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Unlocking healthcare data potential: A comprehensive integration approach with GraphQL, openEHR, Redis, and Pervasive Business Intelligence

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06 November 2024

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Abstract
This paper investigates the transformative potential of integrating technical and methodological tools such as GraphQL, openEHR, Redis, and Pervasive Business Intelligence in healthcare. Modern healthcare systems face data silos, interoperability, and efficient data communication challenges. The integration of these technologies offers innovative solutions to address these challenges. GraphQL, known for its flexible data retrieval capabilities, simplifies data communication and integration. openEHR, a standards-based approach to healthcare data management, fosters interoperability through an unified data model. Redis, a scalable data storage and caching system, enhances application performance and real-time data processing. Pervasive Business Intelligence empowers healthcare analytics, aiding data-driven decision-making. This paper explores these technologies’ benefits, integration possibilities, and synergies. The practical implications of this integration are demonstrated through a real-world case study. The findings underscore the potential to revolutionise healthcare data management, communication, and analysis, improving patient care and operational efficiency.
Keywords: 
Subject: 
Computer Science and Mathematics  -   Information Systems

1. Introduction

Amidst the ever-evolving of healthcare, particularly in terms of systems and technology, a pressing conundrum persists — data silos, interoperability hurdles, and the complexities of efficient communication. The point is that we should look at healthcare ecosystem where these challenges are not impediments but opportunities for transformation.
Healthcare systems grapple with fragmented data and communication bottlenecks. This paper posits that a synergy of cutting-edge technologies can be the antidote. GraphQL, renowned for its prowess in efficient data retrieval, promises streamlined communication. openEHR, a stalwart in healthcare data management, champions interoperability through an unified data model. Redis steps in as the stalwart of scalable data storage, unlocking possibilities for real-time data processing. Pervasive Business Intelligence, the herald of data analytics, empowers informed decision-making in the healthcare domain.
The convergence of these technologies presents an opportunity to address the challenges healthcare systems face. By integrating them, healthcare organisations can overcome data silos, enhance interoperability, improve data access and retrieval, and leverage advanced analytics for informed decision-making. This paper delves deeper into each technology, exploring its individual benefits, integration possibilities, and potential synergies in the context of healthcare systems. Through in-depth analysis and a real-world case study, we aim to highlight the transformative potential of this integration and provide insights for future implementations in the healthcare domain.
The manuscript follows a clearly defined structure, consisting of six essential sections. Section 1 provides the necessary contextualisation for the background clarification. Following that, Section 2 establishes the necessary technical and theoretical foundations. Expanding on this basis, Section 3, named as the State of the Art, examines existing literature from a critical perspective, identifying comparable research and their results. This comparative study aims to strengthen the research motivation and outline the main contributions of this work. Section 4 provides a thorough examination of the particular case study, carefully describing its implementation and results. Following a thorough analysis, Section 5 delves into a rigorous discourse on the obtained outcomes, analysing their importance and their consequences. Ultimately, the text concludes with a thorough and all-encompassing part. This final section, Section 6, emphasises the conclusions on the actualised implementation and outcomes. Furthermore, it lays the foundation for future research by pinpointing prospective directions for further investigation that are inspired by the established research trajectory.

2. Background

While embarking on an exploration of healthcare’s technological landscape, it is extremely important to provide a foundational understanding of key concepts shaping the industry. Therefore a technical explanation of the technologies — GraphQL, openEHR, Redis, and Pervasive Business Intelligence is provided. Each plays a distinctive role, from GraphQL’s prowess in data retrieval to openEHR’s standards-based healthcare data management, Redis’ scalable data storage capabilities, and Pervasive Business Intelligence’s role in comprehensive analytics. By navigating these essential concepts, we set the stage for an in-depth exploration of their integration, aiming to redefine healthcare practices and pave the way for an era marked by advanced data processing, informed decision-making, and elevated patient care standards.

