Digital maturity index instrument development for primary health care in Indonesia: a sequential exploratory mixed-methods approach.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Health Serv Res . 2026 Feb 21;26:523. doi: 10.1186/s12913-026-14193-y Search in PMC Search in PubMed View in NLM Catalog Add to search Digital maturity index instrument development for primary health care in Indonesia: a sequential exploratory mixed-methods approach Steward John Anwar Steward John Anwar 1 Master Program of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia Find articles by Steward John Anwar 1, ✉ , Deni Kurniadi Sunjaya Deni Kurniadi Sunjaya 2 Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia Find articles by Deni Kurniadi Sunjaya 2, ✉ , Lukman Hilfi Lukman Hilfi 2 Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia Find articles by Lukman Hilfi 2 , Fedri R Rinawan Fedri R Rinawan 2 Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia Find articles by Fedri R Rinawan 2 , Mulya Nurmansyah Ardisasmita Mulya Nurmansyah Ardisasmita 2 Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia Find articles by Mulya Nurmansyah Ardisasmita 2 , Dewi Marhaeni Diah Herawati Dewi Marhaeni Diah Herawati 2 Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia Find articles by Dewi Marhaeni Diah Herawati 2 Author information Article notes Copyright and License information 1 Master Program of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia 2 Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia ✉ Corresponding author. Received 2025 Dec 4; Accepted 2026 Feb 9; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13081542  PMID: 41723455 Abstract Background Digital health transformation in Indonesia is a comprehensive digitalization strategy aimed at improving health services, particularly in primary health care (PHC). There are no digital maturity instrument tools available to measure the digitalization level in PHC yet. This study aims to develop a particular digital maturity index (DMI) instrument to measure the digital maturity in the PHC. Method The research design employed a Sequential Exploratory Mixed-Methods Approach. First phase, A qualitative data collection conducted through a Focused Group Discussion. Forty-one health care workers in PHCs from South Sulawesi and West Java provinces were involved as participants. Participants were selected using criterion-based purposive sampling. Qualitative data were processed using the ScreenQ software, and content analysis was conducted. Afterward, two quantitative research instrument prototypes were built using a predetermined theory. The quantitative phase of the survey involved 30 health care workers in the same provinces, with one respondent representing one PHC. The data obtained is transformed and analyzed using Rasch modeling. The maturity levels are categorized into “Basic”, “Utilization”, “Development”, “Coordination”, and “Sustainable”. A comparison of the two prototypes was then analyzed to determine the most significant one based on the most valid and reliable result. Result Qualitative analysis generated five dimensions of the digital maturity index: Stewardship, Human Resources, Infrastructure, Information System, and Health Facility. The reliability, index separation, and Cronbach’s alpha for prototype version 1 are 0.91, 3.10, and 0.95, respectively. The reliability, index separation, and Cronbach’s alpha for prototype version 2 are 0.76, 1.79, and 0.98, respectively. The maturity index based on both prototypes showed that 87% of total PHCs are in the development level. Conclusion The Digital Maturity Instrument version 1 demonstrates promising preliminary psychometric properties and is suitable for further large-scale validation. Supplementary information The online version contains supplementary material available at 10.1186/s12913-026-14193-y. Keywords: DMI instrument, Digitalization and information system, Primary health care (PHC) Introduction Digital technologies are rapidly reshaping how health systems generate, manage, and intensify information. Globally, digital health is recognized not merely as a set of tools but as a strategic enabler that supports universal health coverage (UHC), improves system efficiency, and strengthens data-driven decision making. [ 1 , 2 ] In Indonesia—an archipelagic nation with vast geographic and demographic diversity—the promise of digital health for expanding access and improving quality is particularly salient. [ 3 ] Primary health centers (PHC), namely Puskesmas, are the linchpins of Indonesia’s primary care network; their readiness to adopt, sustain, and benefit from digital technologies determines whether digital transformation will translate into better population health outcomes. Despite national policy momentum, systematic, context-sensitive measurement of digital maturity at the primary care level remains limited. Indonesia has begun building and developing a digital health system since the emergence of various health programs that require comprehensive recording and reporting. Furthermore, the WHO standardized each country to develop a digital-based health system (eHealth) to improve the health services and access, and to expand the affordable and universal health coverage. The government of Indonesia supported the development of this digitalization system, since the information plays a vital role in decision-making and policy considerations. While a government policy is the legal basis for the implementation of the information system, and has become a reference in the implementation of the information system, in practice, the information system still encounters several obstacles, including a suboptimal and non-integrated system. The development of a Digital Maturity Index (DMI) instrument, often guided by SOCI, involves assessing various aspects of digital capability, infrastructure, and strategic alignment within a health system [ 4 – 6 ]. This systematic approach is critical given the complexity of healthcare environments and the significant investments required for successful HIS implementations Indonesia, through the Ministry of Health’s Digital Transformation Office (DTO), in collaboration with the USAID Center of Health and Information System (CHISU), implemented an assessment tool known as the HIS Stages of Continuous Improvement or SOCI. SOCI itself is a measurement with 5 continuous measurement scales that assess: (1) Emerging (ad hoc), (2) Repeatable, (3) Defined, (4) Managed, and (5) Optimal. Each stage in SOCI measures components of the HIS, determines the level of future maturity, and explains the possible change plans to realize a good and strong HIS. SOCI itself has been piloted in DKI Jakarta, East Java, and South Sulawesi provinces. The use of the SOCI method by the DTO and the Ministry of Health is a long-standing choice that has been made by consensus nationally, which also takes into account sub-national conditions, indicating that digital maturity is at level 2.49 [ 7 ]. The DMI measurement tool was developed but limited to macro-level DMI measurement (in Health offices and hospitals). The measurement tool for assessing PHC level has not yet been developed. This study aims to develop and pilot a Digital Maturity Instrument tailored for PHC, with field testing in two contrasting provinces: West Java and South Sulawesi. Research method Context of study This study was conducted in South Sulawesi and West Java as part of Indonesia’s broader digital health transformation strategy, which seeks to strengthen health services through systematic digitalization. Primary Health Centers (PHCs), serving as the frontline of healthcare delivery, were the central focus of the research due to their critical role in providing accessible and equitable services. Despite ongoing efforts in digital health reform, no standardized instrument has been available to assess the level of digital maturity in PHCs. To address this gap, the study was designed to develop and validate a Digital Maturity Index (DMI) instrument tailored to the Indonesian context. The selected province was chosen due to its progress in digitalization, ongoing primary healthcare transformation efforts, its diverse population, and its relevance as a representative area of primary healthcare diversity. These challenges include limited governance support and funding, health worker capacity, and