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Designing an Interoperable Health Library and Medicine Database System for Seamless
Integration with Healthcare Information Standards
Sai Akhil Potru
Sacred Heart University
Capstone Project-HINF-660T-I
Prof. Stephen Burrows & Todd Price
13
th
Nov. 2023
2
Table of Contents
Abstract
..........................................................................................................................................
4
Designing an Interoperable Health Library and Medicine Database System for Seamless
Integration with Healthcare Information Standards
.................................................................
6
Background of the Topic
..............................................................................................................
6
Statement of the Problem
.............................................................................................................
7
Purpose of the Study
....................................................................................................................
8
Significance of the Proposed System
.........................................................................................
10
Summary of Literature
...............................................................................................................
12
Topic Overview
..........................................................................................................................
12
Summary Of the Problem/ Existing Issue
..................................................................................
13
Identify Themes in the Articles
..................................................................................................
13
Benefits Of Interoperability in Health Information Systems
..................................................
13
Challenges Of Interoperability in Health Information Systems
.............................................
15
Current Trends in HL7 and SNOMED-CT Adoption
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18
Rationale
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19
Methodology
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21
Search Strategy
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21
Selection Criteria
........................................................................................................................
21
Analysis of Selected Studies
......................................................................................................
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3
Synthesis of Results
...................................................................................................................
22
Results
...........................................................................................................................................
23
Theme/ Findings from Selected Articles
....................................................................................
23
Benefits of interoperability in health information systems
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23
Challenges of interoperability in health information systems
................................................
26
Current Trends in HL7 And SNOMED-CT Adoption
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28
Discussion
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29
Review of Findings
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30
Significance of Findings to Health Informatics Practice and Policy
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Limitations of the Study
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Conclusion
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Application of the Results to Clinical Practice
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Future Research Directions
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38
References
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40
Appendices
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Appendix A: Overview of Mid-NET
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Appendix B: HL7-Based Health Database
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Abstract
This study investigated the evidence on the effective integration of health library and medicine
database systems with existing healthcare information systems and standards, such as HL7 and
SNOMED CT, to ensure seamless interoperability and enhance healthcare data management. The
fragmentation of healthcare data and the lack of interoperability between healthcare information
systems pose significant challenges to the effective delivery of healthcare. Health libraries and
medicine database systems can play an important role in addressing these challenges by
providing a centralized repository for healthcare data and by facilitating the exchange of data
between different healthcare systems. A systematic literature review and synthesis was conducted
to identify and assess the evidence on the effective integration of health library and medicine
database systems with existing healthcare information systems and standards. The search
included articles distributed somewhere in the range of 2018 and 2023 in licensed academic
diaries. The literature review identified several benefits of interoperability, including seamless
interoperability, enhanced healthcare data management, improved clinical decision support, and
enhanced research capabilities. The studies reviewed also identified a few challenges that can
hinder the effective integration of interoperability solutions, such as technical complexity, lack of
standards, differing priorities, and lack of funding. The findings of the literature review suggest
that the effective integration of health library and medicine database systems with existing
healthcare information systems and standards is feasible and can lead to several important
benefits for patients, clinicians, and researchers. However, the challenges identified in the
literature review need to be addressed to achieve the full potential of interoperability.
Keywords
: interoperability, health library, medicine database, healthcare information systems,
HL7, SNOMED CT
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Designing an Interoperable Health Library and Medicine Database System for Seamless
Integration with Healthcare Information Standards
Background of the Topic
The landscape of healthcare data management has been shaped by the dynamic
interaction of historical trends and technology advancements throughout the growth of healthcare
information systems. Healthcare information systems were created historically in response to the
growing complexity of medical data and the demand for effective mechanisms for data storage
and retrieval. Early computer systems were crude, concentrating mostly on simple patient
records and appointment scheduling. But as time went on, the number and variety of health-
related information increased, calling for more complex solutions.
The administration of healthcare data has undergone a transformation in recent decades.
Electronic health records (EHRs) have played a crucial role in the transition away from paper-
based records. EHRs make it possible to thoroughly document patient data, including their
medical history and results of diagnostic testing, encouraging a more all-encompassing approach
to healthcare. The accuracy and depth of medical data are further improved by the incorporation
of digital imaging and diagnostic technologies (Cardoso et al., 2018). Despite these
developments, data integration in the modern healthcare environment still presents significant
difficulties. Data being dispersed across many systems due to the fragmentation of health
information is a serious problem that makes it difficult for healthcare practitioners to collaborate
and obtain information easily (Cardoso et al., 2018). In addition to impeding continuity of care,
this fragmentation makes it difficult to gather data for community health management (
Cardoso
et al., 2018
).
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Furthermore, the issue is made worse by the lack of smooth interoperability. Healthcare
businesses frequently use a variety of information systems that cannot successfully connect with
one another. This interoperability gap restricts the real-time accessibility necessary for prompt
decision-making in healthcare settings, impeding the smooth flow of patient data (Dobrow et al.,
2019). The necessity to close these gaps emphasizes the significance of unified health data
management by promoting an ecosystem where information flows smoothly, assuring the best
possible patient care, and supporting research projects that depend on integrated, comprehensive
datasets. In order to advance healthcare information systems toward a future that is more
connected and effective, addressing these difficulties becomes essential.
Statement of the Problem
As shown by gaps that impair effective data management and interchange, the modern
landscape of health information systems is characterized by considerable problems. The
widespread inefficiencies in the sharing of data between health systems are a serious problem.
The current systems frequently do not seamlessly interoperate, which hinders the efficient
exchange of information across various healthcare organizations. This fragmentation jeopardizes
the accuracy and comprehensiveness of patient records in addition to the timeliness of data
transmission (Dobrow et al., 2019). Comprehensive healthcare is hampered by data exchange
inefficiencies since timely access to precise patient data is essential for healthcare practitioners to
make educated decisions (Benson et al., 2021).
Furthermore, the absence of defined processes is a fundamental drawback of the present
health information systems. The interoperability and integration of various health databases are
hampered by the lack of standardization in data representation and coding. This restriction is
more obvious in heterogeneous healthcare settings because different institutions may use
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dissimilar coding schemes and data formats. The collection and analysis of health data on a
larger scale are made more difficult by the lack of established procedures. As a result, this makes
it more difficult to realize a comprehensive understanding of public health trends, which is
essential for efficient resource allocation and policymaking.
Patient treatment and health outcomes are directly impacted by the highlighted
deficiencies in the present health information systems. The timely provision of healthcare
services is hampered by inconsistencies in data sharing and standards, which also increase the
risk of mistakes and omissions in patient records (Wang et al., 2018). This ultimately degrades
the standard of care given and may have a negative impact on patient outcomes. The absence of
established processes also prevents a thorough study of health data, which hinders the
advancement of evidence-based practices and medical research. The advancement of healthcare
systems toward a more integrated, effective, and patient-centric paradigm depends on closing
these gaps.
