eHealth competency needs in the Mekong border region: a GLMM analysis of primary care personnel in Nong Khai, Thailand

Abstract

Background

The digital transformation of health systems is crucial for enhancing healthcare delivery, particularly in border areas like Nong Khai province, Thailand, situated along the Mekong River. This strategic region faces unique challenges in cross-border disease surveillance and health data interoperability between Thailand and the Lao People’s Democratic Republic (Lao PDR). Consequently, healthcare personnel in this area require robust eHealth competencies to manage these complexities effectively.

Objective

This study aimed to identify the determinants associated with the need for eHealth competency development among healthcare personnel working in the primary care network of a border province along the Mekong River.

Methods

A cross-sectional analytical study was conducted among 460 healthcare personnel in Nong Khai province. Data were collected using a structured questionnaire assessing socio-economic status , social support, organizational innovation management , and digital intelligence (DQ). Generalized Linear Mixed Models (GLMM) were employed to analyze the factors associated with high needs for eHealth competency development, accounting for the clustering effects of professional differences within the health service network.

Results

The study found that 53.26% of personnel reported a high need for eHealth competency development. The GLMM analysis revealed that personnel working in Sub-district Health Promoting Hospitals (SHPHs) were significantly more likely to require training compared to those in community hospitals (Adjusted Odds Ratio [AOR] = 1.82; 95% CI: 1.08–3.05; p = 0.023). Strong predictors of development needs included an organizational climate conducive to innovation (AOR = 4.03; 95% CI: 2.27–7.15; p < 0.001) and continuous learning systems (AOR = 1.95; 95% CI: 1.09–3.49; p = 0.024). Furthermore, specific components of digital intelligence, including skills in cyberbullying management (AOR = 3.77; 95% CI: 2.14–6.66; p < 0.001), screen time management (AOR = 2.67; 95% CI: 1.42–5.03; p = 0.002), and privacy management (AOR = 2.19; 95% CI: 1.14–4.21; p = 0.019), were significantly associated with higher needs for competency development.

Conclusions

A substantial proportion of primary care personnel in the Mekong border region require eHealth upskilling, particularly those in peripheral sub-district health hospitals. To effectively implement national eHealth policies, interventions should extend beyond technical training to foster an innovative organizational culture and enhance digital resilience, specifically focusing on data privacy and cyber-safety skills.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-026-14104-1.

Introduction

The current transformation of digital technologies in medicine and public health has necessitated urgent adaptation within global health systems, particularly regarding the application of health information technology (eHealth) to enhance service quality and system efficiency. In response, the World Health Organization established the Global Strategy on Digital Health 2020–2025 to drive the development of digital health systems in alignment with the Sustainable Development Goals [1]. In Thailand, national policies and strategies have been formulated to align with these global directives. These include the National Policy and Plan on Digital Economy and Society Development (2018–2037), which emphasizes the development of digital infrastructure and the enhancement of citizens’ quality of life [2]. Furthermore, the 13th National Health Development Plan (2023–2027) and the Five-Year Action Plan of the Office of the Permanent Secretary, Ministry of Public Health, prioritize the advancement of health technology systems, with a specific focus on primary care [3]. Additionally, the Ministry of Public Health has implemented the eHealth Strategy (2017–2026) to develop digital health systems in parallel with the “Smart Hospital” policy, which aims to elevate healthcare management and service delivery through digital technologies [4]. This initiative includes the development of the Smart Health ID system to facilitate the interoperability of health data for citizens nationwide [5]. These efforts are consistent with the Thailand 4.0 policy, which seeks to promote innovation and digital technology within the healthcare system to reduce disparities in service access [6].

Developing the competency of healthcare personnel to drive health information technology (eHealth) constitutes a paramount strategic mechanism for reforming the public health system to bridge gaps in service access and elevate patient safety standards. The essential competencies requiring development encompass six core components aligned with the Ministry of Public Health’s strategy: governance, enterprise architecture, health data standards and interoperability, innovation and intellectual property protection, legal and standard advocacy for ICT, and human capital development in eHealth [4]. The demand for such competency development is intrinsically linked to multidimensional contextual factors, beginning with socio-economic status, which serves as the fundamental determinant of readiness for learning. Specifically, higher education levels and continuous technical training enhance self-efficacy, bridge the gap between theory and practice, and empower personnel to effectively apply complex technologies in their operations [7–9]. Simultaneously, social support from supervisors and colleagues functions as a psychological buffer that mitigates anxiety during the digital transition; receiving emotional, informational, and tangible support stimulates job satisfaction and fosters positive attitudes toward adapting to eHealth systems [10, 11]. This must be coupled with organizational innovation management that prioritizes creating a climate conducive to novelty and continuous learning systems to cultivate a culture of knowledge sharing, which is critical for encouraging personnel to utilize technology for problem-solving [12, 13]. Furthermore, awareness of digital intelligence (DQ) encompassing cybersecurity skills, personal data protection, and digital ethics serves as a crucial driver compelling personnel to recognize the urgent need for upskilling. This ensures the establishment of robust digital resilience, enabling the secure and ethical management of patient health data amidst escalating cyber threats [14, 15]. Nong Khai, a border province located in the northeastern region of Thailand, shares a boundary with Vientiane Capital of the Lao People’s Democratic Republic (Lao PDR). Consequently, it holds significant strategic importance in terms of economy, tourism, and public health. Cross-border communication and health data exchange play a pivotal role in the surveillance of international communicable diseases, the health management of border populations, and the provision of public health services to both Thai citizens and foreign nationals, particularly Laotians seeking medical care on the Thai side. Health information technology (eHealth) is, therefore, critical for integrating border health systems, facilitating communication, and delivering public health services. It serves as a key mechanism for developing health policies tailored to the demographic characteristics of an ethnically and culturally diverse population.