the level of digitalization in Health informatics. Study design This study employs a sequential exploratory mixed-methods design within a constructivist paradigm , emphasizing the role of social interactions and knowledge in shaping readiness and capacity. The application of a mixed-methods approach, specifically an exploratory sequential design, is strategically employed to ensure that qualitative insights gathered in an initial phase directly inform and enrich the subsequent quantitative measurement of complex constructs. [ 8 , 9 ] This methodological choice is particularly beneficial for developing contextually relevant and theoretically grounded quantitative instruments. The phases of an exploratory sequential design typically involve: Phase 1. Qualitative study (focus group discussion) The first phase investigated health care reporting and recording workers (HCW RR) knowledge of Digital maturity or digitalization in the PHC using a qualitative methodology. In cooperation with the Province and District Health Office (DHO) and local PHCs, this qualitative phase involved multiple focus group discussions (FGDs) conducted across Primary Health Centers (PHCs) in West Java and South Sulawesi, with a total of 41 health care workers (HCWs) responsible for reporting and recording (RR) participating. Participants were selected using criterion-based purposive sampling, with inclusion criteria comprising active responsibility for RR or health information system (HIS) tasks at the PHC level, a minimum of one year of work experience, and direct involvement in digital health systems or applications. All FGDs were audio-recorded and transcribed verbatim. Transcripts were imported into ScreenQ, a computer-assisted qualitative data analysis software (CAQDAS), and analyzed using an inductive content analysis approach. Initial open coding generated 55 codes, which were iteratively reviewed, refined, and clustered into 14 sub-themes. These sub-themes were subsequently synthesized into five overarching domains reflecting key dimensions of digital maturity at the PHC level. Theme development was guided by frequency, consistency across FGDs, and relevance to digital maturity, rather than numerical weighting. The resulting domains and sub-themes directly informed the construction of items for the quantitative Digital Maturity Index (DMI) instrument. Formulating the questionnaire for FGD, following Front End of Innovation (FEI) by Tramblay et al., (Appendix 1 ) the following topics were identified as pertinent to each stage: scope, goals, important success factors and difficulties, process, methods and tools, and inputs and outputs.[ 10 ] The result of the collected FGD transcript, analyzed using ScreenQ, a computer-assisted qualitative data analysis program (CAQDAS), was utilized for thematic creation and coding to methodically examine the data, allowing for the emergence of new themes through inductive analysis. Findings from Phase 1 were directly translated to the development of the DMI instrument Prototype (quantitative survey) in Phase 2, ensuring the instrument captured local dimensions of Digital maturity in PHC. Connecting Phase (Instrument Development): The qualitative findings are used to inform the design and content of the quantitative phase. The development of items for the Digital Maturity Index (DMI) instrument followed an exploratory sequential mixed-methods approach. Items were generated inductively from the qualitative phase, in which themes and sub-themes emerged from focus group discussions with health care workers responsible for reporting and recording at the PHC level. The resulting 55 items reflect empirically grounded dimensions of digital maturity, encompassing stewardship, human resources, infrastructure, information systems, and health facility operations. This approach ensured that item content was closely aligned with real-world PHC digital practices and contextual challenges. Formal quantitative assessment of content validity, such as the Content Validity Index (CVI) or Content Validity Ratio (CVR), was not conducted in this initial instrument development phase. Instead, construct validation was approached through Rasch modelling, which was used to evaluate item functioning, internal consistency, category structure, and unidimensionality. Rasch analysis provided empirical evidence on whether the items coherently measured the underlying construct of digital maturity and whether response categories functioned as intended. While Rasch modelling does not replace expert-based content validation, it offers a robust initial assessment of construct validity and measurement performance, particularly in early-stage instrument development. Future research will include formal content validation procedures involving expert panels in digital health, health information systems, and primary health care. These expert reviews will be used to assess item relevance, clarity, and representativeness using CVI or CVR methods, and to further refine the DMI instrument prior to large-scale implementation. This might involve developing survey questions, identifying variables, or structuring experimental conditions based on the qualitative insights. [ 11 ] Phase 2. Quantitative study (DMI tools trials) The second phase employed a cross-sectional survey to measure the HCW RR, recognizing the maturity level of their PHC. Developing DMI instrument (quantitative) questions from an FGD (qualitative survey) involves a systematic process of identifying key themes and concepts from open-ended responses and then formulating structured, measurable questions based on these insights that possess the statistical power of quantitative analysis.[ 12 , 13 ] The questionnaire was developed into two different prototypes (Appendix 2 and Appendix 3 ) option for better understanding and answering items efficiently. The participant will be requested to answer both prototypes to perceive their PHC level. The prototype was then distributed to 30 HCW RR that represent their PHC in 2 subdistricts in South Sulawesi and West Java. The result was then analyzed with Rasch modelling to obtain the prominent prototype and its choice option with the highest value of validity and reliability. The sample size for the quantitative phase was determined by feasibility and the pilot nature of the study rather than by requirements for definitive scale calibration. The primary objective at this stage was to conduct an initial field test of the Digital Maturity Index (DMI) instrument, assess item functioning, response category performance, and overall measurement structure using Rasch modelling. A sample of 30 respondents was considered adequate for exploratory Rasch analysis and instrument refinement in a pilot context, particularly for identifying poorly functioning items and comparing alternative instrument versions. However, larger samples are required for stable parameter estimation, robust category calibration, and the establishment of normative or criterion-referenced maturity thresholds. Accordingly, the results of the present study should be interpreted as preliminary, and future studies with substantially larger and more diverse samples will be necessary to support definitive Rasch calibration and broader generalizability. The English version of the questionnaire is provided as a supplementary file (Appendix 2 and Appendix 3 ). Data analysis In the qualitative phase, data were analyzed using ScreenQ, a computer-assisted qualitative data analysis software (CAQDAS). The analytical process within CAQDAS typically begins with importing transcript raw data, followed by organizing and preparing it for analysis [ 14 , 15 ]. Researchers then engage in “coding data,” which involves attaching labels (codes) to segments of a transcript of serial discussion, ideas, or categories [ 16 , 17 ]. This systematic coding is a crucial first step, particularly in methods like thematic analysis or interpretative phenomenological analysis [ 18 ]. After initial coding, the software assists in identifying “themes,” which are overarching patterns or concepts emerging from the coded data [ 19 ]. These themes are then interrelated to build a more comprehensive understanding of the data, ultimately leading to the interpretation of their meaning in the context of the research question.