Purpose of the Study
This study has several objectives. It aims to offer a conceptual model for an HL7 and
SNOMED CT-integrated Interoperable Health Library and Medicine Database System, describe
the integration with those standards, and highlight the overall goal of improving healthcare data
management. A complete conceptual model for an Interoperable Health Library and Medicine
Database System is the first goal of this research. A unified and interoperable platform is
essential in the landscape of changing healthcare information systems (Maxhelaku & Kika,
2019). The proposed system's basic architecture and functionality will be included in the
conceptual model, which will serve as a solid foundation for the system's seamless integration
with current healthcare infrastructures.
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The conceptual model of the system will include components like data structures,
information flow, and interaction methods. The concept intends to provide a flexible framework
adaptable to various healthcare settings by emphasizing flexibility and scalability. This study
addresses the 'what' and 'how' of developing an interoperable health database, attempting to lay
the framework for the practical implementation of the suggested system.
The integration of the Health Library and Medicine Database System with recognized
healthcare information standards, particularly HL7 (Health Level Seven) and SNOMED CT
(Systematized Nomenclature of Medicine Clinical Terms), is another important goal of this
work. Both clinical terminology and the interchange of healthcare data heavily rely on these
standards. Interoperability at the level of data interchange is promoted by the proposed system's
integration with HL7, which guarantees seamless contact with other healthcare information
systems.
Additionally, the database system's use of SNOMED CT enables the standardization of
clinical terminology. This not only improves the accuracy and consistency of medical data but
also guarantees that it is compliant with global norms (Chang & Mostafa, 2021). While
highlighting the significance of standardization in attaining interoperability across the healthcare
ecosystem, the research will delve into the technical complexities of bringing the proposed
system into compliance with these standards. The third goal of the project is to greatly improve
healthcare data management. Healthcare businesses struggle with enormous amounts of
heterogeneous data from numerous sources in the big data era. In order to effectively store,
retrieve, and manage healthcare data, a unified platform is offered by the planned Health Library
and Medicine Database System. The system intends to optimize the use of healthcare
information for clinical decision-making, research, and policy development through advanced
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data management approaches. The study will investigate how the proposed system may simplify
data administration procedures, lessen duplication, and enhance the general caliber of healthcare
data.
Significance of the Proposed System
To solve the current constraints in current health information systems, the proposed
Health Library and Medicine Database System is of utmost importance. The current state of
healthcare data management is characterized by fragmentation, ineffective data interchange, and
a dearth of smooth interoperability, all of which offer serious obstacles to the provision of patient
care and the improvement of health outcomes.
The necessity to address the shortcomings present in current health information systems
is one of the main imperatives guiding the development of the proposed system. Modern systems
frequently function in silos, resulting in fragmented health data that makes it difficult to fully
comprehend a patient's medical background and course of treatment. By offering a
comprehensive platform that unifies many sources of health information into a logical and
integrated whole, the suggested solution aims to address this. In addition to improving patient
data accessibility, this also enables healthcare professionals to make more educated choices
based on a comprehensive understanding of the patient's health profile.
The technology also aims to reduce problems with data completeness and correctness.
The accuracy of healthcare data is threatened by data entry errors, consistency issues, and
information gaps in many current systems. A sophisticated data validation system and extensive
data quality protocols are intended to be implemented by the proposed Health Library and
Medicine Database System. The system supports evidence-based practices in healthcare and
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improves clinical decision-making overall by assuring the accuracy and completeness of health
data.
A key component of efficient healthcare delivery is seamless data interchange, which
enables essential information to move smoothly between hospitals, clinics, laboratories, and
pharmacies, among other healthcare entities. The seamless flow of data necessary for patient care
is hampered by the interoperability issues that plague today's health information systems. In
order to ensure that health data may be transmitted seamlessly and uniformly across various
healthcare settings, the proposed system is constructed with an emphasis on interoperability
standards, particularly HL7 and SNOMED CT.
The suggested system overcomes the restrictions of proprietary data formats and
communication protocols by conforming to existing standards. This commitment encourages
compatibility with a wide range of existing health information systems in addition to facilitating
effective data interchange. In doing so, the suggested approach contributes significantly to the
development of a linked healthcare ecosystem where data can be easily shared, promoting
cooperation and coordination between healthcare facilities and experts.
The suggested system's major contribution to the broader landscape of healthcare
information technology is the improvement of health data management. Beyond the immediate
enhancements in data interchange and accessibility, the system advances health data management
techniques. To extract valuable insights from the accumulated health data, it makes use of
powerful data analytics and decision support tools.
In order to extract useful knowledge from the enormous repositories of health
information, the suggested system includes advanced data management techniques, such as data
warehousing, data mining, and artificial intelligence algorithms. This helps population health
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management initiatives as well as improving the diagnostic and prognostic skills of healthcare
professionals. The system makes substantial progress in the development of health data
management by encouraging a data-driven approach to healthcare that is in line with the larger
industry trend toward personalized medicine and evidence-based practice.
Summary of Literature
Topic Overview
It is impossible to overestimate the significance of information system interoperability in
the modern healthcare environment. The effective interchange of crucial medical information is
frequently hampered by fragmented healthcare infrastructures, which compromises patient care
and decision-making. Aiming to establish a concept for a Health Library and Medicine Database
System that not only closes existing interoperability gaps but also provides a seamless
integration with internationally acknowledged healthcare information standards, the
current research subject emerges as a response to this urgent challenge.
The research question's inclusion of HL7 and SNOMED CT as key standards emphasizes
the importance of these standards in the administration of healthcare data. The smooth
transmission of clinical and administrative data among healthcare systems is made possible by
HL7, a widely used standard. In addition, SNOMED CT offers a comprehensive and
standardized clinical terminology that supports the appropriate description and interpretation of
medical data. The system's incorporation of these standards shows a dedication to attaining
syntactic and semantic compatibility.
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Summary Of the Problem/ Existing Issue
The proposed Health Library and Medicine Database System faces significant obstacles
when attempting to effectively integrate and interoperate with current healthcare information
systems and standards, specifically HL7 and SNOMED CT. The current landscape of healthcare
information systems is characterized by a fragmented and heterogeneous infrastructure. The
efficient communication of crucial medical information is hampered, collaborative decision-
making processes are hampered, and the overall efficacy of healthcare data management is
compromised by the lack of a consistent and seamless data exchange framework. In order to
ensure that the proposed system not only complies with recognized standards but also enables the
seamless interchange of data across various healthcare platforms, it is vital to solve the
challenges related to interoperability and integration. The identified issue emphasizes the need
for a strong, standardized, and interoperable solution that can fill in the gaps in the healthcare
information systems and improve the usability, accuracy, and accessibility of medical data for
stakeholders and healthcare professionals.
