Web-based Application Integration to Improve Patient Satisfaction: A Model for Improving the Quality of Health Services in Hospitals

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Rahman Kadir, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7446810/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To develop and evaluate an integrated web-based application model (SIKAP) to improve patient satisfaction by optimising both medical and non-medical service quality, and to assess the application’s mediating role in linking service quality to satisfaction. Methods: We conducted a cross-sectional analytical study at Dr. Sumantri Hospital, Parepare, Indonesia, from July to December 2024. A total of 500 patients were recruited using proportional random sampling. Data were collected via a structured web-based questionnaire measuring perceptions of service quality, application usage, and satisfaction. Structural Equation Modelling (SEM) with AMOS v24.0 was used to test direct, indirect, and feedback relationships. Results: Non-medical service quality exerted a stronger influence on SIKAP adoption than medical service quality. Both service domains significantly predicted patient satisfaction, with SIKAP serving as a mediator that improved access to information, reduced waiting times, and enhanced communication. Feedback effects indicated that application use positively influenced perceptions of hospital services. Conclusion: Integrating web-based applications like SIKAP with high-quality medical and non-medical services substantially enhances patient satisfaction. These findings extend existing healthcare informatics models by demonstrating the importance of non-clinical factors in digital adoption. healthcare informatics patient satisfaction service quality web-based application digital health transformation Figures Figure 1 Introduction In recent decades, the development of information technology has revolutionised various sectors, including healthcare. Digital transformation in the healthcare system is increasingly becoming an urgent need as patients demand faster, more efficient and accessible services. One of the rapidly growing innovations is the integration of web-based applications in hospital management systems, which enables the optimisation of various aspects of services, from patient registration, medical record management, to scheduling and communication between patients and medical personnel. 1 , 2 Despite the widespread adoption of these technologies, hospitals still face challenges in effectively integrating digital systems into their operations, resulting in suboptimal service quality and patient satisfaction levels. Therefore, this study focuses on the integration of web-based applications as a model for improving the quality of healthcare services aimed at increasing patient satisfaction. Several previous studies have examined the impact of digital hospital management systems on improving healthcare efficiency. 1 , 3 – 5 In a study showed that web-based hospital management systems can improve coordination between patients and healthcare personnel through real-time information updates. 1 , 6 Similarly, integrating Firebase and an intelligent chatbot in a hospital management system proved that the application of web-based technology can accelerate access to information, improve medication compliance, and simplify administrative processes. 7 In addition, research at an Atlantic clinic revealed that a web-based service system can overcome inefficiencies in recording and searching patient data, provide more accurate queue time estimates, and increase transparency and security of medical data. 8 These studies confirm that digitisation of healthcare systems has a significant impact on hospital operational efficiency as well as improved patient experience and satisfaction. However, there are still some gaps in previous research that need to be addressed. Most previous studies have focused on administrative efficiency and medical record management, but few have explored the holistic integration of web-based applications with the overall patient experience, including non-medical services such as administrative convenience, staff friendliness, and facility cleanliness. 9 In addition, many of the systems developed are still siloed, where functionality is limited to certain aspects without any interoperability between different healthcare subsystems. Previous studies have highlighted the critical role of cloud-based systems in integrating multiple hospital functions, enabling real-time data sharing, enhancing operational efficiency, and improving the quality of patient care. Such systems facilitate interoperability across administrative, clinical, and support services, ultimately contributing to more coordinated and patient-centred healthcare delivery. 10 , 11 However, there is no integration model that explicitly examines the relationship between medical and non-medical service quality and patient satisfaction through web-based systems in the context of Indonesian hospitals. The novelty of this study lies in the development of a web-based application integration model that not only focuses on administrative efficiency, but also incorporates aspects of medical and non-medical service quality as a major factor in improving patient satisfaction. In contrast to previous studies that have focused more on the implementation of general hospital information systems. 12 This study will explore how web-based applications can serve as a catalyst for the digitisation of healthcare by improving accessibility, transparency of information, and interaction between patients and healthcare providers. The model developed in this study will consider the integration of technology with the overall user experience, including the effectiveness of apps as mediators in improving patient satisfaction with hospital services. Thus, the main objective of this study is to develop and evaluate a web-based application integration model that can improve patient satisfaction through optimising the quality of medical and non-medical services in hospitals. Specifically, this study will identify the main factors that contribute to increased patient satisfaction through the use of web-based applications, analyse the influence of medical and non-medical services on digital system adoption, and explore the role of applications as mediators in the patient experience in hospitals. This study is expected to make theoretical and practical contributions in the development of digital healthcare systems that are more adaptive and responsive to patient needs. Conceptually, this study departs from the Technology Acceptance Model (TAM) and Expectation-Confirmation Model (ECM-IT) theories which emphasise that technology adoption in healthcare depends on perceived usefulness, ease of use, and user satisfaction after system implementation. 13 , 14 With reference to this model, the study will analyse how web-based applications can meet patients' expectations of service quality, as well as how this level of satisfaction can increase long-term sustainability of system use. This approach will provide new insights into understanding the factors that drive technology adoption in the healthcare sector and the implications for patient experience. Based on theoretical reviews and previous research results, this article hypothesises that the integration of web-based services in hospital systems has a positive impact on patient satisfaction, with the quality of medical and non-medical services as the main factor in improving the effectiveness of the application. By exploring this relationship, the research is expected to fill a gap in the literature regarding the role of digital technology in healthcare system transformation, while providing policy recommendations for hospitals in optimising web-based services to improve patient satisfaction. Methods A cross-sectional analytical design was employed, conducted at Dr. Sumantri Hospital, Parepare, between July and December 2024. The target population included 10,000 patients who had received care within the preceding three months. A representative sample of 500 respondents (5% of the population) was selected using proportional random sampling based on Cochran's formula. 