Autonomous nursing professional development framework using blockchain technology.

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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Mar 3;16:11825. doi: 10.1038/s41598-026-38750-x Search in PMC Search in PubMed View in NLM Catalog Add to search Autonomous nursing professional development framework using blockchain technology Chia-Chen Lin Chia-Chen Lin 1 Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan Find articles by Chia-Chen Lin 1, ✉ , Yen-Heng Lin Yen-Heng Lin 2 Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung, Taiwan Find articles by Yen-Heng Lin 2 , I-Chieh Hsu I-Chieh Hsu 3 Graduate Institute of Human Resource Management, National Changhua University of Education, Changhua, Taiwan Find articles by I-Chieh Hsu 3 Author information Article notes Copyright and License information 1 Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan 2 Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung, Taiwan 3 Graduate Institute of Human Resource Management, National Changhua University of Education, Changhua, Taiwan ✉ Corresponding author. Received 2025 Nov 3; Accepted 2026 Jan 30; 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: PMC13065954  PMID: 41776251 Abstract This study presents a blockchain-based enabling autonomous nursing professional development framework, known as BCeANPDF. The framework aims to enhance transparency, security, and professional autonomy in nursing credential management. It is grounded in the principles of competency-based human resource management. Blockchain and smart contract technologies are integrated to support independent recording, verification, and management of professional and non-professional credentials by nurses. At the same time, hospital human resource administrators continue to have the authority to conduct regulatory oversight and ensure compliance. The framework employs a three-layer architecture that includes controller, service, and repository components. These components coordinate access control, data processing, and blockchain-related operations. Seven smart contracts are designed within the framework. They automate credential ownership verification, credential updates, and compliance review processes. This design strengthens data integrity and reduces administrative workload. A prototype was implemented in a private blockchain environment to evaluate system performance. The results demonstrate stable and efficient operation. The average on-chain processing time per credential was 12.3 s. Median query latency ranged from 5 to 9 ms. These findings confirm that the framework achieves scalability and responsiveness comparable to Ethereum, while preserving data privacy and immutability. By combining decentralized trust mechanisms with credential management practices, the BCeANPDF framework offers a practical approach to supporting autonomous professional development. It also facilitates flexible management of the nursing workforce. Overall, the framework contributes to the development of transparent and competency-oriented healthcare institutions without increasing operational complexity. Keywords: Nursing resource management, Autonomous professional development, Blockchain technology, Smart contract, Adaptive healthcare institution Subject terms: Engineering, Health care, Mathematics and computing Introduction The concept of competency can be traced back to 1953, when management theorist McClelland 1 identified competence as a human trait that significantly contributes to individual success in the workplace. Since then, the concept and various frameworks of competencies have been widely applied in human resource management (HRM) to assess, manage, and enhance employee performance. Following McClelland’s early work 1 , several influential scholars contributed to the development of competency-based HRM. In 1954, Flanagan 2 introduced the critical incident technique (CIT), a method used to identify behaviors linked to job effectiveness in professions such as education and military service. Flanagan broadly defined competence as the ability to perform tasks effectively through critical actions, providing early insight into behavioral aspects of job performance 2 , 3 . Building on his own earlier ideas in 1953, McClelland 4 challenged the predictive value of traditional intelligence and aptitude tests for job success and life outcomes. He instead emphasized the importance of identifying specific competencies as reliable indicators of occupational performance and personal achievement. Later, competency-based HRM (CBHRM) has been explored in practice as a strategic approach to align workforce capabilities or talent development with organizational goals for competitive advantages 5 – 8 . For example, in the Indian steel industry, the application of competency mapping has been examined to strategically enhance employee effectiveness in their job roles and strengthen overall organizational success 9 . Similarly, in the Indian IT industry, the role of competencies for employee development has been successfully empirically investigated. The results also showed the importance of competencies for organizational success 10 . The healthcare sector has also benefited from the development of a generic competencies framework for nurse managers, as well as specific competencies for three levels of managers: senior managerial level, middle manager level, and front-line managers, to enhance service quality and patient outcomes 11 . Building upon CBHRM, competency-based human resource information systems (CBHRIS) integrate competency data into digital HR platforms, enabling organizations to map, monitor, and manage employee competencies through technology 12 . However, conventional CBHRIS often rely on centralized, legacy information systems, which creates inherent risks related to data security, integrity, and non-repudiation when managing sensitive and critical competency data. Blockchain technology provides far more than a secure and efficient distributed ledger for storing employee records, including human resource competency information. Its distinctive technical characteristics, such as immutability, decentralized consensus, resistance to tampering, transparent data access, and automated execution through smart contracts, create a level of trust, reliability, and autonomy that traditional information systems cannot achieve. Conventional human resource information systems rely on centralized databases, where data integrity depends on a single administrative authority. As a result, employee credentials remain vulnerable to unauthorized modification, inconsistent verification procedures, and single-point failures. With blockchain technology, once a credential is recorded, it cannot be altered without agreement from the entire network, and its validation can be automatically performed through smart contracts. This reduces the need for manual processing, strengthens data integrity, and minimizes administrative risk. These advantages explain the increasing interest in the use of blockchain technology in human resource management research. Recent studies have examined its application in recruitment and selection, training and certification, performance evaluation, and employee relations 13 – 25 . Through secure, transparent, and automated credential management, blockchain-based human resource systems provide superior verifiability, operational efficiency, and protection against unauthorized data manipulation compared to traditional database-driven solutions. Furthermore, in 2024, two research themes, Employee-Systems Interaction (ESI) and Blockchain Framework (BcF) for HR, have also been identified as crucial for blockchain adoption in CBHRM 26 . Over the years, CBHRM enabled by blockchain technologies has been discussed on various occasions, including Industry 4.0 14 , the public sector 15 , and the technology industry 16 . Likewise, in healthcare industries, several studies have explored blockchain-based frameworks for managing electronic health records (EHRs), aiming to improve security, privacy, and