Identify Themes in the Articles
Benefits Of Interoperability in Health Information Systems
The revolutionary potential of interoperability in healthcare data systems is highlighted
by a number of studies. In the context of big data analytics, where the integration of many and
complex data sources presents obstacles, the development of interoperable systems is especially
important. The development of data mining technology and its function in obtaining important
knowledge from sizable datasets are stressed by Yang et al. (2020). As a result of the integration
of such disparate datasets made possible by interoperable systems, more thorough analyses and
insights are possible (Yang et al., 2020; Dobrow et al., 2019).
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MID-NET®, a distributed network linking several healthcare institutions through a main
data center in Japan, serves as an example of how interoperable health information systems
might be implemented (Yamaguchi et al., 2019). This technology offers a trustworthy and useful
medical information database and exhibits about 100% consistency with the actual data found in
hospitals. The usefulness of MID-NET® for drug safety evaluations and other healthcare
research activities is enhanced by the availability of a wide range of clinical and administrative
data (Yamaguchi et al., 2019).
In Taiwan, the Chang Gung Research Database (CGRD) surpasses the National Health
Insurance Research Database (NHIRD) in terms of comprehensive clinical data, serving as an
example of the advantages of interoperability. Due to the CGRD's inclusion of some NHIRD
patients and its more thorough coverage of pathological and laboratory results, it can be a useful
tool for actual epidemiological investigations (Shao et al., 2019).
Cardoso et al. (2018) introduce AIDA, a multi-agent and service-based platform created
to improve interoperability among healthcare information systems, in response to the
interoperability challenge. The proactive agents of AIDA make it easier to manage and save
information, reply to information requests, and communicate with other systems. In order to
successfully address issues with distribution, fault tolerance, standards, and communication in
healthcare systems, a multi-agent paradigm is employed (Cardoso et al., 2018). Benson and
Grieve (2021), who underline that the complexity of healthcare interoperability necessitates the
adoption of standards to handle the enormous number of links necessary to connect various
systems, emphasize the significance of standards in encouraging interoperability. Effective
interoperability can be hampered by issues including translating standards into a universal
exchange language and misaligning user and vendor incentives (Benson & Grieve, 2021).
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Healthcare 4.0 is emerging as a result of the adoption of Industry 4.0 technologies, including as
the Internet of Things (IoT), Cloud and Fog Computing, and Big Data (Aceto et al., 2020). These
technologies emphasize the beneficial effects of interoperability on healthcare outcomes through
enhancing patient care, efficiency, and data-driven decision-making (Aceto et al., 2020).
The adoption of HL7 FHIR appears as a common thread in numerous research as a means
of resolving the limits in healthcare interoperability. The "Devices on FHIR" (DoF) program,
which demonstrates a commitment to staying current with developing technologies and
standards, focuses on assuring semantically consistent sharing of device information, regardless
of integration techniques. Additionally, it has been determined that the use of HL7 FHIR in
Albanian healthcare data exchange is essential for overcoming the limited patient data sharing
between various entities, which in turn improves treatment quality and decision-making
processes (Maxhelaku & Kika, 2019).
Several papers demonstrate the usage of SNOMED CT, illustrating how it has progressed
from theoretical applications to actual use in healthcare settings (Chang & Mostafa, 2021). By
bringing down information missingness issues and upgrading the accuracy of information
investigation calculations, SNOMED CT-based normalized e-clinical pathways empower large
information examination in health care. (Alahmar & Benlamri, 2020).
Challenges Of Interoperability in Health Information Systems
The body of recent material as a whole underlines how crucial interoperability is to
achieving the potential benefits of cutting-edge healthcare information systems. According to
Yang et al. (2020), one significant issue is the complexity of healthcare data itself. Unbalanced
data types and the existence of numerous complicated and different data sources present a
substantial barrier to seamless integration. The sophisticated nature of medical knowledge
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concepts adds to this complexity, demanding advances in knowledge reasoning to guarantee
meaningful interoperability (Yang et al., 2020).
Interoperability is made more difficult by the fact that healthcare information systems are
scattered. This problem is successfully solved by MID-NET®, a closed network system that
connects various healthcare organizations, by combining a variety of clinical and administrative
data using defined coding standards (Yamaguchi et al., 2019). However, maintaining
synchronization with the original hospital data and guaranteeing consistent data quality remain
issues.
Shao et al. (2019) highlight the differences between comprehensive multi-institutional
electronic medical records databases like the Chang Gung Research Database (CGRD) and
national databases like the National Health Insurance Research Database (NHIRD) in the context
of electronic medical records (EMRs). Variations in patient demographics, clinical information
coverage, and the representation of particular medical problems are among these discrepancies.
To ensure thorough and representative data interchange, it is necessary to overcome these
disparities in order to achieve interoperability.
A recurring theme in tackling interoperability issues is the usage of standards. According
to Cardoso et al. (2018), AIDA is a multi-agent platform that focuses on integrating,
disseminating, and preserving medical data. In order to address distribution, fault tolerance, and
communication issues in healthcare systems and promote interoperability, the usage of a multi-
agent paradigm is particularly stressed.
Standards are crucial for controlling the complexity of healthcare interoperability,
according to Dobrow et al. (2019) and Benson et al. (2021). The former highlights the need for
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standardized measuring items while highlighting the benefits of interoperable electronic health
records (iEHRs). The latter examines the complicated components of electronic health records,
such as addresses and clinical data, highlighting the demand for precise standards and methods to
handle these complications.
According to Aceto et al. (2020) and Wang et al. (2018), the adoption of Industry 4.0 and
big data analytics in the healthcare sector adds new levels of complexity. Big data analytics can
offer insightful information, but healthcare organizations struggle to fully grasp its potential
advantages. A paradigm shift in how healthcare services are delivered is required by the
introduction of Industry 4.0 technologies, such as the Internet of Things and Cloud Computing,
necessitating careful consideration of interoperability issues.
The literature also discusses difficulties that are unique to particular technology. Through
the Internet of Things (IoT), Farahani et al. (2018) highlight the shift from clinic-centric to
patient-centric healthcare, highlighting the necessity for effective data handling in IoT eHealth
ecosystems. Additionally, as stated by Nguyen et al. (2019), the implementation of blockchain in
mobile cloud-based EHRs creates security and privacy concerns in assuring trustworthy sharing
of EHRs among mobile users. Healthcare standards like HL7 FHIR and SNOMED CT must be
taken into account when making efforts to improve interoperability. The research of Silva et al.