15 , 16 Data collection utilised a structured, web-based questionnaire to assess perceptions of medical and non-medical service quality, application usage, and satisfaction. Structural Equation Modelling (SEM) with AMOS 24.0 was applied to evaluate direct, indirect, and mediating effects. Ethical approval was granted by the Research Ethics Committee of Hasanuddin University, and informed consent was obtained from all participants. The structured web-based questionnaire used in this study was specifically developed for the purposes of this research. The instrument was designed based on dimensions adapted from the SERVQUAL model and the Expectation-Confirmation Model of Information Technology (ECM-IT). Content validity was assessed by a panel of experts from the Faculty of Public Health and Faculty of Medicine, Hasanuddin University, while reliability was evaluated using Cronbach’s alpha test. The final English version of the questionnaire has been provided as supplementary material and is cited in this manuscript. Results The analysis demonstrates that both medical and non-medical dimensions of service quality significantly affect patient engagement with the SIKAP web-based system. Notably, aspects related to non-medical services—such as administrative convenience, environmental cleanliness, and courteous interaction—exerted a stronger influence on the application’s usage rates than clinical parameters like punctuality and diagnostic precision. The structural model supports this finding, showing a standardized coefficient (β) of 1.336 (p = 0.002) for non-medical services, which exceeds the coefficient for medical services (β = 0.169, p = 0.037). These values highlight the pivotal role of patient-centered facility experience in shaping attitudes toward digital feedback tools in healthcare environments. Statistical analysis using SEM confirms that the SIKAP application functions as a significant mediating construct, reinforcing the pathway between service quality and patient satisfaction by facilitating real-time feedback and enhancing user engagement. Patients who perceived higher service quality were more likely to engage with the application, which in turn, enhanced their satisfaction through improved access to information, reduced waiting times, and better communication. To assess the overall model adequacy, we evaluated several goodness-of-fit indices. These indices are standard measures in SEM to ensure that the proposed model appropriately reflects the observed data. The model’s goodness-of-fit was tested using SEM, and the results are summarised in Table 1 . Table 1 Model Fit Indices (Goodness-of-Fit Summary) Fit Index Recommended Threshold Result Interpretation Chi-square (χ²) p > .05 254.87 Acceptable Degrees of Freedom (df) — 127 — CMIN/DF (Normed Chi-square) 0.90 0.938 Good fit Tucker-Lewis Index (TLI) > 0.90 0.922 Acceptable fit Root Mean Square Error of Approximation (RMSEA) < 0.08 0.045 Good fit Standardized Root Mean Square Residual (SRMR) < 0.08 0.039 Excellent fit The results in Table 1 demonstrate that the model fits the data well. The normed chi-square (CMIN/ DF) is within the acceptable range (< 3.00), and other indices like CFI, TLI, and RMSEA meet the recommended thresholds, indicating that the hypothesized structural model is statistically sound and appropriate for further interpretation. The path coefficients of service quality, installations, and SIKAP usage are presented in Table 2 . Table 2 Standardized Path Coefficients and Significance – Service Quality, Installations, and SIKAP Application Usage Hypothesized Path Estimate (β) C.R. p-value Sig. Description Medical Service Quality → SIKAP App Usage 0.24 3.113 0.002 Significant Positive effect Non-Medical Service Quality → SIKAP App Usage 0.994 2.01 0.044 Significant Stronger positive effect Medical Installation → Patient Satisfaction 0.632 2.729 0.006 Significant Direct positive effect Medical Installation → SIKAP App Usage 0.399 2.385 0.017 Significant Indirect link to satisfaction Non-Medical Installation → Patient Satisfaction 1.362 4.079 *** Significant Strongest direct effect Non-Medical Installation → SIKAP App Usage 0.657 3.064 0.003 Significant Positive influence on adoption ***p < .001 In Table 2 , both medical and non-medical service quality exhibited positive and significant effects on the adoption of the SIKAP application. Notably, non-medical service quality (β = 0.994, p = 0.044) exerted a stronger influence compared to medical service quality (β = 0.240, p = 0.002), underscoring the importance of administrative efficiency, cleanliness, and interpersonal service in encouraging digital engagement. Similarly, both medical and non-medical installations had a direct positive effect on patient satisfaction, with non-medical installations demonstrating the largest coefficient in the model (β = 1.362, p < 0.001). This suggests that physical comfort and supportive facilities play a critical role in shaping the patient experience. Furthermore, both installation domains positively influenced SIKAP usage, indicating that infrastructure quality both clinical and non-clinical supports patient interaction with the hospital’s digital platform. The mediating and feedback roles of the SIKAP application are illustrated in Table 3 . Table 3 Standardized Path Coefficients and Significance – Mediation and Feedback Effects of SIKAP Application Hypothesized Path Estimate (β) C.R. p-value Sig. Description SIKAP App Usage → Patient Satisfaction (mediation) 0.307 5.073 *** Significant Mediated positive effect SIKAP App Usage → Patient Satisfaction (mediation) 0.276 4.552 *** Significant Mediated effect on experience Medical Service Quality → SIKAP App Usage 0.169 2.082 0.037 Significant Smaller positive effect Non-Medical Service Quality → SIKAP App Usage 1.336 3.039 0.002 Significant Stronger predictor in combined model SIKAP App Usage → Medical Installation 0.313 2.307 0.021 Significant Feedback effect on medical operations SIKAP App Usage → Non-Medical Installation 0.374 6.459 *** Significant Feedback effect on support services SIKAP App Usage → Patient Satisfaction (direct) 0.349 5.78 *** Significant Direct and strong positive effect ***p < .001 In Table 3 , the mediating and feedback roles of the SIKAP application become evident. The application significantly mediated the relationship between installations and patient satisfaction, as shown by the paths from medical installations (β = 0.307, p < 0.001) and non-medical installations (β = 0.276, p < 0.001) to satisfaction through the app. In the combined model, non-medical service quality remained the stronger predictor of SIKAP usage (β = 1.336, p = 0.002), further highlighting the role of non-clinical services in digital adoption. The feedback effects were also notable: SIKAP usage improved perceptions of both medical installations (β = 0.313, p = 0.021) and non-medical installations (β = 0.374, p < 0.001), illustrating how digital engagement can enhance overall service delivery. Finally, SIKAP usage had a direct and substantial positive impact on patient satisfaction (β = 0.349, p < 0.001), emphasizing its central role as both a service facilitator and a satisfaction driver. The combined evidence from Table 2 and tabel 3 confirms that both medical and non-medical service dimensions significantly contribute to the adoption of the SIKAP application and to overall patient satisfaction, with non-medical factors exerting a consistently stronger influence than medical factors. This underscores the pivotal role of administrative efficiency, service