Standardized Pathology Assessment Template Design

Abstract

Information system design and implementation require generally accepted norms, principles, and standards. Lately, the challenge for achieving a high degree of general acceptance has increased with the presence of formal governance structures. Compliance with norms for information system development depends on the shared recognition of regulations and standards. The research problem in this study concerns standards and their role in the development of a pathology laboratory information system. In this paper, in the theoretical background section, the authors present regulations, standards, and disease classification, which are necessary for planning the pathology laboratory information system. Next, in the template design project section, the authors focus on development of a new, ontology-based, and standard-oriented approach for elaboration of a standardized template of the pathological assessment of histopathology material. Authors use the World Health Organization (WHO) ICD-11 classification to elaborate on that template, which permits the precise coding of diagnoses and medical procedures. The main findings concern the proposed ontology-based document template, which can further be used in the Laboratory Information Management System (LIMS), and as such can be considered a pattern for the development of other LIMS documents. In conclusion, the authors emphasized the standardized method application for designing and implementing medical documents. This original contribution concerns the assessment template design based on existing ontologies ICD-10 and ICD-11.

1. Introduction

Pathology Laboratory Information Management System (PLIMS) development requires implementation of norms and rules that underpin system design and implementation for generating diagnosis reports. Although standards regulate software product development, they also allow for innovative technology usage, which is only possible with a certain degree of flexibility, compatibility, and compliance.

The mandatory standardized compliance is achievable if all uncertain issues are resolved and the solution is not only documented but also applied in a wide community. According to Fellmann and Zasada [1], compliance means an assurance that business processes are aligned with commonly accepted regulations and norms. Regulations may be required by law, but the norms can be derived from best practice frameworks, company internal standards, and inter-company business contracts [2]. Compliance with standards is a requirement of PLIMS quality management. The software product quality model defined in ISO/IEC 25010:2023 [3] comprises the following quality characteristics: functional suitability, performance efficiency, compatibility, interaction capability, reliability, security, maintainability, flexibility, and safety. In ISO/IEC 25010:2023 [3], compatibility has two sub-characteristics, i.e., co-existence, which means sharing a common environment and resources with other products, and interoperability, which means that the system can exchange information with other products. In this study, the PLIMS quality is perceived as the degree of excellence of the diagnostic process, in which documents, e.g., diagnosis reports, are compliant with ICD-11 and interoperable for exchange with other systems. According to Mukherjee [4], quality also means the degree of achievement of the formulated objectives. Therefore, a product, i.e., pathomorphological diagnosis, is of good quality if, at a minimum cost, it provides the maximum contribution to the health of people, to whom the tissue belongs to, while the specimen processing, transport, use, maintenance, and even recycling come with a minimum use of energy and other resources and with an acceptable impact on society and on the environment.

While the issues of quality and standardization in diagnostic laboratories are well known and developed, there is still a gap in the field of pathology laboratories, and that gap concerns issues of quality, standards, and standardization. Therefore, this study includes the following research questions:

• What standards and regulations may have an impact on Pathology Assessment Template design and implementation?
• What are the key challenges in changing from ICD-10 to ICD-11 for pathology laboratory systems?
• What design procedure can be applied for Pathology Assessment Template development?

To answer these questions, the authors propose the following structure. In the next section, this study presents regulations and standards underpinning the development of Pathology Laboratory Information Management Systems. To achieve this goal, the authors applied the ISO standards and literature survey. The other part covers the application of the ICD-11 ontology for development of the pathological diagnosis template. This contribution includes an adaptive approach for the development of a standardized template for evaluation of histopathological material. This proposed approach is expected to support clinicians with clear and detailed reports, and the pathologists will have an efficient preparation of diagnosis results with minimized errors and workload. The final section includes the conclusions.