[ 15 ] To ensure the validity of qualitative findings, this study employs strategies and criteria to establish trustworthiness, which is often considered an equivalent concept to validity and reliability in qualitative research. [ 20 , 21 ]. The primary trustworthiness applied is credibility, often seen as the qualitative equivalent of internal validity, which focuses on establishing confidence in the “truth” of the findings [ 22 ]. This involves ensuring that the research findings accurately represent the participants’ experiences and the phenomenon under study [ 23 ]. Strategies to enhance credibility include Thick Description, which involves providing rich, detailed, and contextualized descriptions of the research setting, participants, and findings. This approach allows readers to assess the applicability of the findings to their own contexts and provides evidence of the researcher’s immersion in the data. [ 22 , 24 , 25 ] Another trustworthiness to ensure the validity of the qualitative findings is Transferability, Dependability, and Confirmability. Transferability was addressed through the provision of a thick description of the research context, participants, and study settings. [ 26 ] Detailed accounts of the organizational structure of PHCs, variations in infrastructure, governance arrangements, and human resource capacity across South Sulawesi and West Java were intentionally included to allow readers to assess the applicability of the findings to other primary healthcare contexts. By clearly describing participant roles (health care workers responsible for reporting and recording), eligibility criteria, and provincial diversity, the study enables informed judgment regarding the potential relevance of findings to similar health systems undergoing digital transformation. Dependability was ensured by maintaining a transparent and systematic analytic process throughout data collection and analysis. [ 27 ] The study followed a clearly defined qualitative protocol, including purposive sampling, standardized FGD guides, audio recording, verbatim transcription, and structured coding using computer-assisted qualitative data analysis software (ScreenQ). An audit trail documenting coding decisions, theme development, and iterative refinement of categories was maintained to ensure consistency over time. The use of predefined thematic domains, informed by existing digital maturity and HIS frameworks, further strengthened analytic stability while still allowing inductive insights to emerge. Confirmability was strengthened by minimizing researcher bias and ensuring that findings were grounded in participants’ perspectives rather than researchers’ assumptions. This was achieved through systematic coding procedures, reflexive engagement during analysis, and the inclusion of verbatim quotations to support key themes. [ 28 ] The use of CAQDAS facilitated transparent linkage between raw data, codes, and emergent themes. Additionally, analytic decisions were discussed among the research team to challenge interpretations and enhance objectivity, ensuring that conclusions were clearly traceable to the underlying data. In the quantitative phase, statistical analyses were performed to evaluate the instrument’s validity and reliability and to compare the maturity level of PHC and knowledge of the HCW in DMI using the developed prototype. The Rasch model was employed for evaluating both prototype instrument properties, including unidimensionality, item fit statistics, person and item reliability, and item difficulty calibration. In the process, the developed prototype will be compared for its validity and reliability. Reliability and validity are two fundamental features in evaluating any measurement instrument. [ 29 ] Reliability refers to the consistency and stability of a measurement, indicating the degree to which an instrument produces consistent results under the same conditions. Validity, conversely, concerns whether an instrument accurately measures what it is intended to measure. [ 30 ] The trial DMI Instrument (quantitative research) provides the essential framework for developing, validating, and applying measurement tools for digital maturity indexes across 5 major themes. By employing a prominent DMI instrument, PHC will be able to effectively gauge its digital progress, identify areas for improvement, and strategically navigate the complexities of digital transformation. The emphasis on reliability and validity ensures that these instruments yield accurate and consistent results, crucial for informed decision-making and successful digital enablement.[ 31 ] Maturity index value Maturity Index values were categorized based on Rasch person measure scores and illustrative classifications, and were mapped into five distinct levels: Basic (below −2 standard deviation), Utilization (below −1 standard deviation), Development (between −1 and +1 standard deviation), Coordination (above +1 standard deviation), and Sustainable (above +2 standard deviation). This approach, commonly applied in Rasch analysis, allows for the most appropriate classification of the level of maturity spectrum, ensuring that the resulting categories are empirically grounded in the actual data distribution rather than relying on arbitrary cut-off points.[ 32 ] Ethical consideration This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Universitas Padjadjaran on January 8, 2024, Approval No: 37/UN6.KEP/EC/2022. All participants provided verbal informed consent prior to participation. Result Qualitative result The findings of the qualitative phase are categorized into five main predetermined themes, which are: stewardship, Human Resources (HR), Infrastructure, Information System (IS), and Health facility (HF). The Application Theme in the hypothesis was integrated into the Information System since most of the coding refers to the implementation of IS rather than application development. The characteristics of the participant supporting this phase are detailed in Table 1 , which provides further information on their demographic and professional distribution. The following section presents Table 2 , which outlines 55 Coding of 14 sub-themes within the five main themes, providing a comprehensive overview of the qualitative findings. This coding will later be translated into the questionnaire in the quantitative phase. Traceability of Qualitative Codes to Sub-Themes, Domains, and Respondent quotations available in Appendix 4 . Table 1. Respondent demographic and professional attributes Qualitative n = 41 % Gender Male 14 34,1 Female 27 65,9 Age (year) 21–40 29 70,7 41–60 12 29,3 >60 0 0,0 Profession HCW (IT) 13 31,7 HCW (NON-IT) 28 68,3 Education Level IT Bach. 6 14,6 Paramedic Bach. 9 22,0 Other Bach. 12 29,3 Medical Record Dip. 7 17,1 Paramedic Dip. 6 14,6 Other Health Dip. 1 2,4 Open in a new tab Table 2. Coding and category identified from qualitative analysis Theme Category Coding Stewardship (STWP) Regulation Role of the Health Office to support DMI assessment HIS regulation implementation mechanism HIS regulation implementation process Budgeting IT HCW budget allocation Supporting Group HIS Monitoring Technical Working Group (TWG) Supporting Group Structure Governance Role of the government to support HIS HIS Organization and Structure Government monitoring the HIS System Human Resources (HR) Human Resources (HR) quality Programming IT staff HCW knowledge on a specific application HR monitoring and evaluation HCW capacity for running HIS HCW capacity for developing HIS Human Resources quantity HCW requirement HR demand providence IT HCW Task shifting IT HCW limitation Infrastructure Electricity Infrastructure uniform Infrastructure support from the government Computer System Computer availability Software providence Personal Computer Usage Cellular network Cellular capacity threshold Cellular capacity financing in PHC Cellular capacity troubleshooting Information System (IS) and Application Storage Medical Record and interoperability implementation Digitalization of the application Providence and coordination of HIS from a private vendor Interoperability HIS interoperability DMI Implementation PHC RR system HIS operation troubleshooting HIS utilization in the Health Program Medical Record implementation and HIS interoperability Application usage for HIS e-Medical record with e-PHC (PHC electronic system base) Data collection in the district/government server Data Security Data