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Interoperability in Healthcare: Benefits, Challenges and Resolutions

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Information and Communication Technologies (ICTs) play significant roles in the improvement of patient care and the reduction of healthcare cost by facilitating the seamless exchange of vital information among healthcare providers. Thus, clinicians can have easy access to patients' information in a timely manner, medical errors are reduced, and health related records are easily integrated. However, as beneficial as data interoperability is to healthcare, at present, it is largely an unreached goal. This is chiefly because electronic Health Information Systems used within the healthcare organizations have been developed independently with diverse and heterogeneous ICT tools, methods, processes and procedures which result in a large number of heterogeneous and distributed proprietary models for representing and recording patients' information. Consequently, the seamless, effective and meaningful exchange of patients' information is yet to be achieved across healthcare systems. This paper therefore appraises the concepts of interoperability in the context of healthcare, its benefits and its attendant challenges. The paper suggests that the adoption of a standardized healthcare terminology, education strategy, design of useable interfaces for ICT tools, privacy and security issues as well as the connection of legacy systems to the health network are ways of achieving complete interoperability of electronic based Health Information Systems in healthcare.
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International Journal of Innovation and Applied Studies
ISSN 2028-9324 Vol. 3 No. 1 May 2013, pp. 262-270
© 2013 Innovative Space of Scientific Research Journals
http://www.issr-journals.org/ijias/
Corresponding Author: Olaronke Iroju (irojuolaronke@gmail.com) 262
Interoperability in Healthcare: Benefits, Challenges and Resolutions
Olaronke Iroju
1
, Abimbola Soriyan
2
, Ishaya Gambo
2
, and Janet Olaleke
1
1
Computer Science Department,
Adeyemi College of Education,
Ondo, Ondo State, Nigeria
2
Department of Computer Science and Engineering,
Obafemi Awolowo University,
Ile-Ife, Osun State, Nigeria
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A
BSTRACT
:
Information and Communication Technologies (ICTs) play significant roles in the improvement of patient care and
the reduction of healthcare cost by facilitating the seamless exchange of vital information among healthcare providers. Thus,
clinicians can have easy access to patients’ information in a timely manner, medical errors are reduced, and health related
records are easily integrated. However, as beneficial as data interoperability is to healthcare, at present, it is largely an
unreached goal. This is chiefly because electronic Health Information Systems used within the healthcare organizations have
been developed independently with diverse and heterogeneous ICT tools, methods, processes and procedures which result
in a large number of heterogeneous and distributed proprietary models for representing and recording patients’ information.
Consequently, the seamless, effective and meaningful exchange of patients’ information is yet to be achieved across
healthcare systems. This paper therefore appraises the concepts of interoperability in the context of healthcare, its benefits
and its attendant challenges. The paper suggests that the adoption of a standardized healthcare terminology, education
strategy, design of useable interfaces for ICT tools, privacy and security issues as well as the connection of legacy systems to
the health network are ways of achieving complete interoperability of electronic based Health Information Systems in
healthcare.
KEYWORDS:
Interoperability, Information Communication Technologies, electronic Health Information System, healthcare,
patient care.
1 I
NTRODUCTION
Since the 1990s, advances in Information and Communication Technologies (ICTs) in healthcare have created new ways of
managing patients’ information through the digitization of health-related information. The use of ICTs in healthcare has the
potential of reducing medical errors, improving collaboration between healthcare providers, reducing the cost of healthcare
and dramatically improving the delivery and quality of healthcare [1]. The enhancement of ICTs in healthcare has also led to
the generation of huge amount of information relating to the diagnosis, testing, monitoring, treatment and health
management of patients, billing for healthcare services and asset-management of healthcare resources [2]. These
information are stored in heterogeneous distributed Health Information Systems, in different file formats which are mainly
proprietary [3]. These information need to interact and be accessed by healthcare practitioners in a uniform and transparent
way, anywhere and anytime, as required by the treatment path of the patients. For instance, healthcare providers need to
exchange information, such as clinical notes, observations, laboratory tests, diagnostic imaging reports, treatments,
therapies, drugs administered, allergies and letters, x-rays, and bills. However, these information may be heterogeneous in
terminologies, schema, syntax, semantics, data types, data formats and data constraints. This heterogeneity often results in
Olaronke Iroju, Abimbola Soriyan, Ishaya Gambo, and Janet Olaleke
ISSN : 2028-9324 Vol. 3 No. 1, May 2013 263
severe data interoperability problems [4]. Consequently, the healthcare system is characterized by increased costs, high
error rate, and knowledge mismanagement. Thus, this could result in high rate of mortality.
Numerous solutions have been proposed to achieve total interoperability in the healthcare with degrees of success.
These include the use of standards, archeytpes, web services, healthcare service bus, and interface engines and ontologies.
However, in spite of these diverse solutions, interoperability within the healthcare domain is yet to be completely achieved
[5]-[7]. Consequently, this paper discusses the concept of interoperability, the benefits and barriers of interoperability and
also suggests ways in which complete interoperability can be achieved within the context of healthcare.
2 C
ONCEPT OF
I
NTEROPERABILITY
In broad terms, interoperability is the ability of different information and communications technology systems and
software applications to communicate, to exchange data accurately, effectively, and consistently, and to use the information
that has been exchanged [8]. Data interoperability is the ability to correctly interpret data across systems or organizational
boundaries [9]. The key points are illustrated below in Figure 1. In the scenario below, it is assumed that the people on the
left have information needed by the people on the right, and that data in one system is accessible to the other. Hence,
interoperability will only be achieved if the receiving system and users properly understand the meaning of information they
receive and they are able to use this information [10].