(2020) and Maxhelaku and Kika (2019) emphasizes the value of HL7 FHIR in enabling
interoperability in the exchange of medical information. Additionally, SNOMED CT is
acknowledged as a crucial component in standardizing clinical pathways and nursing data
(Alahmar & Benlamri, 2020; Silva et al., 2020; Kim et al., 2020), demonstrating its wide range
of applications but also posing adoption difficulties (Park et al., 2021).
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Current Trends in HL7 and SNOMED-CT Adoption
A key role in promoting interoperability is the HL7 standard known as Fast Healthcare
Interoperability Resources (FHIR). Studies like Silva et al. (2020), where the "Devices on FHIR"
program highlights the dedication to semantically consistent transmission of device information,
demonstrate its modular and flexible character. According to Maxhelaku and Kika (2019),
effective data exchange is crucial for improving treatment quality, healthcare services, and
decision-making processes. They point to HL7 FHIR as a major contributor to these
improvements. Furthermore, Chatterjee, Pahari, and Prinz (2022) show how FHIR can be used to
achieve semantic and structural interoperability in the context of personal health data.
SNOMED-CT is undergoing a transition from theoretical applications to practical
implementation in healthcare settings (Chang & Mostafa, 2021). SNOMED-CT is known for its
adaptable use in nursing across varied care settings (Silva et al., 2020). A number of scenarios,
including standardized e-clinical pathways for big data analytics (Alahmar & Benlamri, 2020)
and collaborative clinical practice and research documentation systems (Direito et al., 2023),
show the usage of SNOMED-CT. These applications highlight SNOMED-CT's role in
standardizing and organizing healthcare data for in-depth research and teamwork.
A major development is the fusion of developing technologies with HL7 and SNOMED-
CT. Wang et al. (2018) promote the use of big data analytics in the healthcare industry. Their
model demonstrates three important path-to-value chains and shows the potential advantages of
using big data analytics. Similarly, Farahani et al. (2018) stress the significance of smoothly
integrating healthcare agents through the Internet of Things by proposing a patient-centric IoT
eHealth ecosystem built upon HL7 and other technologies.
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The literature offers a comprehensive view on the adoption of HL7 and SNOMED-CT. In
Park et al.'s (2021) discussion of adoption and implementation strategies for SNOMED-CT in
Korea, they place particular emphasis on elements including vendor capabilities, governance,
and education. AlQudah et al.'s presentation of a successful integration of queue management
with electronic medical records in the United Arab Emirates in 2021 emphasizes the favorable
effects of using HL7 protocols and XML on patient experience.
Rationale
Benson et al.'s (2021) emphasis on the ongoing difficulties and complexities related to
interoperability in healthcare systems serves as the primary justification for the study.
Interoperability's multi-layered structure, which includes technical, semantic, process, and
clinical dimensions, has been noted as a major barrier to efficient healthcare data management.
By attempting to offer a practical answer to this complex issue, the proposed study fills a crucial
gap in the state of the art.
The literature emphasizes the widespread use and importance of healthcare information
standards, particularly HL7 and SNOMED CT. Studies carried out in nations including Japan
(Yamaguchi et al., 2019), Taiwan (Shao et al., 2019), and the United Arab Emirates (AlQudah et
al., 2021) highlight the global recognition of these norms. The study's justification is enhanced
by the understanding that fostering seamless interoperability not just at a national level but also
on a worldwide scale depends on aligning a Health Library and Medicine Database System with
these widely acknowledged standards.
The literature indicates how incorporating cutting-edge technology, including big data
analytics, into healthcare systems has the potential to be transformative (Wang et al., 2018). By
examining the interaction between a big data analytics platform’s capability and an interoperable
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database system, the suggested study places itself at the cutting edge of technological
advancement. This dynamic integration is intended to not only address current issues but also
open up new opportunities for data-driven insights and healthcare decision-making.
The study justifies its goals by utilizing knowledge from practical applications, drawing
on successful implementations like the Chang Gung Research Database in Taiwan and the MID-
NET® medical information database network in Japan (Yamaguchi et al., 2019). The proposed
research aims to provide useful advice for the design and implementation of an interoperable
Health Library and Medicine Database System by extracting best practices from these success
examples.
The examination of the literature demonstrates the complex value of strategic integration
with well-known standards like HL7 and SNOMED CT. In the proposed study, it is
acknowledged that such integration is not only a technological necessity but also a tactical
necessity for guaranteeing not only syntactic interoperability but, more importantly, semantic
interoperability. This acknowledgment is based on the knowledge that correct representation and
interpretation of health information depend on smooth integration with standards.
The study's justification goes beyond technical factors to account for the larger impact on
medical procedures and results. The research seeks to contribute to a paradigm shift in healthcare
data management by conceiving a Health Library and Medicine Database System in accordance
with international standards. Improved patient care, improved decision-making techniques, and
the development of a model that can be customized and applied in various healthcare situations
are some of the potential advantages.
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Methodology
This systematic literature review and synthesis examined the evidence on the effective
integration of health library and medicine database systems with existing healthcare information
systems and standards, such as HL7 and SNOMED CT, to ensure seamless interoperability and
enhance healthcare data management (
Hutchinson et al., 2018). The review followed the Favored
Announcing Things for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Search Strategy
A comprehensive search was conducted of the following electronic databases: The Sacred
Heart Library, Google Scholar, PubMed, MEDLINE, Cochrane Library, EMBASE, and Scopus.
The accompanying search terms were utilized:
“Health library and medicine database systems”
“Healthcare information systems”
“Interoperability”
“HL7”
“SNOMED CT”
The search was limited to articles published between 2018 and 2023, and only those
published in credible scholarly journals were included. The search of articles offered 2367
articles but only articles that attained inclusion criteria were used, which were 35 articles.
Selection Criteria
The following selection criteria were applied:
Studies that investigated the effective integration of health library and medicine database
systems with existing healthcare information systems and standards, such as HL7 and
SNOMED CT
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Studies that assessed the impact of integration on seamless interoperability and enhanced
healthcare data management
Studies published between 2018 and 2023 and in English
The following exclusion criteria were be applied:
Studies that did not focus on the integration of health library and medicine database
systems with healthcare information systems and standards
Studies that did not assess the impact of integration on seamless interoperability and
enhanced healthcare data management
Studies that were not fouund in accredited scholarly journals
Analysis of Selected Studies
After the elimination of duplicates, the titles and abstracts of all identified articles were
screened for relevance. The full texts of all applicable articles were then investigated to evaluate
their qualification. A literature matrix was developed to track the selection process and to
document the reasons for excluding studies. Data extraction was performed using a literature
matrix. The extricated information incorporated the study title, author, journal, year of
distribution, and key discoveries.