environment, and interpersonal quality in driving digital health engagement. Moreover, the SIKAP application not only mediates the effects of hospital installations on patient satisfaction but also generates positive feedback loops that enhance perceptions of both medical and non-medical facilities. The combined direct, mediated, and feedback effects demonstrate that integrating digital platforms into hospital operations has a transformative impact not merely streamlining processes, but actively shaping patient experiences and improving service delivery outcomes. While clinical quality remains a critical component of hospital services, the findings reveal that non-clinical factors such as the care environment, administrative efficiency, and interpersonal engagement exert a stronger influence on patients’ willingness to adopt digital platforms like SIKAP. The application thus functions not merely as a passive access point but as an active agent in shaping patient perceptions of service quality, underscoring the strategic importance of embedding digital tools into both clinical and non-clinical dimensions of hospital operations. The findings underscore that effective hospital digital transformation requires a balanced focus on both clinical excellence and non-clinical service quality. Embedding digital tools such as the SIKAP application within a comprehensive service enhancement framework ensures that technological innovation not only optimises operational processes but also strengthens patient trust, fosters active engagement, and sustains long-term satisfaction. The final structural model, depicting the associations among service quality, SIKAP adoption, and patient satisfaction, is shown in Fig. 1. llustrates the structural equation model constructed through SEM analysis, highlighting both direct and mediated associations among the study variables. The diagram captures statistically meaningful linkages between the perceived quality of medical and non-medical services, utilization of the SIKAP application, and overall patient satisfaction. The model’s fit indices indicate a well-constructed and empirically supported framework, underscoring the critical role of digital system integration in improving service responsiveness and patient-centered outcomes in hospital setting Discussion This study provides robust empirical evidence on the pivotal role of both medical and non-medical service quality in shaping the adoption of the SIKAP web-based hospital application and its subsequent impact on patient satisfaction. The results indicate that while both service dimensions are significant predictors of application usage, non-medical service quality exerts a stronger influence (β = 0.994, p = 0.044) compared to medical service quality (β = 0.240, p = 0.002). This suggests that patients’ willingness to engage with hospital digital platforms is driven more by administrative efficiency, environmental comfort, and interpersonal interactions than by clinical factors alone. Furthermore, the analysis revealed that hospital installations both medical and non-medical—are important determinants of patient satisfaction. Non-medical installations demonstrated the largest direct effect (β = 1.362, p < 0.001), highlighting the significance of physical comfort, cleanliness, and supportive facilities in patient experience. Medical installations, while still influential (β = 0.632, p = 0.006), appeared to play a comparatively smaller role in fostering satisfaction. The mediating role of the SIKAP application was evident. It significantly mediated the relationship between medical installations and patient satisfaction (β = 0.307, p < 0.001), as well as between non-medical installations and satisfaction (β = 0.276, p < 0.001). This finding underscores the function of digital platforms as integrative tools that bridge the gap between service delivery and patient perception. By streamlining access to information, simplifying administrative procedures, and facilitating communication, the SIKAP application amplifies the positive effects of service quality on satisfaction. In addition to its mediating function, the SIKAP application exhibited feedback effects, enhancing perceptions of medical installations (β = 0.313, p = 0.021) and non-medical installations (β = 0.374, p < 0.001). This bidirectional influence illustrates how sustained use of digital platforms can improve patient perceptions of service infrastructure, potentially creating a cycle of positive engagement and satisfaction. The stronger influence of non-medical service quality on digital adoption aligns with studies by Gu et al. (2018) and Lee (2018), which reported that patient engagement with hospital IT systems is often shaped more by service convenience and communication quality than by clinical parameters. However, the magnitude of this effect in the present study is notably higher, suggesting that in emerging healthcare systems such as Indonesia, service environment and administrative processes may carry greater weight in influencing patient behaviour. The novelty of this research lies in empirically demonstrating that non-medical service quality is a stronger determinant of digital health application adoption than medical service quality. This finding extends the applicability of the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model of Information Technology (ECM-IT) by integrating both clinical and non-clinical service dimensions into a unified adoption–satisfaction framework. The results also enrich the SERVQUAL model by providing evidence that, in the context of hospital digitalization, non-clinical dimensions such as empathy, responsiveness, and tangible facilities may outweigh reliability and assurance in predicting technology use. For hospital administrators, the findings emphasize that digital transformation initiatives should not focus solely on clinical excellence or technological sophistication. Investments in digital platforms like SIKAP should be complemented by parallel improvements in patient-facing administrative processes, staff training in communication and hospitality, and the upgrading of non-clinical facilities. Such an integrated approach can foster higher patient trust, engagement, and satisfaction. Future studies should employ longitudinal and multi-site designs to capture changes over time and enhance external validity. Additionally, exploring moderating variables such as digital literacy, age, and health status could provide deeper insights into patient adoption patterns. Incorporating qualitative methods may also help to contextualize quantitative findings, offering richer understanding of patient experiences with hospital digital platforms. This study has several limitations. First, its cross-sectional design prevents us from drawing causal inferences between variables. Second, data were collected from a single hospital, which may limit the generalisability of the findings to other healthcare settings. Third, patient satisfaction was assessed through self-reported questionnaires, which are subject to potential response bias. Future research should employ longitudinal designs across multiple hospitals and integrate qualitative approaches to capture deeper insights into patient experiences with hospital digital platforms. Conclusion The findings of this study demonstrate that the integration of a web-based hospital application such as SIKAP with both clinical and non-clinical service quality can substantially enhance patient satisfaction, strengthen trust, and support sustainable digital transformation in healthcare. A key contribution of this research lies in its empirical evidence that non-clinical factors such as administrative efficiency, service environment, and interpersonal interactions are more decisive in driving digital adoption than clinical quality alone. By embedding these dimensions alongside clinical excellence within a