2.1. GraphQL

Efficient data management and seamless information exchange are pivotal in the evolving healthcare landscape. GraphQL, a robust query language, is a promising technology to address these needs. This chapter delves into the capabilities and potential applications of GraphQL within healthcare systems, highlighting its benefits and integration possibilities [1].
GraphQL is a query language designed to facilitate efficient data communication between clients and servers by allowing clients to request specific data structures and properties. Unlike traditional REST APIs, which often return fixed data structures, GraphQL enables clients to define the data they require. This ability to retrieve tailored data reduces over-fetching and under-fetching, improving data transmission efficiency [2,3].
In healthcare systems, where data accuracy and timely access are paramount, GraphQL offers notable advantages. The flexibility of GraphQL queries accommodates the diverse information needs of different stakeholders, from medical professionals seeking patient records to administrators monitoring resource allocation. This adaptability streamlines data retrieval processes, contributing to faster decision-making and improved patient care [4,5].
Furthermore, GraphQL’s hierarchical nature allows for retrieving complex and interconnected healthcare data in a single query, reducing the need for multiple requests. This design pattern is particularly advantageous when retrieving patient data spanning various medical domains, such as medical history, lab results, and prescriptions. The reduced latency in data retrieval enhances the overall user experience and facilitates real-time monitoring and intervention [6].
Moreover, GraphQL’s support for introspection enables automated documentation generation. In healthcare, where data accuracy and compliance are critical, automatically generated documentation ensures that developers and stakeholders are aligned in understanding data structures and available queries. It promotes transparency and reduces the potential for misinterpretation [7]. While GraphQL presents numerous benefits, it is essential to consider its challenges. Table 1 presents the summary of advantages and disadvantages of GraphQL usage:

2.2. openEHR

The effective management of healthcare data is central to delivering high-quality healthcare services. However, disparate data formats and systems often hinder interoperability and data exchange. This chapter delves into the role of openEHR in addressing these challenges by providing a standardized framework for healthcare data management. It explores the architecture, benefits, and integration possibilities of openEHR within healthcare systems.
openEHR is an open-source standard designed to support the sharing, exchanging, and storing of Eletronic Health Record (EHR). It offers a dual-level modelling approach: the Reference Model -providing the foundational data structures and Archetypes - offering domain-specific definitions for clinical concepts. This two-tiered approach ensures both flexibility and consistency in healthcare data representation [9].
The openEHR architecture consists of three core components: the Reference Model, Archetypes, and Templates. The Reference Model establishes a standard structure for representing health information, ensuring semantic interoperability across different systems. Archetypes capture domain-specific clinical concepts, while Templates provide practical instances of Archetypes for specific use cases. This modular architecture promotes data reusability and adaptability [10,11]. Figure 1 provides an overview of the openEHR architecture.
The integration of openEHR with GraphQL enhances data accessibility. GraphQL queries can be constructed to retrieve structured healthcare data directly from openEHR repositories, allowing for precise data retrieval tailored to user needs. Combining openEHR with Redis accelerates data access and caching, reducing frequently accessed clinical information response times. Integrating openEHR’s standardized data with Pervasive Business Intelligence platforms enables advanced analytics and informed decision-making [12].
The adoption of this standard must be carefully considered since, while it has many benefits, it may also provide certain obstacles. Table 2 presents a resume of the advantage and disadvantages of the openEHR use.

2.3. Redis

Efficient data storage and rapid data retrieval are indispensable in modern healthcare systems. The exponential growth of healthcare data calls for innovative solutions to manage and access information in real time. This section delves into the capabilities of Redis, a versatile in-memory data store, and explores its application as a scalable data storage and caching system within healthcare contexts.
Redis, short for Remote Dictionary Server, is an open-source, in-memory data structure store that supports various data types such as strings, lists, sets, and hashes. It is known for its exceptional speed and ability to handle high-throughput scenarios. Redis employs an in-memory caching approach, ensuring rapid data access and reducing the need to query databases for frequently accessed information repeatedly [14].
Redis’s publish-subscribe feature facilitates real-time data updates and event notifications. It proves valuable in scenarios like remote patient monitoring, where continuous data streams from wearable devices must be relayed to medical professionals promptly for timely intervention [14,15].
Redis’s integration with other technologies like GraphQL and openEHR enhances its utility. Combining Redis with GraphQL optimises data fetching by storing frequently accessed data in memory, reducing the load on backend systems, and improving response times [16,17]. When integrated with openEHR, Redis accelerates data retrieval by caching standardized health records, minimising the need to repeatedly fetch data from distributed repositories [18].
The advantages and disadvantages of the use of Redis are outlined in Table 3.