Synthesis of Results
A narrative synthesis of the results has been conducted and is presented in the subsequent
section (Xiao & Watson 2018). The findings were summarized and categorized according to the
following themes:
1.
Benefits of interoperability in health information systems
2.
Challenges of interoperability in health information systems
3.
Current trends in HL7 and SNOMED-CT adoption
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The findings of this systematic literature review and synthesis were used to develop a set
of recommendations for the effective integration of health library and medicine database systems
with existing healthcare information systems and standards. These recommendations were
informed by the best available evidence and were tailored to the specific needs of different
healthcare settings.
Results
Theme/ Findings from Selected Articles
Benefits of interoperability in health information systems
Numerous studies have emphasized the transformative potential of interoperability in
healthcare data systems. The creation of interoperable systems is crucial in the context of big
data analytics, where the integration of numerous complex data sources poses challenges. Yang
et al. (2020) emphasizes the advancement of data mining technology and its role in drawing
significant knowledge from huge datasets. More complete analyses and insights are available as
a result of the integration of such different datasets made feasible by interoperable systems (Yang
et al., 2020; Dobrow et al., 2019). In the study by Dobrow et al. (2019), productivity and quality
were the main evaluation criteria, and most measurement results were favorable (57.1%).
Another example of how interoperable health information systems could be used is MID-
NET®, a distributed network connecting multiple healthcare facilities through a major data
center in Japan (Yamaguchi et al., 2019). Figure 1 in the appendices provides an overview of the
system. This method provides a reliable and practical library of medical data that roughly 100%
accurately matches the information actually discovered in hospitals. The accessibility of a broad
range of clinical and administrative data improves the utility of MID-NET® for drug safety
studies and other healthcare research activities (Yamaguchi et al., 2019).
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As an illustration of the benefits of interoperability, the Chang Gung Research Database
(CGRD) in Taiwan outperforms the National Health Insurance Research Database (NHIRD) in
terms of complete clinical data. The CGRD, which is depicted in Figure 2 in the appendices, can
be a helpful tool for actual epidemiological investigations due to the inclusion of some NHIRD
patients and its more extensive coverage of pathological and laboratory results (Shao et al.,
2019). Taiwan’s study showed that The CGRD includes a portion of the patients from NHIRD,
accounting for 6.1% of outpatients and 10.2% of hospitalized patients in 2015. Notably, it has a
higher representation of elderly outpatients (23.5% vs. 12.5%) and pediatric inpatients (19.7%
vs. 14.4%) compared to NHIRD. Patient sex distributions in CGRD and NHIRD are similar, but
CGRD has higher coverage rates for severe health conditions, such as cancer, compared to other
health conditions. In response to the interoperability challenge, Cardoso et al. (2018) present
AIDA, a multi-agent and service-based platform developed to enhance interoperability among
healthcare information systems. It is simpler to handle and save information, respond to
information requests, and interface with other systems owing to AIDA's proactive agents. A
multi-agent paradigm is used to successfully handle problems with distribution, fault tolerance,
standards, and communication in healthcare systems (Cardoso et al., 2018). The importance of
standards in promoting interoperability is emphasized by Benson and Grieve (2021), who stress
that the complexity of healthcare interoperability needs the establishment of standards to handle
the huge number of links required to connect disparate systems. Other common issues include
misaligning user and vendor incentives and converting standards into a universal interchange
language. These challenges might hinder effective interoperability (Benson & Grieve, 2021). The
reception of Industry 4.0 advancements, like the Internet of Things (IoT), Cloud and Haze
Registering, and Enormous Information, has prompted the development of medical services 4.0
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(Aceto et al., 2020). Through improved patient care, increased efficiency, and data-driven
decision-making, these technologies highlight the positive effects of interoperability on
healthcare outcomes (Aceto et al., 2020).
In several studies, the adoption of HL7 FHIR is mentioned as a solution to the problems
with healthcare interoperability. The "Devices on FHIR" (DoF) program emphasizes
semantically consistent sharing of device information, regardless of integration methods, and
indicates a commitment to staying current with evolving technologies and standards. The usage
of HL7 FHIR in Albanian healthcare data exchange has also been found to be crucial for
overcoming the low patient data sharing across various entities, which in turn enhances treatment
quality and decision-making procedures (Maxhelaku & Kika, 2019).
In a number of studies, SNOMED CT is used to show how it has advanced from
theoretical applications to practical use in healthcare settings. AlQudah et al. (2021) found that
using HL7 protocols and XML integration between a queue management system (QMS) and
electronic medical records (EMR) dramatically enhanced the patient experience in a healthcare
facility in the United Arab Emirates. The outpatient department's patient wait times were reduced
due to this integration. It was discovered that, compared to the conventional "routine-based
identification" process for scheduled appointments, the proposed solution decreased the
"patient's journey time" by more than 14 minutes and the "time to identify" patients by 10
minutes in a simulation experiment involving 517 valid appointments. Big data analytics in
healthcare is rendered possible by SNOMED CT-based standardized e-clinical pathways because
they reduce data missingness issues and increase the accuracy of data analytics algorithms
(Alahmar & Benlamri, 2020; Chang & Mostafa, 2021).
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Challenges of interoperability in health information systems
Recent research as a whole emphasizes how essential interoperability is to realizing the
potential advantages of cutting-edge healthcare information systems. The complexity of
healthcare data itself is a big concern, according to Yang et al. (2020). A significant obstacle to
seamless integration is the existence of several complex and disparate data sources and
unbalanced data types. This complexity is increased by the complex nature of medical
knowledge concepts, necessitating improvements in knowledge reasoning to ensure meaningful
interoperability (Yang et al., 2020).
The dispersed nature of healthcare information systems makes interoperability more
difficult. By merging a range of clinical and administrative data using predefined coding
standards, MID-NET®, a closed network system that connects multiple healthcare organizations,
successfully resolves this issue (Yamaguchi et al., 2019). The challenges still exist in maintaining
synchronization with the original hospital data and ensuring consistent data quality.
In the context of electronic medical records (EMRs), Shao et al. (2019) highlight the
distinctions between comprehensive multi-institutional electronic medical records databases like
the Chang Gung Research Database (CGRD) and national databases like the National Health
Insurance Research Database (NHIRD). Among these differences are variations in patient
demographics, clinical information coverage, and the portrayal of specific medical issues. It is
vital to eliminate these differences in order to establish interoperability in order to guarantee
thorough and representational data transmission.
The use of standards is another frequent element in addressing interoperability concerns.
Multi-agent platform that prioritize integrating, disseminating, and protecting medical data are an
effective contingent to this particular issue according to Cardoso et al. (2018). The use of a
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multi-agent paradigm is specifically emphasized in order to overcome distribution, fault
tolerance, and communication concerns in healthcare systems and promote interoperability.
