.9)
	Internet	140 (50.2)
	Mass media	50 (17.9)
	Training	78 (28.0)
I know at least one telemedicine application type (video, audio, text, M-health)	140 (33.9)	273 (66.1)
I have awareness of E-health types like EMR, telemonitoring, personal health records, and wearables	280 (67.8)	133 (32.2)
Telemedicine improves the quality of healthcare delivery	350 (84.7)	63 (15.3)
Telemedicine reduces overload and hospital overcrowding	310 (75.1)	103 (24.9)
Telemedicine reduces transportation costs due to unnecessary appointments that could be done remotely	365 (88.4)	48 (11.6)
Telemedicine reduces patients’ time to get care	290 (70.2)	123 (29.8)
Telemedicine reduces clinician time to deliver care	220 (53.3)	193 (46.7)
Did you know about any Telemedicine guidelines (guidelines of Ethiopia or others)?	30 (7.3)	383 (92.7)
Does Telemedicine reduce the gap between the availability of health services and demand for them?	260 (62.9)	153 (37.1)
Have you ever seen a Telemedicine process previously?	75 (22.2)	338 (77.8)
Did you know about Telemedicine infrastructures?	80 (24)	333(76)

Factors associated with healthcare professionals’ readiness to implement telemedicine services

To determine the variables associated with TR, a logistic regression model was fitted. Health professionals with prior digital training were 2.5 times more likely to be telemedicine-ready (AOR = 2.56, CI (1.54–4.26). Those receiving organizational support had 2 times greater readiness for telemedicine (AOR = 2.14, CI (1.29–3.56). Higher digital health literacy independently increased the likelihood of TR (AOR = 2.21, CI (1.29–3.78). Equipment used for telehealth, like smartphones and computer access, was also significantly associated with readiness. Having a smartphone makes 5 × more readiness, and computer access makes 4 × more readiness. In contrast, variables such as sex, age, educational level, and attitude toward telemedicine had no statistically significant association (p > 0.05) (Table 5).

Table 5.

Bivariable and multivarible analysis of factors associated with TR among HCPs working in primary hospitals of Wolaita zone, 2025 (n = 413).

Variables		Ready (Yes)	Not Ready (No)	COR (95% CI)	AOR (95% CI)	p-value	VIF
Sex	Male	118	75	1	1
	Female	169	51	2.09 (1.36–3.20)	0.92 (0.58–1.45)	0.721	1.15
Age (years)	< 30 (Ref)	65	36	1	1
	30–39	78	34	1.27 (0.72–2.24)	1.05 (0.63–1.76)	0.842	1.62
	40–49	81	34	1.32 (0.75–2.33)	0.94 (0.54–1.63)	0.819	1.65
	≥ 50	69	25	1.53 (0.83–2.82)	0.90 (0.47–1.71)	0.733	1.68
Education level	Diploma	86	37	1	1
	BSc	115	40	1.24 (0.73–2.10)	1.15 (0.67–1.99)	0.625	1.21
	MSc & Above	99	36	1.18 (0.69–2.03)	0.88 (0.48–1.62)	0.693	1.22
Years of experience	0–5 (Ref)	61	34	1	1
	6–10	76	35	1.21 (0.68–2.16)	0.96 (0.59–1.56)	0.811	1.54
	11–15	83	33	1.40 (0.78–2.51)	1.02 (0.61–1.70)	0.942	1.57
	> 15	75	30	1.39 (0.77–2.53)	0.88 (0.48–1.63)	0.682	1.58
Smartphone access	No	11	22	1	1
	Yes	297	83	7.16 (3.33–15.36)	5.21 (2.78 – 9.63)	0.001	1.24
Computer access	Yes	211	57	5.13 (2.08–6.20)	4.39 (2.45 – 7.24)	0.001	1.31
	No	46	99	1	1
Organizational Support	No (Ref)	89	70	1	1
	Yes	205	49	3.29 (2.16–5.38)	2.14 (1.29–3.56)	0.003	1.12
Attitude	Unfavorable	206	68	1	1
	Favorable	81	58	1.02(0.65–1.55)	1.17 (0.74–1.53)	0.451	1.33
Knowledge	Inadequate	176	77	1	1
	Adequate	111	49	0.99 (0.64–1.52)	1.32 (0.91 – 1.92)	0.140	1.42
Prior digital training	No (Ref)	95	85	1	1
	Yes	197	36	4.90 (3.09–7.76)	2.56 (1.54–4.26)	0.001	1.18
Internet quality	Poor (Ref)	100	45	1	1
	Moderate	123	72	0.77 (0.49–1.21)	0.83 (0.47–1.44)	0.497	1.26
	Good	41	32	0.58 (0.32–1.03)	0.89 (0.50–1.58)	0.681	1.28
DHL	Low (Ref)	98	77	1	1
	High	194	44	3.11 (2.02–4.80)	2.21 (1.29–3.78)	0.004	1.45

COR Crude odds ratio, AOR Adjusted odds ratio, CI Confidence interval.