unified adoption–satisfaction framework, the study advances the theoretical scope of the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model of Information Technology (ECM-IT) toward a more holistic approach to healthcare digitalization. From a practical perspective, these insights suggest that hospital digital transformation strategies should maintain a balanced emphasis on clinical and non-clinical domains, ensuring that digital tools like SIKAP are implemented within a comprehensive service improvement framework. Such integration ensures that technological innovation not only streamlines operational processes but also fosters patient trust, engagement, and sustained satisfaction. While these findings provide strong evidence for the role of web-based applications in enhancing patient satisfaction, they should be interpreted within the context of the study’s limitations. Further research across diverse healthcare settings is required to confirm the applicability of the SIKAP model more broadly. Declarations Acknowledgements The authors would like to thank the management and staff of Dr. Sumantri Hospital, Parepare, for their full cooperation during data collection. We also acknowledge the valuable assistance of the research team from the Faculty of Public Health and Faculty of Medicine, Hasanuddin University, for their support in study design, instrument validation, and technical guidance. Special thanks are extended to all patients who participated and provided valuable feedback to improve the SIKAP application. Contributors All authors contributed to the study design, data collection, analysis, interpretation of results, and preparation of the manuscript. A.N.A. is the guarantor responsible for the integrity of the work. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Competing interests None declared. Patient consent for publication Not applicable. Ethics approval Ethics approval was obtained from the Research Ethics Committee of Hasanuddin University (Approval No: 009/KEPK FKG-RSGMP UH/EE/X/2024). All procedures performed in this study were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. All participants provided informed consent before participating in the study. Data availability statement The datasets generated and/or analysed during the current study are not publicly available due to ethical restrictions, as they contain information that could compromise the privacy of research participants. However, data are available from the corresponding author on reasonable request. Requests should be directed to Asram Nur Anas ( [email protected] ). Provenance and peer review Not commissioned; externally peer reviewed. Data availability statement Data are available upon reasonable request. Due to ethical restrictions, the dataset contains information that could compromise the privacy of research participants and is therefore not publicly available. Requests for data access should be directed to the corresponding author. References Anandan A. Hospital Management System Using Python Django. Interantional J Sci Res Eng Manage. 2024;08(09):1–15. Dotel S. Hospital Management System Based on Web. Interantional J Sci Res Eng Manage. 2024;08(05):1–5. 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Structural model generated from AMOS SEM analysis, showing standardized coefficients, error terms, and the associations among service quality, application engagement, and patient satisfaction. Fit indices confirm excellent model alignment (CMIN/DF = 1.176, RMSEA = 0.0035, CFI = 0.963).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446810/v1/9a11c86de30d587c5bc46ca8.jpeg"},{"id":93655181,"identity":"806e19ff-0a17-4f52-9bd6-8a558713b0ee","added_by":"auto","created_at":"2025-10-16 06:53:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":882416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7446810/v1/47d7bf7a-2fab-4151-8e9b-ab0bd4084dc2.pdf"},{"id":91510709,"identity":"32d4cf7e-3e2d-4daa-aa21-cd570d35202d","added_by":"auto","created_at":"2025-09-17 08:45:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":230590,"visible":true,"origin":"","legend":"","description":"","filename":"Doc111.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7446810/v1/1d8253606d7c51b4fd791a6f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Web-based Application Integration to Improve Patient Satisfaction: A Model for Improving the Quality of Health Services in Hospitals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent decades, the development of information technology has revolutionised various sectors, including healthcare. Digital transformation in the healthcare system is increasingly becoming an urgent need as patients demand faster, more efficient and accessible services. One of the rapidly growing innovations is the integration of web-based applications in hospital management systems, which enables the optimisation of various aspects of services, from patient registration, medical record management, to scheduling and communication between patients and medical personnel.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Despite the widespread adoption of these technologies, hospitals still face challenges in effectively integrating digital systems into their operations, resulting in suboptimal service quality and patient satisfaction levels. Therefore, this study focuses on the integration of web-based applications as a model for improving the quality of healthcare services aimed at increasing patient satisfaction.\u003c/p\u003e\u003cp\u003eSeveral previous studies have examined the impact of digital hospital management systems on improving healthcare efficiency.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In a study showed that web-based hospital management systems can improve coordination between patients and healthcare personnel through real-time information updates.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Similarly, integrating Firebase and an intelligent chatbot in a hospital management system proved that the application of web-based technology can accelerate access to information, improve medication compliance, and simplify administrative processes.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In addition, research at an Atlantic clinic revealed that a web-based service system can overcome inefficiencies in recording and searching patient data, provide more accurate queue time estimates, and increase transparency and security of medical data.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e These studies confirm that digitisation of healthcare systems has a significant impact on hospital operational efficiency as well as improved patient experience and satisfaction.\u003c/p\u003e\u003cp\u003eHowever, there are still some gaps in previous research that need to be addressed. Most previous studies have focused on administrative efficiency and medical record management, but few have explored the holistic integration of web-based applications with the overall patient experience, including non-medical services such as administrative convenience, staff friendliness, and facility cleanliness. \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e In addition, many of the systems developed are still siloed, where functionality is limited to certain aspects without any interoperability between different healthcare subsystems. Previous studies have highlighted the critical role of cloud-based systems in integrating multiple hospital functions, enabling real-time data sharing, enhancing operational efficiency, and improving the quality of patient care. Such systems facilitate interoperability across administrative, clinical, and support services, ultimately contributing to more coordinated and patient-centred healthcare delivery. \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e However, there is no integration model that explicitly examines the relationship between medical and non-medical service quality and patient satisfaction through web-based systems in the context of Indonesian hospitals.