2. Regulations and Standards for PLIMS Development

The functioning of a pathology laboratory in Poland is regulated by a number of legal acts that address both organizational issues and the rules for performing medical tests. In order for a pathology laboratory to function properly in accordance with the regulations, it must meet the requirements set forth in the following legal acts and quality standards. The regulations on organizational standards, personnel qualifications, diagnostic equipment, and personal data protection are extremely important. The key regulations and guidelines include, in particular,

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The Law of 15 April 2011 on therapeutic activity [5], which defines the rules for conducting medical activity, including the operation of laboratories as therapeutic entities or their organizational units.

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The Law of 27 July 2001 on Laboratory Diagnostics [6], which regulates the activities of diagnostic laboratories, including requirements for personnel qualifications, equipment, and quality standards in laboratory diagnostics.

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The Law of 5 December 1996 on the professions of physician and dentist [7], which defines the rules and conditions for practicing the professions of physician and dentist in Poland.

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The Law of 1 July 2005 on the collection, storage, and transplantation of cells, tissues, and organs [8]; sanctioning rules for the collection, storage, transplantation, and use of cells in humans, including hematopoietic cells of bone marrow, peripheral blood and umbilical cord blood, and tissues and organs derived from a living donor or from a cadaver; the testing, processing, storage, and distribution of human cells and tissues; and the donation, collection, collection, testing, and release of tissues and cells intended for the manufacture of advanced therapy medicinal products.

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The Act of 6 November 2008 on Patients’ Rights and Patients’ Ombudsman [9], which in turn regulates, among other things, the rules for sharing medical records and the obligations of healthcare providers related to patients’ rights.

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The Law of 27 August 2004 on publicly funded healthcare services [10], defining—among other things—the conditions for the provision and scope of healthcare services.

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Law of 31 January 1959 on cemeteries and burial of the dead [11].

From the point of view of the quality of operation of the histopathology laboratory, the legal acts detailing the above-mentioned regulations are very important. The additional acts are as follows:

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Regulation of the Minister of Health of 18 December 2017 on organizational standards of healthcare in the field of pathology [12];

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Announcement of the Minister of Health of 24 September 2021 on accreditation standards for the provision of health services and the operation of pathology diagnostic units [13];

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Regulation of the Minister of Health of 23 March 2006 on quality standards for medical diagnostic and microbiological laboratories [14];

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Decree of the Minister of Health and Welfare of 3 August 1961, on the determination of death and its cause [15];

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Regulation of the Minister of Health of 7 November 2007 on the type and scope of preventive, diagnostic, therapeutic, and rehabilitative services provided by a nurse or midwife independently without a doctor’s order [16], which confirms the authority of midwives to collect material for gynecological cytology.

When considering an adaptive approach to the quality of a standardized template for pathological assessment, it should be noted that the concept of “quality” itself is multifaceted and defined differently depending on the context and field of study. Definitions of quality vary depending on the context—from philosophy to management to marketing. Each definition emphasizes different aspects, such as compliance with requirements, user satisfaction, or reliability. The common denominator, however, is the drive to meet customer expectations and needs. The classical definition of the concept of quality is derived from the Greek word “poiothes”—proposed by Plato. Its proper concept was introduced by Cicero, who proposed the term “qualitas”. Selected contemporary definitions in the context of quality management include the modern approach to product quality, which was one of the first to be proposed by Deming. According to his theory, proper personnel management, planning, product design, and process monitoring can be used for this purpose. In the 1980s, both Porter and Deming linked quality to competitiveness and considered it an important factor in determining customer perception of a company [17].

Crosby defined quality as compliance with external and internal requirements, while Fiegenbaum considered quality to be the usefulness of a product and its ability to perform tasks [18]. In contrast, Juran viewed quality as usefulness beyond the statistical dimension of quality management or compliance with specifications. He argued that quality is determined by the user, not the producer [19]. The definition provided by Garvin should be considered the most common approach in viewing quality in connection with relationships. He distinguished “four major eras of quality”, i.e., inspection, quality control, quality assurance, and quality management [20]. Garvin’s proposal to describe the evolution of quality management is based on the criterion of the maturity of the solutions used and the completeness of the management functions considered [20,21]. The common denominator among the various definitions of quality is its association with expectations and requirements. Regardless of perspective, quality refers to the ability of a product, service, or process to meet user expectations while minimizing errors and waste.