Discussion

This study assessed healthcare professionals’ readiness for telemedicine in primary hospitals of Southern Ethiopia. About 69.5 percent of the participants were found to be ready for the adoption of telemedicine. Therefore, the level of readiness identified in this study highlights both the potential and the gaps in integrating telemedicine into primary healthcare settings.

This level of readiness is comparable to findings from Ethiopia’s previous studies, which reported readiness levels around 70%, and is slightly higher than reports from Uganda (64.7%) and Mauritius (64.5%)^18,19,38. However, not all Ethiopian studies report comparable levels of telemedicine readiness. A study assessing awareness and readiness to use telemonitoring for diabetes care among healthcare providers at teaching referral hospitals in Ethiopia found that the majority of participants had low readiness and limited awareness of telemonitoring technologies, indicating persistent gaps in preparedness for specialized telehealth tool³³. In addition, research on healthcare professionals’ acceptance of telemedicine services in public hospitals of the North Shewa Zone reported that only about half of providers were willing to use telemedicine in routine care, suggesting variability in digital health adoption across settings⁴⁰. These findings imply that telemedicine readiness in Ethiopia may vary by clinical domain, exposure to digital technologies, and institutional support structures, necessitating tailored capacity-building and organizational strategies^20,41. However, it remains lower than readiness levels observed in some high-income settings, where digital infrastructure, consistent internet connectivity, and established policies support higher adoption⁴². Overall, these findings suggest that primary healthcare providers in this region are generally open to implementing telemedicine but require stronger system-level support to translate readiness into effective practice⁴³.

The observed readiness level of 69.5% suggests a significant transition toward digital health adoption within Southern Ethiopia’s primary care sector. This level of preparedness, despite infrastructural constraints, may be attributed to the Ethiopian Ministry of Health’s recent emphasis on the developed National eHealth Strategy³⁰.

The other objective achieved in this study was identifying factors associated with telemedicine readiness. Accordingly, the findings demonstrated factors that were associated with telemedicine readiness. These include digital literacy, prior training, organizational support, and access to digital devices. Digital literacy is a significant predictor of TR in healthcare systems. This finding is supported by the former study, which states that digital literacy is a key factor related to telemedicine effectiveness⁴⁴. Another study further reinforced this, noting that higher population DHL consistently correlates with positive health behaviors and enhanced engagement with digital health tools⁴⁵. Thus, digital literacy is not only advantageous but also becoming more and more necessary for contemporary healthcare systems, especially when navigating intricate digital health environments^46,47.

Organizational support also emerged as a significant factor associated with TR. Multiple studies provide strong evidence for this claim. Other studies conducted on readiness for telemedicine found that the organizational environment is crucial in determining telemedicine application success^48,49. Another study conducted in the US also identified key organizational facilitators for telemedicine, including stakeholder buy-in, adequate staffing, and alignment with existing systems⁵⁰. When healthcare institutions provide digital infrastructure, supportive supervision, and recognition for digital innovation, this organizational support was positively associated with the integration of telemedicine. In contrast, environments lacking organizational endorsement may experience resistance to change even when individual competencies are high⁵¹. These findings suggest that readiness for telemedicine isn’t just about having the right technical tools; it is deeply rooted in the hospital’s culture. When leaders prioritize digital health, it gives healthcare workers the ‘green light’ to spend time learning new systems without feeling they are neglecting their clinical duties.

Previous training on digital technologies like telemedicine has a significant association with TR. This finding is consistent with a previous study conducted to assess the readiness and associated factors^31,52,53. This is straightforward in that training is linked to higher skill levels and knowledge, which correlates with increased confidence and readiness. On the other hand, in developed countries, some studies didn’t report previous training as a significant predictor in TR, which may be associated that other organizational or personal factors might be stronger predictors for TR than training⁴⁹.

Access to electronic devices like smartphones and computers was significantly associated with readiness, highlighting how both mobile and traditional computing devices play complementary roles in facilitating digital health engagement. This finding is consistent with previous research, which states that smartphone ownership is associated with better health care access and health literacy, signaling that mobile devices serve as critical gateways to telehealth services⁵⁴. Moreover, another study depicted that the absence of a computer has been associated with poorer telehealth attendance, indicating smartphone alone is not adequate⁵⁵. The importance of ensuring equitable device access is further supported by evidence from scoping reviews, which show that the provision of computers or tablets can significantly improve telemedicine uptake⁵⁶.