interoperability 27 – 29 , 31 . These systems enable patients and healthcare providers to have a unified view of medical histories, potentially reducing treatment costs and improving diagnosis. Researchers have further argued that blockchain applications in healthcare could extend beyond EHRs to areas such as pharmaceutical research, supply chain management 30 – 35 , customer order management 35 , and telemedicine 31 , 33 , given the omnipotence of blockchain technology. Despite the growing body of work in this area, existing studies have paid limited attention to how blockchain technology can support nurses in self-managing and applying their professional competencies during active service, as reflected in our literature review. The primary contributions of this paper are as follows: A blockchain-based enabling autonomous nursing professional development framework (BCeANPDF) allows nurses to efficiently record and manage their professional development activities in a secure, immutable, and tamper-proof manner. By leveraging the proposed smart contract mechanism, nurses are empowered to proactively identify the certifications, credentialing requirements, and regulatory standards relevant to their roles, allowing them to independently plan and schedule their professional competency development based on their availability. The proposed BCeANPDF enables the Nursing Human Resources Department to streamline the certification of professional roles through smart contracts. The framework ensures that each nurse’s qualifications, certifications, and completed service hours meet hospital standards. It also supports institutional compliance with qualification and certification requirements established by government authorities, including the Ministry of Health and Welfare, as well as relevant regional regulations. In a healthcare system with multiple branches or departments, the proposed BCeANPDF can also be extended to help nurses evaluate whether their competencies meet the requirements before attempting to transfer to another branch or department. The remainder of this paper is structured as follows. Section “Literature Review” presents the background knowledge, including a brief overview of blockchain technology and its applications in blockchain-based human resource management. Section "The proposed BCeANPDF" provides a detailed description of the proposed BCeANPDF. Section “System Implementation” outlines the system implementation, while Section "Prototype Demonstration and Evaluation" discusses the prototype demonstration and evaluation. Finally, Section “Conclusions” concludes the paper and offers directions for future work. Literature review In this section, a brief review of blockchain technology and its applications in human resource management will be presented. Blockchain technology Blockchain technology, first conceptualized by Satoshi Nakamoto in 2008, is a distributed ledger system that enables secure, decentralized, and immutable data management 36 . By employing cryptography, hashing techniques, and a linked structure of blocks, blockchain ensures that recorded transactions are tamper-evident and verifiable. Unlike traditional distributed databases, blockchain networks achieve data consistency through various consensus mechanisms, such as Proof-of-Work (PoW) 37 , Proof-of-Stake (PoS), or Practical Byzantine Fault Tolerance (PBFT). These consensus mechanisms allow participants to agree on the state of the ledger without relying on a central authority 38 . Beyond simply storing transactions, many blockchain platforms integrate smart contracts, which are self-executing programs embedded in the blockchain and automatically enforce rules when predefined conditions are met 39 , 40 . This programmability extends blockchain’s utility from a secure record-keeping tool to a foundation for decentralized applications (DApps), enabling innovation in fields such as healthcare, supply chain management, education 41 , 42 , credentialing systems 43 , and e-voting 44 . While blockchain offers transparency, security, and automation through its consensus protocols and smart contract execution 23 , it also raises privacy concerns, particularly when sensitive information is stored or processed on public ledgers. Researchers are actively developing privacy-preserving techniques such as zero-knowledge proofs 45 and secure multiparty computation to mitigate these risks. Despite the remaining challenges, blockchain technology continues to attract interest from academics, industry, and governments due to its ability to enhance trust, improve data governance, and streamline operational processes. Its technical properties also enable the development of secure peer-to-peer multiparty transaction schemes 47 and robust authentication mechanisms 48 , further expanding its value across diverse application domains. Blockchain-based human resource management In 2023, Chanda and Singh conducted a literature analysis to examine the primary and secondary research topics related to the application of blockchain technology in human resource management. Their study identified research gaps and limitations within the existing literature and outlined directions for future research 46 . Based on their review of the literature, Chanda and Singh identified four principal applications of blockchain technology in HRM as (1) recruitment and onboarding, (2) performance management, (3) payroll and benefits, and (4) training and development. Each blockchain-based HRM application can leverage multiple blockchain features, including decentralization, immutability, and smart contract automation, which collectively address long-standing challenges in the development and implementation of HRM information systems 46 . These challenges include ensuring the credibility of employee data, preserving data integrity across disparate systems, and enhancing the efficiency of administrative processes. The detailed explanations of blockchain-based applications for each HRM domain are listed below: Recruitment and onboarding. Blockchain can facilitate secure and transparent verification of job candidates’ qualifications. Academic degrees, certifications, and employment histories can be issued as VCs, stored in a candidate’s digital wallet, and shared only with explicit consent. Revocation registries enable issuers to invalidate outdated or fraudulent credentials without altering the blockchain’s history. Payroll and benefits administration. Payroll and benefits transactions can be automated through smart contracts that execute payments when predefined conditions are met, such as the end of a month or the fulfillment of performance bonuses. These contracts ensure accuracy, reduce manual processing, and prevent payment delays. Performance management. Employee goals, achievements, and feedback can be recorded on an immutable ledger, ensuring that neither managers nor employees can alter historical performance data. ZKPs can be applied to verify the achievement of certain targets without revealing all underlying data, protecting confidentiality. Training and development. Completion of training courses and acquisition of new skills can be recorded as blockchain-issued digital credentials. These records are portable across organizations and can be revoked when outdated, ensuring skill verification remains current. In this section, Table 1 summarizes the key blockchain-based HRM studies based on the four HRM domains defined by 46 , and three other perspectives: (1) blockchain design patterns and technologies, (2) operational mechanisms, and (3) the key benefits of adopting blockchain-based HRM. It is noted that the concepts discussed in the management literature have been mapped to the corresponding HRM domain and technical concepts presented in Table 1 . For example, the authors of 18 stated that “the HR blockchain will execute an automatic process to reach consensus among the parties involved” and proposed a blockchain-based, decentralized rating system to address HR skills shortages. Accordingly, their work 18 is classified under the training and development HRM