Standards are essential, according to Dobrow et al. (2019) and Benson et al. (2021), for
limiting the complexity of healthcare interoperability. While stressing the advantages of
interoperable electronic health records (iEHRs), the first emphasizes the need for standardized
measuring objects. The second highlights the need for specific standards and procedures to deal
with these issues by examining the complex elements of electronic health records, such as
addresses and clinical data.
The use of Industry 4.0 and big data analytics in the healthcare industry, according to
Aceto et al. (2020) and Wang et al. (2018), adds new degrees of complexity. Healthcare
businesses struggle to properly understand the benefits of big data analytics, despite the fact that
it can provide valuable insights. The emergence of Industry 4.0 technologies, such as the Internet
of Things and Cloud Computing, calls for a paradigm shift in the way healthcare services are
provided, needing careful consideration of interoperability issues.
The literature also covers issues that are special to a given technology. Farahani et al.
(2018) highlight the movement in healthcare from clinic-centric to patient-centric through the
Internet of Things (IoT), emphasizing the need for efficient data management in IoT eHealth
ecosystems. Furthermore, Nguyen et al. (2019) note that the use of blockchain in mobile cloud-
based EHRs raises security and privacy issues with regard to ensuring reliable EHR sharing
across mobile users. When attempting to increase interoperability, it is important to take into
account healthcare standards like HL7 FHIR and SNOMED CT. Silva et al.'s (2020) study and
Maxhelaku and Kika's (2019) research both highlight the importance of HL7 FHIR in facilitating
interoperability in the transmission of medical data. SNOMED CT is also regarded as being a
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key element in standardizing clinical pathways and nursing data (Alahmar & Benlamri, 2020;
Silva et al., 2020; Kim et al., 2020), highlighting its broad variety of uses but also facing
adoption challenges (Park et al., 2021).
Current Trends in HL7 And SNOMED-CT Adoption
The HL7 standard known as Fast Healthcare Interoperability Resources (FHIR) plays a
significant role in fostering interoperability. Studies like Silva et al. (2020), which highlight the
"Devices on FHIR" program's commitment to semantically consistent device information
transmission, show how modular and adaptable it is. Effective data interchange is essential for
enhancing treatment quality, healthcare services, and decision-making processes, according to
Maxhelaku and Kika (2019). They credit HL7 FHIR as being a key factor in these advancements.
Furthermore, Chatterjee, Pahari, and Prinz (2022) demonstrate how semantic and structural
interoperability can be attained using FHIR in the context of personal health data.
In healthcare contexts, SNOMED-CT is transitioning from theoretical applications to
actual deployment (Chang & Mostafa, 2021). According to Silva et al. (2020), SNOMED-CT is
well known for its adaptive application in nursing across a variety of care contexts. SNOMED-
CT is used in several settings, including collaborative clinical practice and research
documentation systems and standardized e-clinical pathways for big data analytics (Alahmar &
Benlamri, 2020; Direito et al., 2023). These examples demonstrate how SNOMED-CT
standardizes and arranges healthcare data for in-depth analysis and collaborative work.
The combination of emerging technologies with HL7 and SNOMED-CT is a significant
development. Big data analytics are supported by Wang et al. (2018) for usage in the healthcare
sector. They present three significant path-to-value chains in their model, which also illustrates
some possible benefits of big data analytics. Similar to how Farahani et al. (2018) propose a
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patient-centric IoT eHealth ecosystem built upon HL7 and other technologies, they emphasize
the importance of seamlessly integrating healthcare agents through the Internet of Things.
The available literature provides a thorough analysis of the adoption of HL7 and
SNOMED-CT. In their discussion of SNOMED-CT adoption and implementation techniques in
Korea in Park et al. (2021), they give particular attention to factors including vendor capabilities,
governance, and education. The United Arab Emirates' implementation of queue management
with electronic medical records in 2021, as described by AlQudah et al., highlights the positive
effects of employing HL7 protocols and XML on patient experience.
Discussion
There are a lot of problems in the field of health information systems nowadays, most of
which appear as barriers to effective data interchange and management. In this setting, the
pervasive inefficiency of data sharing amongst healthcare systems is a serious concern. Many
times, current systems are not seamlessly interoperable, which makes it difficult for information
to flow between various healthcare institutions. In addition to endangering the completeness and
accuracy of patient records, this fragmentation causes a delay in the transfer of vital information.
The lack of defined procedures in the current health information systems is another
serious issue. Interoperability and integration of different health databases are hampered by this
lack of uniformity in data representation and coding, which is especially evident in
heterogeneous healthcare environments when institutions use different coding methods and data
formats. In the absence of defined protocols, gathering and analyzing health data becomes even
more difficult. This hinders the ability to fully comprehend public health trends, which is
essential for effective resource allocation and policymaking.
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Patient care and health outcomes are directly impacted by these deficiencies. The timely
provision of healthcare services is hampered by inconsistent data sharing and standards, which
also increase the possibility of mistakes and omissions in patient records and, ultimately,
jeopardize the standard of treatment and patient outcomes. Furthermore, the absence of well-
established procedures impedes the thorough examination of health data, impeding the
advancement of medical research and evidence-based treatments. The closing of these crucial
gaps will determine how healthcare systems develop into a more unified, effective, and patient-
centered model.
Review of Findings
The results regarding the adoption rates of SNOMED-CT and HL7 today highlight how
important these standards are to improving interoperability and streamlining healthcare systems.
One important factor promoting interoperability is the implementation of HL7's Fast Healthcare
Interoperability Resources (FHIR) standard. According to Silva et al. (2020), the "Devices on
FHIR" program's dedication to semantically consistent device information transmission is an
excellent example of the flexibility and versatility of FHIR. Maxhelaku and Kika (2019) have
noted that the flexibility and modularity of FHIR offer a strong basis for efficient data
interchange. They attribute the improvement of treatment quality, healthcare services, and
decision-making processes to HL7 FHIR. In the context of personal health data, Chatterjee,
Pahari, and Prinz (2022) show how FHIR may be used to establish semantic and structural
interoperability, highlighting its adaptability and efficacy.
In the meantime, SNOMED-CT acceptance is moving from theoretical to practical
applications, which is especially noticeable in healthcare settings. This shift is clarified by Chang
and Mostafa (2021), who demonstrate the expanding use of SNOMED-CT across a range of care
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settings. Its adaptability in nursing across various care settings is highlighted by Silva et al.
(2020), and its extensive use in research documentation systems, standardized e-clinical
pathways for big data analytics, and collaborative clinical practice is well-documented (Alahmar
& Benlamri, 2020; Direito et al., 2023). The examples demonstrate the standardization and
organization of healthcare data using SNOMED-CT, hence facilitating comprehensive analysis
and cooperative endeavors. This highlights the system's significance and widespread
implementation in modern healthcare.