The healthcare infrastructure in Nong Khai comprises two general hospitals, seven community hospitals, and 74 sub-district health promoting hospitals (SHPHs). These facilities are currently transitioning towards a digital health system, having initiated the implementation of Telemedicine and Telehealth services at both the hospital and primary care levels. Furthermore, several hospitals within the province have been certified as Smart Hospitals [16], reflecting the province’s potential and readiness to become a model for eHealth implementation. However, significant opportunities remain to enhance the efficiency of the health information technology system. Specifically, achieving seamless interoperability between software programs such as JHCIS and HOSxP [17], expanding technology coverage to all service units, and upgrading information technology standards to achieve higher quality certification are essential steps. These measures will reinforce trust, security, and the overall efficiency of the health system.

Consequently, developing the competency of healthcare personnel in driving eHealth is of paramount importance and necessity within the context of Nong Khai province. Healthcare personnel are regarded as the critical workforce for transitioning the health system towards a digital health ecosystem. Investigating the factors associated with the need for eHealth competency development among healthcare personnel in the primary care network of Nong Khai province serves as a fundamental starting point for developing a sustainable digital health system appropriate for the border context. The findings of this study will help determine the direction for enhancing personnel capacity to adapt to technological changes. This will facilitate the delivery of modern, convenient, and rapid health services, reduce financial burdens on the public, and effectively increase access to personal health information. Moreover, it will elevate the operational efficiency of healthcare personnel within the service system, reduce procedural redundancy, and promote cybersecurity within the health system at both provincial and national levels.

Research objective

To investigate the factors associated with the need for eHealth competency development among healthcare personnel working in the primary care network of Nong Khai Province.

Methods

Study design

This study employed a cross-sectional analytical research design collecting quantitative data via structured questionnaires from September 22, 2025, to October 11, 2025. The research aimed to investigate the associations between socio-economic status, social support from supervisors and colleagues, organizational innovation management, and digital intelligence (DQ) with the need for eHealth competency development among healthcare personnel in the primary care network of Nong Khai Province.

Population

The population for this study comprised 800 multidisciplinary healthcare personnel working within the primary care network under the jurisdiction of the Nong Khai Provincial Public Health Office [17]. The personnel included registered nurses, public health technical officers, public health officers, dental health officers, and traditional Thai medicine practitioners. All participants were actively practicing in either Community Hospitals or Sub-district Health Promoting Hospitals (SHPHs) within the province. The inclusion criteria for participants were: (1) personnel currently employed in the primary care network (SHPHs and Community Hospitals) under the Nong Khai Provincial Public Health Office; (2) personnel with active duty status (excluding those on maternity leave, study leave, training leave, or ordination leave); (3) personnel free from health conditions that would hinder data provision; and (4) personnel willing to participate in the research. The exclusion criterion was refusal to participate in the study.

Sample size

Given the study’s objective to identify determinants associated with the need for eHealth competency development involving multiple independent variables, the sample size was calculated to meet the requirements for regression analysis [18]. Initially, the minimum required sample size was estimated based on a comparable study by Wubante and Tegegn [9], which reported a minimum sample size of 168 participants. However, to adequately satisfy the methodological requirements of multiple logistic regression analysis [18], ensure sufficient statistical power, control for potential confounding factors, and reduce the risk of model overfitting, the sample size was subsequently adjusted upward. This adjustment accounted for the potential correlation among independent variables by incorporating the Variance Inflation Factor (VIF) into the sample size estimation. For this purpose, an a priori R-squared (R²) or rho (ρ) value of 0.75 was assumed, corresponding to a VIF of 4.0. The a priori assumption of VIF = 4.0 was based on Hsieh et al. [18]. who described its application for sample size estimation in logistic regression models with multiple predictors. This value falls within the generally acceptable range for rho (0.50–0.80), which corresponds to VIF values between 2.0 and 5.0. The VIF is a commonly used indicator of multicollinearity among independent variables and is calculated as the reciprocal of tolerance (1/tolerance). While a VIF value greater than 10 typically indicates severe multicollinearity [19], a value close to 1 suggests minimal correlation among predictors. In the present study, a VIF of 4.0 reflects a moderate and acceptable level of intercorrelation that is unlikely to compromise the stability or interpretability of the regression model. Based on the adjusted sample size estimation table incorporating this VIF value, the final required sample size was determined to be 460 health personnel.

Sampling technique

A multi-stage sampling technique was employed to ensure a representative sample of the population. The process was executed in three sequential steps: first, personnel were stratified by district (Amphoe) to guarantee comprehensive geographic coverage across the province; second, quotas were assigned based on the proportion of each professional category within each district; and finally, simple random sampling without replacement was conducted within each professional category. Random numbers were generated using a drawing lots without replacement to select participants until the required sample size of 460 was achieved. This rigorous selection procedure yielded a final sample that comprehensively represents all professional categories, consisting of 264 registered nurses, 142 public health technical officers// public health officers, 27 public health technical officers (dental)/ dental assistant, and 27 Thai traditional medicine practitioners.

Outcome variable

The primary outcome of this study was the need for competency development in driving eHealth implementation among healthcare personnel in the primary care network of Nong Khai Province. Conceptually, this variable reflects the extent to which personnel perceive a need to strengthen their competencies in order to effectively implement and utilize health information technology (eHealth) within their routine practice. The construct was operationalized in alignment with the Ministry of Public Health’s national eHealth strategy and encompasses six core domains: (1) establishment of organizational structures for eHealth management; (2) development of enterprise architecture to support eHealth service delivery; (3) standardization of health information systems and effective data integration and exchange; (4) promotion of innovation in health information technology and intellectual property protection; (5) implementation of laws and standards to support ICT utilization in the health system; and (6) development of human capital in eHealth and health information technology, including knowledge management for the public.

Empirically, the outcome was measured using a 30-item scale developed by the researchers based on relevant policies, concepts, and empirical literature. Items are distributed across the six domains and ask respondents to indicate the degree to which they feel additional competency development is required in each area. Responses are recorded on a 5-point Likert scale ranging from 1 (very low need) to 5 (very high need). For each participant, a mean score was calculated for the overall scale, with higher scores indicating a greater perceived need for eHealth competency development. Consistent with established cut-off criteria, mean scores were classified into three levels: 1.00–2.33 (low), 2.34–3.67 (moderate), and 3.68–5.00 (high). For the purpose of regression analysis, the variable was dichotomized into two categories: low-to-moderate need (reference group: mean score ≤ 3.67) and high need (comparison group: mean score ≥ 3.68). The internal consistency of this 30-item scale was excellent, with a Cronbach’s alpha coefficient of 0.971, supporting its reliability for use in this study.

