Digital health integration and technological advancements in Saudi Arabia’s healthcare system: a comparative analysis of clustered and non-clustered hospitals

Study design

This study employed a convergent mixed methods design to evaluate digital health integration in Saudi Arabia [11]. The quantitative component analyzed hospital-level performance indicators from 2016 to 2024, while the qualitative component explored stakeholder perspectives through semi-structured interviews conducted in 2024. Findings from both components were integrated through triangulation.

The research was conducted in the Western region (Mecca city) and the Eastern Province, both selected for their demographic diversity and strategic relevance to the Saudi SHSTP. Four hospitals were included: two clustered and two non-clustered, each with comparable capacity (300–400 beds). Clustered hospitals were tertiary care centers, while non-clustered hospitals comprised secondary and tertiary facilities.

Ethical approval was obtained from the Research Ethics Committee of Swansea University (Ref: 280818) and the Institutional Review Board of Umm Al-Qura University (Ref: UQU-IRB-2024-05-221). Written informed consent was obtained from all participants before interviews. The study adhered to the principles of the Declaration of Helsinki. Hospital-level data were de-identified and used with institutional permission; no individual patient records were accessed.

Quantitative methodology
Data sources and variables

Hospital-level data were extracted from MOH databases and hospital information systems for the years 2016–2024.

Variables included:

• EMR adoption rates (%)

• Telemedicine consultations per 100 patients

• Mobile health application usage

• Patient-Centered Care Index (PCCI)

• Chronic Disease Management Score (CDMS)

• Staff training hours per clinician

• Patient satisfaction (survey-based mean scores)

• Follow-up compliance rates (%)

• Health outcome improvement measures (e.g., glycated hemoglobin A [HbA1c], hospital readmissions).

All metrics were aggregated at the hospital level and reported as annual averages from 2016 to 2024. No individual-level data were used. Data were cleaned and cross validated with internal MOH quality reports to ensure consistency and completeness. Access to MOH administrative datasets was granted through formal agreements and approved by both ethics committees; only de-identified hospital-level data were provided, and no individual patient records were accessed.

Sampling and hospital selection

A purposive comparative sampling approach was used to ensure that hospitals represented both governance structures under the Saudi SHSTP. Four hospitals met the eligibility criteria: two clustered and two non-clustered facilities with comparable bed capacity (300–400 beds) and complete reporting of digital health indicators for the full 2016–2024 period.

Inclusion criteria for hospitals included:

• availability of continuous digital health reporting,

• classification as either clustered or non-clustered under SHSTP,

• comparable service scope and bed capacity.

Exclusion criteria included:

• incomplete annual reporting,

• hospitals undergoing restructuring that could distort performance trends.

The sample size of 4 hospitals is justified because the objective was comparative evaluation of two structural models rather than statistical generalization, and only a limited number of hospitals met all inclusion criteria across the 9 years.

A structured algorithm guided site selection: (1) list all hospitals in Mecca and the Eastern Province with complete 2016–2024 digital reporting; (2) classify as clustered or non-clustered under SHSTP; (3) apply comparability criteria (300–400 beds, similar service scope); (4) exclude facilities with incomplete data or ongoing restructuring. The final sample included two clustered tertiary hospitals (C1, C2) and two non-clustered secondary/tertiary hospitals (NC1, NC2).

Composite indicator

Technology Utilization Score (TUS) was developed to capture overall adoption of digital tools:

TUS = (0.6 × EMR adoption rate) + (0.4 × telemedicine consultations per 100 patients). This composite measure was pilot-tested against other digital health metrics to ensure internal consistency.

The PCCI combined metrics such as care coordination, shared decision-making, and patient-provider communication. The CDMS reflected hospital-level chronic care indicators including HbA1c control rates, adherence to clinical pathways, and readmission avoidance. Both indicators followed MOH standard dashboards and were scaled from 0 to 100.