Fig. 1. Concept of Interoperability [1]
In general, there are seven basic levels of different levels of interoperability [11]. These levels include:
Level 0 or No Interoperability: This is usually characterized by stand-alone systems which have no interoperability.
Level 1 or Technical Interoperability: This level of interoperability involves the use of a communication protocol for
the exchange of data between systems. Technical interoperability establishes harmonization at the plug and play,
signal and protocol level.
Level2 or Syntactic interoperability: This is the ability of two or more systems to exchange data and services using a
common interoperability protocol such as the High Level Architecture (HLA).
Level3 or Semantic Interoperability: Semantic interoperability refers to the ability of two or more systems to
automatically interpret the information exchanged meaningfully and accurately in order to produce useful results as
defined by the end users of the systems [12]. Semantic interoperability is also used in a more general sense to refer
to the ability of two or more systems to exchange information with an unambiguous and shared meaning [13].
Semantic interoperability implies that the precise meaning of the exchanged information is understood by the
communicating systems. Hence, the systems are able to recognize and process semantically equivalent information
homogeneously, even if their instances are heterogeneously represented, that is, if they are differently structured,
and/or using different terminology or different natural language [7]. Semantic interoperability can thus be said to be
distinct from the other levels of interoperability because it ensures that the receiving system understands the
meaning of the exchange information, even when the algorithms used by the receiving system are unknown to the
sending system.
Interoperability in Healthcare: Benefits, Challenges and Resolutions
ISSN : 2028-9324 Vol. 3 No. 1, May 2013 264
Pragmatic Interoperability: This level of interoperability is achieved when the interoperating systems are aware of
the methods and procedures that each other are employing. This implies that the use of the data or the context of
its application is understood by the participating systems.
Dynamic Interoperability: Two or more systems are said to have attained dynamic interoperability when they are
able to comprehend the state changes that occur in the assumptions and constraints that they are making over
time, and they are able to take advantage of those changes.
Conceptual Interoperability: Conceptual interoperability is reached if the assumptions and constraints of the
meaningful abstraction of reality are aligned.
However, in the context of healthcare, there is no standard definition of interoperability [14]. Nevertheless, the National
Alliance for Health Information Technology defined interoperability in the context of healthcare as the ability of different
information technology systems and software applications to communicate, to exchange data accurately, effectively, and
consistently, and to use the information that has been exchanged [14]. Interoperability in healthcare can be investigated in
different categories such as the interoperability of the messages (information) exchanged between healthcare applications,
interoperability of Electronic Healthcare Records (EHRs), interoperability of patient identifiers, coding terms, clinical
guidelines and healthcare business processes. However, all these categories of interoperability can be classified in two major
layers which are syntactic interoperability layer and the semantic interoperability layer [8]. Syntactic interoperability also
referred to as the messaging layer involves the ability of two or more systems to exchange information.
Syntactic interoperability in e-health involves several layers which include the following:
Network layer: The network layer provides the functional and procedural means of transferring variable length data
sequences from a source host on one network to a destination host on a different network while maintaining the
quality of service requested by the transport layer.
Transport layer: The successful exchange of health-related information amongst healthcare applications requires
the transport protocols, such as Internet. At present, the Transport Communication Protocol /Internet Protocol
(Internet) is the de-facto communication standard for the exchange of health-related messages.
Application protocol layer: This layer supports application and end-user processes. The functions of this layer
typically include the identification of communicating systems, user authentication and privacy. This layer also
identifies any constraints on data syntax. It also provides application services for file transfers and e-mail. Example
of protocols at this layer is the File Transfer Protocol.
Message protocol and messaging format layer: The message layer defines the structure and format for the
messages that are exchanged between end-points. An example of a protocol used at this layer is the Simple Object
Access Protocol (SOAP).
Sequencing of the messages: There is a need to standardize the sequencing of the messages in healthcare. For
example, in Health Level 7 standards, when “I05 RQC Request Clinical Information” message is sent, the expected
return message is “I05 RCI Return Clinical Information”. There are also different types of messages. These
messages could be a message with the intent of action or an acknowledgment message indicating the successful
exchange of a message or an error message. However, for the message content to be processed correctly by the
receiving application, the message content structure and the data items in the message must be standardized [8].
Syntactic interoperability only ensures that the message is received by the receiving system. It does not guarantee that
the content of the received information will be processable by the receiving system. Hence, it can be said that without
syntactic interoperability, data and information cannot be handled properly with regards to its formats, encodings,
properties, values and data types. Therefore, to guarantee total interoperability in healthcare, semantic interoperability must
be provided. Semantic interoperability is the ability for information shared by systems to be understood at the level of
formally defined domain concepts [15]. Semantic interoperability can also be defined as the ability of two or more computer
systems to exchange information in such a way that the meaning of that information can be automatically interpreted by the
receiving system accurately enough to produce useful results to the end users of both systems [16]. From the definition
above, it can be deduced that semantic interoperability is yet to be achieved in healthcare. This is because ‘being useful to
end users’ in the definition refers to end users who are human beings and who have the capacity to make sense of the data
exchanged even when it is incomplete, contains errors, redundant, full of duplications, ambiguous and lack adequate
formalization. Computers, however, do not such capacity. This is because the computer does not capture the semantics of
information and it has no pre-existing repository of contexts, but instead requires a semantic representation that is simpler
and more precise [17]. In recent times, the challenge of semantic interoperability is to ensure that information exchanged are
understood not only by the human beings on both ends of the ICT communication channel, but also, the exchanged
information must be understood by the computer systems and their associated software. Hence, it can be said that, without
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ISSN : 2028-9324 Vol. 3 No. 1, May 2013 265
semantic interoperability, the meaning of the used language, terminology and metadata values cannot be negotiated or
correctly understood. The levels of semantic interoperability in healthcare according to [7] include:
Full semantic interoperability: This level of interoperability also referred to as co-operability is the highest level of
semantic interoperability. It is reached if users of system B are able to use information acquired automatically from
system A with equivalent meaning to its local data, and the information can be processed homogenously with data
captured natively within System B, as if they were entered by a user B directly into system. At this level, neither
language nor technological differences prevent the system to seamlessly integrate the received information.