Research instruments

The research instrument employed for data collection was a structured questionnaire developed by the researcher based on a comprehensive review of relevant literature, concepts, and theories. The instrument comprises five distinct sections: Part 1 assesses socio-economic status using 10 items adapted from Sirivan Serirat et al. [7] ; Part 2 measures social support based on Jacobson’s theory [10] with 15 items; Part 3 evaluates organizational innovation management using 20 items adapted from Tidd & Bessant [12]; Part 4 assesses digital intelligence (DQ) through 40 items adapted from the Thai Health Promotion Foundation [14]; Part 5 determines eHealth competency development needs using 30 items aligned with the Ministry of Public Health’s strategy [4]. As shown in Fig. 1. The full English-language version of the questionnaire is provided as Additional file 1 (Supplementary questionnaire).

Fig. 1.

Conceptual framework of study

Scoring and Interpretation Parts 2 through 5 utilized a 5-point Likert rating scale [20]. Mean scores were interpreted as follows: 1.00–2.33 (Low), 2.34–3.67 (Moderate), and 3.68–5.00 (High) [21]. For logistic regression analysis, scores were dichotomized into Reference Group (Low/Moderate) and Comparison Group (High). Parts 2 through 5 utilize a 5-point Likert rating scale for assessment. For the purpose of logistic regression analysis, mean scores were dichotomized, with low and moderate levels serving as the reference group and high levels as the comparison group.

Validity and Reliability The quality of the research instrument was rigorously validated through assessments of both content validity and reliability. Content validity was verified by three experts who evaluated the accuracy of the content and the clarity of the language. The Index of Item-Objective Congruence (IOC) was calculated, with all items yielding an IOC value greater than 0.50, indicating acceptable content validity. Subsequently, reliability was assessed through a pilot test (try-out) with 30 participants working in a neighboring area with similar characteristics to the study population. Internal consistency was measured using Cronbach’s Alpha Coefficient. The results demonstrated high reliability across all domains: the coefficients for social support, organizational innovation management, and digital intelligence were all 0.958, while the coefficient for eHealth competency development needs was 0.971. The overall questionnaire achieved a reliability coefficient of 0.987, confirming the instrument’s suitability for data collection.

Statistical analysis

Data analysis for this study was conducted using STATA version 15 (licensed to Khon Kaen University) [22]. Statistical methods were selected based on the study objectives and the characteristics of the variables.

Inferential Statistics: Simple Logistic Regression was utilized to analyze the relationship between each independent variable (univariate analysis) and the dependent variable, the need for eHealth competency development. Each variable was analyzed individually to present Crude Odds Ratios (Crude OR) and 95% Confidence Intervals (95% CI). Additionally, this method served as an initial screening tool to identify variables with potential associations for inclusion in the subsequent multivariate analysis.

Simple Logistic Regression and Generalized Linear Mixed Model (GLMM) To analyze predictive factors associated with eHealth competency needs, the researcher utilized the Generalized Linear Mixed Model (GLMM) [23], which extends multiple logistic regression to enhance estimation precision and to reflect the hierarchical data structure based on professional categories of personnel. The dependent variable was defined as a binary outcome (high vs. low-to-moderate eHealth competency needs) using a logit link function. To account for variance between professional groups that might influence competency needs, the professional category of healthcare personnel was specified as a random effect.

Independent variables with p-values < 0.25 in univariate logistic regression were entered into the multivariable generalized linear mixed model. A backward elimination procedure was then applied to sequentially remove non-significant variables while retaining the random effect of professional category. This approach was adopted to identify the most parsimonious model while controlling for potential confounding and interprofessional clustering. The model’s suitability was assessed using a goodness-of-fit test [24], in which a p-value greater than 0.05 indicated an appropriate model fit. The final results are reported as adjusted odds ratios (AORs) and 95% confidence intervals (95% CIs) at a significance level of 0.05, with estimates reflecting both fixed effects and random effects within the GLMM framework to identify truly associated predictive factors.

Results

A total of 460 healthcare personnel working in primary health care networks in Nong Khai Province participated in the study. The majority of respondents were female (81.09%). The most prevalent age group was 31–40 years (39.13%), with a mean age of 37.09 years (SD = 9.27). Nearly half of the participants were married (47.83%). The majority held a bachelor’s degree or equivalent (87.39%) and reported a monthly income between 20,000 and 29,999 Baht (28.26%), with an average monthly income of 34,379.83 Baht (SD = 13,969.04). More than half of the participants were professional nurses (57.39%), and half held positions at the skilled or professional rank (50.21%). Regarding work experience, 42.83% had worked for fewer than 11 years, with an average of 13.78 years (SD = 9.43). Over half had never attended any eHealth-related training (57.83%), and the average number of training sessions was 1.94 times (SD = 1.35 ). Most respondents worked in Sub-district Health Promoting Hospitals (65.65%).

The analysis of social support from supervisors and colleagues revealed that participants predominantly reported high levels across all dimensions. Among the three domains assessed, emotional support had the highest proportion of high-level ratings, with 70.87% of respondents reporting strong emotional support. This was followed by tangible support, in which 59.35% of participants indicated high levels of support.

With respect to organizational innovation management, participants also reported high levels across all domains. The developing innovation-supportive leaders domain demonstrated the highest proportion of high-level ratings, with 67.39% of respondents perceiving that their supervisors or organizational leaders actively fostered and supported innovation. This was followed by the vision creation for collaborative innovation domain, with 62.17% of participants reporting high levels.

The analysis of digital intelligence showed that respondents reported high levels across all domains. The highest proportion of high-level evaluations was observed in the digital empathy domain (85.22%), followed by the digital citizen identity domain (75.22%).

The analysis of the need for competency development in driving eHealth implementation revealed that respondents reported high levels across all competency domains. The domain with the highest proportion of high-level evaluations was the development of human capital in eHealth and health information technology, including knowledge management for the public (61.52%), followed by the standardization of health information systems and effective data integration and exchange (58.26%) (Table 1).