Tool development and validation

All composite indicators were derived from MOH national dashboards. The TUS was pilot tested against other digital indicators for internal consistency and aligns with national digital maturity metrics [10]. The weighting structure (0.6 for EMR, 0.4 for telemedicine) reflects MOH prioritization benchmarks for core digital enablers. The PCCI is based on the MOH Patient Experience Program, which undergoes annual psychometric validation through national survey cycles and incorporates domains such as communication, coordination, shared decision-making, and continuity of care. The CDMS follows standardized MOH chronic care dashboards and is validated annually through clinical audits and benchmarking reports. All 3 indicators use a 0–100 scale to support consistent comparison across facilities and years.

Rationale for analytical techniques

Analytical choices were guided by the structure of the dataset and the study objectives. Binary logistic regression was used to explore structural predictors of digital adoption because the outcomes represent institutional-level decisions (adopted/not adopted), and the results are interpreted descriptively given the small number of hospitals. ARIMA modelling was applied to the nine-year dataset as it is appropriate for assessing longitudinal trends in digital uptake across institutions. Covariates were included to adjust for structural differences rather than to infer causality, and together these analytical approaches provide exploratory insights while acknowledging limitations related to precision and generalizability.

Qualitative methodology

Participants and sampling. A purposive sampling strategy was employed [12]. Eighty participants were recruited (40 patients, 40 physicians). Inclusion criteria required patients to have experience with EMRs, telemedicine, or mobile applications, while physicians were senior clinicians managing chronic diseases. Of these, 53 participants (30 patients, 23 physicians) were interviewed until saturation was reached.

Data collection

Semi-structured interviews were conducted in Arabic during 2024 [13]. Interviews were audio-recorded with consent, transcribed verbatim, and translated into English. The interview guide, informed by prior literature and SHSTP policy, explored usability, accessibility, barriers, facilitators, and perceived impacts of digital health [14]. To ensure anonymity and systematic organization.

All participants were assigned unique identifiers. For senior physicians, IDs began with the prefix SP, followed by the hospital type (C1, C2, NC1, NC2), department acronym (e.g., Cardiology [CARD], Oncology [ONC], Endocrinology [END]), and a sequential number (e.g., Senior Physician from Clustered Hospital 1, Cardiology Department, first interviewee [SP-C1-CARD-01]). For patients, IDs consisted of the hospital type, department acronym, and sequence number (e.g., Patient from Clustered Hospital 1, Oncology Department, fourth interviewee [C1-ONC-04]). This coding system ensured confidentiality while enabling clear comparison between clustered and non-clustered hospitals and across specialties. Hospital-level quantitative data were obtained from MOH databases and internal hospital information systems for the years 2016–2024.

Data saturation was assessed continuously and reached when no new codes or perspectives emerged after consecutive interviews. Coding reliability was ensured through iterative coding, cross-checking of emerging themes, and maintaining an audit trail in NVivo to ensure transparency and replicability.

Quantitative analysis

In addition to descriptive and group comparison analyses, a binary logistic regression model was conducted to assess the predictive value of hospital type (clustered vs. non-clustered) on two main outcomes: EMR adoption and telemedicine utilization. Independent variables included hospital type, bed capacity, regional location, and patient chronic disease prevalence. Results were reported as odds ratios (OR) with 95% confidence intervals (CI). Data were analyzed using R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria) and IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics (mean, SD, median) were computed. Group comparisons were performed using independent t-tests, ANOVA, and Wilcoxon tests as appropriate. Effect sizes (Cohen’s d) and 95% CI were reported. ARIMA forecasting models were applied to examine adoption trends. Missing data (<10%) were addressed using multiple imputation by chained equations. Implausible values or outliers were identified through exploratory plots and were excluded if inconsistent with internal MOH data.

Qualitative analysis

Thematic analysis followed Braun and Clarke’s 6-step framework [15]. Transcripts were coded inductively and deductively using NVivo 12 (QSR International).

Integration of findings

Quantitative and qualitative findings were integrated through triangulation [16], enabling convergence, complementarity, and identification of discrepancies. Hospital performance metrics were compared with stakeholder perspectives to enhance interpretive validity. Quantitative and qualitative results were integrated through an explanatory triangulation approach, in which qualitative insights from patients and physicians were used to interpret and contextualize the 9-year performance trends observed in the quantitative analysis. Convergence was identified where both datasets highlighted stronger digital adoption, improved patient communication, and more coordinated workflows in clustered hospitals. Complementarity occurred when qualitative narratives explained the organizational and cultural mechanisms underlying quantitative differences, such as leadership alignment and digital readiness. No major discrepancies emerged; instead, qualitative findings enhanced the interpretability of temporal trends and clarified why adoption trajectories differed between hospital types.