One important development is the incorporation of new technologies with SNOMED-CT
and HL7. Wang et al. (2018) provide a model outlining the possible advantages of this
technology and advocate for the use of big data analytics in the healthcare industry. They stress
the significance of seamlessly integrating healthcare agents through the Internet of Things,
ushering in a new era of data-driven healthcare, which is similar to Farahani et al.'s (2018)
proposal of a patient-centric IoT eHealth ecosystem based upon HL7 and other technologies.
The extant literature offers a thorough examination of the adoption of HL7 and
SNOMED-CT, emphasizing elements like vendor capabilities, governance, and education. Park
et al.'s (2021) discourse on SNOMED-CT adoption and implementation methodologies in Korea
provides further details on this topic. Furthermore, the report by AlQudah et al. (2021) on the
queue management system that the United Arab Emirates implemented in 2021 using electronic
medical records shows the observable benefits of using XML and HL7 protocols on the patient
experience. Together, these results highlight how crucial HL7, and SNOMED-CT are to defining
the state of healthcare today, promoting interoperability, and improving patient outcomes. They
represent how healthcare standards have dynamically evolved in response to the always shifting
healthcare environment.
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The study's findings further highlight the importance of interoperability in health
information systems and highlight the value of these discoveries. The development of
interoperable systems appears to be a revolutionary undertaking in the field of healthcare data
systems, with the potential to completely change data administration and analysis. Big data
analytics benefits greatly from the integration of various, complicated data sources, which is
made possible by interoperable systems. This integration enables more thorough and perceptive
analysis (Yang et al., 2020).
A notable illustration of the advantages of interoperability is the MID-NET® network in
Japan, which links several healthcare institutions. With over 100% data accuracy, this distributed
network serves as a strong repository for medical data, increasing its usefulness for a range of
healthcare research endeavors (Yamaguchi et al., 2019). In a similar vein, Taiwan's Chang Gung
Research Database (CGRD) is a great resource for epidemiological investigations, outperforming
the National Health Insurance Research Database (NHIRD) in terms of clinical data
completeness (Shao et al., 2019).
AIDA's multi-agent platform is one example of an effort to improve interoperability;
other initiatives address problems with distribution, fault tolerance, standards, and
communication in healthcare systems and streamline information management, information
requests, and system interface (Cardoso et al., 2018). However, it is impossible to exaggerate the
significance of standards in fostering interoperability since they provide the essential connections
across dissimilar systems, hence navigating the complexities of healthcare interoperability
(Benson & Grieve, 2021).
The rise of healthcare 4.0, which is upheld by Industry 4.0 advancements like Huge
Information, Cloud and Haze Registering, and the Web of Things, features how interoperability
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further develops medical services results. According to Aceto et al. (2020), this technology
revolution has the potential to improve patient care, increase efficiency, and make data-driven
decision-making easier. Moreover, HL7 FHIR adoption is clearly a crucial answer to the
problems with healthcare interoperability. The "Devices on FHIR" initiative highlights a
dedication to adjusting to new standards and technologies while emphasizing the uniform
exchange of device data regardless of integration techniques. As seen in Albanian healthcare,
HL7 FHIR implementation improves patient data sharing, which in turn improves treatment
quality and decision-making processes (Maxhelaku & Kika, 2019).
Furthermore, the usefulness of SNOMED CT in healthcare environments is
demonstrated; this is particularly evident in the United Arab Emirates, where XML integration
and HL7 protocols between an electronic medical record and queue management system have
greatly enhanced patient experiences and decreased wait times for outpatient appointments. By
reducing data missingness problems and improving the accuracy of data analytics algorithms, the
use of SNOMED CT-based standardized e-clinical pathways enhances the viability of big data
analytics in the healthcare industry (Alahmar & Benlamri, 2020; Chang & Mostafa, 2021).
A recurrent issue in addressing interoperability concerns is the introduction of standards.
Cardoso et al. (2018) emphasize the use of a multi-agent paradigm to overcome distribution,
fault tolerance, and communication issues in healthcare systems, promoting interoperability.
They advocate for multi-agent platforms that prioritize the integration, dissemination, and
protection of medical data. As mentioned by Dobrow et al. (2019) and Benson et al. (2021),
standards are not only varied but also crucial. Benson emphasizes the need for particular
standards and methods to address complicated parts of electronic health records, such as
addresses and clinical data, whereas Dobrow emphasizes the requirement for standardized
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measuring objects in interoperable electronic health records (iEHRs). The intricacy of healthcare
interoperability is significantly reduced thanks in large part to these standards. As noted by Aceto
et al. (2020) and Wang et al. (2018), the integration of Industry 4.0 and big data analytics in
healthcare introduces a new level of complexity. Healthcare companies struggle to fully utilize
big data analytics' promise to obtain insightful information. The advent of Industry 4.0
technologies, such as cloud computing and the Internet of Things, calls for a thorough analysis of
interoperability concerns and a reevaluation of healthcare service delivery.
Moreover, technologies provide difficulties. Farahani et al. (2018) highlight how the
Internet of Things (IoT) is enabling a shift in healthcare from a clinic-centric approach to a
patient-centric one, emphasizing the importance of effective data management in IoT eHealth
ecosystems. Furthermore, Nguyen et al. (2019) explore how the usage of blockchain in mobile
cloud based EHRs creates security and privacy concerns in the context of dependable EHR
sharing among mobile users.
The implementation of healthcare standards, like SNOMED CT and HL7 FHIR, is
essential for resolving interoperability issues. While SNOMED CT is essential for standardizing
clinical pathways and nursing data, Silva et al. (2020) and Maxhelaku and Kika (2019)
emphasize the significance of HL7 FHIR in promoting the interoperability of medical data
(Alahmar & Benlamri, 2020; Silva et al., 2020; Kim et al., 2020). It is imperative to
acknowledge that the implementation of these standards may face obstacles (Park et al., 2021).
All things considered; these results highlight how important interoperability is to
healthcare systems—from data integration to patient care to the application of new technology.
The practical value of these ideas is emphasized by the examples from real-world situations that
are provided, which also show how they might enhance patient experiences, research outcomes,
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and healthcare quality. The pursuit of efficient interoperability, supported by cutting-edge
technologies and standards, is seen as a major force behind improvements in the healthcare
industry.