\u003c/p\u003e\u003cp\u003eThe novelty of this study lies in the development of a web-based application integration model that not only focuses on administrative efficiency, but also incorporates aspects of medical and non-medical service quality as a major factor in improving patient satisfaction. In contrast to previous studies that have focused more on the implementation of general hospital information systems.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e This study will explore how web-based applications can serve as a catalyst for the digitisation of healthcare by improving accessibility, transparency of information, and interaction between patients and healthcare providers. The model developed in this study will consider the integration of technology with the overall user experience, including the effectiveness of apps as mediators in improving patient satisfaction with hospital services.\u003c/p\u003e\u003cp\u003eThus, the main objective of this study is to develop and evaluate a web-based application integration model that can improve patient satisfaction through optimising the quality of medical and non-medical services in hospitals. Specifically, this study will identify the main factors that contribute to increased patient satisfaction through the use of web-based applications, analyse the influence of medical and non-medical services on digital system adoption, and explore the role of applications as mediators in the patient experience in hospitals. This study is expected to make theoretical and practical contributions in the development of digital healthcare systems that are more adaptive and responsive to patient needs.\u003c/p\u003e\u003cp\u003eConceptually, this study departs from the Technology Acceptance Model (TAM) and Expectation-Confirmation Model (ECM-IT) theories which emphasise that technology adoption in healthcare depends on perceived usefulness, ease of use, and user satisfaction after system implementation.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e With reference to this model, the study will analyse how web-based applications can meet patients' expectations of service quality, as well as how this level of satisfaction can increase long-term sustainability of system use. This approach will provide new insights into understanding the factors that drive technology adoption in the healthcare sector and the implications for patient experience.\u003c/p\u003e\u003cp\u003eBased on theoretical reviews and previous research results, this article hypothesises that the integration of web-based services in hospital systems has a positive impact on patient satisfaction, with the quality of medical and non-medical services as the main factor in improving the effectiveness of the application. By exploring this relationship, the research is expected to fill a gap in the literature regarding the role of digital technology in healthcare system transformation, while providing policy recommendations for hospitals in optimising web-based services to improve patient satisfaction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA cross-sectional analytical design was employed, conducted at Dr. Sumantri Hospital, Parepare, between July and December 2024. The target population included 10,000 patients who had received care within the preceding three months. A representative sample of 500 respondents (5% of the population) was selected using proportional random sampling based on Cochran's formula. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Data collection utilised a structured, web-based questionnaire to assess perceptions of medical and non-medical service quality, application usage, and satisfaction. Structural Equation Modelling (SEM) with AMOS 24.0 was applied to evaluate direct, indirect, and mediating effects. Ethical approval was granted by the Research Ethics Committee of Hasanuddin University, and informed consent was obtained from all participants. The structured web-based questionnaire used in this study was specifically developed for the purposes of this research. The instrument was designed based on dimensions adapted from the SERVQUAL model and the Expectation-Confirmation Model of Information Technology (ECM-IT). Content validity was assessed by a panel of experts from the Faculty of Public Health and Faculty of Medicine, Hasanuddin University, while reliability was evaluated using Cronbach\u0026rsquo;s alpha test. The final English version of the questionnaire has been provided as supplementary material and is cited in this manuscript.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis demonstrates that both medical and non-medical dimensions of service quality significantly affect patient engagement with the SIKAP web-based system. Notably, aspects related to non-medical services\u0026mdash;such as administrative convenience, environmental cleanliness, and courteous interaction\u0026mdash;exerted a stronger influence on the application\u0026rsquo;s usage rates than clinical parameters like punctuality and diagnostic precision. The structural model supports this finding, showing a standardized coefficient (β) of 1.336 (p\u0026thinsp;=\u0026thinsp;0.002) for non-medical services, which exceeds the coefficient for medical services (β\u0026thinsp;=\u0026thinsp;0.169, p\u0026thinsp;=\u0026thinsp;0.037). These values highlight the pivotal role of patient-centered facility experience in shaping attitudes toward digital feedback tools in healthcare environments.\u003c/p\u003e\u003cp\u003eStatistical analysis using SEM confirms that the SIKAP application functions as a significant mediating construct, reinforcing the pathway between service quality and patient satisfaction by facilitating real-time feedback and enhancing user engagement. Patients who perceived higher service quality were more likely to engage with the application, which in turn, enhanced their satisfaction through improved access to information, reduced waiting times, and better communication. To assess the overall model adequacy, we evaluated several goodness-of-fit indices. These indices are standard measures in SEM to ensure that the proposed model appropriately reflects the observed data.\u003c/p\u003e\u003cp\u003eThe model\u0026rsquo;s goodness-of-fit was tested using SEM, and the results are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Fit Indices (Goodness-of-Fit Summary)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFit Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecommended Threshold\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChi-square (χ\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e254.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcceptable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegrees of Freedom (df)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMIN/DF (Normed Chi-square)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparative Fit Index (CFI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTucker-Lewis Index (TLI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcceptable fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoot Mean Square Error of Approximation (RMSEA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandardized Root Mean Square Residual (SRMR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExcellent fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrate that the model fits the data well. The normed chi-square (CMIN/ DF) is within the acceptable range (\u0026lt;\u0026thinsp;3.00), and other indices like CFI, TLI, and RMSEA meet the recommended thresholds, indicating that the hypothesized structural model is statistically sound and appropriate for further interpretation.