Partial semantic interoperability: In partial semantic interoperability, the users of system B are able to access the
information from system A and are able to detect, interpret and meaningfully present to the information to the
attending physician.
3 T
HE
C
RITICAL
N
EEDS FOR
I
NTEROPERABILITY IN
H
EALTHCARE
The healthcare domain currently is undergoing a fundamental change in its approach to delivering care as ICTs is
becoming an indispensible component of healthcare. However, with the rising cost of healthcare, incessant inefficiencies and
healthcare quality failures experienced by healthcare providers and patients, there is a need to understand the critical role
that interoperability plays in data sharing and re-use among disparate healthcare applications and devices, reduction of
healthcare costs and the improvement in the quality of care. Thus, this section critically appraises the benefits of complete
interoperability in healthcare.
3.1 E
ASY
A
CCESS TO
P
ATIENTS
R
ECORDS
Patients usually get care from a wide range of care givers (such as hospitals, laboratory, pharmacy, urgent care centers,
physician group, solo physicians and nurses, school clinics, and public health sites) based on their proximity, bedside manner,
quality of care received and cultural attitude [18]. This has led to the fragmentation of the patients’ information in
proprietary heterogeneous systems across healthcare organizations. Consequently, vital information stored in these systems
cannot be easily accessed to present a clear and complete picture of the patient. For instance, a study in an outpatient clinic
found that pertinent patient data were unavailable in 81% of cases, with an average of four missing items per case. The
entire medical record was unavailable 5% of the time [19]. In addition, the patients’ information are often in a non-standard,
non-structured and non-coded (text) form which makes the exchange of information a challenge [20]. Hence, the
healthcare’s current fragmented state results in injury, wasted resources, and loss of lives. In addition, avoidable deaths and
injuries occur because of poor communication between healthcare practitioners annually. In spite of these challenges,
healthcare practitioners are required to have access to the detailed and complete records of their patients across
heterogeneous systems in order to manage the safe and effective delivery of healthcare services. However, through
healthcare information exchange and interoperability, clinicians can have access to longitudinal patients’ records stored in
proprietary heterogeneous systems in a timely manner. This will improve healthcare processes by giving care providers the
patient-specific information they need to effectively consult on a case. Also, with complete interoperability in healthcare,
patients can also have full access to fragmented medical records maintained by each of their healthcare providers which will
enable to better manage their health. Thus, interoperability establishes a seamless continuum of healthcare. The major
benefit of interoperability in healthcare is to facilitate the easy access to health-related information that are stored in
heterogeneous systems irrespective of the geographical locations of the healthcare providers as well as the patients. This
concept is depicted in Figure 2.
Interoperability in Healthcare: Benefits, Challenges and Resolutions
ISSN : 2028-9324 Vol. 3 No. 1, May 2013 266
Fig. 2. Easy Access to Patients Information
3.2 E
ASY
C
OMPREHENSION OF
M
EDICAL
T
ERMS
The application of interoperability in the healthcare domain will provide care givers with the ability to better understand
terms and concepts as data is transmitted from one system to another, while preserving the meaning of the content. Thus,
interoperability will contribute to the improvement of healthcare because it ensures that the right meanings of medical
terminology are delivered to communicating systems. Hence, physicians can easily analyze data from all collaborating
systems for diagnosis and decision making.
3.3 R
EDUCTION OF
M
EDICAL ERRORS
The delivery of healthcare often involves moving the locus of care among diverse sites and providers. Hence, patients’
records are scattered across several physicians’ offices, laboratories and hospitals. This process is usually fraught with errors
as a result of lack of interoperability among healthcare systems. For instance, a study conducted in an inpatient setting
estimated that 18% of medical errors that result in an adverse drug event were due to inadequate availability of patients’
information [19]. Thus, medical errors are of great concern to healthcare because they are the sixth leading cause of death in
hospitals [2]. Also, it was reported in [2] that at least 44,000 and perhaps as many as 98,000 Americans die in hospitals each
year as a result of medical errors, which results in considerable loss of life. Also, more than one million patients are injured
each year as a result of broken health care processes and system failures [19]. One of the ways of avoiding medical errors is
to ensure complete interoperability in healthcare by ensuring that health related data are formatted in a way that allows
disparate computer systems to understand both the structure and content of the exchanged information.
3.4
R
EDUCED
H
EALTHCARE
C
OST
One of the major challenges that the healthcare industry is facing is increasing costs [2]. For instance it was estimated
that healthcare costs in the United States of America were about 14.9 % of the Gross Domestic Product, which accounted for
$1.6 trillion in 2002, and $1.9 trillion in 2005 [2]. Furthermore, healthcare is projected to rise to $3.6 trillion by 2014 in the
United States of America [2]. Interoperability among healthcare ICT systems would deliver a national savings of $77.8 billion
dollars every year [2]. Hence, the effective sharing and communication of data, information, and knowledge among various
stakeholders in the healthcare network is an essential factor for reducing healthcare cost.