2.4. Pervasive Business

The use of data-driven technology is essential in healthcare to improve patient outcomes and optimise resource usage. Pervasive Business Intelligence (BI) plays a vital role in this transformation by offering advanced analytics and visualisation capabilities to users at all levels of an organization [20]. In this chapter, we will explore the significance of Pervasive BI in healthcare, its applications, and its integration with GraphQL, openEHR, and Redis for comprehensive insights and improved healthcare delivery .
Pervasive BI enables users to explore data and gain insights independently, going beyond traditional executive dashboards to make data accessible to everyone in an organization [21,22]. This means that clinicians, administrators, and analysts can use data to improve patient care, streamline operations, and plan strategically. In healthcare, Pervasive BI has many applications, including real-time monitoring of patient vitals, effective resource allocation, and identification of potential breakthroughs [22]. Integration with GraphQL, openEHR, and Redis enhances data consistency and accuracy, expediting query processing for real-time analytics. Though Pervasive BI has many benefits in healthcare, implementing robust data governance frameworks is crucial to overcome challenges such as data security, privacy concerns, and data accuracy and validity. As healthcare generates more data, Pervasive BI’s role will continue to grow. Integrating AI and machine learning with Pervasive BI can provide predictive analytics, aiding early disease detection and treatment planning. Wearable and Internet of Things (IoT) devices can also be integrated with Pervasive BI for holistic patient insights. Pervasive BI empowers stakeholders with actionable insights and fosters data-driven decisions that can transform patient care, operational efficiency, and strategic planning.

3. State of the Art

When searching the topic under discussion in this paper, there are various topics and respective applications that need to be addressed. Therefore, this section has covered topics ranging from the use of the technologies listed above in healthcare institutions to the trends and innovations expected in the future.
Starting with GraphQL, in recent years, the healthcare industry has recognized the potential of GraphQL to address data integration challenges [4]. Currently, GraphQL is employed to efficiently retrieve patient records, lab results, and other health-related data, improving the responsiveness and speed of EHR systems [12,23]. In accordance, this query language also facilitates seamless data exchange between different healthcare systems and organizations, promoting interoperability and collaboration [4,5]. More recently, GraphQL is being used to fetch relevant clinical data, aiding in real-time decision-making for healthcare professionals [24].
The use of GraphQL in healthcare has been explored in various studies, with promising results. Mukhiya et al. [4] demonstrated the performance, cost-effectiveness, scalability, and flexibility of a GraphQL and HL7 FHIR-based approach for Healthcare Information Exchange (HIE). Singh and Kaur [25] proposed a methodology, SQL2Neo, for converting health-care data from relational to graph databases, which are better suited for handling highly-related data. Park et al. [26] presented a framework for efficient data management and data services in large-scale healthcare systems using graph databases, which can reduce complexity and enhance data accessibility. Mohammed et al. [27] reported success in prototyping a problem-oriented medical record for connected health using TypeGraphQL, which can connect to HL7 FHIR medical records and biomedical repositories. These studies collectively highlight the potential of GraphQL in improving data exchange, data management, and data services in healthcare.
Now focusing on interoperability, using worldwide accepted standards is the write path. openEHR was the choice. openEHR plays a pivotal role in promoting interoperability among heterogeneous healthcare systems. Healthcare organizations can facilitate seamless data exchange across different platforms by adhering to a standardized data model. In addition, openEHR’s focus on semantic interoperability ensures that data retains its intended meaning regardless of its originating system - crucial in maintaining the accuracy and context of clinical information [9,28]. openEHR is actively collaborating with the Health Level Seven International (HL7) in specific Fast Healthcare Interoperability Resources (FHIR) standards, promoting a synergistic approach to healthcare interoperability [29].
openEHR has been evaluated for storing computable representations of EHR phenotyping algorithms, demonstrating its potential to accelerate precision medicine Papež et al. [30]. It has also been identified as a key component in achieving semantic interoperability among International Patient Summary sources in the European Union Krastev et al. [31]. The use of openEHR archetypes has been shown to be feasible and scalable, with significant reuse across diverse health data sets Leslie [32]. These studies collectively highlight the versatility and potential of openEHR in various healthcare applications.
The implementation of openEHR in Portuguese healthcare facilities has been explored in several studies. Oliveira et al. [28] and Hak et al. [33] both discuss the initial steps and the adoption of openEHR in these settings, highlighting the potential for improved knowledge acquisition and clinical decision support.
In the rapidly evolving landscape of healthcare technology, Redis stands out as a crucial asset, revolutionising the industry with its unparalleled capabilities. As evidenced by Muradova et al. [34], Redis has proven to be invaluable in optimising the search for medical supplies through the innovative integration of geospatial data. This not only enhances efficiency but also demonstrates a substantial improvement in system working speed when compared to traditional databases. Moreover, the exploration of real-world data in healthcare, with a keen examination of the potential role of Redis, offers valuable insights into the challenges and limitations inherent in this transformative approach, as highlighted by Rudrapatna et al. [35].
The use of GraphQL, openEHR, and Redis in a synergistic manner has been explored in various studies. Helou et al. [36] demonstrated the performance benefits of using graph databases, such as Neo4j, for implementing openEHR clinical repositories. This approach could be further enhanced by the use of GraphQL, as suggested by Werbrouck et al. [37], who compared SPARQL with GraphQL for querying building datasets. Finally, Tian et al. [38] presented a system called IBM Db2 Graph, which supports synergistic graph and SQL analytics, potentially enhancing the performance of openEHR systems. These studies collectively suggest that the use of GraphQL, openEHR, and Redis in a synergistic manner could lead to improved performance and efficiency in healthcare data management.
Conclusively, while the studies presented underscore the significant relevance of this technologies in healthcare, it is noteworthy that none of them have explored the tripartite synergy of Redis, openEHR, and GraphQL. This trifecta presents a unique and promising avenue for efficiently managing vast datasets in accordance with globally recognized data standards. The integration of Redis for rapid data retrieval, openEHR for standardized health information exchange, and GraphQL for streamlined data querying and manipulation holds the potential to redefine the landscape of healthcare information systems. Future research endeavours should consider delving into this unexplored territory to unveil the collective power of these technologies, ultimately contributing to the advancement of data management practices in healthcare on a global scale.