domain, with the related operational mechanism identified as digital training badges stored on-chain, as shown in Table 1 . Table 1. HRM domains, enabling blockchain design patterns, operational mechanisms, benefits, and selected sources. HRM domain Blockchain design patterns/technologies Operational mechanisms Key benefits Selected sources Recruitment & onboarding VCs; DIDs; immutable ledger; revocation registries; consent-based access control; smart contract Credentials stored in a decentralized and tamper-proof way; shared only with consent; outdated ones revoked on-chain Fraud reduction; faster verification; improved privacy; builds trust in HR systems Onik et al. 14 ; Chillakuri & Attili 16 ; Adel et al. 20 ; Kişi 22 ; Chanda et al. 49 Chen 50 ; Herbke et al. 51 Payroll & benefits Smart contract triggers; event-driven automation Encoded payroll/bonus rules auto-execute when conditions are met Reduced administrative costs; timely and transparent payments; eliminating the need for banks and third parties Chillakuri & Attili 16 ; Salah et al. 19 ; Chen 50 ; Hacioglu 52 ; Madhani 53 Performance management Immutable ledger; ZKPs Performance logs locked on blockchain; ZKPs verify metrics without exposing sensitive data Trustworthy evaluations; privacy protection, dispute reduction, enhanced transparency, and reduced bias; better auditability Chillakuri & Attili 16 ; Salah et al. 19 ; Chen 50 ; Monteiro et al. 54 Training & development VCs; revocation registries; immutable and decentralized ledger Digital training badges stored on-chain; revocable when outdated Talent mobility; reduced verification effort Chillakuri & Attili 16 ; Fachrunnisa & Hussain 18 ; Salah et al. 19 ; Chen 50 ; Yeganegi et al. 55 ; Horváth et al. 63 ;Madumidha et al. 64 Open in a new tab VCs: Verifiable credentials; DIDs: Decentralized Identifiers; ZKPs: zero-knowledge proofs. According to the summary in Table 1 , more studies focus on the first three HRM domains: recruitment and onboarding, payroll and benefits, and performance management. This focus is driven by the immutable and decentralized ledger, a key feature of blockchain technology, which has garnered significant attention from scholars. For the four primary HRM applications, verifiable credentials are utilized to store employee profiles and records of skills and training. Through our literature review, we found no research discussing the application of blockchain to HRM in the healthcare sector. Although blockchain-based HRM applications have been explored in other industries, there is limited discussion on how blockchain-based HRM can help firms address employee mobility across different subsidiaries or departments. Other, less discussed issues include how blockchain can enable employees to view their own competency or training records and compare them with the functional requirements of new departments or subsidiaries, as well as how blockchain technology can help firms assess employee human capital before undertaking transformation planning. Furthermore, the extent to which blockchain technology can help firms more efficiently evaluate whether employee competencies comply with specific government regulations remains an issue that has yet to be examined. To address the research gap mentioned above, this paper focuses on human resource management for nurses in the healthcare industry. We aim to explore the following issues within the context of nursing HRM: (1) Whether a blockchain-based HRM system can help nurses not only maintain their professional credentials but also effectively manage, review, and systematically acquire the required training programs and hours. (2) Whether blockchain-based HRM can help hospitals evaluate whether nurses comply with regulatory requirements, thereby ensuring hospital readiness for accreditation and government oversight by authorities such as the Ministry of Health and Welfare. (3) In a multi-branch healthcare system, blockchain-based HRM can assist nurses in assessing whether their competencies align with the requirements before applying for a transfer to another branch. We hope that through the discussion of this study, we can delve deeper and more broadly into the issues that have yet to be explored in current blockchain-based HRM applications. The proposed BCeANPDF A blockchain-based enabling autonomous nursing professional development framework (BCeANPDF) is proposed in this paper to achieve the following four objectives: To design a blockchain-based enabling autonomous nursing professional development framework (BCeANPDF) that supports nurses in recording and managing their professional development trajectories. To empower nurses, through smart contract mechanisms, to proactively identify role-relevant certifications, credentialing requirements, and regulatory standards, and to independently plan and schedule their professional competency development based on their availability. To enable the Nursing Human Resource Department (NHRD) to conduct license verification and renewal tracking, manage professional certifications, and perform credentialing for specialized roles via smart contracts, ensuring compliance with national and regional regulatory standards. To assist nurses in evaluating whether their competencies meet the requirements before attempting to transfer to another department or branch. Framework overview and architecture Figure 1 presents the architectural components of the proposed BCeANPDF and the interaction among participating entities. It illustrates how participants interact with the blockchain through predefined smart contracts and access databases to either perform on-chain and verification of nurses’ credentials, or to retrieve information on nurses, hospitals, training subjects, and credentials, which are maintained in the databases. Participating entities can access the off-chain storage for the different credential hashes available on the chain. By examining the credential details, participating entities can further verify whether the hospital’s nursing workforce complies with regulatory requirements. Fig. 1. Open in a new tab Framework of the proposed BCeANPDF. Note : The blue line represents the end user’s access line; the orange line represents the HR administrator’s access line; the purple line indicates the on-chain process, which refers to writing data or transactions onto the blockchain ledger; and the pink line represents the nursing association’s upload operation. NA stands for Nursing Association. Participating entities and layered design Our BCeANPDF involves three participating entities: end users (nurses), the HR administrator, and the certification officer of the Nursing Association (NA), and three-layer: controller, service, and repository layers. It is noted that two authorities: internal authority and external authority, may not directly interact with our proposed BCeANPDF, but directly interact with the HR administrator, which is one of the participating entities. To provide sufficient background knowledge for our BCeANPDF, descriptions and definitions regarding three participating entities and two authorities are presented in Table 2 . Table 2. Definitions and operations for three participating entities and two authorities. Parties: Definitions Operations End users Refer to the nurses DB CRUD/ on-chain hashes of credentials/ VC (upload) HR administrator ． Refer to the director or staff members of the HR administrative office who handle nurse management within a hospital DB R/VC Certification officer ． Refers to the certification officer of the Nursing Association (NA) responsible for uploading the credentials of nurses who have either attended the training courses or successfully passed the evaluations ． DB CR Internal authority ． Refers to the CAO within a hospital system ． The CAO within a hospital system, responsible for formulating internal compliance policies and auditing the human resource management of branch hospitals ． Formulating internal compliance policies/ auditing the HRM of branch hospitals External authority ． Refers to regulatory authority (RA) ． Responsible for establishing