\u003c/p\u003e\u003cp\u003eThe path coefficients of service quality, installations, and SIKAP usage are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStandardized Path Coefficients and Significance \u0026ndash; Service Quality, Installations, and SIKAP Application Usage\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesized Path\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC.R.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Service Quality \u0026rarr; SIKAP App Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePositive effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Medical Service Quality \u0026rarr; SIKAP App Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStronger positive effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Installation \u0026rarr; Patient Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDirect positive effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Installation \u0026rarr; SIKAP App Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIndirect link to satisfaction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Medical Installation \u0026rarr; Patient Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrongest direct effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Medical Installation \u0026rarr; SIKAP App Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePositive influence on adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, both medical and non-medical service quality exhibited positive and significant effects on the adoption of the SIKAP application. Notably, non-medical service quality (β\u0026thinsp;=\u0026thinsp;0.994, p\u0026thinsp;=\u0026thinsp;0.044) exerted a stronger influence compared to medical service quality (β\u0026thinsp;=\u0026thinsp;0.240, p\u0026thinsp;=\u0026thinsp;0.002), underscoring the importance of administrative efficiency, cleanliness, and interpersonal service in encouraging digital engagement. Similarly, both medical and non-medical installations had a direct positive effect on patient satisfaction, with non-medical installations demonstrating the largest coefficient in the model (β\u0026thinsp;=\u0026thinsp;1.362, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that physical comfort and supportive facilities play a critical role in shaping the patient experience. Furthermore, both installation domains positively influenced SIKAP usage, indicating that infrastructure quality both clinical and non-clinical supports patient interaction with the hospital\u0026rsquo;s digital platform.\u003c/p\u003e\u003cp\u003eThe mediating and feedback roles of the SIKAP application are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStandardized Path Coefficients and Significance \u0026ndash; Mediation and Feedback Effects of SIKAP Application\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesized Path\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC.R.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIKAP App Usage \u0026rarr; Patient Satisfaction (mediation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMediated positive effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIKAP App Usage \u0026rarr; Patient Satisfaction (mediation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMediated effect on experience\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Service Quality \u0026rarr; SIKAP App Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSmaller positive effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Medical Service Quality \u0026rarr; SIKAP App Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStronger predictor in combined model\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIKAP App Usage \u0026rarr; Medical Installation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFeedback effect on medical operations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIKAP App Usage \u0026rarr; Non-Medical Installation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFeedback effect on support services\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIKAP App Usage \u0026rarr; Patient Satisfaction (direct)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDirect and strong positive effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the mediating and feedback roles of the SIKAP application become evident. The application significantly mediated the relationship between installations and patient satisfaction, as shown by the paths from medical installations (β\u0026thinsp;=\u0026thinsp;0.307, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and non-medical installations (β\u0026thinsp;=\u0026thinsp;0.276, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) to satisfaction through the app. In the combined model, non-medical service quality remained the stronger predictor of SIKAP usage (β\u0026thinsp;=\u0026thinsp;1.336, p\u0026thinsp;=\u0026thinsp;0.002), further highlighting the role of non-clinical services in digital adoption. The feedback effects were also notable: SIKAP usage improved perceptions of both medical installations (β\u0026thinsp;=\u0026thinsp;0.313, p\u0026thinsp;=\u0026thinsp;0.021) and non-medical installations (β\u0026thinsp;=\u0026thinsp;0.374, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), illustrating how digital engagement can enhance overall service delivery. Finally, SIKAP usage had a direct and substantial positive impact on patient satisfaction (β\u0026thinsp;=\u0026thinsp;0.349, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), emphasizing its central role as both a service facilitator and a satisfaction driver.\u003c/p\u003e\u003cp\u003eThe combined evidence from Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and tabel 3 confirms that both medical and non-medical service dimensions significantly contribute to the adoption of the SIKAP application and to overall patient satisfaction, with non-medical factors exerting a consistently stronger influence than medical factors. This underscores the pivotal role of administrative efficiency, service environment, and interpersonal quality in driving digital health engagement.\u003c/p\u003e\u003cp\u003eMoreover, the SIKAP application not only mediates the effects of hospital installations on patient satisfaction but also generates positive feedback loops that enhance perceptions of both medical and non-medical facilities. The combined direct, mediated, and feedback effects demonstrate that integrating digital platforms into hospital operations has a transformative impact not merely streamlining processes, but actively shaping patient experiences and improving service delivery outcomes. While clinical quality remains a critical component of hospital services, the findings reveal that non-clinical factors such as the care environment, administrative efficiency, and interpersonal engagement exert a stronger influence on patients\u0026rsquo; willingness to adopt digital platforms like SIKAP. The application thus functions not merely as a passive access point but as an active agent in shaping patient perceptions of service quality, underscoring the strategic importance of embedding digital tools into both clinical and non-clinical dimensions of hospital operations.\u003c/p\u003e\u003cp\u003eThe findings underscore that effective hospital digital transformation requires a balanced focus on both clinical excellence and non-clinical service quality. Embedding digital tools such as the SIKAP application within a comprehensive service enhancement framework ensures that technological innovation not only optimises operational processes but also strengthens patient trust, fosters active engagement, and sustains long-term satisfaction.