collection security issue Data collection improvement strategy DMI Implementation Health Facility Management Routine data provision Data information availability in the unit work Dashboard display strategy for health infographic. Data Management A quality of Routine data usage Time management in HIS RR Routine data for Health program indicators Routine data utilization Application usage for HIS Autonomous development and usage application Digital platform-based usage vs RR with a simple application (Excel platform) HIS utilization in the Health Program Application-based usage for HIS RR system in the PHC Usage and development of the HIS district platform IT HCW scarcity Open in a new tab The following sections provide a thick description and detailed exploration of each theme. A. Stewardship (Governance, Supporting Group, Regulation, Budgeting) The findings indicate that stewardship gaps are not primarily technical, but structural. While national-level digital health policies exist, participants consistently described ambiguity at the district and PHC levels regarding authority, accountability, and operational responsibility for HIS implementation. The absence of a formally mandated HIS supporting group and dedicated leadership role results in fragmented governance, where monitoring, evaluation, and system improvement occur inconsistently across PHCs. Budgeting constraints further exacerbate this gap, as HIS investments are often limited to hardware procurement without parallel investment in human resources or system maintenance. Together, these findings suggest that digital maturity in PHCs is constrained less by willingness to adopt technology and more by weak institutional arrangements that fail to translate policy into operational and implementation practice. Practice implication: Strengthening stewardship requires formalizing HIS governance at the district level through dedicated technical working groups, clear role definitions or support system, and protected budgets for IT human resources as well as information ecosystem development. Without these structural supports, improvements in digital infrastructure alone are unlikely to translate into sustainable digital maturity at the PHC level. Supporting Group The Participant agrees that a specific group needs to be established and funded to monitor the HIS activities conducted at the PHC level, and a particular person should lead the group to ensure that the HIS implementation runs appropriately. A discussion is being made with the district Office, and if it is possible, such a position (support Group) could be published and promoted to be HCW (Res. 021, Bach. PH) 2. Governance The role of the government is required to support the implementation of HIS, not only for monitoring and evaluating functions, but needs to establish and ensure that the implementation of HIS is conducted by other district offices appropriately for health program development. If it is possible, the local government can support our HIS, and facilitate coordination with other district offices, so the HIS Implementation can be comprehensively implemented by the district to enhance the health program (Res. 014, Nurse) 3. Budgeting The budget policy needs to be revised, so a specific budget for IT HCW procurement could be inserted. Most of the PHCs are not able to allocate a specific budget for IT HCW procurement. PHC with big revenue might be able to procure IT HCW independently . (Res. 01, IT, DHO) There is no such procurement; the support for HIS is only for tools and stationery (Res. 016, Nurse, PHC) 4. Regulation Specific and clear regulations need to be established as guidelines in supporting the HIS implementation at the PHC level. An electronic-based government system is present, but not specifically mentioned in the HIS implementation and its process (Res. 08, Nurse, DHO) B. Human Resources (HR) Human resource constraints emerge as a central bottleneck in PHC digital maturity. Although many health workers demonstrate basic operational capacity in conducting reporting and recording, advanced competencies—such as system troubleshooting, data analysis, and application development—remain limited. Task shifting has become a pragmatic coping strategy, with nurses, midwives, and public health graduates assuming IT-related responsibilities in the absence of, or in the limited number of, dedicated IT personnel. However, this informal redistribution of tasks often increases workload and contributes to inefficiencies, particularly when digital responsibilities compete with clinical and programmatic service duties. These findings suggest that HR-related maturity is shaped not only by skills deficits but also by systemic misalignment between workforce planning and digital transformation goals. Practice implication: Digital maturity efforts should move beyond ad hoc task shifting toward structured capacity-building pathways, including standardized IT training, regular capacity strengthening, competency frameworks, and formal recognition of digital roles within PHCs. Aligning workforce planning with digital health demands is essential to prevent burnout and ensure consistent system performance. Human Resources quality The quality of the HCW is measured by the capacity to operate the HIS and or develop the HIS application. The capacity could be part of the education background profile or any other training or education process to advance the level of IT. A role of Bach. PH could be set to support HIS, so nurses or midwives can focus on their patient services. (Res. 019, Nurse PHC) An IT HCW profile should be hired to support Health programming (Res. 021, BAC. PH) An IT HCW procurement is in process to be set; hopefully, it could fulfill all positions at the PHC level. (Res. 013, Nurse) IT capacity To improve the quality of the HCW, a specific training or monitoring and evaluation needs to be established to support the capacity of HCW to advance in operating the HIS application and programming The digital platform is good and easy to use; it would be better if DHO provides some operational training for that platform to all PHCs. (Res. 012, BAC. PH) 2. Human Resources quantity HCW requirement The fulfillment of HCW to support health services and HIS needs to be improved, as it is basically affected by the health program workload and the vast area of PHC. Most of the PHCs have improvised by procuring more health staff to support various health programs, and adapted those staff as IT or RR HCW as well. Most of my time working alone, and I have to recap all referral reports as well before creating an analysis of the 10 disease burden in PHC. (Res. 019, Nurse PHC) Some specific IT HCWs need to run a specific Ipuskesmas application (Res. 01, IT, DHO) IT HCW limitation A specific strategy to deal with the scarcity of IT HCW in PHC, through empowering functional HCW or task shifting, enables them to operate the HIS at the PHC level. In recent times, most of our HCW can operate applications, but it is a basic operation, such as RR report, but for advanced purposes or specific role in data analysis, the IT HCW is limited, and that becomes our problem. (Res. 03, BAC. PH, PHC) We lack IT HCW, which is somehow important (Res. 09, BAC. PH, PHO) IT HCW task shifting A strategy to fulfill the requirement to support the HIS implementation, through specific IT training for other HCWs. It is expected that the trained HCW can perform a complex HIS, such as data collection, analysis, and translation to a health program report. Most of the HCWs do not receive any particular IT training; I voluntarily took on that charge to run the HIS program in PHC (Res. 018, Nurse PHC) We have not received or have any basic IT training in operating HIS (Res. 03, Bac. PH, PHC) C. Infrastructure Infrastructure inequities strongly influence the pace and depth of digitalization across PHCs. While urban PHCs benefit from stable electricity, adequate bandwidth, and sufficient computing devices, facilities in rural or geographically challenging areas rely on generators, fluctuating cellular networks, and personal laptops. These disparities directly affect the feasibility of implementing advanced digital solutions such as EMRs and interoperable systems. Importantly, participants framed infrastructure not as a binary condition (available vs. unavailable) but as a continuum that determines whether digital tools