regulations and inspecting compliance (e.g., government agencies or professional accreditation bodies) Regulatory oversight Open in a new tab DB CRUD refers to the create (C), read (R), update (U), and delete (D) operations performed on a database (DB), CV refers to compliance verification, CAO refers to chief administrative officer in a hospital system, and RA refers to regulatory authority. In general, an HR administrator is responsible for verifying the credentials of nurses across the organization and monitoring nursing human resources within the hospital to ensure compliance with the regulations enforced by the RA. Moreover, in a hospital system with multiple branches, the HR administrator must comply not only with the RA’s regulations but also with the additional requirements established by the system’s Chief Administrative Office (CAO). The detailed descriptions of the three layers are listed below: (1) Controller layer: This layer functions as the entry point through which end users, HR administrators, and the certificate officer of the nursing association interact with the proposed BCeANPDF. It defines six primary operations. The first four functions correspond to standard CRUD operations, which include create, read, update, and delete. These operations apply to data stored in the database, such as information on nurses, hospitals, training courses, and credentials. Through these functions, end users (i.e., nurses) can create their own professional or non-professional credential records, and subsequently read, update, or delete them as necessary. In contrast, the hospital HR administrator is granted extended privileges. Here, professional and non-professional credentials are defined in line with relevant regulations. In Taiwan, the Ministry of Health and Welfare’s (MHW) regulations for the Practice Registration and Continuing Education of Medical Personnel specify the corresponding categories. Professional subjects, classified as category 1, include topics such as primary and secondary trauma assessment, management of traumatic shock and airway conditions, and introductory training on thoracic, abdominal, head and neck, spinal, and musculoskeletal trauma. Non-professional subjects, defined as categories ranging from 2 to 4, include topics such as applications of artificial intelligence in healthcare, issues of clinical ethics and law, and practical sharing of clinical teaching in the context of digital technology, among others. The Nursing Association (NA), which is approved by the MHW, defines the credit points assigned to each credential. Given that hospitals may provide internal training courses for their staff, it is operationally efficient for the HR administrators to update the credential records of participating nurses once they meet qualification requirements. Accordingly, HR administrators are permitted to create, and update credential records; however, read and delete permissions are intentionally excluded to preserve the integrity of the credential database. Because certain professional training courses are offered externally by nursing associations, the system also authorizes certification officers from the nursing association to upload and manage credential data relevant to those courses. The granted DB CRUD relationship among end users (nurses), HR administrators, and the certificate officer of the Nursing Association (NA) is shown in Fig. 2 . It should be noted that the HRM system is shown in Fig. 2 but is not included in Table 2 , as it only provides a list of approved professional and non-professional credentials suitable for nurses in different specialties to accumulate points. This list is intended for the proposed BCeANPDF to reference when verifying the credentials uploaded by the nurses. Fig. 2. Open in a new tab Relationship of DB CRUD among three entities: the Certificate office, HR administrator, and end users. The fifth operation, upload, specifically refers to the on-chain process, whereby authorized end-users (nurses) can record the hashes of their credentials on the blockchain to enable future compliance verification. The final operation is compliance verification, which allows nurses to determine whether their credentials satisfy applicable regulatory requirements. Simultaneously, the HR administrator is empowered to review all credential records to ensure that the hospital’s nursing workforce complies with regulatory authority standards. (2) Service layer: This layer encapsulates the business logic and serves as the bridge between the controller layer and the repository layer. Three core services are defined as follows: (a) Access control service: Responsible for authentication (identity verification) and authorization. It ensures that end users are permitted to access only their own data, while HR administrators can retrieve organization-wide information, which includes hospital, nurse, training subject, and credential records. HR administrators can also verify nurses’ credentials across the organization. (b) Data service: Provides end users with the ability to perform CRUD operations (create, read, update, delete) on traditional databases, referred to as DB CRUD. Role-based restrictions apply: end users are limited to CRUD (Create, Read, Update, Delete) operations on their own records, whereas HR administrators are restricted to read-only (R) access to hospital-wide data. (c) Blockchain service: Manages all interactions with the blockchain. Through this service, the upload operation defined at the controller layer is executed, enabling on-chain of credential hashes. In addition, compliance verification requests from the controller layer are processed, invoking either the professional smart contract (P. Smart Contract) to examine nurses’ professional credentials or the non-professional smart contract (NP. Smart Contract) to examine non-professional credentials. Repository layer: This layer represents the data storage component of the BCeANPDF, encompassing both traditional database records and blockchain-based records. Two types of records are stored in the traditional database: nurse information and credential records. The nurse records database stores personal and professional details of nurses, which cover basic profile information. The credential records database stores credential-related information, such as issued certificates and licenses, in a traditional database format. The blockchain ledger stores hashes of credential records, which enables decentralized and tamper-resistant storage. With a blockchain-based record, credentials are ensured that they cannot be altered once uploaded. Additionally, smart contracts reside in this layer, as they form an integral part of the blockchain execution environment. Two types of smart contracts are defined in the BCeANPDF. One is the professional smart contract (P. Smart Contract) to examine nurses’ professional credentials, and the other is the non-professional smart contract (NP. Smart Contract) to examine non-professional credentials. Here, we assume that an HR administrator attempts to verify whether nurses in a hospital possess the required professional and non-professional credentials in accordance with regulatory requirements. As shown in Fig. 1 , the HR administrator performs credential verification by following the orange workflow through the three-layer architecture implemented in BCeANPDF. The verification process starts at the Controller Layer, where a nurse’s compliance verification is initiated. The corresponding request and the HR administrator’s user identifier are then transmitted to the Service Layer for evaluation by the Access Control Service. After the request and the administrator are validated, the Blockchain Service and Database Service are triggered, and the nurses’ off-chain professional and non-professional credentials, together with their associated on-chain records, are retrieved via the corresponding smart contracts in the Repository Layer. A non-repudiation verification is subsequently conducted, followed by statistical analysis, and the verification results are finally returned to