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8
Fostering the Global Dimension
of the Health Data Trust
INTRODUCTION
The ability to draw broadly from anywhere across the globe to provide
relevant insights for health and healthcare improvement is a long-term goal
for the learning health system. Meanwhile, the ability to learn from the
experiences of other countries and to apply health information technology
(HIT) for biosurveillance can actively facilitate progress toward this and
other goals. This chapter reviews several activities relevant to exploring the
global dimension of the digital infrastructure for a learning health system.
In his paper, Brendan Delaney from Kings College London describes
the TRANSFoRm project. TRANSFoRm, a European Union (EU) effort to
develop a learning health system driven with HIT, has been designed based
on carefully chosen clinical use cases and is aimed at improving patient
safety as well as supporting and accelerating clinical research. Dr. Delaney
outlines several of the challenges that have arisen such as system interoper-
ability, a need for advanced functionalities, and the support of knowledge
translation. He also describes several techniques being employed to address
these challenges, including clinical research information models, service-
based approaches to semantic interoperability and data standards, detailed
clinical data element representations built on archetypes, and an effort to
prioritize electronic health record (EHR) and workflow integration in the
development of clinical decision support systems that are designed to cap-
ture and present fine-grained clinical diagnostic cues.
Drawing from his involvement with SHARE, an EU-funded project
to define the path toward greater implementation of grid computing ap-
197
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198 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
proaches to health, Tony Solomonides, from the University of the West
of England, discusses his current work to automate policy and regulatory
compliance to allow health information sharing. He describes the imple-
mentation of attribute-based access controls to ensure enforcement of pri-
vacy obligations which—due to variations in their interpretation between
EU countries—require a logic-based computed approach.
HIT holds great promise to increase quality and improve patient safety
in developing and transitional countries. Harvard University’s Ashish Jha
describes how a dearth of reliable information has impeded efforts to better
understand and design solutions to higher rates of adverse event–associated
morbidity in developing countries, as well as obtain an accurate calculation
of global disease burden. Dr. Jha describes an effort by the World Health
Organization to maximize the impact of HIT in resource-poor settings
through the development of a minimum dataset that would allow for sys-
tematic data collection to address safety issues.
David Buckeridge and John Brownstein from McGill University de-
scribe how HIT is enabling dramatic changes in domestic and international
infectious disease surveillance. Detailing how the digital infrastructure can
enhance existing systems through the use of automation and decision sup-
port, the authors also address novel approaches to surveillance enabled
by recent informatics innovations. Using the DiSTRIBuTE project as an
example of innovations in syndromic surveillance that drastically improve
coverage and speed, they call for a renewed science of disease surveillance
that embraces information technology as well as the potentially disruptive
changes it brings to improve disease control.
TRANSFoRm:
TRANSLATIONAL MEDICINE AND PATIENT SAFETY IN EUROPE
Brendan Delaney, M.D.
King’s College London
The underlying concept of TRANSFoRm is to develop a “rapid learn-
ing healthcare system” driven by advanced computational infrastructure
that can improve both patient safety and the conduct and volume of clinical
research in Europe.
The European Union (EU) policy framework for information society
and media, identifies e-health as one of the principal areas where advances
in information and communications technology (ICT) can create better
quality of life for Europe’s citizens (Europe’s Information Society, 2009).
ICT has important roles in communication, decision making, monitoring,
and learning in the healthcare setting. TRANSFoRm recognizes the need
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
to advance the underpinning information and computer science to address
these issues in a European and international context.
The Challenge of Interoperability
Providing interoperability between different clinical systems (which span
national boundaries) and integrating those systems with the research enter-
prise lies at the heart of the eHealth Action Plan (Iakovidis and Purcarea,
2008). In both domains fragmentation of records and proprietary systems
that do not adhere to uniform standards are as much of a challenge as the
legal and ethical issues that complicate access to clinical data for researchers
(Delaney, 2008). However, significant advances in international standards
and in computational technology to support interoperability offer a way to
overcome these challenges. Furthermore, advances in the understanding of
clinical judgment and decision making—as well as the ways of supporting
them via ICT—can inform the design of more “intelligent”electronic health
record (EHR) systems.
Interoperability of data is underpinned by shared concepts and a com-
mon terminology (or at least an agreed and maintained mapping between
terminologies). In research, interoperability of concepts between domains is
promoted by the Biomedical Research Integrated Domain Group (BRIDG)
Model (Fridsma et al., 2008). In primary care, the Primary Care Research
Object Model defines the necessary domain-specific data classes, mapped
to BRIDG (Speedie et al., 2008). In addition to terminologies, the system
needs to enable multilanguage representations of the clinical terms, which
is particularly important from an EU perspective.