can be used reliably, concurrently, and at scale. Practice implication: Infrastructure investments should be calibrated to digital maturity targets, ensuring minimum standards for electricity reliability, internet bandwidth, and shared computing resources. Without addressing these foundational inequities, expectations for interoperability and real-time reporting may be unrealistic for many PHCs Electricity The electric availability to support all electric devices and their operation is essential. National Electric is the main source, but for some remote areas, PHCs depend on their own generator to supply electricity to support the health services and programs in the PHC. We have several electric resources, from National Electric, our own generator, and the other come from a turbine in our village (Res. 014, Nurse) In PHC accreditation, to be set as a comprehensive PHC, one must have electricity for 24 hours. (Res. 015, BAC. PHO) There are 80 PHCs in Bandung city, and their infrastructure varies. (Res. 01, IT, DHO) 2. Cellular network The Cellular network is a crucial infrastructure to support RR in the PHC. The cellular network itself varies depending on the geographic conditions or the capacity that fits to run all cellular devices in the PHC at the same time (minimum 100Mbps). The Cellular network depends on the weather; once it is raining, the cellular network will be troubled, as well as the electricity. (Res. 014, Nurse) To upgrade Electronic Medical Record (EMR) from E-Pus, including e-que application, requires at least 100 Mbps for cellular speed. (Res. 04, BAC. PH, DHO) 3. Computer System Another crucial instrument in RR in the PHC. Almost all of the HCWs do their RR reports on personal computers, since PHC PCs were used for PHC property and were limited in number and technology. There are not many PC for each program, so basically most of the programs were reported with personal laptops. (Res. 018, Nurse PHC) Some PHCs like ours in the Urban area were equipped properly with PC. All rooms and program systems are already using computer-based based, it is already digitalized. (Res. 02, Nurse, PHC) D. Information System (IS) and Application The qualitative findings reveal that interoperability is perceived as a defining marker of advanced digital maturity. Participants emphasized that fragmented applications—often developed by different vendors or administrative levels—limit data integration and reduce the usefulness of HIS for decision-making. While digital platforms are widely used, parallel reliance on manual or Excel-based reporting persists, reflecting both infrastructural constraints and limited system integration. Concerns regarding data storage and security further highlight the tension between expanding digital data use and ensuring confidentiality. These findings suggest that digital maturity is less about the number of applications deployed and more about the coherence and governance of the overall information ecosystem. Practice implication: Advancing digital maturity requires prioritizing system interoperability, standardized data architectures, and clear data governance frameworks. Investments in new applications should be accompanied by integration strategies and security protocols to avoid reinforcing fragmentation. Interoperability EMR implementation and HIS interoperability HIS and interoperability implementation are crucial in monitoring and evaluating the health program, as well as running health services in the PHC efficiently. The digitalization level is measured through the capability of health services to conduct interoperability. As a PHC IS administrator, we have one server for many programs, which will assist us in controlling any updates before sending them to the health office. (Res. 020, Nurse, PHC) In the district considered success is considered when most of its PHCs can report to the one health reporting system and use EMR, and bridge with the P-care application . (Res. 015, BAC. PH, PHO) 2. DMI Monitoring and Evaluation DMI Monitoring and evaluation is a method to observe the whole process of digitalization and implementation at the PHC level. The DMI monitoring will give a picture of which areas need to improve or maintain further to support the digitalization process. In some part, we need assistance from our supervisor since it is regarding the policy of PHC (Res. 05, BAC. PH, DHO) DMI measurement use as a tools to asses our weakness and strength in health facilities, (Res. 09, BAC. PH, DHOprov) 3. Reporting and Recording HIS Usage for health program implementation PHC IS is part of HIS, and it was developed to assist PHC in analyzing their health program implementation. Additional features are required to improve PHC IS to display or give a dashboard of the reporting health programs. There was no data integration existed, so we expected that all data could be integrated. So, the program PIC can easily retrieve the data from the HIS (Res. 018, Nurse, PHC) Digital platform usage vs conventional data report (with excel program) Digital platform is considered an important tool that is efficient in the RR of any health program. In some areas with limited resources of infrastructure, the usage of a conventional report is important before submitting it to the health office or any available digital platform. We report manually in the manual data (Excel data), just like other PHCs before submitting it to the district office ( Res. 018, Nurse, PHC) It would be better if we do not send the manual report, just by submitting through the specific application, then it will be there. (Res. 012, BAC. PH) 4. Application Development Application development as part of the digitalization, since it gives a picture of the development IS, that present or being adopted by the PHCs. Most health program application is developed at the National level, and PHC mostly develops local applications that bridge inter-departmental issues in PHCs or supervised health care. Still not ready, we are still figuring out the need and kind that is suitable for our PHC (Res. 014, Nurse) Recent application development runs by a private vendor, even though under Telkom (Indonesia Telecommunication Bureau), and is somehow not linked to the HIS. (Res. 04, BAC. PH, DHO) Private vendor approach to facilitate application development (Res. 08, Nurse, DHO) 5. Data Storage and Security Storing data and information of the RR requires a large storage capacity, as many programs will depend on it. The security of the storage is also important to ensure the confidentiality of the patient in the PHC. The server should be placed in the district office (Res. 01, IT, DHO kota) The most weakness lies in the security system (Res. 07, Nurse, DHO) E. Health Facility At the facility level, routine data practices reflect the cumulative effects of governance, HR, and infrastructure constraints. Delayed reporting, incomplete data entry, and end-of-period workload accumulation were commonly described, indicating that HIS use is often reactive rather than embedded in daily workflows. Although platforms such as E-PHC offer functionality for EMR and program monitoring, their effective use varies widely across facilities. This variation suggests that digital maturity is not solely determined by system availability but by how well digital processes are integrated into routine service delivery and managerial practices. Practice implication: Improving digital maturity at the facility level requires workflow redesign that integrates reporting and data use into daily operations. Supportive supervision and performance feedback mechanisms can help shift HIS use from a compliance-driven task to a routine management tool which impacted on the PHC health program deliveries. Reporting and recording in the PHC Time management in HIS RR RR management includes time to report; in many conditions, there is a delay in submitting the report, caused by time management in reporting and recording. Most of the HCWs handle multiple tasks and jobs, and leave the RR task accumulated at the end of work. At the end, most of the RR were not reported properly due to a lack of information. Many of the program administrator delayed their input time, so basically most of the program reports were left blank. They will work at the end of reporting time and in haste mode. (Res. 018, Nurse, PHC) 2. E-PHC Usage E-PHC as HIS in the local setting