Table 1.

Number and percentage of the need for competency development in driving health information technology (eHealth) among healthcare personnel in the primary health care network of Nong Khai Province (n = 460)

need for competency development in driving health information technology (eHealth) among healthcare personnel in the primary health care network of Nong Khai Province		Number	Percentage
Establishment of organizational structures for eHealth management
Low level		10	2.17
Moderate level		183	39.78
High level		267	58.04
Development of enterprise architecture to support eHealth service delivery
Low level		29	6.30
Moderate level		205	44.57
High level		226	49.13
Standardization of health information systems and effective data integration and exchange
Low level		15	3.26
Moderate level		177	38.48
High level		268	58.26
Promotion of innovation in health information technology (eHealth) and intellectual property protection
Low level		18	3.91
Moderate level		214	46.52
High level		228	49.57
Implementation of laws and standards to support ICT utilization in the health system
Low level		19	4.13
Moderate level		192	41.74
High level		249	54.13
Development of human capital in eHealth and health information technology, including knowledge management for the public
Low level		13	2.83
Moderate level		164	35.65
High level		283	61.52

Overall, 53.26% of healthcare personnel reported a high need for eHealth competency development (95% CI: 48.67–57.79) (Table 2).

Table 2.

Prevalence levels of the need for competency development in driving health information technology (eHealth) among healthcare personnel in the primary health care network of Nong Khai Province (n = 460)

need for competency development in driving health Information technology (eHealth) among healthcare personnel in the primary health care network of Nong Khai Province	Number	Percentage	95% confidence interval
Low - Moderate level	215	46.74	42.20–51.33
High level	245	53.26	48.67–57.79

Variables associated with the need for eHealth competency development at a significance level of p < 0.25 in the univariate analysis were entered into the multivariable generalized linear mixed model. A backward elimination procedure was then applied to derive the final model, sequentially removing variables that did not retain statistical significance after adjustment for other covariates and the random effect of professional category.

The analysis using both simple logistic regression and a generalized linear mixed model (GLMM), in which professional discipline was specified as a random effect to control for interprofessional variance, examined the associations of socio-economic status, social support, organizational innovation management, and digital intelligence with the need for eHealth competency development. The results indicated that workplace setting was significantly associated with competency development needs. Personnel working in subdistrict health-promoting hospitals were 1.82 times more likely to require competency development than those in community hospitals (AOR = 1.82; 95% CI: 1.08–3.05; p = 0.023).

Organizational innovation management factors were also significantly associated with competency development needs. Creating an innovation-conducive environment was associated with a fourfold higher likelihood of needing competency development (AOR = 4.03; 95% CI: 2.27–7.15; p < 0.001), while establishing continuous learning systems was associated with a 1.95-fold higher likelihood (AOR = 1.95; 95% CI: 1.09–3.49; p = 0.024).

Several components of digital intelligence were significantly associated with competency development needs. Privacy management was associated with a 2.19-fold higher likelihood (AOR = 2.19; 95% CI: 1.14–4.21; p = 0.019), screen time management with a 2.67-fold higher likelihood (AOR = 2.67; 95% CI: 1.42–5.03; p = 0.002), and cyberbullying management with a 3.77-fold higher likelihood (AOR = 3.77; 95% CI: 2.14–6.66; p < 0.001), with further details presented in Table 3.

Table 3.

Factors associated with the need for competency development in driving health information technology (eHealth) among healthcare personnel in primary healthcare networks, Nong Khai Province (n = 460)