However, simply providing a mechanism for the high-level interoper-
ability of data will not provide sufficient functionality for a learning health
system. System integration and shared detailed clinical data representations
are also required. The system needs to have a common business model
with a shared model of processes driven by a suite of open source middle-
ware. Further, the integration of systems requires a much deeper level of
interoperability than simple “diagnosis.” Although SNOMED-CT has an
underlying classification and allows for the concatenation of terms as well
as representing diagnostic concepts such as clinical signs, it is probably not
rich enough to represent all the symptoms and signs required for a diag-
nosis. Furthermore, these concepts need to be linked in an ontology rather
than just a classification.
Building a Learning Health System
The single richest source of routine healthcare data lies within the
records of Europe’s general practitioners. Primary care providers are re-
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200 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
sponsible for first contact, continuing, and generalist care of the entire
population from birth to death (Schade et al., 2006). Any project that
aims to comprehensively support the integration of clinical and research
data should begin with primary care. In addition, even in countries where
general practitioners do not fulfill a “gatekeeper” function—controlling
access to specialist services—the quality of initial diagnosis at the primary
care level determines much of the future course for an individual patient.
In order to support patient safety in both clinical and research settings, sig-
nificant ICT challenges need to be overcome in the areas of interoperability,
common standards for data integration, data presentation, recording, scal-
ability, and security (Ohmann and Kuchinke, 2009).
To explore these issues in more depth, it is useful to consider a list of
requirements for a learning health system:
1. Supports complex queries of existing data, distributed and with
support for various mapped terminologies.
2. Supports real-time recruitment of subjects with workflow-inte-
grated prompts based on reason for encounter or any other data
item within the clinical encounter.
3. Supports real-time prompts for data or sample collection based on
data items within the clinical encounter.
4. Supports jointly controlled data entry into research and clinical
records.
5. Supports real-time diagnostic and therapeutic decision support.
6. Supports all relevant requirements of data privacy, consent, and
security.
7. Supports full audit and provenance of data.
To support this level of functionality a sharing of concepts at the very
deepest level is required. The international standard CEN/ISO 13606 sup-
ports the use of archetypes (Kalra et al., 2005). Archetypes are computable
expressions of a domain content model in the form of structured constraint
statements based on a reference information model. They are often encap-
sulated together in templates, sit between lower level knowledge resources
and production systems, and are independent of the interface and system.
The latter is essential to the development of a sustainable business model
whereby core shared work on archetypes can be deployed via a variety of
commercial EHR systems.
Efficient support of knowledge translation is the final piece in the jig-
saw. While decision support systems for management, quality improvement,
and prescribing have all been shown to be effective, no system for diag-
nostic decision support has been positively evaluated or widely deployed
(Garg et al., 2005). The principal reason for this is the failure of clinicians
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
to use the systems routinely. Not only do they not integrate seamlessly with
the EHR—for the technical reasons described above—but they have been
developed without an understanding of the cognitive workflow involved
in diagnosis. Much recent work in the field of medical decision making
indicates that there may be specific points within the diagnostic process
where decision support, in the form of alerts or prompts, may be effective.
Accurate diagnosis has been shown to be related to the acquisition and
interpretation of critical clinical cues. This process should be amenable to
support by a well-specified ontology of diagnostic cues (Kostopoulou et al.,
2008). In order for this to be achieved, it is necessary to provide an EHR
interface that readily supports the capture and presentation of fine-grained
clinical diagnostic cues. Given that “failure to diagnose promptly”is the
single most common cause of litigation against primary care physicians,
detailed justification of a diagnosis—richly recorded and linked to a knowl-
edge base—will be one means by which clinicians may reduce the risk of
litigation while improving patient care (Singh et al., 2007).
The TRANSFoRm Project
International cooperation in this area is essential. Working with
and extending international standards for the representation of data and
machine-readable clinical trial protocols, archetypes, and terminology ser-
vices require international consensus and models of shared ownership. In
addition, the market within which EHR systems are developed needs to be
opened up to allow for widespread adoption of innovative user interfaces,
decision support, terminology, and archetype services, and the export and
linkage of data. The restriction of access to EHR data and systems is anti-
competitive and restricts innovation in this field.