In light of the above considerations, it is important to point out the inextricable link between quality issues in the broadest sense and quality standards—including, in particular, ISO (International Organization for Standardization) standards. For the proper management of a pathomorphology laboratory, the following categories should be indicated:

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ISO 9001—which classifies the requirements for quality management systems, including, but not limited to, introducing supervision of documentation and records, involving management in building a quality management system, systematizing resource management, establishing product realization processes, or making systematic measurements (customer satisfaction, products, processes) [22],

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ISO 14001—describes environmental management systems and confirms the consistent implementation of all elements aimed at reducing the organization’s negative impact on the environment as much as possible [23];

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ISO 27001—regulates information security management in the broadest sense: it helps organizations manage and protect their information assets so that they remain secure, and helps them continually review and improve the way they operate, not only for today, but also for the future [24];

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ISO 15189—deals with requirements for medical laboratories and aims to ensure the high quality and competence of medical laboratories that provide diagnostic services and conduct tests on human material in order to provide information for diagnosis in medical processes or prevention in the assessment of a patient’s health, and the results of a laboratory accredited to ISO 15189 are unquestionable, while the laboratory itself with an accreditation certificate is perceived as a facility with the highest quality of the testing service provided [25].

It should be emphasized that ISO standards—specifically related to healthcare—are designed to improve patient safety, streamline supply chains, and foster innovation in digital health and sustainable healthcare. The following standards set by the Polish Society of Pathologists complement the above-mentioned sources of law and standards that affect the organization of a pathology laboratory:

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A set of accreditation standards for pathology diagnostic units [26].

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Pathology standards and examples of good practice and elements of differential diagnosis. Guidelines for pathology departments/staff [27].

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Organizational standards and standards of practice in pathology. Guidelines for pathomorphology departments/departments [28].

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Implementation of accreditation standards in pathology diagnostic units. Guidelines and recommendations [29].

The presented standards and regulations concern the quality management at a business organization, i.e., pathology laboratory. The quality is understood as the interpretable and measurable characteristic of the whole business unit, further applicable for software system development. Paszko and Turner [30] proposed the traditional procedure for the development of software systems for pathology laboratories. That approach includes project definition, definition of functional requirements, functional design, implementation planning, system integration, and system evaluation by the end-user. That procedure is very general and the document template design is not emphasized there. Other authors emphasize the need to integrate ISO 17025:2017 and ISO 9001:2015 [22] in a Laboratory Information Management System, because the integration offers significant benefits in terms of efficiency, data integrity, compliance, and overall quality management [31]. The ISO 17025:2017 standard focuses on information technology and highlights the use of computer systems, electronic records, and the production of electronic results and reports [32].

The automatic reporting function in many Laboratory Information Management Systems allows for time saving, providing fully configurable reports, and combining the reports with graphs, which are also automatically generated [31]. Wiener and Puetter [33] argue that information system development requires generally accepted norms, rules, and principles. They allow for a higher degree of general acceptance and compliance even in the case of absence of formal governance structures [34]. Stegmeier et al. [35] also share the opinion that technical standards support rationalization and quality management in business units. Standards are needed for the exchange of data, messages, and documents. Information system development is realized in circumstances of standardized programming languages, application servers, and processes. The methodologies for development of the Laboratory Information Management System focus mostly on functional structure modeling and the implementation of functionalities [30,36,37]. The document design, particularly designing reports, is a consequence of the system implementation of functionalities. However, reports are to be considered as fundamental to laboratory data processing for patient diagnosis decision making. Reports should be issued, reissued, modified, and changed according to the end user requirements. They should be clearly labeled. Their content and reason for omissions should be clearly presented in the reports [37]. Wang and Strong [38] highlighted data quality features, which were to be included in the certification-compliant modeling. The features are as follows: data faultlessness, accessibility by simple procedures, timeliness, completeness, and availability at the designed time and in the respective process steps. ISO8000-61:2016 addresses the data management problems and incorporates the Total Data Quality Management (TDQM) method developed by the MIT Chief Data Officer [39,40].