\u003c/p\u003e\u003cp\u003eThe final structural model, depicting the associations among service quality, SIKAP adoption, and patient satisfaction, is shown in Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003ellustrates the structural equation model constructed through SEM analysis, highlighting both direct and mediated associations among the study variables. The diagram captures statistically meaningful linkages between the perceived quality of medical and non-medical services, utilization of the SIKAP application, and overall patient satisfaction. The model\u0026rsquo;s fit indices indicate a well-constructed and empirically supported framework, underscoring the critical role of digital system integration in improving service responsiveness and patient-centered outcomes in hospital setting\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides robust empirical evidence on the pivotal role of both medical and non-medical service quality in shaping the adoption of the SIKAP web-based hospital application and its subsequent impact on patient satisfaction. The results indicate that while both service dimensions are significant predictors of application usage, non-medical service quality exerts a stronger influence (β\u0026thinsp;=\u0026thinsp;0.994, p\u0026thinsp;=\u0026thinsp;0.044) compared to medical service quality (β\u0026thinsp;=\u0026thinsp;0.240, p\u0026thinsp;=\u0026thinsp;0.002). This suggests that patients\u0026rsquo; willingness to engage with hospital digital platforms is driven more by administrative efficiency, environmental comfort, and interpersonal interactions than by clinical factors alone.\u003c/p\u003e\u003cp\u003eFurthermore, the analysis revealed that hospital installations both medical and non-medical\u0026mdash;are important determinants of patient satisfaction. Non-medical installations demonstrated the largest direct effect (β\u0026thinsp;=\u0026thinsp;1.362, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting the significance of physical comfort, cleanliness, and supportive facilities in patient experience. Medical installations, while still influential (β\u0026thinsp;=\u0026thinsp;0.632, p\u0026thinsp;=\u0026thinsp;0.006), appeared to play a comparatively smaller role in fostering satisfaction.\u003c/p\u003e\u003cp\u003eThe mediating role of the SIKAP application was evident. It significantly mediated the relationship between medical installations and patient satisfaction (β\u0026thinsp;=\u0026thinsp;0.307, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as between non-medical installations and satisfaction (β\u0026thinsp;=\u0026thinsp;0.276, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding underscores the function of digital platforms as integrative tools that bridge the gap between service delivery and patient perception. By streamlining access to information, simplifying administrative procedures, and facilitating communication, the SIKAP application amplifies the positive effects of service quality on satisfaction.\u003c/p\u003e\u003cp\u003eIn addition to its mediating function, the SIKAP application exhibited feedback effects, enhancing perceptions of medical installations (β\u0026thinsp;=\u0026thinsp;0.313, p\u0026thinsp;=\u0026thinsp;0.021) and non-medical installations (β\u0026thinsp;=\u0026thinsp;0.374, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This bidirectional influence illustrates how sustained use of digital platforms can improve patient perceptions of service infrastructure, potentially creating a cycle of positive engagement and satisfaction.\u003c/p\u003e\u003cp\u003eThe stronger influence of non-medical service quality on digital adoption aligns with studies by Gu et al. (2018) and Lee (2018), which reported that patient engagement with hospital IT systems is often shaped more by service convenience and communication quality than by clinical parameters. However, the magnitude of this effect in the present study is notably higher, suggesting that in emerging healthcare systems such as Indonesia, service environment and administrative processes may carry greater weight in influencing patient behaviour.\u003c/p\u003e\u003cp\u003eThe novelty of this research lies in empirically demonstrating that non-medical service quality is a stronger determinant of digital health application adoption than medical service quality. This finding extends the applicability of the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model of Information Technology (ECM-IT) by integrating both clinical and non-clinical service dimensions into a unified adoption\u0026ndash;satisfaction framework. The results also enrich the SERVQUAL model by providing evidence that, in the context of hospital digitalization, non-clinical dimensions such as empathy, responsiveness, and tangible facilities may outweigh reliability and assurance in predicting technology use.\u003c/p\u003e\u003cp\u003eFor hospital administrators, the findings emphasize that digital transformation initiatives should not focus solely on clinical excellence or technological sophistication. Investments in digital platforms like SIKAP should be complemented by parallel improvements in patient-facing administrative processes, staff training in communication and hospitality, and the upgrading of non-clinical facilities. Such an integrated approach can foster higher patient trust, engagement, and satisfaction.\u003c/p\u003e\u003cp\u003eFuture studies should employ longitudinal and multi-site designs to capture changes over time and enhance external validity. Additionally, exploring moderating variables such as digital literacy, age, and health status could provide deeper insights into patient adoption patterns. Incorporating qualitative methods may also help to contextualize quantitative findings, offering richer understanding of patient experiences with hospital digital platforms.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, its cross-sectional design prevents us from drawing causal inferences between variables. Second, data were collected from a single hospital, which may limit the generalisability of the findings to other healthcare settings. Third, patient satisfaction was assessed through self-reported questionnaires, which are subject to potential response bias. Future research should employ longitudinal designs across multiple hospitals and integrate qualitative approaches to capture deeper insights into patient experiences with hospital digital platforms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study demonstrate that the integration of a web-based hospital application such as SIKAP with both clinical and non-clinical service quality can substantially enhance patient satisfaction, strengthen trust, and support sustainable digital transformation in healthcare. A key contribution of this research lies in its empirical evidence that non-clinical factors such as administrative efficiency, service environment, and interpersonal interactions are more decisive in driving digital adoption than clinical quality alone. By embedding these dimensions alongside clinical excellence within a unified adoption\u0026ndash;satisfaction framework, the study advances the theoretical scope of the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model of Information Technology (ECM-IT) toward a more holistic approach to healthcare digitalization. From a practical perspective, these insights suggest that hospital digital transformation strategies should maintain a balanced emphasis on clinical and non-clinical domains, ensuring that digital tools like SIKAP are implemented within a comprehensive service improvement framework. Such integration ensures that technological innovation not only streamlines operational processes but also fosters patient trust, engagement, and sustained satisfaction. While these findings provide strong evidence for the role of web-based applications in enhancing patient satisfaction, they should be interpreted within the context of the study\u0026rsquo;s limitations. Further research across diverse healthcare settings is required to confirm the applicability of the SIKAP model more broadly.