4. Case Study

The focal point of this case study is the Centro Hospitalar Universitário do Porto, a community hospital located in Portugal, which endeavours to enhance its medical services through the integration of technology. The hospital’s core mission is to improve the sharing of data across different departments, implement real-time monitoring of patients, and promote data-driven decision-making. In pursuit of these objectives, the hospital has embraced GraphQL to streamline data retrieval, incorporated openEHR to standardise health records, employed Redis for accelerated data access, and implemented Pervasive Business Intelligence for informed insights.
The overall system structure comprises several key components, such as GraphQL middleware, openEHR repositories, legacy information systems, Redis cache, and a Pervasive BI platform. The Pervasive BI platform is supported by the front-end application, which enables medical professionals to efficiently interact with the system. The GraphQL middleware streamlines data requests, while openEHR guarantees the storage of standardized healthcare data. The Redis cache is used for swift access to cached data. For a better understanding of the system architecture, please refer to the accompanying visual diagram present in Figure 2.
Pervasive BI is a platform that offers patients personalised analytics and visualisation of their clinical data. With advanced GraphQL Introspection Capabilities, the platform delivers contextualised analytics and customised user interfaces. The development process involved thorough data collection and analysis, including conducting semi-structured interviews, meetings, and ongoing communication with work task groups. The accompanying image, present in Figure 3 displays an early prototype of the platform’s User Interface (UI) with simulated data.
The integration yielded remarkable outcomes, resulting in expedited retrieval of data through GraphQL’s tailored queries and Redis’s cache system. Standardisation of health records using openEHR enhanced data consistency and enabled accurate analysis. The pervasive BI facilitated the creation of dynamic dashboards that displayed real-time patient data and operational metrics, thereby improving resource optimization and enabling timely interventions.
The case study shed light on the significance of cross-functional collaboration among medical professionals, Information Technology (IT) experts, and data analysts. It was crucial to uphold proper data governance to ensure data security and compliance.