Quantitative findings

This study analyzed longitudinal hospital performance data from 2016 to 2024 across two clustered and two non-clustered hospitals in Saudi Arabia. Table 1 presents institutional averages of key digital health and performance indicators during this period. Overall, clustered hospitals demonstrated consistently higher scores across all categories.

Between 2016 and 2024, the PCCI improved from 70.0% to 95.1% in clustered hospitals, compared to 68.0%–92.2% in non-clustered hospitals. The CDMS followed a similar pattern, increasing from 65.0% to 96.1% in clustered hospitals, and from 62.9% to 92.8% in non-clustered hospitals. These trends reflect enhanced coordination and adherence to chronic care protocols.

The TUS, a composite metric combining EMR and telemedicine adoption, rose from 60.0 to 95.9 in clustered hospitals, while increasing from 58.0 to 83.9 in non-clustered hospitals, indicating a steeper trajectory in digital uptake among clustered institutions.

Other performance metrics also showed consistent year-over-year gains. Patient satisfaction improved from 75.1% to 97.0% in clustered hospitals and from 72.9% to 94.0% in non-clustered hospitals. Staff training hours per clinician increased from 16.0 to 29.4 in clustered hospitals, compared to 15.0 to 21.8 in non-clustered ones, suggesting more intensive capacity-building efforts in the former. Follow-up compliance and health outcomes improvement both improved across the board, with clustered hospitals maintaining a consistent edge.

To assess statistical significance, binary logistic regression was used to examine the relationship between hospital type and key digital adoption outcomes. After controlling for covariates (age, gender, comorbidities), clustered hospitals had:

• 1.8 times higher odds of EMR adoption (95% CI: 1.3–2.5)

• 2.1 times higher odds of telemedicine utilization (95% CI: 1.5–2.8)

All variables represent hospital-level annual averages, not individual patient data. The percentages shown (e.g., 85% EMR adoption) reflect aggregate administrative records rather than interview responses. Group comparisons using independent t-tests and ANOVA were conducted for descriptive purposes, though statistical power was limited by the small sample (n = 4 hospitals). These results should therefore be interpreted as indicative, not inferential. The graph below illustrates the temporal trends in EMR and telemedicine adoption from 2016–2024, comparing clustered and non-clustered hospitals. The graph shows a clear divergence beginning in 2018, with clustered hospitals demonstrating faster and more consistent increases in digital adoption. In contrast, non-clustered hospitals displayed slower, irregular progression with periods of stagnation before 2021. These patterns align with SHSTP implementation phases and support the interpretation that structural integration accelerated digital readiness.

These trends reflect enhanced coordination and adherence to chronic care protocols. Figure 1 illustrates the longitudinal trajectory of digital health adoption across clustered and non-clustered hospitals between 2016 and 2024. Clustered hospitals demonstrate a steeper and more consistent increase in digital utilization compared with non-clustered facilities.

Figure 1.

Overall, the longitudinal data revealed a consistent pattern: clustered hospitals demonstrated earlier digital readiness, faster adoption of EMRs and telemedicine, and higher gains in patient-centered care and chronic disease management. These improvements were not isolated indicators but reflected broader organizational coherence and alignment with national digital transformation policies. The divergence between clustered and non-clustered hospitals widened after 2018, corresponding with the phased implementation of SHSTP reforms.

Qualitative findings

Participants in clustered hospitals emphasized integrated systems and interoperability. One physician explained, “The cluster system makes referrals faster; patients move between hospitals without repeating tests” (SP-C1-CARD-02). A patient similarly noted, “It feels like one hospital even if I go to another branch” (C2-ONC-05).

By contrast, respondents in non-clustered hospitals reported frustrations with fragmented systems, inconsistent support, and weaker training. As one patient described, “Sometimes I have to carry my papers because the system in one hospital cannot see my records from another” (NC1-END-03). A physician added, “Technical support is not always available, so we often revert to manual processes” (SP-NC2-NEU-01).