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the management and staff of Dr. Sumantri Hospital, Parepare, for their full cooperation during data collection. We also acknowledge the valuable assistance of the research team from the Faculty of Public Health and Faculty of Medicine, Hasanuddin University, for their support in study design, instrument validation, and technical guidance. Special thanks are extended to all patients who participated and provided valuable feedback to improve the SIKAP application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study design, data collection, analysis, interpretation of results, and preparation of the manuscript. \u003cstrong\u003eA.N.A.\u0026nbsp;\u003c/strong\u003eis the guarantor responsible for the integrity of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was obtained from the Research Ethics Committee of Hasanuddin University \u003cstrong\u003e(Approval No: 009/KEPK FKG-RSGMP UH/EE/X/2024).\u003c/strong\u003e All procedures performed in this study were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. All participants provided informed consent before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to ethical restrictions, as they contain information that could compromise the privacy of research participants. However, data are available from the corresponding author on reasonable request. Requests should be directed to Asram Nur Anas ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProvenance and peer review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot commissioned; externally peer reviewed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request. Due to ethical restrictions, the dataset contains information that could compromise the privacy of research participants and is therefore not publicly available. Requests for data access should be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnandan A. Hospital Management System Using Python Django. Interantional J Sci Res Eng Manage. 2024;08(09):1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDotel S. Hospital Management System Based on Web. Interantional J Sci Res Eng Manage. 2024;08(05):1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarbieri C, Neri L, Stuard S, Mari F, Mart\u0026iacute;n-Guerrero JD. From electronic health records to clinical management systems: how the digital transformation can support healthcare services. Clin Kidney J. 2023;16(11):1878\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeasberry J, Scott IA, Sullivan C, Staib A, Ashby R. Going digital: A narrative overview of the clinical and organisational impacts of eHealth technologies in hospital practice. Aust Health Rev. 2017;41(6):646.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Kahtani N, Alruwaie S, Al-Zahrani BM, Abumadini RA, Aljaafary A, Hariri B et al. Digital health transformation in Saudi Arabia: A cross-sectional analysis using Healthcare Information and Management Systems Society\u0026rsquo; digital health indicators. Digit Health. 2022;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoshiura VT, Azevedo-Marques JM, Rzewuska M, Vinci ALT, Sasso AM, Miyoshi NSB, et al. A web-based information system for a regional public mental healthcare service network in Brazil. Int J Mental Health Syst. 2017;11(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhanem SI, Hamshary E, Matar L, Morsy A. Enhancing Healthcare Service with Firebase Integration and Intelligent Chatbot Deployment. 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE; 2024. p. 550.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValentin MRB, Butsianto S, Fatchan M. Website Based Clinic Health Service Application Model. Riwayat: Educational Journal of History and Humanities. 2024;7(3):971\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTiriteu S, Cosma A, Pacuraru M, Zamfir A, Chirvase S. Improved Healthcare Quality Through Integrated Hospital Management and Digitalization. IBIMA Business Review. 2024;2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen PT, Chen JH. Implementing cloud-based medical systems in hospitals and strategic implications. Technol Anal Strateg Manag. 2015;27(2):198\u0026ndash;218.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarcu R, Popescu D, Danila I. Healthcare integration based on cloud computing. UPB Sci Bull Ser C: Electr Eng Comput Sci. 2015;77(2):31\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHemkiran S, War MM, Aadhithiyan KS, Kabilan K. Web-Based Patient Health Management System with Doctor Recommendations and Medicine Alternatives using Machine Learning. In: 2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC). IEEE; 2024. pp. 68\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu D, Yang X, Li X, Jain HK, Liang C. Understanding the role of mobile internet-based health services on patient satisfaction and word-of-mouth. Int J Environ Res Public Health. 2018;15(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee D. Strategies for technology-driven service encounters for patient experience satisfaction in hospitals. Technol Forecast Soc Chang. 2018;137:118\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapadopoulos T, Abrahim A, Sergelidis D, Bitchava K. Original article Ερευνητική. 2011;2(January):119\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMehrabian F, Gilani MHN, doust, Almaee A. Patient Satisfaction with the Quality of Health Services Provided by Public Hospitals in Rasht, Iran. J Holist Nurs Midwifery. 2021;31(1):17\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"healthcare informatics, patient satisfaction, service quality, web-based application, digital health transformation","lastPublishedDoi":"10.21203/rs.3.rs-7446810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7446810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo develop and evaluate an integrated web-based application model (SIKAP) to improve patient satisfaction by optimising both medical and non-medical service quality, and to assess the application’s mediating role in linking service quality to satisfaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a cross-sectional analytical study at Dr. Sumantri Hospital, Parepare, Indonesia, from July to December 2024. A total of 500 patients were recruited using proportional random sampling. Data were collected via a structured web-based questionnaire measuring perceptions of service quality, application usage, and satisfaction. Structural Equation Modelling (SEM) with AMOS v24.0 was used to test direct, indirect, and feedback relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eNon-medical service quality exerted a stronger influence on SIKAP adoption than medical service quality. Both service domains significantly predicted patient satisfaction, with SIKAP serving as a mediator that improved access to information, reduced waiting times, and enhanced communication. Feedback effects indicated that application use positively influenced perceptions of hospital services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIntegrating web-based applications like SIKAP with high-quality medical and non-medical services substantially enhances patient satisfaction. These findings extend existing healthcare informatics models by demonstrating the importance of non-clinical factors in digital adoption.\u003c/p\u003e","manuscriptTitle":"Web-based Application Integration to Improve Patient Satisfaction: A Model for Improving the Quality of Health Services in Hospitals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 08:44:58","doi":"10.21203/rs.3.rs-7446810/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7f4cc78-73a3-4238-80c0-1004a717ce7d","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-16T06:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 08:44:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7446810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7446810","identity":"rs-7446810","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}