This study contributes new evidence showing that even when individual digital literacy levels are reasonably high, and health professionals are willing to use telemedicine, their readiness is still held back by broader structural limitations such as unreliable digital infrastructure and the absence of clear telemedicine policies. These findings reinforce that system-level capacity, including dependable technological systems, supportive regulations, and active managerial engagement, was a stronger predictor of readiness than individual skills. In practical terms, improving telemedicine readiness cannot be achieved through staff training in isolation. Instead, it requires coordinated investments in infrastructure, clear operational guidelines, and consistent organizational support⁵⁷. By addressing these foundational gaps, primary health facilities in resource-limited settings will be better positioned to adopt telemedicine in a meaningful, sustainable, and routine way.

These findings have significant implications for digital health policy in Ethiopia. Policy should shift from generic ICT training to creating facility-specific digital health roadmaps. Furthermore, since access to devices was a major hurdle, national policy should consider incentive schemes such as duty-free imports or subsidized procurement of laptops and tablets for healthcare professionals. This ensures that readiness is not stifled by a lack of basic hardware.

Strengths and limitations

The strength of this study lies in its comprehensive assessment of telemedicine readiness by integrating variables from organizational, infrastructural, and individual levels. However, several limitations must be acknowledged.

First, the cross-sectional nature of the study captures data at a single point in time, which precludes the establishment of causal relationships between the identified predictors and telemedicine readiness. While significant associations were identified, we cannot determine a temporal sequence of whether predictors led to telemedicine readiness.

Second, the use of self-reported measures for digital health literacy and readiness may have introduced social desirability bias, as participants might overstate their competencies to align with institutional expectations.

Third, as the study focused on primary hospitals in Southern Ethiopia, the findings may not be fully generalizable to tertiary facilities or private healthcare settings with different resource levels. Future longitudinal and mixed-methods research is recommended to explore how telemedicine readiness evolves following specific interventions, such as structured training programs or infrastructural upgrades.

Conclusion

This quantitative study assessed the level of healthcare professionals’ readiness for telemedicine in primary hospitals of Southern Ethiopia. Digital literacy, prior digital training, organizational support, and access to essential digital devices were significant predictors of readiness. These findings indicate that both individual capacity and institutional factors play important roles in shaping the adoption of telemedicine. Improving telemedicine implementation in similar settings, therefore, requires strengthening the digital competencies of health professionals while simultaneously improving organizational structures and resources that support digital health initiatives. Given that the study was conducted during the Marburg virus outbreak season, strengthening telemedicine readiness is also crucial for maintaining essential health services and enhancing health system resilience during high-consequence infectious disease emergencies.

Recommendations

Strengthen digital capacity

Hospital administrators should implement continuous, hands-on digital skills training programs to improve healthcare professionals’ digital literacy and confidence in using telemedicine tools. These trainings should be incorporated into routine professional development systems to ensure sustained competency.

Enhance organizational support

Hospital leadership and regional health authorities should promote supportive supervision, actively engage staff in digital initiatives, and establish formal internal structures, including designated telemedicine coordinators, to strengthen organizational readiness and accountability.

Improve digital infrastructure

Policymakers and health system planners should prioritize investments in reliable internet connectivity, stable electricity, and access to essential digital devices such as computers and smartphones, particularly in primary healthcare facilities where infrastructure gaps are greatest.

Develop institutional guidelines

The Ministry of Health of Ethiopia should strengthen the implementation of the existing national Telehealth guideline by supporting the development of institutional-level operational frameworks, standard operating procedures, and capacity-building initiatives tailored to facility-level contexts. Coordinated action across institutional and policy levels is required to improve the technical and organizational foundations necessary for sustainable telemedicine adoption and to advance digital health efforts in Ethiopia.

Abbreviations

AOR

Adjusted odds ratio

BitPH

Bitana primary hospital

BodPH

Bodity primary hospital

BomPH

Bombe primary hospital

GPH

Gasuba primary hospital

HCPs

Healthcare professionals

CI

Confidence interval

DHL

Digital health literacy

TR

Telemedicine readiness

Author contributions

All authors contributed to data analysis, drafting or revising the article, and gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Funding

The research was financially supported by Walailak University, Thailand. The funders had no role in research design, data collection and analysis, decision to publish, and preparation of the manuscript.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The Institutional Review Board (IRB) of Wolaita Sodo University, College of Health Sciences and Medicine, approved this study (Ref. No: 07213/2025; Date: January 2025). Subsequently, formal cooperation letters were submitted to the Chief Executive Officers of each participating hospital. Written informed consent was obtained from all participants prior to data collection. Participants were informed of their right to withdraw at any time, and all data were treated with strict confidentiality to safeguard participant privacy. This research was conducted in full accordance with the ethical principles of the Declaration of Helsinki for medical research involving human subjects.