Going further, Data Quality Management can be supported by the shared ontology development and implementation [41]. In general, ontologies are important for software development, because of four distinguished aspects, i.e., domain modeling, which reveals the application domains and provides their knowledge to the software; structural or syntactive modeling, which describes the interfaces and the application architecture; and static and dynamic semantics [42]. Ontologies facilitate the interoperations of different systems and sharing technology-independent knowledge in heterogeneous contexts [43]. The fundamental usage of ontology is presented in the Ontology-Driven Software Development (ODSD) approach [42] or Ontology-Driven Requirement Engineering (ODRE), which is an ontology-based approach for the specification and validation of requirements, and as such it allows inconsistency to be avoided and the completeness of the identification of requirements to be ensured [44]. Romao et al. [45] presented the development of the intraoperative neurophysiological monitoring (IOM) documentation ontology (IOMDO) and the associated tool. The initial phase of the proposed approach focuses on the ontology’s creation, the software prototype is developed, and the last phase covers the software evaluation within real-world documentation settings. Provided by the authors, an ontology-based documentation tool serves as an aid to non-technical users in knowledge base querying. Lee and Tu [46] developed an ontology-based documenting approach, and they proposed an Ontology-based Document Clustering (ODC) technique to support document clustering at the conceptual level. Their research is an interesting inspiration to this paper’s research on an ontology-based document template design.

3. Ontology Servers Used in Medical Service Development

The increasing amount of information and complexity of the architecture of information systems in pathology laboratories necessitates the implementation of solutions aimed at

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Supporting the integration of various information systems that process data in different formats,

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The possibility of inference based on available medical data;

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The understanding of the meaning of data, both by the participants of the processes and GenAI solutions;

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The use of medical data description standards that allow systems to be integrated not only at the syntactic level but also the semantic one.

In the context of pathological assessments, where accurate and standardized documentation is of key importance, ontologies offer formal frameworks for representing the concepts and relations used in pathology processes. Due to the complexity of ontology described for instance in the OWL language, dedicated servers are created for easier access to knowledge. These are called ontology servers and provide the functions of searching the knowledge written in the ontology’s structure. They facilitate the integration of currently used solutions such as RESTful API network services with complicated mechanisms of ontology searches. The aim of network services is to reduce the problems related to the use of a dedicated programming API existing in solutions such as OntoCat, which provides Java API to various ontologies [47]. The use of web services eliminates the limitation in the form of implementation of a given API in a specific programming language. The concept of providing access to ontologies via web services is used in various projects such as NCBO BioPortal, which has a service architecture and makes homogeneous REST API available to hundreds of ontologies in various formats [48], and Ontoserver [49], which provides infrastructure for building your own ontology server. However, its use free of charge is limited. In the literature, one can also find solutions dedicated to specific uses, such as OntoCRF—Ontology Clinical Research Forms [50]; OntoPharma [51]; or ICD-11 [52]. The first solution allows the generation of medical forms based on the available ontology. The second concerns the analysis of medicine’s effect on the patient, while the third solution is made available by the WHO and provides access to the ICD-11 classification.

The International Classification of Diseases (ICD) is the standard diagnostic tool used for epidemiology, health management, and clinical purposes supported by the World Health Organization (WHO). It is applicable to determining healthcare payments and the cost reimbursement of healthcare service providers in hospitals [53]. The ICD-11 classification may foster advancements in artificial intelligence (AI) and analytics through detailed and interoperable data. This classification is to ensure semantic interoperability and reusability of recorded data for the different use cases beyond mere health statistics, including decision support, resource allocation, and cost reimbursement guidelines. In particular, the classification enables a taxonomy of the public health causes of death; surveillance of clinical term records; functioning assessment decision support; primary care prevention and research; safety of patients, drugs, and devices; and cancer registration. The classification is a conceptual framework independent of language and culture. The ICD ensures an integration of terminology and classification, and it supports end-to-end digital solutions, i.e., API, online and offline tools, actual scientific knowledge, comparable statistics, and semantic interoperability. The WHO provides the ICD-11 version in multiple languages (https://icd.who.int, accessed on 5 April 2025). A disease is usually defined using a set of relevant aspects and it is a set of dysfunctions defined by symptomatology, manifestations, etiology, course, outcome, treatment response, linkage to genetic factors, and linkage to environment factors. The ICD has categories for diseases, disorders, syndromes, signs, symptoms, findings, injuries, external causes of morbidity and mortality, factors influencing health status, and traditional medicine.