3.5 I
NTEGRATION OF HEALTH
-
RELATED RECORDS
The healthcare system is an information-intensive activity that produces enormous data from its diverse sub systems such
as laboratories, wards, operating theatres, primary care organizations, and from wearable and wireless devices. However,
integrating information from autonomously developed applications is a difficult task, as individual applications usually are
not designed to cooperate and they often based on differing conceptualizations [21]. Nevertheless, the management of
information across healthcare systems and organizations requires collaboration, portability and data integration.
Interoperability ensures that disparate applications within diverse healthcare facility “talk to and understandone another.
With this benefit of interoperability, healthcare organizations can integrate the information in disparate applications such as
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registration systems, laboratory systems, core measure tracking and surgical software suites. In addition, interoperability
allows a healthcare system to seamlessly integrate with other healthcare vendors, organizations, providers and national level
organizations.
3.6 E
NHANCED SUPPORT FOR THE MANAGEMENT OF CHRONIC DISEASES
The treatment of chronic diseases often involves multiple physicians and healthcare providers. In recent times, half the
U.S. population lives with chronic disease [19]. An interoperable healthcare system however, will make it easier for patients
to find information to help them prevent such conditions, since many chronic illnesses are preventable. Thus, individuals can
improve their lifestyle to avoid chronic diseases.
4 BARRIERS TO INTEROPERABILITY IN HEALTHCARE
There is no doubt that interoperability has a major positive impact on healthcare. However, the lack of interoperability in
healthcare systems and services has long been identified as one of the major challenges in healthcare. For instance, a
practitioner in a private practice may have difficulty obtaining complete information about a patient who is currently being
hospitalized; also a practitioner may repeat tests and procedures because he or she does not have prior information about
the patient. Consequently, this section appraises the barriers impeding interoperability in healthcare.
4.1 C
OMPLEXITY OF THE HEALTHCARE DOMAIN
The healthcare domain is a very complex one because it involves a lot of actors such as doctors, radiologists, nurses,
pharmacists, laboratory technicians who collaboratively participate in the treatment of patients. Each of these actors
generates information that is needed by one another. The information in the healthcare domain is also enormously complex,
because it covers different types of data such as patient administration, organizational information, clinical data and
laboratory/pathology data [4]. However, safe and effective healthcare relies heavily on the ability to exchange data from one
software to another, and from one person to another, and also on the ability to understand that information so that it can be
used. However, care givers may be unwilling to share health-related information, but even when they are in agreement to
share information, individual entities may have their customized or vendor-driven software that is incompatible and not
interoperable with other systems.
4.2 S
TANDARDIZATION PROBLEMS IN HEALTHCARE
The operational goal of standardization is to provide sets of consistent specifications called “standards” to be shared by
all parties manufacturing the same products, or providing the same services [17]. Standards are agreed-upon specifications
that allow independently manufactured products, whether physical or digital, to work together, or in other words, to be
interoperable. The major goal of standards in the healthcare domain is to improve patient care by allowing interoperability
among disparate systems. However, standards are often too general and subject to local interpretation and implementation.
For instance, there is a “standard” that every patient admitted to a U.S. hospital undergoes nursing assessment processes
which are not uniform or standardized from one hospital to the other. A serious error or omission in this process can lead to
the untimely death of a patient [19]. In addition, abbreviations are barely standardized within the healthcare domain.
Moreover, there are a lot of standards used in healthcare. These include the Health level 7 standards, OpenEHR, Digital
imaging and communications in medicine (Dicom), CEN/ISO EN13606, International Classification of Disease etc. Healthcare
institutes however do not conform to a single standard, and the use of multiple standards breeds confusion [20]. Thus, the
pursuit of high patient care and safety is futile in the absence of uniformity or standardization of the basic means of
communication.
4.3 U
SE OF INCOMPATIBLE CLINICAL ONTOLOGIES
/
TERMINOLOGIES
Existing efforts aimed at achieving semantic interoperability within the healthcare domain rely on agreements about the
understanding concepts stored in terminology systems such as nomenclatures, vocabularies, thesauri, or ontologies. This is
based on the fact that all computer systems would understand one another perfectly if they use the same terminology or
ontologies, or mutually compatible ones [17]. However, the growth of incompatible terminologies and ontologies within the
healthcare domain is exponential. Thus, the use of incompatible and heterogeneous terminology and ontologies in
healthcare contribute to the problem of interoperability. This is because heterogeneous terminologies and ontologies consist
of multiple representations for the same clinical concept.
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4.4 L
EGACY SYSTEMS
Legacy systems (usually electronic medical record systems) with limited interoperability capabilities are those systems
implemented prior to the introduction of common national standards. These systems are still in use today. Their data
storage, input, and inventory of data items are unique and often proprietary. The problem associated with legacy systems is
that they are designed for a particular task or facility. Moreover, many of these systems are designed to prevent
interoperability with other vendors’ applications to protect market share and to encourage purchases by hospitals or clinic
chains .
4.5 R
ESISTANCE TO CHANGE
The healthcare industry unlike most industries (e.g. banking industry) still relies on piles of papers/ handwritten notes
(paper records) for patients care. This is because most healthcare providers are resistant to change from their traditional
paper based system to electronic health system because of the following reasons which were emphasized in [22].