has the capability to facilitate health program RR and store any data related to health program, but varies throughout each PHC. We are using E-PHC to store our EMR (Res. 02, Nurse, PHC) All Program Administrators are requested to input all the data and report in E-PHC . (Res. 020, Nurse PHC) Quantitative result The quantitative analysis included 30 HCWs who worked in RR at PHC in South Sulawesi and West Java. The survey showed slight disparities, with more HCW (Non-IT) representing 56.7% (17) compared to HCW (IT) who have an IT background, at 43.3% (13). Subjects were selected with careful consideration to ensure representation from two provinces. Table 3 details their demographic and professional attributes. These characteristics are crucial for understanding the data’s context and ensuring the sample’s representativeness in relation to the study’s overall objectives, contributing to a more comprehensive analysis. Table 3. Respondent demographic and professional attributes Quantitative n = 30 % Gender Male 17 56,7 Female 13 43,3 Age (year) 21–40 22 73,3 41–60 8 26,7 >60 0 0,0 Profession HCW (IT) 13 43,3 HCW (NON-IT) 17 56,7 Education Level IT Bach. 5 16,7 Paramedic Bach. 8 26,7 Other Bach. 6 20,0 Medical Record Dip. 8 26,7 Paramedic Dip. 1 3,3 Other Health Dip. 2 6,7 Open in a new tab Based on the qualitative Analysis in Table 1 , the development of the DMI instrument follows an outline of 55 codes of 14 sub-themes within the five main themes. The choice of each Coding will follow a Likert scale that describes the condition PHC, from lowest to highest, where “Basic”, “Utilization”, “Development”, Coordination”, and “Sustainable respectively. A. DMI Instrument Versi1 For Version 1, each answer option follows a description or narrative from the lowest phase to the comprehensive phase. It will allow the respondent to analyze the condition of their PHC according to the description on each option and choose the option that suits their PHC condition. For example, the question regarding Computer availability, the choice option from lowest to highest is on the basic phase is no hardware available, utilization phase is available but limited use, development phase is a personal PC used more than a PHC PC, coordination phase is equally used of personal PC and PHC PC, sustainable phase, PHC PC is mainly used and updated regularly. B. DMI Instrument versi2 The explanation of each phase is described or narrative on the prolog of the questionnaire, the choice only displays the basic phase (as choice number 1), the Utilization phase (as choice number 2), the Development phase (as choice number 3), Coordination Phase (as choice number 4), and the Sustainable phase (as choice number 5). The respondent only requested to choose with answer/phase that best fit with their present PHC Profile. The main purpose of version 2 is to efficiently respond in answering the question. Rasch modelling in selecting a prominent instrument Using Rasch modelling from 30 respondents with 55 fundamental instrument measurement items, collected 1645 data. Chi-Square results are 4207, 99 version 1 and 4013,30 for version 2, with d.f 1558 for version 1 and 1503 for version 2 ( p = 0.000 and p < 0,01). This shows a significant test for both instruments. Table 4 compares the Rasch modelling reliability of two instrument versions. Overall, Version 1 also shows higher item reliability (0.91) compared to Version 2 (0.76). While both versions report excellent internal consistency, with Cronbach’s alpha values of 0.95 and 0.98, respectively, Version 1 achieves a notably higher separation index (3.10 vs. 1.79). [ 33 ] These results confirm that Version 1 offers more reliable and stable measurement outcomes. Table 4. The comparison of instrument reliability Instrument Version Version 1 Version 2 Item Reliability 0.91 0.76 Cronbach alpha 0.95 0.98 Index separation 3.10 1.79 Open in a new tab The analysis of instrument validity through eigenvalue units, especially within the framework of the Rasch model, offers key insights into construct validity and unidimensionality. Unidimensionality is a core assumption in psychometric models like the Rasch model, stating that a single latent trait drives responses to scale items. [ 34 ] In Table 5 , the validity analysis using eigenvalue units shows that Version 1 of the instrument exhibits better construct validity and unidimensionality compared to Version 2. A variance explained by measures above 40% indicates acceptable unidimensionality. Version 1 accounts for 44.5 (44.7%) of the total variance, while Version 2 accounts for 40.4 (42.3%), revealing a stronger representation of the latent construct in Version 1, which suggests that its items more cohesively measure a single underlying trait. [ 31 ] Additionally, a low value for unexplained variance in the first contrast suggests fewer multidimensional tendencies, indicating that the instrument primarily measures a single construct without substantial interference from secondary dimensions. The unexplained variance in the first contrast for Version 1, 8.7 (8.8%), is notably lower than that of Version 2, 17.6 (18.4%), indicating fewer signs of multidimensionality. A higher unexplained variance in the first contrast, as seen in Version 2, implies that a greater portion of the item variance is not accounted for by the primary construct, potentially pointing to the presence of secondary dimensions or measurement error that reduce unidimensionality. [ 34 ] The total unexplained variance remains consistent at 55.0 for both versions, implying similar residual patterns. Therefore, Version 1 can be considered more psychometrically reliable and aligned with Rasch model expectations, whereas Version 2 requires further development to enhance dimensional coherence and construct validity. Table 5. The comparison of instrument validity in eigenvalue unit Instrument Version Version 1 Version 2 Raw Variance explained measure 44.5 (44.7%) 40.4 (42.3%) Raw unexplained Variance (Total) 55.0 (55.3%) 55.0 (57.7%) Unexplned variance in 1st contrast 8.7 (8.8%) 17.6 (18.4%) Unexplned variance in 2nd contrast 6.6 (6.6%) 7.1 (7.5%) Unexplned variance in 3rd contrast 5.1 (5.2%) 5.0 (5.2%) Unexplned variance in 4th contrast 4.1 (4.2%) 4.5 (4.7%) Unexplned variance in 5th contrast 3.4 (3.5%) 3.4 (3.6%) Open in a new tab The comparison of Rasch modelling Andrich thresholds between the two instrument versions is shown in Fig. 1 . The category probability curves were examined to assess the functioning of the response categories and the ordering of Andrich thresholds. In the version 1 plot, each category demonstrated a distinct modal peak along the latent trait continuum, with the intersections between categories occurring in the expected sequential order (approximately at −2.0, −1.0, 0.0, and +1.0 logits). This orderly progression indicates that respondents were able to meaningfully differentiate among the five response options, suggesting that the rating scale functions effectively and that each category represents a unique level of the underlying construct. Fig. 1. Open in a new tab The comparison of Rasch modelling Andrich threshold for instrument (version 1 left; and version 2 right) In contrast, the version 2 plot also displayed an ordered structure of thresholds, though the spacing between adjacent categories was less uniform. Notably, overlap between Categories 1 and 2 and between Categories 3 and 4 suggests that respondents may have experienced some difficulty distinguishing between these neighboring options. Despite this, the thresholds remained ordered, implying that the response structure is still consistent with Rasch model expectations. Both plots indicate that the response categories are ordered and operational, supporting the scale’s functional validity. The uneven threshold spacing observed in the version 2 plot suggests that minor refinement in category labeling or descriptor clarity could improve differentiation and enhance the precision of responses across the latent continuum. Version 1 presents a more coherent threshold structure, reinforcing its stronger category functioning and stability compared to Version 2, which shows greater irregularity and potential misfit.