Factor		Number	Percentage	Need for competency development in driving eHealth implementation		Crude OR (95%CI)	p-value	GLMM AOR (95%CI)	p-value
				Low - Moderate level (%)	High level (%)
Gender							0.001
	Female	373	81.09	188 (50.40)	185 (49.60)	Ref.
	Male	87	18.91	27 (31.03)	60 (68.97)	2.25 (1.37–3.71)	0.001
Age							0.920
	Less than 30 years	120	26.09	54 (45.00)	66 (55.00)	Ref.
	31–40 years	180	39.13	83 (46.11)	97 (53.89)	0.95 (0.60–1.52)	0.850
	41–50 years	102	22.17	49 (48.04)	53 (51.96)	0.88 (0.52–1.50)	0.651
	More than 51 years	58	12.61	29 (50.00)	29 (50.00)	0.81 (0.43–1.53)	0.531
Marital status							0.053
	Single	188	40.87	94 (50.00)	94 (50.00)	Ref.
	Married	220	47.83	91 (41.36)	129 (58.64)	1.41 (0.95–2.09)	0.081
	Widowed/Divorced/Separated	52	11.30	30 (57.69)	22 (42.31)	0.73 (0.39–1.36)	0.327
Education							0.867
	Diploma or below	21	4.57	11 (52.38)	10 (47.62)	Ref.
	Bachelor’s degree or equivalent	402	87.39	187 (46.52)	215 (53.48)	1.26 (0.52–3.04)	0.600
	Master’s degree or higher	37	8.04	17 (45.95)	20 (54.05)	1.29 (0.44–3.78)	0.638
Average Monthly Income							0.229
	Less than 20,000 Baht	56	12.17	29 (51.79)	27 (48.21)	Ref.
	20,000–29,999 Baht	130	28.26	61 (46.92)	69 (53.08)	1.21 (0.64–2.27)	0.543
	30,000–39,999 Baht	116	25.22	44 (37.93)	72 (62.07)	1.75 (0.92–3.34)	0.086
	40,000–49,999 Baht	70	15.22	35 (50.00)	35 (50.00)	1.07 (0.53–2.11)	0.842
	50,000 Baht and above	88	19.13	46 (52.27)	42 (47.73)	0.98 (0.50–1.91)	0.955
Professional position							0.342
	Registered Nurse	264	57.39	129 (48.86)	135 (51.14)	Ref.
	Public Health Technical Officer / Public Health Officer	142	30.87	58 (40.85)	84 (59.15)	1.38 (0.91–2.09)	0.123
Age (years) : mean = 37.09, standard deviation (SD) = 9.27, range = 22–59.
Average Monthly Income (THB): mean = 34,379.83, standard deviation (SD) = 13,969.04, range = 8,050–100,000
	Public Health Technical Officer (Dental) / Dental Assistant	27	5.87	13 (48.15)	14 (51.85)	1.02 (0.46–2.27)	0.944
	Thai traditional medical practitioner	27	5.87	15 (55.56)	12 (44.44)	0.76 (0.34–1.69)	0.509
Professional rank							0.673
	Government employee / Ministry officer / Temporary employee	101	21.96	48 (47.52)	53 (52.48)	Ref.
	Operational level / Junior professional	97	21.09	41 (42.27)	56 (57.73)	1.23 (0.70–2.16)	0.457
	Skilled level / Professional	231	50.21	113 (48.92)	118 (51.08)	0.94 (0.59–1.51)	0.815
	Senior level / Senior professional	31	6.74	13 (41.94)	18 (58.06)	1.25 (0.55–2.82)	0.585
Length of Working Experience							0.193
	More than 31 years	30	6.52	19 (63.33)	11 (36.67)	Ref.
	1–10 years	197	42.83	90 (45.69)	107 (54.31)	2.05 (0.92– 4.54)	0.076
	11–20 years	160	34.78	69 (43.13)	91 (56.88)	2.27 (1.01–5.09)	0.045
	21–30 years	73	15.87	37 (50.68)	36 (49.32)	1.68 (0.70–4.02)	0.244
History of eHealth Training							0.002
	Never attended	266	57.83	140 (52.63)	126 (47.37)	Ref.
	Attended	194	42.17	75 (38.66)	119 (61.34)	1.76 (1.21–2.56)	0.003
Workplace							< 0.001		0.023
	Community hospitals	158	34.35	98 (62.03)	60 (37.97)	Ref.		Ref.
	Sub-district Health Promoting Hospital	302	65.65	117 (38.74)	185 (61.26)	2.58 (1.73–3.83)	< 0.001	1.82 (1.08–3.05)
Social Support from Supervisors and Colleagues – emotional support							< 0.001
	Low - Moderate level	134	29.13	96 (71.64)	38 (28.36)	Ref.
	High level	326	70.87	119 (36.50)	207 (63.50)	4.39 (2.83–6.81)	< 0.001
Social Support from Supervisors and Colleagues – informational support							< 0.001
	Low - Moderate level	189	41.09	130 (68.78)	59 (31.22)	Ref.
	High level	271	58.91	85 (31.37)	186 (68.63)	4.82 (3.22–7.19)	< 0.001
Length of Work experience (years): mean = 13.78, standard deviation (SD) = 9.43, range = 1–39
Training sessions (times): mean = 1.94, standard deviation (SD) = 1.35, range = 1–5
Social Support from Supervisors and Colleagues – tangible support							< 0.001
	Low - Moderate level	187	40.65	135 (72.19)	52 (27.81)	Ref.
	High level	273	59.35	80 (29.30)	193 (70.70)	6.26 (4.14–9.46)	< 0.001
Vision Creation for Collaborative Innovation							< 0.001
	Low - Moderate level	174	37.83	128 (73.56)	46 (26.44)	Ref.
	High level	286	62.17	87 (30.42)	199 (69.58)	6.36 (4.17–9.69)	< 0.001
Creating an Innovation-Conducive Environment							< 0.001		< 0.001
	Low - Moderate level	194	42.17	151 (77.84)	43 (22.16)	Ref.		Ref.
	High level	266	57.83	64 (24.06)	202 (75.94)	11.08 (7.13–17.21)	< 0.001	4.03 (2.27–7.15)
Developing Innovation-Supportive Leaders							< 0.001
	Low - Moderate level	150	32.61	116 (77.33)	34 (22.67)	Ref.
	High level	310	67.39	99 (31.94)	211 (68.06)	7.27 (4.63–11.41)	< 0.001
Establishing Continuous Learning Systems							< 0.001		0.024
	Low - Moderate level	175	38.04	132 (75.43)	43 (24.57)	Ref.		Ref.
	High level	285	61.96	83 (29.12)	202 (70.88)	7.47 (4.86–11.46)	< 0.001	1.95 (1.09–3.49)
Digital Citizen Identity							< 0.001
	Low - Moderate level	114	24.78	87 (76.32)	27 (23.68)	Ref.
	High level	346	75.22	128 (36.99)	281 (63.01)	5.48 (3.38–8.90)	< 0.001
Critical Thinking							< 0.001
	Low - Moderate level	135	29.35	104 (77.04)	31 (22.96)	Ref.
	High level	325	70.65	111 (34.15)	214 (65.85)	6.46 (4.07–10.26)	< 0.001
Cybersecurity Management							< 0.001
	Low - Moderate level	157	34.13	118 (75.16)	39 (24.84)	Ref.
	High level	303	65.87	97 (32.01)	206 (67.99)	6.42 (4.15–9.92)	< 0.001
Privacy Management							< 0.001		0.019
	Low - Moderate level	127	27.61	102 (80.31)	25 (19.69)	Ref.		Ref.
	High level	333	72.39	113 (33.93)	220 (66.07)	7.94 (4.85–13.00)	< 0.001	2.19 (1.14–4.21)
Screen Time Management							< 0.001		0.002
	Low - Moderate level	141	30.65	117 (82.98)	24 (17.02)	Ref.		Ref.
	High level	319	69.35	98 (30.72)	221 (69.28)	10.99 (6.67– 18.11)	< 0.001	2.67 (1.42–5.03)
Digital Footprints							< 0.001
	Low - Moderate level	126	27.39	104 (82.54)	22 (17.46)	Ref.
	High level	334	72.61	111 (33.23)	223 (66.77)	9.49 (5.68–15.86)	< 0.001
Cyberbullying Management							< 0.001		< 0.001
	Low - Moderate level	158	34.35	129 (81.65)	29 (18.35)	Ref.		Ref.
	High level	302	65.65	86 (28.48)	216 (71.52)	11.17 (6.95–17.94)	< 0.001	3.77 (2.14–6.66)
Digital Empathy							< 0.001
	Low - Moderate level	68	14.78	56 (82.35)	12 (17.65)	Ref.
	High level	392	85.22	159 (40.56)	233 (59.44)	6.83 (3.55–13.16)	< 0.001