Large number of physicians in individual or small group practices with very limited administrative support for IT and
related practice changes;
The lack of uniformity and interoperability of IT systems from different vendors;
Regulatory limitations on hospital funding of IT for physicians;
Lack of trust and other legal concerns with respect to joint IT solutions; and
Privacy and security concerns
Thus, the transition from a paper-based system to an electronic interoperable system in healthcare still remains a
challenge for healthcare providers. The paper based process is inherently error-prone, as the multiple actors involved in the
patients care may not communicate complicated results appropriately, leading to medical errors. The paper based system
also adversely affects the management of medical information and the secure sharing of information across the continuum
of care.
5 T
HE WAYS FORWARD
The achievement of a fully interoperable healthcare delivery system is a daunting task that is characterized by numerous
barriers. However, the following solutions can be adapted to achieve complete interoperability in healthcare.
5.1 A
DOPTION OF A STANDARDIZED TERMINOLOGY IN HEALTHCARE
The establishment and adoption of a standard terminology/vocabulary, that is, a common language for the description
and exchange of data is essential for the achievement of complete interoperability in healthcare. This is because
interoperability in healthcare requires standardization of the format and content of health-related data so that they can be
understandable to computer programs as well as to the patients and care givers.
5.2
CONNECTION OF LEGACY SYSTEMS TO HEALTH NETWORK
The connection of legacy systems to the healthcare network, either temporarily or permanently is one of the solutions to
complete interoperability in healthcare. This is because legacy systems are critical to medicine and thus they cannot be shut
down from interoperable systems. Interoperating legacy systems with the healthcare network can be achieved via the use of
a middleware software or hardware which translates the input and output of the system so it can interact with other
connected healthcare systems.
5.3 E
DUCATION STRATEGY
Healthcare providers should be taught the use of ICT tools in healthcare and their importance. Healthcare providers
should be made to acquire the technical skills and knowledge needed to make full use of electronic systems in healthcare.
Healthcare providers should be made to realize that adopting interoperable electronic healthcare information is in their best
interest in terms of time and professional convenience. In addition, opportunities for health informatics training and
introductory virtual courses on topics such as standards, application development and eHealth are essential for healthcare
providers.
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5.4
PRIVACY AND SECURITY POLICIES
Privacy and security policies should be considered as a part of design of an interoperable healthcare system. Healthcare
Policies must be widely agreed by patients and practitioners on the terms and conditions for access to and dissemination of
patient data. Adequate protection for the privacy of health information should also be considered in the development of
interoperable healthcare system. Legislation and regulation should be frequently considered to reevaluate emerging
technologies and capabilities. Also, authentication techniques such as passwords, fingerprints, retina scans and biometric
devices such as fingerprint readers and voice-scanning systems should be used to help ensure data and networks are secure.
5.5
USABILITY ISSUES
The ability of care givers to use ICTs successfully depends on how well the technologies have been designed at the level of
human-computer interaction (i.e. the user interface). The display of health-related information in a disorderly, illogical, or
confusing manner leads to decreased user performance and satisfaction. Moreover, a poorly designed user interface
contributes to medical errors. Addressing user interface issues requires greater attention to the cognitive and social factors
influencing clinicians in their daily workflow and interaction with technologies
6 CONCLUSION
The major goal of interoperability in healthcare is to facilitate the seamless exchange of health-related information
amongst caregivers and patients for clinical decision making. However, interoperability within the context of healthcare is yet
to be realized. Thus, the lack of interoperability amongst healthcare systems further strengthens the information silos that
exist in today’s paper-based medical files, which results in proprietary control over health information. This has resulted in
increased healthcare cost, declining quality of patients care, and the inability to integrate patients’ information across
healthcare systems. Consequently, this paper appraised the concepts of interoperability and its relevance to healthcare and
attendant challenges. The paper also suggests solutions to achieving interoperability in healthcare.
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... Since heavy regulation is an accepted staple of doing business in the healthcare domain, long product development cycles and high costs have become commonplace, leading to the weighty issue of outdated infrastructure [47]. Historically, incumbents have not been incentivized to regularly update old systems, and thus IoT practitioners face the challenge of interfacing new technologies with legacy systems-which in many cases is a Herculean task [47]. ...
... Since heavy regulation is an accepted staple of doing business in the healthcare domain, long product development cycles and high costs have become commonplace, leading to the weighty issue of outdated infrastructure [47]. Historically, incumbents have not been incentivized to regularly update old systems, and thus IoT practitioners face the challenge of interfacing new technologies with legacy systems-which in many cases is a Herculean task [47]. Other business challenges facing the widespread adoption of IoT include: (i) the inherent complexity associated with coordinating collaboration between di↵erent key stakeholders, all of whom have di↵ering sets of expertise and potentially inconsistent interests [47]; (ii) a lack of agreed upon IoT standards, leading healthcare decision makers to be reluctant to invest in nascent, as yet unproven technologies [5]; (iii) a risk averse culture that incentivizes maintaining the status quo instead of innovative experimentation [45]; (iv) a lack of clear metrics to define exactly what a successful IoT implementation looks like from the outset [45]; ...
... Historically, incumbents have not been incentivized to regularly update old systems, and thus IoT practitioners face the challenge of interfacing new technologies with legacy systems-which in many cases is a Herculean task [47]. Other business challenges facing the widespread adoption of IoT include: (i) the inherent complexity associated with coordinating collaboration between di↵erent key stakeholders, all of whom have di↵ering sets of expertise and potentially inconsistent interests [47]; (ii) a lack of agreed upon IoT standards, leading healthcare decision makers to be reluctant to invest in nascent, as yet unproven technologies [5]; (iii) a risk averse culture that incentivizes maintaining the status quo instead of innovative experimentation [45]; (iv) a lack of clear metrics to define exactly what a successful IoT implementation looks like from the outset [45]; ...