The ICD-coded entities or categories can be used in conjunction with other relevant health classifications and terminologies to fully document an episode of care, or a case of research. Standardization of the medical documentation provides many societal benefits, particularly to the organizations related to health. Beyond the ICD-11, there are some other classifications, e.g., SNOMED CT, NANDA, HPO, and RGO, which are developed in cooperation with ICD-11. The SNOMED CT is a clinical reference terminology to enable the semantic interoperation and knowledge representation of healthcare data. In the SNOMED CT, each concept has a unique numeric identifier, enabling meaning-based queries, descriptions linking human-readable terms to concepts, help searching for concepts, linking concepts to others, and providing multi-lingual support. The SNOMED CT is used for research, data integration, clinical information sharing, and various analytics applications. This classification is embedded in clinical information systems to improve the information quality in these systems [54]. NANDA-I is standardized taxonomy for the decision making in clinical nursing, regarding diagnoses, interventions, and outcomes [55]. The Human Phenotype Ontology (HPO) is used to describe the phenotype traits in human diseases [56]. However, clinical features, laboratory measurements, and anatomical and functional phenotypes of patients are often described with various qualities, which does not sufficiently well support diagnostic efficiency nor global data support [57]. The Radiology Gamuts Ontology (RGO) is the ontology of 16,918 diseases, interventions, and imaging observations, and it provides a resource for various diagnoses and automated textual report understanding in radiology [58]. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge.

The ICD-11 standard allows the easy classification of disease entities, and also provides additional mechanisms facilitating the creation of medical documentation. Due to the functionality of the available ICD-11 ontology server (the WHO provides a public access point REST (https://icd.who.int/browse/2025-01/mms/en, accessed on 5 April 2025)), the ICD-11 ontology is not directly accessible in the form of OWL files. To refer to the ICD-11 ontology, one needs to use certain services which allow the querying of the ontology server. This facilitates integration of the ICD-11 with other information systems based on SOA architecture or micro-services, but it limits the possibility of integrating the ontology with other ontologies through tools such as Protege. The shift from a flat ICD-10 list to the ICD-11 ontology-based format allows advanced analyses of clinical data as well as the linking of concepts related to disease entities with other concepts applied in medicine.

There are two fundamental issues when using various solutions that provide ontologies for creating the software solution. The first is a lack of integration between the existing ontologies. Languages such as OWL have the capability to merge various ontologies at the level of used concepts. However, this requires that they are already merged in the design stage. In the case of solutions with a closed architecture such as the ICD-11, the use of a dedicated ontology server makes this task more difficult. The second issue refers to the encapsulation of ontology servers. A server may have a pre-defined structure of provided services, which may be non-editable. In this case, even if the ontology is remodeled, the pre-defined services will prevent access to its resources. As a result, merging ontologies can only take place when their contents are exposed via a given web service. Consequently, when building applications based on ontology servers, you have to consider two approaches. The first one is ontology-oriented, with the basic layer being the ontology layer, its structure, and integration with other ontologies. In this case, ontologies are merged in the design stage, and exposed via dedicated services, which must be changed after each change of the ontology. The second approach, used in the case of ontology server encapsulation, is service-oriented. In this case, the integration with ontology is possible in the stage of accessing a network service and requires indicating unique concepts that allow the creation of references between the provided ontology and integrated ontology.

The solution presented in the next section combines both these approaches, with integration executed partially at the level of ontology and partially at the level of exposed web services. An additional problem with providing services is combining them into specified business processes occurring in an organization. A service connected with a given ontology only enables the search of concepts and their relations without indicating a broader context of its use within a business process occurring in the organization.