5. Discussion

Through meetings and collaborative work with a taskforce of healthcare professionals, a significant milestone in the field of medical care has been achieved: the development of a mobile application tailored to the medical context. This chapter aims to highlight the main contributions of this innovative work, outlining its distinctive features and its potential impact on improving healthcare. The foremost and most notable contribution of this work is the creation of a mobile application tailored to meet the specific demands of the healthcare sector. Through extensive meetings and collaboration with a taskforce of healthcare professionals, it was possible to identify critical needs and challenges faced in the clinical environment.
A distinctive feature of this application is its ability to offer simultaneous vertical navigation across a variety of subsets of medical information. This enables healthcare professionals to quickly access different aspects of relevant data, promoting informed and agile decision-making. The application also offers advanced cross-information and check functionality, allowing healthcare professionals to verify and validate information across different contexts. This not only increases the reliability of data but also reduces the likelihood of diagnostic or treatment errors.
One of the most notable contributions of this work is the significant improvement in clinical efficiency and speed. By consolidating dispersed information that would previously be scattered across various pages or applications, the application streamlines and accelerates daily tasks for healthcare professionals.
Lastly, and perhaps most importantly, this application has the potential to transform the quality of healthcare delivery. By facilitating access to critical information, promoting informed decision-making, and reducing waiting times, it is expected that this application will significantly contribute to enhancing clinical outcomes and patient satisfaction.
In summary, the development of this mobile application represented an innovative collaboration between healthcare professionals and technology experts, resulting in a powerful tool that has the potential to revolutionise healthcare delivery. Its distinctive features, such as simultaneous vertical navigation and cross-information and check functionality, promise not only to increase clinical efficiency but also to elevate the overall standard of medical care.

6. Conclusions

This article delves into the potential advantages of combining GraphQL, openEHR, Redis, and Pervasive Business Intelligence in healthcare systems. Each technology offers distinct benefits. GraphQL streamlines data retrieval, openEHR standardises healthcare data, Redis accelerates data access, and Pervasive Business Intelligence enables data-driven decision-making. This exploration has yielded significant findings and contributions.
Primarily, integrating these technologies provides a comprehensive solution to long-standing healthcare system issues. It promotes interoperability, enhances data accessibility, accelerates data processing, and empowers stakeholders with valuable insights. As a result, patient care is improved, operations are streamlined, and informed decision-making is possible across all levels of healthcare organizations.
Furthermore, a real-world case study presented in this article showcases the practical advantages of integration. It demonstrates how these technologies can be seamlessly integrated to create a more efficient and effective healthcare system. The results of the case study highlight tangible benefits of this integration, ranging from reduced data retrieval times to enhanced patient monitoring and resource optimization.

Author Contributions

The Conceptualization was performed by R.S., V.A. and H.P.; The methodology design by R.S., V.A., H.P. and J.M.; Software development by R.S. and V.A.; Validation was performd by H.P. and J.M.; Investigation by R.S., V.A., H.P. and J.M.; The first writing—original draft preparation, V.A., supervision was performed by J.M., and the writing—review and editing, R.S., H.P. and J.M..

Acknowledgments

This work has been supported by FCT (Fundacão para a Ciência e Tecnologia) within the R&D Units Project Scope: UIDB/00319/2020.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. openEHR architecture.
Figure 1. openEHR architecture.
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Figure 2. System Architecture.
Figure 2. System Architecture.
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Figure 3. Prototype Implementation.
Figure 3. Prototype Implementation.
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Table 1. Advantages and Disadvantages of GraphQL usage [8]
Table 1. Advantages and Disadvantages of GraphQL usage [8]
Advantages Disadvantages
Efficient Data Communication Server Resource Strain
Customizable Queries Learning Curve
Flexible for Diverse Stakeholders Security Concerns
Hierarchical Data Retrieval Queries Complexity
Compatibility and Integration Versatility
Automated Documentation Network Overhead
Table 2. Advantages and Disadvantages of openEHR usage [13]
Table 2. Advantages and Disadvantages of openEHR usage [13]
Advantages Disadvantages
Semantic Interoperability High Learning Curve
Standardised Clinical Information Models Complex and resource-intensive
Long-term flexibility Different adoption rates across regions
Neutral Architecture Requires Data Migration
International Standards Compliance Resistance to Change
Supports Multilingual Health Records Complex Governance Structure
Table 3. Advantages and Disadvantages of Redis usage [19]
Table 3. Advantages and Disadvantages of Redis usage [19]
Advantages Disadvantages
High Performance Data Size Limitations
In-Memory Data Storage Persistence Configuration Complexity
Versatile Data Structures Single-Threading Limitation
Built-in Replication Limited Query Language
Atomic Operations Not Suitable for Complex Analytics
Scalability Memory Management
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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