[ 35 ] Using Rasch modelling for the sum, and based on the Maturity Index value category derived from Rasch person measure scores, the result is presented in the pie chart (Fig. 2 ). Overall, 87% of PHCs are in the Development level, 3% are already in the Sustainable Level, 7% are in the Coordination Level, and only 3% are still in the Basic Level. No PHC in the Utilization Level. Fig. 2. Open in a new tab DMI version for PHC results in South Sulawesi and West Java Discussion The global assessment of digital maturity in healthcare employs multidisciplinary frameworks that integrate diverse indicators to capture a comprehensive view of system readiness. The World Health Organization (WHO) and International Telecommunication Union (ITU) have developed toolkits supporting national eHealth strategies, addressing areas such as governance, investment, infrastructure, interoperability, workforce, legislation, and compliance. [ 36 , 37 ] These frameworks acknowledge the inherent complexity of digital public health (DiPH) systems and help countries identify gaps and prioritize investments. They also assess aspects such as digital health literacy, societal engagement, workforce readiness, and legal maturity, thereby facilitating cross-country comparisons and highlighting universal challenges and best practices in digital health transformation [ 36 ]. Emphasizing those ideas, the five key domains—Stewardship, Human Resources, Infrastructure, Information Systems, and Health Facilities—are interpreted through the lens of digital maturity theory and global frameworks, comparing findings from this research, LMIC and PHC-focused digital health literature, and linking them to policy and implementation implications for Indonesia’s PHC system. Stewardship Stewardship, or leadership and governance, is paramount for digital health transformation, providing the strategic vision and oversight necessary to steer the entire system [ 38 ]. The WHO Health System Framework identifies leadership and governance as a foundational building block, ensuring accountability and quality [ 39 ],, [ 40 ]. In the context of digital maturity, robust stewardship implies not only the presence of policies but also their effective implementation and adaptation to evolving digital landscapes [ 41 ]. A high level of digital maturity in stewardship involves clear strategic direction, established legal frameworks, defined roles and responsibilities, and mechanisms for coordination across various stakeholders [ 42 ]. In LMICs, including Indonesia, governance for digital health is often challenging due to a multiplicity of external players and insufficient operational guidance. For instance, Indonesia has enacted a National Health Information System regulation, but challenges persist in adoption from a healthcare management perspective [ 43 ]. The Indonesian government’s prioritization of developing an integrated healthcare technology ecosystem highlights a recognition of the need for improved stewardship [ 44 ]. However, for many PHC systems globally, stewardship remains at a developmental level, characterized by fragmented policy efforts and reactive rather than proactive strategic planning [ 45 ]. Advancing towards coordination and sustainability requires a shift from fragmented, disease-focused thinking to an integrated view that leverages digital tools for strategic planning and monitoring [ 46 ]. This involves establishing clear digital health strategies, ensuring sustained political commitment, and allocating dedicated budgets for digital initiatives [ 42 ]. Human resources The human resources domain encompasses the availability, competence, and appropriate deployment of the health workforce, including their digital literacy and capacity to utilize digital health tools [ 39 , 42 ]. Digital maturity in human resources signifies a workforce that is not only proficient in using existing digital systems but also adaptable to new technologies and equipped with the skills for data analysis and digital communication [ 42 ]. The WHO framework emphasizes the importance of a qualified health workforce whose skills match population health needs. In LMICs, the health workforce often faces significant constraints, including shortages, maldistribution, and limited training in digital health [ 47 ]. Studies in Indonesia reveal that while digital strategies are implemented to improve efficiency, there are obstacles related to human resources, particularly in their ability to leverage digital tools effectively [ 48 ]. Many PHC systems in LMICs struggle with low digital literacy among healthcare providers, resistance to new technologies, and a lack of continuous training programs [ 49 ]. The concept of “task-shifting” and “task-sharing” is critical in PHC, enabling less specialized health workers to deliver essential services, which can be further enhanced by digital tools if the workforce is adequately trained. To progress from a development level, PHC systems need to invest in comprehensive digital literacy training, integrate digital health into professional curricula, and create incentive structures for digital adoption. Furthermore, addressing the digital divide among healthcare professionals is crucial for equitable implementation and sustained patient engagement. Infrastructure Digital health infrastructure refers to the underlying basic needs of electricity to the complex technological framework, including internet connectivity, hardware (computers, mobile devices), and network systems necessary for digital health operations [ 50 ]. A high level of digital maturity in infrastructure implies ubiquitous, reliable, and secure connectivity, continuity of electricity, access to appropriate hardware, and robust data storage and processing capabilities [ 51 ]. This also includes ensuring the quality and reliability of the digital platforms that support health services. In countries like Indonesia, infrastructure remains a significant barrier to effective digital health implementation, particularly in remote and rural areas [ 51 ]. The basic needs, such as electricity, become an issue that significantly impedes the effective delivery of essential healthcare services, impacting everything from basic lighting and communication to the operation of medical equipment and vaccine cold chains. Studies examining the use of information systems in Indonesian public health facilities report the use of various and disconnected systems, indicating a fragmented infrastructure without sufficient coordination mechanisms. Despite rapid embrace of digital health, especially during the COVID-19 pandemic, challenges persist in developing an integrated healthcare technology ecosystem [ 44 ]. To move beyond developmental stages, investments must focus on improving broadband access, ensuring stable electricity supply, and providing standardized and interoperable hardware across PHC facilities [ 51 ]. The “appropriate technology” principle from PHC suggests solutions should be scientifically sound, affordable, socially acceptable, and locally maintainable, rather than imposing complex, unsupported technologies. Information systems Information systems encompass the applications, databases, and software used for collecting, managing, analyzing, and disseminating health data. Digital maturity in this domain is characterized by interoperable systems that allow seamless data exchange, robust data security and privacy protocols, and advanced analytical capabilities for evidence-based decision-making [ 52 ]. The goal is to transform raw health data into actionable insights for improved health outcomes. Indonesia’s PHC system faces substantial challenges with information systems, primarily due to fragmentation and lack of interoperability [ 52 ]. The integration of healthcare data using electronic systems has been enabled by regulations, but limited information on barriers from a healthcare management perspective has hindered adoption. This fragmentation leads to disconnected data and inefficient management, making it difficult to monitor and evaluate health service performance [ 52 ]. For example, the lack of a common data language makes it challenging for different parts of the health system to communicate effectively. To achieve higher maturity levels, a national digital health platform strategy is needed to ensure interoperability among different systems. This requires adopting common data standards (e.g., FHIR), implementing privacy-preserving methods for data sharing, and establishing clear guidelines for data governance. Health facilities The