Note : Adjusted odds ratios (AORs) are reported only for variables entered into the multiple logistic regression model (p-value < 0.25)

Note : Adjusted odds ratios (AORs) are presented only for variables retained in the final multivariable model following backward elimination. Variables with significant crude associations that were excluded during model selection are shown with empty cells in the AOR columns

Abbreviations: Ref., reference group; OR, odds ratio; CI, confidence interval; GLMM, generalized linear mixed model

Discussion

This study found that most healthcare personnel in the primary health care network of Nong Khai Province perceived a high need for further development of eHealth competencies, reflecting a persistent gap between national digital health policies and frontline workforce readiness [1]. In the Thai health system, sub-district healthpromoting hospitals (SHPHs) and other primary care units constitute the core mechanism for achieving universal health coverage, being responsible for health promotion, disease prevention, and the management of noncommunicable diseases in the community, particularly in border provinces with complex population structures and substantial cross-border mobility such as Nong Khai, which is connected to Vientiane Capital via the First Thai–Lao Friendship Bridge and key trade and tourism routes along the Mekong River [25]. Studies on cross-border health service networks in the Nong Khai–Vientiane corridor have indicated the coexistence of patient referral, medical service utilization, health tourism, and complex cross-country health information exchange, thereby necessitating greater reliance on robust health information systems and trustworthy eHealth platforms [26]. At the same time, both the World Health Organization and the Thai Ministry of Public Health have emphasized that achieving equitable access to services and transitioning toward a digital health system requires strengthening digital competencies among health personnel at all levels, yet health information systems remain fragmented and poorly integrated, especially at the primary care level [1]. Consequently, the relatively high level of perceived need for eHealth competency development among SHPH staff in border areas may reflect their recognition of their unavoidable frontline role in linking health data and ensuring continuity of care for both Thai citizens and cross-border service users along the Mekong. Nong Khai Province, as a border area connected to Vientiane Capital in the Lao People’s Democratic Republic (Lao PDR) via the First Thai–Lao Friendship Bridge, is a highly dynamic area in terms of economic activity, tourism, and population mobility, all of which directly influence patterns of disease transmission and the management of health information. The analysis in this report therefore covers eight key dimensions, derived from a synthesized conceptual framework based on the literature and empirical evidence, with the aim of formulating policy recommendations that are practically applicable and capable of strengthening the capacity of health personnel to respond effectively to future health challenges [27, 28].

The finding that personnel in primary care facilities reported a greater need to develop eHealth competencies than those in secondary-level facilities is consistent with the role of primary care as the front gate of the Thai health system and as the main data source for the national health information system [29]. Reviews of digital health competencies in primary care indicate that frontline staff must be proficient in recording and using electronic medical records, operating disease surveillance systems, providing teleconsultations, and using health applications to support continuity of care in the community; consequently, they require more extensive competency development than personnel in higher-level facilities, which are supported by dedicated IT teams [30]. Evidence from the Thai context further suggests that although digital health technology knowledge and literacy among healthcare personnel are generally at a reasonably good level, ongoing strengthening is still required across all cadres, particularly within the primary care network [31]. Considered alongside technology acceptance frameworks such as the Technology Acceptance Model and the Diffusion of Innovations, the fact that primary care personnel are frequently tasked with piloting multiple eHealth initiatives and observing tangible benefits for patient care in rural and border settings may heighten their awareness of the need to further strengthen competencies in data-related technologies, telecommunication, and change management to a greater extent than staff in secondary-level facilities [26, 32, 33].

Sub-district healthpromoting hospitals are the frontline units closest to the community, yet they are also the most resource-constrained in terms of budget, hardware, and, most critically, IT human capital. In contrast to community hospitals, which often have computer centers or medical records staff with specialized technical expertise, personnel in SHPHs, who are predominantly professional nurses or public health officers, must simultaneously assume multiple roles as care providers, managers, and de facto technicians when internet systems fail or software problems arise, and they are required to troubleshoot these issues themselves [34]. This situation creates a state of technological isolation, whereby central policies that promote the adoption of new applications or telemedicine systems tend to have more immediate and pronounced impacts on SHPH staff, who lack a technical buffer. The absence of on-site specialists to provide consultation leaves them feeling insecure and drives a stronger need for intensive and comprehensive training in order to remain self-reliant in their day-to-day practice. Policies such as Smart Health ID and digital identity verification further complicate the work of SHPHs in border areas, which must serve a diverse population that includes local residents as well as transient or undocumented groups. Managing personal digital identities and ensuring data security at the primary care level have thus become new challenges with which staff are still unfamiliar. Whereas hospitals are supported by more complex management systems, SHPHs must operate with smaller-scale systems that are nonetheless expected to meet the same security standards. The pressure to uphold these standards acts as a catalyst for SHPH personnel to demand higher levels of competency development so that they do not become the weakest link in the provincial health network’s cybersecurity system [35].

With respect to innovation-oriented organizational management factors, the presence of an innovation-supportive climate within service units was positively associated with personnel’s perceived need to develop competencies for driving eHealth. This finding is consistent with reviews of organizational readiness for digital health transformation, which indicate that an organizational culture open to change, strong leadership support, and an environment that allows safe experimentation with new ideas are key predictors of successful adoption of digital health technologies [36]. Empirical studies in healthcare organizations further demonstrate that an innovation climate that values learning from mistakes, encourages knowledge sharing, and promotes teamwork enhances both psychological readiness and motivation among staff to invest in developing their own digital skills [37, 38]. In the context of border SHPHs, which must simultaneously meet national performance indicators and respond to the needs of local populations and cross-border service users, managers who create spaces for staff to propose new eHealth initiatives may enable personnel to directly experience how strengthening their digital competencies can be translated into tangible innovations, thereby further increasing their perceived need for eHealth competency development [26, 39, 40].