Significance of Findings to Health Informatics Practice and Policy
The findings of this study carry significant implications for health informatics education,
research, practice, and policy, offering valuable insights that can shape and improve the
healthcare landscape in multiple dimensions. As healthcare systems become increasingly
complex and data-intensive, it is imperative that health informatics programs and curricula adapt
to the changing landscape. The prominence of standards like HL7 FHIR and SNOMED-CT, as
highlighted in the study, underscores the importance of integrating these standards into health
informatics education. Future healthcare informaticians must be well-versed in these standards to
bridge the gap between technological advancements and the practical needs of healthcare
systems. Moreover, the adaptability and modularity of standards like FHIR, as exemplified in the
"Devices on FHIR" program, can serve as valuable case studies in health informatics education,
showcasing real-world applications of interoperability standards.
The findings also hold promise for health informatics research. The transition of
SNOMED-CT from theoretical applications to practical deployment signals new research
avenues in healthcare data standardization and management. Researchers can explore the
evolving role of SNOMED-CT in different care contexts and its implications for clinical
practice, data analytics, and collaborative research. The modularity and adaptability of FHIR
offers a rich area for research, enabling scholars to delve into innovative ways to leverage this
standard for enhanced healthcare interoperability. Moreover, the integration of emerging
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technologies with HL7 and SNOMED-CT, as highlighted in the study, opens the door to
interdisciplinary research at the intersection of health informatics, IoT, and big data analytics.
For healthcare practitioners and informaticians, these findings offer practical
implications. The adoption and integration of HL7 FHIR and SNOMED-CT into clinical practice
and health information systems can lead to more effective data management, improved decision-
making, and enhanced patient care. As healthcare systems grapple with the challenges of
interoperability, the adaptability of these standards, as evidenced in the study, provides a
roadmap for practitioners to overcome these obstacles. The study's emphasis on standards and
their role in improving interoperability also underscores the importance of adhering to these
standards in health informatics practice. As such, healthcare organizations and professionals can
consider investing in the training and tools required for the effective implementation of these
standards.
From a policy perspective, these findings are instrumental in guiding healthcare
regulations and standards. Policymakers can leverage the insights from the study to formulate
regulations that encourage and facilitate the adoption of interoperability standards like HL7
FHIR and SNOMED-CT. They can promote the incorporation of these standards in electronic
health records (EHR) systems, facilitating seamless data exchange and enhancing patient care.
Additionally, the study highlights the importance of governance and education in the
implementation of these standards, which can inform policy decisions related to training and
support for healthcare professionals.
Limitations of the Study
The study relies on information from a diverse range of sources, each employing different
methodologies and data collection techniques. This heterogeneity can introduce variability in the
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quality and reliability of the data used to support the findings and recommendations. Variations
in data collection methods and sample sizes across studies may compromise the internal validity
of the synthesis, potentially affecting the accuracy of the study's conclusions. While the study
references multiple sources, it is essential to consider the potential bias in the selection of these
sources. The inclusion of predominantly positive or optimistic studies on interoperability
standards, while excluding research with more critical or unfavorable perspectives, can introduce
bias and affect the credibility of the findings. A more transparent and comprehensive approach to
source selection could enhance the study's objectivity.
Conclusion
The purpose of this research project was to investigate the evidence on the effective
integration of health library and medicine database systems with existing healthcare information
systems and standards, such as HL7 and SNOMED CT, to ensure seamless interoperability and
enhance healthcare data management. The systematic literature review and synthesis found that
there is a growing body of evidence that supports the effective integration of health library and
medicine database systems with existing healthcare information systems and standards. The
studies reviewed found that interoperability can lead to a number of benefits, including seamless
interoperability, enhanced healthcare data management, improved clinical decision support, and
enhanced research capabilities.
However, the studies reviewed also identified a number of challenges that can hinder the
effective integration of interoperability solutions. These challenges include technical complexity,
lack of standards, differing priorities, and lack of funding. Despite these challenges, the findings
of the literature review suggest that the effective integration of health library and medicine
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database systems with existing healthcare information systems and standards is feasible and can
lead to a number of important benefits for patients, clinicians, and researchers.
Application of the Results to Clinical Practice
The results of the literature review can be applied to clinical practice in a number of
ways. First, healthcare organizations should adopt standardized data formats and terminologies,
such as HL7 and SNOMED CT, to facilitate interoperability. This will make it easier for different
healthcare systems to communicate with each other and share data. Second, healthcare
organizations should invest in the technical infrastructure necessary to support interoperability,
such as secure messaging systems and data repositories. This will allow healthcare organizations
to exchange data with each other and with other healthcare stakeholders, such as public health
agencies and research institutions.
Third, healthcare organizations should develop interoperability roadmaps that outline
their goals and strategies for achieving interoperability. This will help healthcare organizations to
prioritize their efforts and ensure that they are making progress towards their interoperability
goals. Fourth, healthcare organizations should form partnerships with other healthcare
organizations and technology vendors to share resources and expertise. This can help healthcare
organizations to overcome the challenges of interoperability and achieve their goals more
quickly and efficiently. Finally, healthcare staff should be educated about the importance of
interoperability and how to use interoperability solutions effectively. This will help to ensure that
healthcare staff are able to take full advantage of the benefits that interoperability can offer.
Future Research Directions
The following topics should be addressed in future research. The first is how to develop
and implement interoperability solutions that are affordable and accessible to all healthcare
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organizations, regardless of size or budget. The second avenue for follow up research may be
how to address the lack of standards for interoperability in healthcare. Another possible area of
investigation is how to align the priorities of different healthcare organizations around
interoperability. Researchers can also explore how to increase funding for the development and
implementation of interoperability solutions, and how to develop and evaluate educational
programs for healthcare staff on interoperability.
In addition to these areas, future research should also focus on specific aspects of the
integration of health library and medicine database systems with existing healthcare information
systems and standards. For example, future research could investigate the most effective ways to
index and retrieve data from health library and medicine database systems. It is also worthwhile
to study the best practices for integrating health library and medicine database systems with
clinical decision support systems.
In addressing these topics, future research projects can help to advance the field of
interoperability and make it easier for healthcare organizations to integrate health library and
medicine database systems with their existing healthcare information systems and standards.
This will ultimately lead to better patient care, improved public health, and accelerated medical
research.
In sum, the effective integration of health library and medicine database systems with
existing healthcare information systems and standards is essential to achieving the goals of
healthcare reform, such as improving the quality and efficiency of care, reducing costs, and
promoting population health. Addressing the challenges of interoperability and implementing the
recommendations outlined above can enable healthcare organizations to realize the many
benefits that interoperability can offer.
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References
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Appendices
Appendix A: Overview of Mid-NET
Figure 1
: Structure of MID-NET®, a distributed network connecting multiple healthcare
facilities through a major data center in Japan (Yamaguchi et al., 2019)
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Appendix B: HL7-Based Health Database
Figure 2:
Structure of the Chang Gung Research Database (CGRD) (Shao et al., 2019)
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