4. Knowledge Elements of the ICD-11 Ontology Pertaining to Prostate Cancer

The foundation for the research presented in this section was the IoTDT-BPMN ontology [59], which enables the linking of concepts related to business-process issues with concepts from the Digital Twin domain (Figure 1).

This ontology facilitates the representation of business processes and the association of various concepts, such as process participants, resources, and software agents, with them. Its aim is to provide context for the process under study and to identify the tasks that can be performed within it. In business processes, knowing only the sequence of steps is insufficient, as each task also requires specific domain knowledge related to the actions carried out by process participants. As previously stated, the ICD-10 and ICD-11 ontologies may be applied to specific activities conducted within PLIMSs.

The basic function of the ICD-10 ontology is defining the code of a disease entity constituting the reference to the resources defined in the ontology. Due to their uniqueness, disease entity codes can be integrated with other ontologies. In the case of the ICD-11 ontology, disease entity codes were changed, complicating direct merging of ICD-10 and ICD-11 ontologies. The use of an ICD-11 ontology server makes it possible to obtain additional information, for instance, concerning disease severity, location, and its effects on the patient. Such information can be helpful when building a Pathological Assessment Form (i.e., Template) and that information adds a broader context to the concepts used in the development of the form. The ICD-11 ontology server allows for the indication of information on the disease entity, which can aid the patient in understanding the results presented to them. Due to the limitation of the dedicated ICD-11 server resulting from the lack of linkage between ICD-11 codes and ICD-10 codes used in Poland, as well as the broader scope of the Pathomorphological Assessment Template, it was necessary to extend the ICD-11 ontology to include the ICD-10 standard and business context.

5. Proposal of Ontology and Pathological Assessment Template

The first step in constructing the Pathological Assessment Form was to identify the process in which it would be applied (Figure 2).

The prepared business process encompasses a series of stages related to tissue preparation for analysis, such as preparing the macroscopic description, drying, sectioning the material, and staining. Dividing the process into these stages made it possible to better understand the requirements imposed on the form under development and to break it down into sections corresponding to each of the histotechnician’s tasks. The next step was to develop the appropriate ontologies and knowledge graphs. Typical stages of the ontology-building process can include term definitions, community/expert involvement, vocabulary development, taxonomy formalization, the development of lightweight and heavyweight ontologies, and ontology alignment and validation. The authors interviewed the PLIS system’s users and experts involved in tissue assessment processes as well as in the analysis of information system requirements. The interviews have facilitated the development of domain-specific ontology elements.

Figure 3 presents the development of the ICD10to11 ontology and a server call allowing verification of the ICD11 and ICD10 linkage. The ontology was constructed based on a range of concepts, including those representing knowledge about disease names and their corresponding codes (ICD11Entity, ICD10Entity). The ontology also includes concepts related to the assignment of ICD codes to appropriate thematic groups (ICDChapter, ICDBlock, ICDCategory).

The indicated ontology has been published on an ontology server and can be queried using JSON or JSON-LD queries. The use of an ontology server supports the publication of static ontologies. However, in the case of data that changes frequently or data integrated with PLISs, it is necessary to apply knowledge graphs that semantically provide information to the Pathological Assessment Template. The data collected through the form includes patient information, macroscopic descriptions, sample staining details, and microscopic data. Figure 4 shows an element of the knowledge graph server built using the Ontop platform [60]. The Ontop platform allows the database to be represented as a knowledge graph and the system to be queried using the SPARQL language. Figure 4 shows a database element relating to the tissue-staining task, the ontology storing information about the material under study, and a SPARQL query that retrieves information from the ontology for a specific instance of the Pathological Assessment Template.

By linking the relational model with the semantic representation of knowledge in the form of knowledge graphs, it becomes possible to associate a specific ICD code with the examined pathology material. This approach enhances the integration of medical processes and facilitates their execution.