the HR administrator. In summary, the controller layer defines three primary actors: end users, the HR administrator, and the certificate officer, along with their associated permissions. The service layer encapsulates the business logic, enforcing access control and coordinating operations on both databases and the blockchain. The repository layer provides persistent storage, maintaining all data in traditional databases and on the blockchain, alongside smart contracts dedicated to credential verification. Within this three-layer architecture, the proposed BCeANPDF enables authorized nurses not only to update their credentials in support of career development but also to satisfy both professional and non-professional requirements mandated by the regulatory authority (RA). Governance mechanisms for smart contracts and blockchain maintenance BCeANPDF is deployed in a permissioned private blockchain environment, where governance responsibilities are clearly assigned to authorized institutional participants. Smart contract deployment, updates, and version control are governed by the hospital IT governance committee in coordination with the Nursing Human Resources Department. Any modification to smart contracts, such as updates to credential validation rules or regulatory thresholds, must undergo an off-chain approval process, including regulatory compliance review, before being redeployed on-chain as a new contract version. To preserve immutability and auditability, existing smart contracts are digitally signed using conventional cryptographic algorithms 56 , ensuring that any unauthorized modification can be readily detected. Moreover, updated contracts are deployed alongside legacy versions, with version identifiers recorded on the chain. Regarding blockchain maintenance, node operation, and network management, these tasks are jointly maintained by authorized hospital units, such as the hospital’s computer center, under a consortium-style governance model. Maintenance responsibilities include monitoring node availability, applying security patches, and conducting periodic system audits. Since the blockchain is private and operates within a controlled healthcare network, consensus participation and node admission are restricted to trusted entities, such as authorized HR administrators, nurses, and the hospital HRM system. System implementation Traditional database definition Before delving into the specifics of the prototype implementation, it is important to place the proposed platform within a practical application context. The blockchain-based enabling autonomous nursing professional development framework (BCeANPDF) is intended to function seamlessly in both single-hospital settings and across multi-hospital systems. In such settings, hospitals not only provide in-house training courses for nurses but also permit participation in educational programs organized by the national Nursing Association (NA). Furthermore, nurses may engage in training sessions facilitated by third-party organizations. In the proposed BCeANPDF, both hospital-provided and Nursing Association-provided courses are categorized as professional training courses. Accordingly, the BCeANPDF authorizes two types of institutional actors: (1) the hospital HR administrator, who is responsible for automatically uploading credential records of nurses who successfully complete internal training programs, and (2) the certificate officer of the nursing association, who is permitted to register professional credentials for nurses obtaining certification through association courses. For hospital-organized training activities, a Subject Record is created to capture detailed metadata concerning the training course, thereby establishing a verifiable linkage between the training course and the issued credential. To enhance extensibility, the prototype database schema consists of four interrelated tables, as shown in Fig. 3 : (1) Nurse_info. Record: maintaining demographic and professional data of nurses. (2) Hospital Record: containing identifiers and storing the institution’s fundamental human resource information, a verification field is also included that is set to true once a nurse accumulates 120 points over six years of service, thereby designating the nurse as qualified. (3) Subject Record: recording the professional training subjects provided by different units and classifying the training subjects into four categories: professional courses, professional quality, professional ethics, and professional regulations. (4) Nurse_credential Record: storing credential data, linked to both the Nurse Information Record and the Subject Record. It is noted that such a relational database structure ensures efficient data retrieval and management while providing the necessary foundation for blockchain integration. Fig. 3. Open in a new tab The relational database structures: Hospital record, Nurse_info. record, Subject record and Nurse_credential record. Blockchain node configuration and on-chain credential management To guarantee immutability and transparency of nurses’ credential records, the BCeANPDF incorporates a blockchain layer that complements the relational database. The blockchain component is designed as a private network consisting only of nodes uploaded by authorized nurses within the hospital. Leaf node configuration. Each credential record selected for anchoring on the blockchain is transformed into a leaf node in the Merkle tree. The leaf payload consists of the following fields: (a) credentialId: A unique identifier for the credential; (b) nurseId_hash: A privacy-preserving identifier for the nurse instead of the nurseId stored in the Nurse_info. Record; (c) subjectId: Identifier of the training course associated with the credential; (d) start_time: The course start time; (e) end_time: The course end time. And (f) points: The number of points awarded for the course. Here, in the proposed BCeANPDF, nurseId_hash is recorded in the leaf node rather than the nurseId stored in Nurse_info. Record. This is because of the following four reasons: Privacy protection: The nurseIds, including national IDs or employee numbers, are personally identifiable information (PII). Storing them directly on-chain would permanently expose sensitive data in a public, immutable ledger. Compliance: Healthcare data is governed by stringent privacy laws, such as HIPAA and GDPR. Although nurseIds are specifically linked to nurses, they could be classified as part of broader medical data. Therefore, using hashed identifiers instead of raw nurseIds on-chain can help minimize the risk of violating these regulations. Selective disclosure: With hashing, the nurse can later prove ownership of a credential by revealing their original nurseId plus the salt used in hashing, without making all identifiers visible to the public. Tamper-resistance: Even though only the hashed ID (nurseId_hash) is stored, it still uniquely binds the credential to the nurse. Any attempt to alter the nurseId would result in a different hash and an invalid Merkle proof. Based on the five selected data from the Nurse_credential Record, a leaf node can be generated by the credential leaf encoding algorithm listed below, once a nurse selects his/her credential records and performs an upload operation at the controller layer. It is noted that a leaf hash is derived by encoding the credential data fields, including credentialId, nurseId_hash, subjectId, start_time, end_time, points, in a canonical order and subsequently applying a cryptographic hash function to the encoded sequence. In practice, the function employed is typically Keccak-256 in Ethereum-based systems or SHA-256 in Bitcoin-based systems. Here, SHA-256 is used in the proposed BCeANPDF, and a 256-bit digest (32 bytes) that uniquely and irreversibly characterizes the credential. (2) On-chain credentials. Once generated for all credentials, these leaf hashes are iteratively paired and re-hashed to form a Merkle tree, whose final root value (the Merkle Root) provides a compact cryptographic commitment to the integrity of the entire set of credentials