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Situated at the intersection of technology and medicine, the Internet of Things (IoT) holds the promise of addressing some of healthcare's most pressing challenges, from medical error, to chronic drug shortages, to overburdened hospital systems, to dealing with the COVID-19 pandemic. However, despite considerable recent technological advances, the pace of successful implementation of promising IoT healthcare initiatives has been slow. To inspire more productive collaboration, we present here a simple—but surprisingly underrated—problem-oriented approach to developing healthcare technologies. To further assist in this effort, we reviewed the various commercial, regulatory, social/cultural, and technological factors in the development of the IoT. We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem. To this end, we explore the key enabling technologies that underpin the fog architecture, from the sensing layer all the way up to the cloud. It is our hope that ongoing advances in sensing, communications, cryptography, storage, machine learning, and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.
... As a result, a comprehensive healthcare management system has grown increasingly important over time [7,8]. To cope up with e-Health demands, interoperable systems provide a platform(s) for multiple stakeholders like patients, doctors, hospital management, lab technicians, nurses, and guardians with their corresponding roles, rights, and access provision [9,10]. In this manner, the different users can have other notions, intends, intelligible inputs, and distinct writing structures, giving rise to a heterogeneous data environment with the databases. ...
... It has alarmed industries to ensure healthcare data security. Towards healthcare data security, different methods like steganography, cryptosystems, and blockchain have been proposed [1,4]; however, the recent attacks like brute force, smart card loss, and impersonation have confined major solutions [10,11]. Most of the existing methods have focused on encryption; however, very few efforts are made towards "data infrastructure security" [11]. ...
... However, being vertical in nature, the classical SQL-driven relational databases struggle in delivering fast responses especially over large inputs [5,6]. To cope with time-efficient and interoperable system demands, NoSQL databases, which are often characterized in the form of their robust dynamic structure, hierarchical data storage, and vertical scalability, have gained widespread attention [9,10]. Their efficacy in coping with more complex queries to serve real-time EHRs or telemedicine purposes makes them suitable for interoperable e-Healthcare [13]. ...
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The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.
... Our use cases demonstrated the need for interaction between different devices and sensors (for example, tablet, smartwatch, and/or smartphone each with integrated move and/or face recognition sensors) and access to multi-dimensional data (daily routines, sensor data, and biographic data). The need for interoperable devices emphasizes data security as well as the importance of compatibility between different (medical) devices and (medical) software (Iroju et al., 2013). ...
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The number of people with dementia is increasing worldwide. At the same time, family and professional caregivers' resources are limited. A promising approach to relieve these carers' burden and assist people with dementia is assistive technology. In order to be useful and accepted, such technologies need to respect the values and needs of their intended users. We applied the value sensitive design approach to identify values and needs of patients with dementia and family and professional car-egivers in respect to assistive technologies to assist people with dementia in institutionalized care. Based on semi-structured interviews of residents/patients with cognitive impairment, relatives, and healthcare professionals (10 each), we identified 44 values summarized by 18 core values. From these values, we created a values' network to demonstrate the interplay between the values. The core of this network was caring and empathy as most strongly interacting value. Furthermore, we found 36 needs for assistance belonging to the four action fields of activity, care, management/administration, and nursing. Based on these values and needs for assistance, we created possible use cases for assistive technologies in each of the identified four action fields. All these use cases already are technologically feasible today but are not currently being used in healthcare facilities. This underlines the need for development of value-based technologies to ensure not only technological feasibility but also acceptance and implementation of assistive technologies. Our results help balance conflicting values and provide concrete suggestions for how engineers and designers can incorporate values into assistive technologies.
... Unlike proprietary software, open-source tools make their underlying code accessible to users. The use of proprietary software in American health care systems generally exceeds the use of open-source software [2]. However, select examples, such as the United States Veterans Affairs' open-source VistA system, have found success in clinics and facilities [3]. ...
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Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and devices, a longstanding culture of open-source products in other industries, and the beginnings of machine learning–friendly regulatory pathways together allow for the development and deployment of open-source machine learning models. Such tools have distinct advantages including enhanced product integrity, customizability, and lower cost, leading to increased access. However, significant questions regarding engineering concerns about implementation infrastructure and model safety, a lack of incentives from intellectual property protection, and nebulous liability rules significantly complicate the ability to develop such open-source models. Ultimately, the reconciliation of open-source machine learning and the proprietary information–driven health care environment requires that policymakers, regulators, and health care organizations actively craft a conducive market in which innovative developers will continue to both work and collaborate.
... In simple terms, interoperability can be defined as the ability of two or more independent systems or components to exchange meaningful information reliably and quickly without errors [41]. Hence interoperability facilitates communication between two or more systems. ...
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The health of every individual in the world is greatly influenced by global health issues and threats which are usually caused by international trade and voyage. These threats which have exposed the inadequacies of healthcare systems across the globe include the rapid spread of non-communicable and infectious diseases, pandemics, hunger and starvation, natural disasters, shortage of healthcare personnel and climate change. These threats have led to economic and social disruption in almost all spheres of human lives such as agriculture and education. Aim: Against this background, this study reviews global health challenges and the importance of robots in global health. This study also appraises the factors hindering the effective use of robotic technology to improve global health. Methodology: A total of 41 literatures relevant to the subject matter were obtained from diverse scientific electronic databases including CiteseerX, Science Direct, Google Scholar, IEEE explore, indexCat, PubMed and National Library of Medicine. Results: The study showed that robots can be used to improve global health by diagnosing and treating infectious diseases, reducing the dangers of human contact during pandemic and delivering food and medicines to infected individuals. The study also showed that robots can be used to reduce harmful gases released into the atmosphere and also limit the anxiety and fear of vaccination. The study also revealed that high cost, privacy-related issues, interoperability challenges and the fear of displacement of jobs by robots are some of the factors hindering the effective use of robotic technology to improve global health. Conclusion: This paper suggests that adopting a common standard for robots of different brands and education strategies are some of the strategies that will facilitate the effective use of robotic systems to improve the health of individuals across the globe.