The organizational innovation management factor of establishing continuous learning systems was likewise associated with the need to develop competencies for driving eHealth. eHealth development cannot be treated as a one-off training event; rather, it aligns with the concept of a learning health system, in which data generated from routine clinical practice are continuously fed back into cycles of learning and service improvement, a process that depends on both IT infrastructure and a culture of ongoing learning within health organizations [38, 41]. International agencies have emphasized that building digital capacity in the health workforce must be embedded within both formal education and continuing professional development, through flexible digital learning modalities such as e-learning, micro-credentialing, and communities of practice [30, 42]. Health systems that continuously invest in the digital upskilling of their personnel are better prepared to transition toward high-quality digital health services than organizations that regard training as merely an occasional activity [43]. Thus, in service units where continuous learning systems are in place, staff are more likely to perceive eHealth competencies as skills that must be constantly replenished.

In the dimension of digital intelligence, the finding that privacy management skills were associated with the need for eHealth competency development is consistent with the fact that digital health services rely heavily on the storage and exchange of highly sensitive personal data [44]. The digital intelligence framework identifies privacy management as a core digital citizenship competency encompassing the ability to configure privacy settings, awareness of one’s digital footprint, and the capacity to prevent data leakage [45]. Reviews of data security in health information systems indicate that major challenges to the expansion of eHealth include risks of system breaches, unauthorized data use, and compliance with personal data protection legislation, all of which require both robust organizational policies and strong individual competencies [46]. In border SHPHs that must manage data for both Thai patients and cross-border service users, personnel with stronger privacy management skills are more likely to recognize the complexities of eHealth implementation and therefore report a greater need for further competency development [47].

Digital intelligence skills related to screen time management were also associated with the need for eHealth competency development and reflect the dimension of personnel’s digital well-being. The introduction of eHealth systems into daily practice inevitably increases screen time and digital workload. In the absence of appropriate workload management and change support, the adoption of new technologies may exacerbate stress and fatigue among health workers [48]. The introduction of eHealth systems into the daily work of primary care personnel such as online data entry, patient follow-up through applications, or teleconsultation inevitably increases screen time and digital workload. Studies on digital transformation in health systems have shown that, in the absence of appropriate workload management and adequate change support, the adoption of new technologies can exacerbate stress and fatigue among health workers [33, 39]. Personnel with strong screen time management skills and digital self-regulation are better able to balance online and offline work demands and may be more likely to seek further competency development to use technology effectively without compromising their own health [40]. This association may also reflect a heightened awareness of inefficiencies and workflow burdens among personnel with higher digital intelligence, leading to a stronger demand for training as a strategy to optimize eHealth use [49]. Personnel with higher digital intelligence are more likely to recognize inefficiencies, redundancies, and cognitive burdens within their current eHealth-related workflows. Rather than indicating resistance to digital health, this heightened awareness reflects a more advanced understanding of how suboptimal system design or limited competencies can increase screen time, workload, and fatigue. Consequently, individuals at this stage may express a stronger demand for training as a strategy to optimize workflow efficiency, reduce unnecessary digital exposure, and use eHealth technologies more effectively and sustainably.

Finally, digital intelligence skills related to cyberbullying management were associated with the need for eHealth competency development, consistent with evidence that violence against healthcare workers has increasingly expanded into online spaces through insults, public shaming, and threats on social media platforms [50, 51]. The digital intelligence framework therefore identifies cyberbullying management as a core competency encompassing the ability to protect oneself, seek help, and support victims of online harassment. For border-area personnel who must communicate with the public through digital channels, those with stronger cyberbullying management skills may be more willing to remain engaged online and to invest in further eHealth competency development [52, 53].

Although several socio-demographic and psychosocial variables were theoretically relevant based on the conceptual framework, not all constructs retained statistical significance in the final multivariable model. This may reflect shared variance and overlapping pathways among predictors, particularly between socio-demographic characteristics, social support, and organizational innovation management. In multivariable adjustment, the stronger explanatory power of organizational and digital intelligence factors may have attenuated the independent effects of other variables, suggesting that these determinants operate indirectly through organizational readiness and digital capability mechanisms rather than exerting direct effects on perceived competency development needs.

In this regard, although gender demonstrated a strong crude association with eHealth competency development needs in the univariate analysis, it did not remain statistically significant after adjustment in the generalized linear mixed model. This attenuation is likely attributable to shared variance between gender and other covariates, particularly organizational climate and digital intelligence dimensions, as well as potential clustering effects by professional category. Given that female personnel constituted the majority of the workforce in primary care settings, gender may function as a proxy for underlying organizational and role-related characteristics rather than as an independent determinant. Consequently, once these contextual and capability-related factors were accounted for, the direct effect of gender on perceived competency development needs was no longer evident. This finding suggests that observed gender differences in unadjusted analyses may be mediated through organizational and digital capability mechanisms rather than reflecting intrinsic gender-based disparities in eHealth competency needs.

Developing healthcare personnel to possess comprehensive eHealth competencies is therefore not merely a routine educational or training task, but a critical long-term strategic agenda for the sustainability and security of the Thai health system. Health workers constitute the key mechanism that translates large-scale investments in digital infrastructure and technological systems into tangible outcomes; without competent personnel, even the most advanced technologies cannot deliver their full value. Investment in people is thus the most worthwhile and sustainable form of investment, in terms of enhancing service efficiency, reducing inequities in access to digital health, and strengthening public trust in the health system, particularly in the Mekong border areas where social, economic, and security conditions are highly complex. Any policy or programme related to the advancement of eHealth should therefore be grounded in the fundamental assumption that, without human capacity development, technology cannot realize its full potential, and the development of eHealth competencies among healthcare personnel must be viewed as a strategic investment for the greatest benefit of all citizens.