Despite these barriers, both groups acknowledged the benefits of digital health. A clustered hospital patient reflected, “Before, I waited weeks for results; now, I get them in a day” (C1-CARD-07). Meanwhile, a non-clustered physician admitted, “Even with our challenges, telemedicine has helped reduce unnecessary hospital visits” (SP-NC1-ONC-04).

The qualitative themes emerging from patient and physician interviews across both hospital types are summarized in Table 2, highlighting key facilitators, barriers, and perceived benefits of digital health adoption.

Policy and practice implications

The results carry clear implications for policymakers. First, investment must be directed to non-clustered and rural hospitals to close adoption gaps, ensuring equity and consistency across the health system [28]. Second, targeted digital literacy campaigns for both patients and providers are essential to maximize the benefits of EMRs, telemedicine, and mobile health applications [29]. Third, embedding digital health training into routine clinical workflows and providing targeted incentives for staff participation could help reduce cultural resistance and normalize technology use [30].

Saudi Arabia’s heavy investment in artificial intelligence and predictive analytics presents an opportunity to build on current progress. Integrating these tools into clustered hospitals could drive efficiencies in chronic disease management, early detection, and personalized care [31]. However, careful governance and ethical frameworks will be needed to manage data security, algorithmic bias, and equitable access.

These findings directly align with the strategic objectives of Saudi Vision 2030 and the SHSTP, which emphasize interoperability, digital readiness, and patient-centered models of care. Clustered hospitals embody the Vision 2030 implementation roadmap by promoting integrated service delivery, unified health information systems, and enhanced accountability. The observed improvements in patient satisfaction, chronic disease management, and workflow efficiency reflect the expected outcomes articulated in national digital health strategies, including the National eHealth Strategy and the Digital Health Maturity Model adopted by the MOH [32].

Internationally, the performance trajectory of clustered hospitals mirrors successful large-scale digital transformation models such as the UK NHS integrated care systems, Estonia’s national EHR ecosystem, and Singapore’s cluster-based digital health architecture. These global examples reinforce the finding that digital transformation is most effective when embedded within coordinated governance structures, supported by workforce training, and aligned with broader policy mandates. By situating the study within both national and international frameworks, the results underscore that Saudi Arabia’s clustering model is consistent with global best practices in driving equitable and system-wide digital transformation [33]. While digital adoption contributed to performance differences, the clustering management structure, characterized by unified governance, shared infrastructure, and coordinated workflows, likely played an equally significant role. Thus, the observed outcomes reflect the combined influence of digital maturity and broader organizational reforms.

This study has several limitations. The quantitative data were derived from only 4 hospitals, limiting statistical power and generalizability. Although longitudinal, the dataset is institution-level rather than patient-level, restricting causal inference. Qualitative data were collected cross-sectionally in 2024, which limited insights into changes in perceptions over time. Additionally, geographic coverage was restricted to Mecca and the Eastern Province, underrepresenting rural and remote hospitals that may face distinct challenges. Despite these limitations, the mixed-methods approach strengthens validity by triangulating hospital records with stakeholder perspectives. Several limitations must be considered when interpreting these findings. First, the small sample of 4 hospitals limits statistical power and restricts the generalizability of model estimates, meaning the observed differences should be viewed as indicative rather than causal. Second, the use of composite indicators such as PCCI, CDMS, and TUS may introduce measurement constraints because these indices rely on administrative reporting cycles and may not capture all aspects of patient-centered care or digital maturity. Third, qualitative data were collected only in 2024, which may not fully reflect earlier phases of digital transformation and could amplify recent improvements in clustered hospitals. Finally, because clustered hospitals received earlier SHSTP investments, the data structure may inherently favor their performance trajectories, introducing a potential upward bias that should be acknowledged in interpretation.

Future studies should extend sampling to include rural and under-resourced hospitals, where digital equity issues may be most pronounced. Longitudinal qualitative designs would help capture evolving user experiences over time. Finally, further research should evaluate the impact of advanced technologies, such as AI, big data analytics, and predictive modeling, on the quality, safety, and efficiency of care in the Saudi context.