within the block. The Merkle Root is recorded in the block header alongside other essential metadata, as shown in Fig. 4 , including the hash of the previous block, a timestamp, and a Proof-of-Work (PoW) token. The Proof of Work mechanism demonstrates that the block producer has performed verifiable computational effort. The Merkle Root ensures that any modification to an individual credential alters the root, making tampering detectable. Collectively, the leaf hash digest, the Merkle Root, and the Proof of Work establish the immutability and verifiability of credential records. They also enable efficient third-party validation of individual entries through Merkle proofs. Fig. 4. Open in a new tab Example of a Merkle tree. Smart contract and algorithm design To facilitate the automated verification and management of credentials, the BCeANPDF framework employs three distinct types of smart contracts, each corresponding to different operations and user roles. For end users (nurses), BCeANPDF enables each nurse to record their credentials on-chain and verify whether their accumulated professional and non-professional credentials meet the hospital’s requirements. To execute the on-chain process, three smart contracts are employed: a) ownership verification, b) add credential, and c) credential leaf encoding. The corresponding algorithms are presented as Algorithms 1 – 3 . To enable authorized nurses to examine their accumulated professional and non-professional credentials, professional credential smart contracts are further defined as Algorithms 4 and 5 . To support compliance auditing by HR administrators, two additional smart contracts are defined: one for professional credential evaluation across all nurses and another for non-professional credential evaluation across all nurses. These functions correspond to Algorithms 6 and 7 , respectively. Ownership verification smart contract: This smart contract ensures that only the authorized nurse can perform the on-chain operation for their own credential(s). The corresponding algorithm is outlined below: Algorithm 1. Open in a new tab Ownership verification (2) Add credential smart contract: This smart contract enforces access control to ensure that only an authorized nurse can submit credential-related information associated with their own identity. The submitted data include the subject identifier, subject name, awarded course points, and other relevant attributes. The procedure for adding a credential is presented in the following algorithm. Algorithm 2. Open in a new tab Add credential (3) Credential leaf encoding smart contract: This smart contract ensures that only the authorized nurse can generate the hash value and other necessary data for their own credentials, and upload the newly created credential node to the blockchain. Algorithm 3. Open in a new tab Credential leaf encoding (4) Professional credential smart contract for the end user (nurse): This smart contract allows authorized nurses to review the professional credit points accumulated from credentials issued by hospitals and the Nursing Association. It enables a nurse to determine whether the accumulated points satisfy the required threshold ( N ) specified by regulatory requirements, or alternatively, to identify the number of additional points still needed to meet this threshold. The algorithm used for this process is outlined below: Algorithm 4. Open in a new tab Professional credential evaluation (5) Non-professional credential smart contract for the end user (nurse): This smart contract allows the authorized end users to verify their non-professional credentials issued by third parties and determine whether the required threshold ( N 1 ) has been satisfied. If the threshold is not met, the contract further indicates the number of additional points required to achieve compliance. The corresponding algorithm is outlined below: Algorithm 5. Open in a new tab Non-professional credential evaluation For the hospital HR administrators, the BCeANPDF provides two smart contracts that assist them in verifying whether all nurses in the hospitals have met the pre-determined thresholds ( N and N 1 ) for both professional credential points and non-professional credential points, as follows: (6) Professional credential smart contract for examining all nurses: This smart contract enables the HR administrator to review each nurse’s credential points, as issued by hospitals and the Nursing Association. It not only calculates the number of nurses who meet the pre-determined threshold ( N ) for professional credential points, but also identifies those who do not meet the threshold ( N ). Finally, the IDs of qualified nurses and unqualified nurses are listed for subsequent management. The corresponding algorithm is outlined below: Algorithm 6. Open in a new tab Professional credential evaluation for all nurses (7) Non-professional credential smart contract for examining all nurses: This contract verifies the total points of each nurse based on credentials issued by third parties. It calculates the number of nurses who meet the pre-determined threshold ( N 1 ) for non-professional credential points, as well as those who fall below it. Finally, it generates lists of qualified and unqualified nurses’ IDs for subsequent management. The corresponding algorithm is outlined below: Algorithm 7. Open in a new tab Professional credential evaluation for all nurses With the proposed BCeANPDF system, the HR administrator can utilize these two audit algorithms to verify nurses’ training hours across the entire hospital and identify nurses who have not yet met the required criteria for further follow-up and management. Once all nurses satisfy the regulatory requirements, the HR administrator can also generate and print the relevant supporting documents when inspections are conducted by the competent authority. Prototype demonstration and evaluation Ethics statement. Although this work is motivated by nursing professional development and credential management, it is a technology-focused study and does not involve experiments on or with human participants. No participants were recruited, and no personal data were collected. All evaluation records were synthetic and generated for system performance testing. Any names shown in the manuscript are fictional and for demonstration only. Therefore, institutional ethical approval and informed consent were not applicable. To demonstrate the functions of the proposed BCeANPDF, a prototype system was developed and deployed on a workstation running Ubuntu 24.04.2 LTS, equipped with an Intel® Core™ i7-9700KF processor (8 cores) and 32 GB of memory. The blockchain application layer was implemented using Spring Boot in combination with JavaScript, providing a robust backend framework for managing data flows between the relational database and the blockchain components. Smart contracts governing credential verification and on-chain logic were written in Solidity, ensuring secure execution within an Ethereum-compatible blockchain environment. This configuration provided both computational efficiency for system operations and flexibility in integrating smart contracts with the prototype’s service logic. Prototype interface demonstration As shown in Fig. 5 , each nurse can record his/her credentials after completing training courses, passing examinations, and receiving certification. Once the credentials are entered, Algorithms 2–3 are executed automatically to register the submitted credentials on the blockchain. Subsequently, through the same “Certificate Management” interface, authorized users can review their recorded credentials and then click the “To Chain” button (as illustrated in Fig. 6 ) and perform an on-chain operation. Fig. 5. Open in a new tab Example of credentials addition by the authorized nurse and recorded on the blockchain. Fig. 6. Open in a new tab Example of on-chain operations and results. Note : Credentials that have completed the on-chain process are highlighted in light green and are shown as the records displayed at the bottom of the figure. For example, consider a case where the nurseId is ‘1,’ the subject ID is ‘501,’ the nurse’s name is ‘John Doe,’ and the corresponding subject name is ‘Emergency Nursing EPAs New Milestone.’ Once the authorized nurse records this credential and clicks the “To chain” button, the on-chain operation is triggered in the background, and a unique chain ID ‘1337’ together with the corresponding hash value is generated using the proposed BCeANPDF as shown in Fig. 7 . Fig. 7. Open in a new tab Example of on-chain operation. To enhance the user experience, the system employs different colors to indicate whether the on-chain process has been completed, as demonstrated in Fig. 8 . Records highlighted in light green represent credentials for which the on-chain operation has been successfully completed. Fig. 8. Open in a new tab Example of the review interface designed for the HR administrator. Our system also provides a review interface to help HR administrators assess the current status of credentials held by nurses across the hospital. As illustrated in Fig. 8 , the orange line represents the total number of nurses in each department, while the blue line indicates the number of nurses who have currently met the required criteria. For nurses who do not meet the requirements, detailed review results are provided in a separate screen, as shown in Fig. 9 . Fig. 9. Open in a new tab Example of the unqualified nurses list. Experimental results and evaluation To practically test the performance of our proposed BCeANPDF, we conducted tests with different numbers of end users: 500, 1000, 3000, and 5000. In our experiments, we assumed that each nurse received 55 non-professional credentials issued by third parties and 125 professional credentials issued by hospitals or the Nurses Association. This setting reflects the situation in Taiwan, where the Nurses Association assigns a credit value to each credential, typically ranging from 0.6 to 1 point. According to the regulations of the Ministry of Health and Welfare, every practicing nurse is required to accumulate 120 points within each six-year period. In other words, assuming there are 2000 nurses, our experiment would include approximately 250,000 professional credentials and 110,000 non-professional credentials in the dataset. Four criteria are used to provide a fair performance evaluation: average latency, the 50th percentile denoted as p50, the 95th percentile denoted as p95, and throughput (TPS). Average latency is defined as Eq. ( 1 ) and refers to the mean time taken for a transaction or request to be processed, measured from submission to confirmation or response. 1 The p50 represents the value below which 50 percent of transactions are completed. It indicates the typical or most common performance observed by users and is less affected by outliers than the average. By contrast, p95 indicates the time within which 95 percent of transactions are completed. It reflects the near worst-case performance and is useful for understanding system stability and responsiveness under higher load conditions. A large gap between the p50 and p95 usually suggests performance variability or congestion. Finally, Throughput (TPS) is defined as the number of transactions the blockchain can process per second. After conducting experiments with different numbers of users (nurses) ranging from 500 to 20,000, as Fig. 10 presents, when 5000 individuals each uploaded 180 certificates, which included 55 non-professional credentials issued by third parties and 125 professional credentials issued by hospitals or the Nurse Association. In other words, a total of 275,000 records were uploaded, and it requires approximately 11,070,000 s. The and it shows in the average each credential took 12.3 s to process. In comparison, the average on-chain time for Ethereum is 13–15 s. This experimental result confirms that BCeANPDF, our Ethereum-compatible blockchain environment, achieves upload times comparable to those of Ethereum. This performance remains consistent for both professional and non-professional credentials across diverse user groups and is supported by an average of 180 successful upload trials. Fig. 10. Open in a new tab Chart showing the on-chain time for 55 non-professional credentials and 125 professional credentials with varying numbers of end users (nurses). In the second experiment, we compared the block query latency by querying the details of both the first and last credentials that were chained. This approach allowed us to conduct an objective data comparison. The experiment focused on the variations in p50 with different numbers of end users (500, 1000, 3000, 5000, and 20,000). It’s essential to note that in distributed systems or during stress testing, p50 represents the median latency, reflecting the “typical user experience.” Fig. 11 aillustrates the p50 execution time, which represents the time taken to first query the first chained credential (first) and then query the last chained credential. In contrast, Fig. 11 b shows the p50 execution time when the last credential is queried first, followed by the first credential. Query order: query the first credential and then the last credential Query order: query the last credential and then the first credential Fig. 11. Open in a new tab “Typical user experience (p50)” with the different query order under different numbers of nurses. By comparing Fig. 11 a, b, and Table 3 , we can observe that Fig. 11 b demonstrates a slightly longer waiting time for querying the last block compared to the first block. This occurs because the last credential was queried first, followed by the first credential. The result suggests that the system experiences a warm-up effect during the second round of queries. In the first round, a TCP connection must be established, the connection pool initialized, and the handshake process completed. In contrast, during the second round, the system can reuse the already established keep-alive connections, thereby avoiding these one-time costs and resulting in a shorter query time for the first block. Table 3 shows stable system behavior of BCeANPDF as the number of concurrent users increases from 500 to 20,000. The p50 write latency remains nearly constant across all experimental conditions. The p50 query latency also remains nearly constant. The p95 write latency shows only minor variation as the number of users increases. The p95 query latency follows the same trend. These results indicate the absence of system congestion as the user load increases. In the test dataset, each nurse is associated with 55 non-professional credentials and 125 professional credentials under the initial experimental configuration. The total number of credentials reaches 900,000 with 5,000 nurses. With 20,000 nurses, the measured processing time is 16.23 s. This value closely matches the theoretical processing time of 16.3 s at a throughput of 37 transactions per second. These results confirm the scalability and robustness of the proposed BCeANPDF system. It is essential to note that the environment used in this study is a private blockchain, where both nodes and clients are located within the same network domain. This configuration results in exceptionally low network transmission costs. Consequently, query latency is reduced to the millisecond level, with the 50th percentile (p50) latency observed between five and nine milliseconds. This performance is substantially better than that of the Ethereum public blockchain, where query latencies are typically in the range of several hundred milliseconds. This superior performance is primarily attributed to the local area network configuration of the private chain, which avoids the delays associated with international data transmission and the computational burden of distributed consensus. Table 3. Write and query latency (p50/p95) under different numbers of nurses and batch sizes. Number of nurses Batch Write latency (unit: s) Query latency (unit: ms) p50 p95 p50 p95 500 55 12.20 16.23 8.90 9.10 500 125 12.20 16.26 9.00 9.30 1000 55 12.19 16.23 6.23 6.31 1000 125 12.20 16.23 6.12 6.15 3000 55 12.20 16.21 6.13 6.18 