... The absolute number of large-scale disease trajectory studies has remained small. 4,7 We think this is because of 2 reasons-first, there is a lack of syntactic and semantic interoperability of health data 8,9 which makes network studies a challenge, and second, there has not been an open-source standardized implementation of an analytical framework for performing this type of analysis. ...
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Objective To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data. Materials and Methods A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example. Results As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations. Conclusions For the first time, there is a complete software package for detecting disease trajectories in health data.
... Semantic Interoperability (Coordinates semantics/meanings) Technical Interoperability (Connectivity, Data Conveyance) Copyright The lack of interoperability in healthcare systems and services has long been identified as one of the major challenges in healthcare [14]. Thus, this section appraises the factors impeding technical, semantic and process interoperability in Nigeria healthcare. ...
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Interoperability of health related information is one of the agendas of many counties in the world, with no exception to Nigeria and other developing countries. This is because healthcare costs are rising exponentially. Ho wever, interoperability of health related informat ion seem largely unattainable in Nigeria due to reluctance to change fro m the traditional paper based healthcare system to the use of e-health systems, inadequate ICT infrastructure, poor utilization of the available ICT resources, erratic power supply, increased burden of underdevelopment, poverty, political instability, shortage of educational capacity in Nigeria rural and urban healthcare centers, low level of ICT awareness, poor maintenance culture as well as corruption. Consequently, the healthcare system in Nigeria is saddled with h igh cost, high rate of d isease outbreak driven by HIV/AIDs, malaria and other infectious diseases which results in a h igh rate of mortality. Nevertheless, the urgent need to meaningfully exchange safe and reliab le health informat ion is a key priority to the healthcare system in Nigeria as the qualities of patients' care depend majorly on the timely acquisition, processing and retrieval of data related to the patient. Thus, this paper attempts to unravel the factors hindering interoperability in the Nigeria healthcare system and suggests ways of making total interoperability a reality in Nigeria healthcare system as well as other developing countries.
Chapter
The importance of healthcare technologies has been made clear in the current pandemic. Healthcare informatics plays an important role in facilitating healthcare and providing healthcare services in real time. Healthcare informatics has developed from Healthcare 1.0 to Healthcare 4.0 in the last few decades. The data generated from the various sources are stored as electronic health record. These data are collected in different forms and formats. The inconsistent data could be handled using various techniques of big data. The information obtained from big data analytics can be used for the prediction of diseases or conditions using artificial intelligence, machine learning, and deep learning techniques. 5G plays an important role in healthcare informatics by enabling real-time remote monitoring and improving augmented reality, virtual reality, and spatial computing. With 5G technologies, a large number of devices can be connected using high-performance computing over large distances to provide healthcare services. Blockchain is applied in healthcare for health record management, insurance claims, drug tracking, authentication, and ensuring the integrity of medical data. Deep learning techniques can be applied to ever-changing data for the detection and prevention of disease. For the classification challenge, deep convolutional neural networks using pictures of diseased regions are often utilized. In many research techniques, AlexNet and GoogLeNet have been utilized to identify plant diseases. This chapter discusses the state of the art for detecting human sickness as well as the associated 5G healthcare framework for improving it.
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The healthcare sector is suffering from inefficiencies in handling its data. Many patients and healthcare organisations are frustrated by the numerous hurdles to obtaining current, real-time patient information. Patients are also frustrated at trying to schedule appointments at health organisations that have outdated contact information. The healthcare sector’s attention has been drawn to blockchain technology as a part of the solution, especially since this technology has been successfully applied in the financial sector to improve the security of transactions. The aspect of interoperability is resolved adequately by blockchain technology, because it has the potential to store, manage and share EMRs safely in the healthcare community. Therefore, the technology is having a positive impact on healthcare outcomes for various stakeholders. Interoperability in healthcare eases the exchange of health-related data, such as EMRs, between healthcare entities so that records may be shared and distributed among clinical systems. To handle data in this sector without violating privacy is a challenge, whether in the collection, storage, or analysis. Poor security, which increases data breaches, endangers patients both mentally, socially, and financially. A lack of data-sharing in the healthcare sector is considered a significant issue worldwide. This research focuses on this gap by investigating the benefits of using blockchain at the Ministry of Health in Saudi Arabia, providing a detailed analysis of the healthcare sector, and evaluating how blockchain technology improves data-sharing security. This research proposes a framework that identifies the factors supporting data-sharing using blockchain among healthcare organisations. It has three categories: healthcare systems factors; security factors; and blockchain factors. A triangulation technique achieved reliable results in three steps: a literature review; an expert review; and a questionnaire. This gave a comprehensive picture of the research topic, validating and confirming the results. To construct the framework, factors were comprehensively extracted from the literature then analysed, cleared of duplicates, and categorised. As a result, the final framework is confirmed as being based on the literature and expert review, and it is supported by the practitioners’ survey.