When the overall findings of this study are synthesized, their policy and practical implications for Mekong border provinces become clear. The high level of perceived need for eHealth competency development among primary care personnel, together with the role of Nong Khai’s sub-district healthpromoting hospitals as the frontline of the border health system, indicates that local agencies should develop a border-specific eHealth competency framework. Such a framework should encompass technical competencies in health information systems, digital health communication, personal data protection, and dimensions of digital intelligence related to well-being and the management of online violence. At the organizational level, the Provincial Public Health Office and the primary care network should design a long-term digital workforce development plan that emphasizes cultivating an innovation-conducive climate and continuous learning systems, for example, through data quality clinics, district-level eHealth innovation forums, and digital change-leader development programmes for border SHPHs. At the policy level, these findings should be aligned with the national digital health strategy and Thai–Lao cross-border health cooperation through targeted investment in health information infrastructure in border areas, the development of eHealth and digital intelligence training curricula for primary care personnel, and the establishment of data protection and digital security measures that are consistent with both Thai law and international obligations, so that health information technology truly becomes a core mechanism for improving health care for populations on both sides of the Mekong River.

Limitations

This study acknowledges several limitations inherent to its design and methodology. The cross-sectional nature of the data captures only a specific snapshot in time, which may not fully reflect the rapid evolution of national eHealth policies or technological systems. Furthermore, the diversity of professional roles within the sample may introduce selection bias, as respondents might predominantly consist of individuals with a pre-existing interest in eHealth, while variations in professional duties could influence the depth of understanding and specific competency requirements. Finally, reliance on self-reported questionnaires limits the ability to objectively assess actual technical proficiency and may introduce social desirability bias, meaning the findings reflect personnel’s self-perceived needs rather than an objective evaluation of their operational performance.

In addition, the generalizability of the findings is limited by the study setting. As the research was conducted in Nong Khai Province, a border province adjacent to the Lao People’s Democratic Republic, the local context is shaped by unique cross-border migration patterns, public health demands, and security considerations. These contextual factors may influence eHealth implementation and workforce competency requirements; therefore, the findings may not be fully applicable to central or non-border provinces in Thailand.

This study has several limitations that should be considered when interpreting the findings. First, the cross-sectional design captures associations at a single point in time and therefore precludes causal inference, particularly in the context of rapidly evolving national eHealth policies and digital health infrastructures.

Second, the study relied on self-reported questionnaires to assess eHealth competency development needs, which limits the ability to objectively evaluate actual technical proficiency and operational performance in real-world digital health tasks. Consequently, the findings primarily reflect personnel’s self-perceived needs and may be influenced by social desirability bias.

In addition, although eHealth competency development needs were initially measured using a multi-level Likert scale, the outcome variable was dichotomized for inferential analysis. This decision was necessitated by the sparse distribution of data in the lower response categories and violations of key assumptions required for ordinal logistic regression. While sensitivity analyses using alternative cut-off points confirmed the consistency and robustness of the main associations, this binary classification may still have obscured more nuanced differences between low and moderate levels of competency needs and may have resulted in some loss of statistical information compared with analyses using continuous or ordinal outcomes. Future research should therefore consider longitudinal designs and analytical approaches that retain the ordinal or continuous nature of competency measures to capture more granular variations over time.

Finally, the generalizability of the findings may be limited by the study setting. As the research was conducted in Nong Khai Province, a Mekong border area adjacent to the Lao People’s Democratic Republic, the local context is characterized by distinctive cross-border migration patterns, public health demands, and security considerations. These contextual features may shape the implementation of eHealth systems as well as workforce competency requirements in ways that differ from those in central, urban, or non-border provinces in Thailand. Consequently, caution should be exercised when extrapolating the findings beyond similar border health system contexts.

Conclusion

The study highlights a critical need for eHealth competency development among primary care personnel in the Mekong border region, particularly within sub-district health promoting hospitals where the demand for upskilling is significantly higher than in community hospitals. The findings demonstrate that this need is driven not merely by technical gaps but is strongly associated with an innovation-conducive organizational climate, continuous learning systems, and specific dimensions of digital intelligence, including privacy preservation, screen time management, and cyberbullying management. Therefore, effective implementation of national digital health policies in this strategic border context requires a holistic approach that extends beyond basic technical training to foster a culture of innovation and enhance digital resilience, ensuring that the frontline workforce is fully empowered to manage the complexities of cross-border health data security and service delivery standards.

Recommendations

Recommendations for future research

Future research should employ longitudinal or mixed-methods designs to better examine causal relationships in eHealth competency development needs. Alternative analytical approaches that avoid dichotomization of outcomes are recommended to reduce information loss. Comparative studies across different geographic and service contexts, including border and non-border areas, are also warranted to enhance generalizability and explore contextual influences.

Recommendations for policymakers

Policymakers should integrate workforce development into national digital health strategies by strengthening innovation-supportive organizational climates and continuous learning systems. Investment in digital intelligence training particularly in privacy protection, screen time management, and cyberbullying management should be prioritized to enhance digital readiness and governance capacity in primary care settings.

Recommendations specifically for sub-district health promoting hospitals (SHPHs)

Given the higher competency development needs among SHPH personnel, targeted capacity-building initiatives should be implemented. These should emphasize continuous learning, leadership support for innovation, and practical training in data privacy and digital well-being to strengthen frontline eHealth implementation.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

SK and NP conceptualized and designed the study. SK developed the research instruments, conducted data collection, and performed the statistical analysis. NP supervised the project and verified the analytical methods. NN, JY, WP, and AN participated in data interpretation and provided critical suggestions. SK wrote the first draft of the manuscript. NP, NN, JY, WP, and AN reviewed and edited the manuscript for intellectual content. All authors read and approved the final version of the manuscript. The authors acknowledge the use of an artificial intelligence–based language tool (ChatGPT, OpenAI) to assist with language editing and grammatical improvement. No generative AI tool was used in the study design, data analysis, or interpretation of results. The authors are solely responsible for the scientific content of this manuscript.

Funding

The authors received no financial support from any funding agency, commercial entity, or not-for-profit organisation for the research, authorship and/or publication of this article.

Data availability

All data generated or analysed during this study are included in this published article.

Declarations

Ethics approval and consent to participate

This study was approved by the Human Research Ethics Committee of Khon Kaen University (Reference No. HE682132; August 28, 2025). All study procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants prior to data collection. Participant confidentiality and anonymity were strictly safeguarded; all data were processed and reported in aggregated form to prevent identification of individuals or their specific workplaces.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.