health facilities domain pertains to the data management where healthcare services are delivered, including their readiness and capacity to support digital health initiatives. Digital maturity for health facilities involves not only the presence of digital equipment but also their functional integration into daily workflows, adequate technical support, and an environment conducive to technology adoption [ 53 ]. A critical issue in many LMICs is the inaccessibility and low maintenance of Health facility medical data, which hinders research output and effective public health measures. Despite the introduction of electronic health data systems, many still lack efficient systems for collecting and archiving data [ 41 ]. Primary health care in LMICs is particularly vulnerable to these challenges. The effective integration of digital technologies in PHC often faces barriers, including limited digital literacy among healthcare workers and patients, and inadequate infrastructure [ 53 ]. The concept of “appropriate technology” is central to PHC, advocating for solutions that are scientifically sound, affordable, socially acceptable, and locally maintainable, rather than sophisticated, imported technologies that may be difficult to sustain. In Indonesia, distinctions between Digital Maturity Index (DMI) tools for hospitals and primary health care (PHC) facilities reflect their operational, structural, and resource differences. [ 54 ] Hospitals, being larger and more complex, require DMI tools assessing advanced digital integration, including electronic medical records (EMR), clinical data analytics, and specialized technologies. Conversely, PHC facilities—smaller and community-oriented—focus on foundational capabilities that enhance basic care, prevention, and public health. Hospitals generally possess greater financial, technological, and human resources, enabling the adoption of sophisticated systems, while PHC centers in low- and middle-income contexts rely on cost-effective, practical solutions.[ 55 ] Service focus further differentiates the two: hospitals prioritize acute and specialized care supported by intricate digital systems, whereas PHCs emphasize promotion and prevention, basic routine consultations, and disease surveillance. Interoperability needs also differ, with hospitals requiring complex internal system integration, and PHCs focusing on data sharing with higher-level facilities (Health office or Hospitals) and health reporting systems. Similarly, digital workforce competencies range from advanced analytical skills in hospitals to basic digital literacy in PHCs. Overall, while generic digital maturity models exist, frameworks like the Digital Public Health Maturity Index offer a holistic approach, adaptable to varying healthcare levels and national contexts. Limitation of the study Although this study provides valuable evidence regarding the psychometric properties of the two versions of the DMI instrument, several limitations should be acknowledged. First, the sample size was relatively small, involving only 30 respondents, which may limit the generalizability of the findings. The selected province is to analyze the two different areas, where West Java is an urban and highly developed area, compared to South Sulawesi, with a rural and mid-developed area. A larger and more diverse sample would strengthen the stability of the Rasch estimates and enhance external validity. Second, the data were collected from a single context, which may not fully capture variability across different populations or healthcare settings where the DMI instrument could be applied. Third, facility-level digital maturity was inferred based on the perceptions of single key informants responsible for reporting and recording (RR) at each PHC, rather than through aggregation of responses from multiple staff within the same facility. While these informants were selected due to their direct involvement in health information systems and digital reporting processes, reliance on individual perspectives may not fully reflect intra-facility variation in digital practices, capacity, or system use. As such, the reported maturity levels should be interpreted as informed proxy assessments rather than comprehensive organizational measures. Finally, item difficulty estimates and maturity classifications reported in this study should be interpreted as provisional outputs of an exploratory pilot calibration rather than as stable or finalized measurement parameters. Strength of the study The main strength of this study is that the DMI Instrument prototype is an initial assessment DMI level in the PHC. It is expected that more similar research will be conducted to improve the DMI instrument that has already been developed, including improving the choices of its phase. More comprehensive DMI instruments will also assist Governance Indonesia in analyzing the improvement of digital health transformation, especially at the PHC level, which aligns with the Indonesian Health Development Plan. Conclusion Developing the Digital Maturity Index (DMI) instrument for Primary Health Care (PHC) represents an essential step in advancing Indonesia’s digital health transformation. This study identified five key domains influencing digitalization—stewardship, human resources, infrastructure, information systems, and health facilities. The validated DMI instrument (version 1), evaluated through Rasch modeling, demonstrated strong reliability, validity, and appropriate category functioning, confirming its suitability for assessing digital maturity in PHC settings. The analysis revealed that the majority of PHCs are currently in the development phase (87%), with fewer in the coordination (7%) and sustainable (3%) phases. These findings highlight the need for targeted capacity-building and policy interventions to accelerate digital readiness across Indonesia’s PHC system and support the transition toward a more integrated, data-driven health ecosystem. This valid and reliable Digital Maturity Index instrument version 1 for PHC is ready to be implemented. Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary material 1 (14.8KB, docx) Supplementary material 2 (55KB, docx) Supplementary material 3 (47KB, docx) Supplementary material 4 (20KB, docx) Supplementary material 5 (188KB, docx) Supplementary material 6 (148.4KB, docx) Supplementary material 7 (57.6KB, docx) Acknowledgements The authors would like to acknowledge and thank the Provincial Health Office, District Health Office, and PHC HCW in South Sulawesi and West Java for their amazing support and participation in this study. This publication charge is funded by Universitas Padjadjaran (UNPAD) through the Indonesian Endowment Fund for Education (LPDP) on behalf of the Indonesian Ministry of Higher Education, Science and Technology, and managed under the EQUITY Program (Contract No. 4303/ B3/DT.03.08/2025 and 3927/UN6.RKT/HK.07.00/2025). Author contributions SJA, DKS, and LH conceived the idea and designed the experiment. SJA experimented and collected the data. SJA, DKS, and LH performed the data analysis. SJA, DKS, and LH wrote the main manuscript text and prepared the tables and figures. DKS, LH, FRR, MNA, and DMDH reviewed and provided feedback on the manuscript. All authors have read and approved the final manuscript. Funding The authors received no financial support for conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript. Data availability The DMI Instrument prototype generated during the current study is available from the corresponding author on reasonable request. Declarations Ethics approval and consent participant This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Universitas Padjadjaran on January 8, 2024, Approval No: 37/UN6.KEP/EC/2022. All participants provided informed consent prior to participation. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Supplementary Materials Supplementary material 1 (14.8KB, docx) Supplementary material 2 (55KB, docx) Supplementary material 3 (47KB, docx) Supplementary material 4 (20KB, docx) Supplementary material 5 (188KB, docx) Supplementary material 6 (148.4KB, docx) Supplementary material 7 (57.6KB, docx) Data Availability Statement The DMI Instrument prototype generated during the current study is available from the corresponding author on reasonable request. 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