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ta Abstract—Health care standards like SCP-ECG (Standard Communication Protocol for Computer Assisted Electrocardiography) aim at enhancing interoperability of digital electrocardiography. However, the complexity of information included in such a file limits the wide adoption of SCP-ECG as the default standard in the field, and restricts its integration with the corresponding EHR (Electronic Health Record). In the present work, focusing on the integration between the ECG-related information and EHR, we have developed SEIA (SCP-ECG Integrated Access), an integrated environment for managing ECGs and other medical information originating from disparate sources. The SEIA system consists of an SCP-ECG Ontology defining SCP-ECG file structure, a query mapping ontology, the database used to retrieve relevant patient data, and a GUI environment used to register SCP-ECG files, pose queries, retrieve results and visualize ECGs.
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One of the key problems in healthcare informatics is the lack of interoperability among different healthcare information systems. Interoperability can be investigated in different categories in the eHealth domain, such as the interoperability of the messages exchanged between healthcare applications, interoperability of Electronic Healthcare Records (EHRs), interoperability of patient identifiers, coding terms, clinical guidelines and healthcare business processes. Furthermore, all these categories can be investigated in two major layers: syntactic interoperability layer and the semantic interoperability layer. This paper describes the concepts involved in eHealth interoperability; briefly assesses the current state in some of the countries in the world and discusses the technical issues to be addressed for achieving interoperability.
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The Levels of Conceptual Interoperability Model (LCIM) was developed to cope with the different layers of interoperation of modeling & simulation applications. It introduced technical, syntactic, semantic, pragmatic, dynamic, and conceptual layers of interoperation and showed how they are related to the ideas of integratability, interoperability, and composability. The model was successfully applied in various domains of systems, cybernetics, and informatics.
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In this paper we assess the value of electronic health care information exchange and interoperability (HIEI) between providers (hospitals and medical group practices) and independent laboratories, radiology centers, pharmacies, payers, public health departments, and other providers. We have created an HIEI taxonomy and combined published evidence with expert opinion in a cost-benefit model. Fully standardized HIEI could yield a net value of dollar 77.8 billion per year once fully implemented. Nonstandardized HIEI offers smaller positive financial returns. The clinical impact of HIEI for which quantitative estimates cannot yet be made would likely add further value. A compelling business case exists for national implementation of fully standardized HIEI.
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In this paper we describe a model of clinical information designed to make health information systems properly interoperable and safely computable. The model is a response to a number of categories of requirements, ranging from the semantic to the performance of software at runtime. We argue that the starting point of a successful model must be an ontological analysis of the process of clinical care delivery, seen as a scientific problem-solving process. From this approach we develop a classification of types of clinical information called the Clinical Investigator Record (CIR) ontology.
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One of the most successful Healthcare Information Models is version 2 of the Health Level 7 (HL7) standard. However, this standard has various prob-lems, mainly its lack of semantic interoperability. This shortfall was addressed in HL7 Version 3, a newer standard which has been designed to solve this problem. Total semantic interoperability cannot be achieved without defined terminology, and to this end the use of the Systemised Nomenclature of Medicine -Clinical Terms (SNOMED-CT) is proposed. The difficulty arrives when deciding how to integrate the information model and the terminology. The line be-tween where one ends and the other begins is often indistinct. This paper describes a proposal for nor-malising the two using ontology mapping and basing HL7 message models on SNOMED-CT concepts and their relationships, in an effort to further total se-mantic interoperability and seamless communication between healthcare entities.
Conference Paper
In modeling and simulation, the need for interoperability can be between simulation models or, more broadly, within simulation environments. For example, simulation of biochemical pathways for glycan biosynthesis will need access to glycomics knowledge bases such as the GlycO, EnzyO and ReactO ontologies and bioinformatics resource/databases. Traditionally, developers have studied these information sources and written custom simulation code with hardlinks into, for example, databases. Our research explores a technique which allows developers to create a conceptual model using domain ontologies, and then use alignment and mapping information between the domain ontologies and the Discrete-event Modeling Ontology (DeMO) to create DeMO instances which represent a model that conforms to a particular simulation world view. Once the DeMO instances have been created, a code generator can be used to produce an executable simulation model. This paper discusses several situations in which DeMO can support interoperability but focuses primarily on interoperability between domain ontologies and DeMO.
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One of the most challenging problems in the healthcare domain is providing interoperability among healthcare information systems. In order to address this problem, we propose the semantic mediation of exchanged messages. Given that most of the messages exchanged in the healthcare domain are in EDI (Electronic Data Interchange) or XML format, we describe how to transform these messages into OWL (Web Ontology Language) ontology instances. The OWL message instances are then mediated through an ontology mapping tool that we developed, namely, OWLmt. OWLmt uses OWL-QL engine which enables the mapping tool to reason over the source ontology instances while generating the target ontology instances according to the mapping patterns defined through a GUI.Through a prototype implementation, we demonstrate how to mediate between HL7 Version 2 and HL7 Version 3 messages. However, the framework proposed is generic enough to mediate between any incompatible healthcare standards that are currently in use.
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Healthcare's hottest topic finally has two things it has badly needed: plain language and a sense of urgency.
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A seamless support of information flow for increasingly distributed healthcare processes requires to integrate heterogeneous IT systems into a comprehensive distributed information system. Different standards contribute to ease this integration. In a research project focussing on the development of a reference architecture for inter-institutional health information systems, we identified concurring standards currently in use. We therefore categorized these integration standards by distinguishing between technical and semantic integration on the one hand, and data and functional integration on the other hand. In addition, standards for semantic integration are roughly categorized according to their scope. By placing standards into a corresponding matrix a "semantic gap" is revealed, which cannot be covered by standards as it contains volatile medical concepts. As a conclusion, it is recommended to conceptually consider the necessity of system evolution in system architectures and also in future integration standards.