The outcome of this work was the development of the Pathological Assessment Template, which integrates the proposed ontologies and enables the recording of and access to information about the processed tissue samples. The completed form is shown in Figure 5. Highlighted in red is the section of the form that retrieves data from the ICD10to11 ontology; green shows the data coming from the knowledge graphs provided via the Ontop platform; and yellow shows the information about the process instance recorded in the PLIMS (IoTDT-BPMN ontology).

The architecture depicted in Figure 5 illustrates the integration of the developed ontology server prototypes, knowledge graphs, and the proposed Pathological Diagnosis Preparation process. In the presented approach, the process-oriented nature of the IoTDT-BPMN ontology allows for the integration of information about the business process with information about the patient involved in the process (Patient Information section). The Disease Information section is generated based on the linkage between the ICD-11 ontology and the ICD10to11 ontology, providing context for the examinations and serving as a source for elements displayed in the Diagnoses, Macroscopic Data, and Specimen Staining sections.

6. Discussion

In this study, the authors proposed a standard-based approach to the design of the Pathology Assessment Template. They argue that standards are necessary for the pathology diagnosis laboratories, for software product development, and for the information system document design. The standardization allows for information document flexibility, compatibility, and compliance. The document standardization supports not only the excellence of the diagnostic process at a pathology laboratory, but it permits the easier communication of many laboratories and an exchange of information reports among them. The pathology diagnosis document exchange is fundamental and essential for the development and maintenance of data repositories for further evidence-based diagnosis verification.

In this paper, the authors have listed regulations and standards that are mandatory for the functioning of pathology diagnosis laboratories. Particularly, they have emphasized the meaning of the ISO standards, i.e., ISO 9001, ISO14001, ISO 27001, and ISO15189. However, for construction of the Pathology Assessment Template, in this study, the authors proposed to use already developed ontologies, i.e., ICD-10 and ICD-11.

The ICD-11 ontology server permits the identification of information on the disease entities. In this study, the authors have expanded their earlier research on using ontology in defining business processes. Therefore, they have integrated the proposal in [56], the IoTDT-BPMN ontology, with the ICD-11 ontology to develop the Pathological Assessment Template.

The advantages of integrating the above-mentioned ontologies include the following issues:

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Maintaining the consistency of concepts across the merged business and medical ontologies;

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Possibility of extending the IoTDT-BPMN ontology to include elements of ICD-11 and ICD-10 ontologies;

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Possibility of making the elements of both ontologies available in the Polish language;

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Facilitating the development of the elements of a Pathological Assessment Form by using ICD-11 and ICD-10 ontologies and indicating its application in a business process using IoTDT-BPMN ontology;

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Extension of the BPMN process to incorporate ontology and knowledge graph elements in the tissue staining and macroscopic data stages.

The designed report template is to be considered as an interior part of a Pathology Laboratory Information Management System (PLIMS). That proposed document template development must be treated as a case study; hence, the proposed design and implementation procedure can also be applied to the development of other PLIMS documents. Such an approach supports the sharing of intraorganizational documents as well as the external (i.e., beyond the one laboratory) interorganizational flow of documents. The ICD11 standardization of documents is a challenge for pathology laboratories, creating new opportunities for more efficient communication among them.

7. Conclusions

In this article, a set of ontologies is presented that not only map patient data but also model the course of medical processes in which the patient participates. Thanks to the proposed knowledge graphs, it is possible to create a unified PLIMS integrating Electronic Medical Record (EMR) and Electronic Health Record (EHR). The key premise of this integration is to encompass both the level of medical data exchange and the structure of processes, both at the medical laboratory level and more broadly across the entire patient care pathway. In particular, the ontological model of the Pathology Assessment Template and its use are discussed, representing a significant step towards an ontology-driven IT architecture for medical laboratories. The authors want to emphasize that the presented process for the pathology laboratory, as well as the assessment template and IoTDT-BPMN ontology, are the original contributions of the authors. The next step in the research work concerns including the template in the PLIMS architecture and the further strategic development of laboratory architecture and its operationalization. The authors are planning to further model the laboratory processes and create templates for other data forms.