3000 125 12.21 16.25 6.25 6.31 5000 55 12.22 16.21 6.21 6.25 5000 125 12.23 16.25 6.25 6.31 20,000 55 16.32 20.61 6.32 6.38 20,000 125 16.32 20.61 6.34 6.43 Open in a new tab Regarding throughput (TPS), the experimental results confirm that the TPS remained at 37 under both a block gas limit of 8,000,000 and a block gas limit of 10,000,000. Ethereum typically achieves a TPS between 15 and 30. The higher TPS observed in our experiments demonstrates that the proposed BCeANPDF provides sufficient performance to support practical application scenarios. In the defined scenarios, including credential uploading, qualification verification, and periodic record updates, transactions are not generated continuously or in real time. Regarding the calculation burden, the proposed BCeANPDF introduces a moderate and well-controlled computational overhead that is suitable for hospital environments. The on-chain computation mainly involves cryptographic hash generation, Merkle tree construction, and lightweight smart contract execution for credential verification, while data-intensive operations are handled off-chain. This design maintains a stable average on-chain processing time of approximately 12.3 s per credential, ensuring predictable scalability. In comparison with Ethereum, which requires more complex consensus and global state management, BCeANPDF achieves comparable transaction latency with a lower and more application-focused computational burden. Such characteristics align well with the hospital nursing professional development framework proposed in this study, where credential-related operations occur at low frequency and do not require real-time processing. Security discussions In the current design of BCeANPDF, smart contracts and the immutability of the blockchain ledger reduce the risk of insider misuse. The controller and service layers define separate permissions for nurses, HR administrators, and certification officers. Each role can perform only operations that match its responsibility. Smart contracts for ownership verification, credential addition, and credential encoding verify the identity of the caller before storing any credential hash on the blockchain. Only the credential owner nurse can trigger the operation that writes this credential to the ledger. HR staff and other insiders cannot silently forge or change records for another person. A credential first becomes a leaf in the Merkle tree. Any later change to the underlying data produces a different leaf hash. The Merkle root in the block header also changes in this situation, and auditors can detect the inconsistency. The system also uses hashed nurse identifiers in the leaf payload. This design conceals the nurse’s true identity while still associating each credential with its correct owner. Smart contracts and the immutable ledger, therefore, work together to limit insider misuse in BCeANPDF. In addition, BCeANPDF can adopt established security approaches 57 – 62 to mitigate and prevent malicious tampering of smart contracts. For instance, conventional cryptographic algorithms can be employed to digitally sign smart contracts, ensuring their integrity after deployment. Simplifying contract logic and leveraging well-tested standard libraries can further reduce vulnerability exposure 57 . Moreover, static analysis and formal verification techniques are effective in identifying and preventing common security flaws 58 . Overall, by adopting these approaches, the security risks associated with smart contracts can be effectively mitigated within the proposed BCeANPDF framework. Limitations This study mainly focuses on how the system works in private blockchain environments, but it does not explore its potential in consortium or hybrid blockchain networks. This is a limitation, especially considering the potential for collaboration between hospitals of different sizes and levels within the healthcare system. Future research could investigates the feasibility of using consortium or hybrid blockchain models, which could help hospitals work together more effectively and share data more easily with regulatory bodies. Additionally, the proposed framework doesn’t yet make use of AI-based tools to help hospital administrators assess the gaps between nursing staff skills and job requirements. Adding AI could provide more personalized recommendations for professional development, helping staff meet the specific needs of their roles. The current framework also doesn’t consider privacy-enhancing technologies, like zero-knowledge proofs or advanced anonymization, which could improve data security and protect patient privacy. These are areas that could be explored further in the future. Conclusions In this study, we introduced BCeANPDF as an innovative system that integrates blockchain technology and smart contracts within a three-layered system architecture. The system allows nurses to independently manage their training and credentials, while providing administrators with tools to ensure compliance with institutional and regulatory standards. Experimental evaluations confirmed the system’s feasibility and scalability. For example, with 5,000 users each uploading around 180 credentials, the system processed each credential in an average of 12.3 s, which iscomparable to Ethereum’s 13 to 15 s. Query latency, ranging from 5 to 9 ms within the private blockchain, was significantly faster than public blockchain benchmarks, highlighting the system’s efficiency for real-world hospital networks. From a credential management perspective, our approach aligns with 64 , which identifies blockchain as an effective tool for sustainable credential lifecycle management. Since healthcare credentials must remain trustworthy across multiple issuing authorities and long careers, blockchain-based verification addresses the limitations of traditional systems that are often manual and dependent on centralized intermediaries. From an organizational and socio-technical perspective, BCeANPDF supports workforce governance, regulatory compliance, and future digital transformation, aligning with the principles outlined in 63 . In this context, our proposed framework serves as a critical foundation for healthcare institutions, helping them address the evolving challenges of workforce management and regulatory compliance. However, as discussed in Section “Limitations”, there are areas for improvement, including exploring consortium and hybrid blockchain models for better collaboration and data sharing across hospitals. Additionally, integrating AI-based analytics to identify competency gaps and recommend professional development, along with incorporating privacy-preserving technologies like zero-knowledge proofs, would further strengthen the system. These enhancements are key areas for future work, and we plan to address them in ongoing and future developments to ensure that BCeANPDF evolves into a more robust, scalable, and secure solution for healthcare systems. Author contributions Chia-Chen Lin, Yen-Heng Lin, and I-Chieh Hsu contributed to writing and revising the main manuscript. Chia-Chen Lin, Yen-Heng Lin, and I-Chieh Hsu contributed to the development of the scenario and ideas. I-Chieh Hsu provided expertise in human resource management. Yen-Heng Lin carried out the implementation. Chia-Chen Lin and Yen-Heng Lin prepared all figures. Chia-Chen Lin applied for the grant. All authors reviewed the manuscript and contributed to the revision. Funding This work is partially supported by funding from National Science and Technology Council under Grant NSTC 113-2410-H-167-012-MY3 and NSTC114-2634-F-005-001-MBK. Data availability The datasets generated and/or analyzed during the current study are synthetic and were created for system performance evaluation. Data are available from the corresponding author upon reasonable request. Declarations Competing interests The authors declare no competing interests. Ethics approval and consent to participate Not applicable. This study did not involve experiments on or with human participants. 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