TRANSFoRm (Figure 8-1) brings together a highly multidisciplinary
consortium where three carefully chosen clinical “use cases” will drive,
evaluate, and validate the approach to the ICT challenges. The project will
build on existing international work in clinical trial information models
(BRIDG and the Primary Care Research Object Model), service-based ap-
proaches to semantic interoperability and data standards (ISO11179 and
controlled vocabulary), data discovery, machine learning, and EHRs based
on open standards (CEN/ISO 13606). We will extend this work to interact
with individual EHR systems as well as operate within the consultation
itself, providing diagnostic support as well as support for the identifica-
tion and follow-up of subjects for research. The approach to system design
will be modular and standards based—providing services via a distributed
architecture—and will be tightly linked with the user community. Four
years of development and testing will end with a fifth year dedicated to
summative validation of the project deliverables in the primary care setting.
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202 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
SAFER CLINICAL
PRACTICE
EPIDEMIOLOGICAL
TRANSFoRm
STUDIES AND RCTS
-INTEGRATION
KNOWLEDGE
-INTEROPERABILITY
TRANSLATION
-SERVICES
MORE RESEARCH
EVIDENCE
FIGURE 8-1 TRANSFoRm and the learning health system
Figure 8-1.eps
HEALTHGRIDS, THE SHARE PROJECT, AND BEYOND
Tony Solomonides
University of the West of England
Grid computing was introduced in the late 1990s to serve as a me-
dium of scientific collaboration and as a more immediate means of high-
performance computing (Foster and Kesselman, 2004). If the Internet is
an apparently inexhaustible information medium, the grid would also add
rapid computation, large-scale data storage, and flexible collaboration by
harnessing the power of large numbers of computers. As a computational
paradigm, the grid was adopted for use in scientific fields—such as particle
physics, astronomy, and bioinformatics—in which large volumes of data,
very rapid processing, or both, are necessary.
The complementary idea of e-science arose from the observation that a
scientist often has to juggle experiments, data collection, data processing,
analysis of results, and their iteration and refinement. There is a need for in-
telligent conduit of information between these processes. Why not facilitate
this through an informatic infrastructure that allows the scientist to pipeline
activities in some way, leaving her free to concentrate on the science? If the
work is being undertaken together with other scientists, this infrastructure
should also support their collaboration but not expose their individual or
joint efforts to anyone outside the specified group of collaborators.
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
Grids and Clouds in Health Care
There have also been several ambitious medical and healthcare applica-
tions of grids. While these initial exemplars have been mainly restricted to
the research domain, there is a great deal of interest in real-world applica-
tions. However, there is some tension between the spirit of the grid para-
digm and the requirements of healthcare applications. The grid maximizes
its flexibility and minimizes overheads by requesting that computations be
performed, and data stored/replicated, at the most appropriate node in the
network. On the other hand, a hospital or other healthcare organization is
required to maintain control of its confidential patient data and to remain
accountable for its use at all times. The very basis of grid computing there-
fore appears to threaten certain inviolable principles: the confidentiality of
medical data, the accountability of healthcare professionals, and the precise
attribution of “duty of care.”
Cloud computing is a more recent but related innovation. Like the
grid, it arises from concepts and forces that were already present in the
field, not least in the world of commercial computing. Precursors include
the ideas of “application service provision” and “virtualization.” Indeed,
early adaptations of concepts from grid computing included the notion of
utility computing—computing power distributed as if it were a “domestic”
utility like gas of electricity. The advantage to a business that outsources its
information systems to a cloud provider is that it need not own the infra-
structure of servers and communications nor concern itself with maintain-
ing the applications.
The current convergence of utility computing with social networking
applications has led to several serious proposals to use clouds for patient-,
or more accurately, carer-managed electronic health records (EHRs): com-
mercial examples include Microsoft’s HealthVault and Google Health,
while in the United Kingdom there is debate on extending the use of
HealthSpace along such lines. Indeed, the idea that personal EHRs could
be “banked” originated with Dr. Bill Dodd in 1997 (Dodd, 1997). The op-
portunity to mine such records to the advantage of public health has also
been noted (Bonander and Gates, 2010).
Healthgrids arose from the observation that healthcare and biomedical
research share many of the characteristics of e-science. Consequently, many
areas of biomedical research—medical imaging and image processing, mod-
eling the human body, pharmaceutical research and development, epidemio-
logical studies, genomic research, and personalized medicine—are expected
to benefit from healthgrid technology. To use a familiar and successful
example, consider a patient in a breast cancer screening program. If a mam-
mogram gives cause for concern, it may be necessary to conduct further
investigation or to seek a second opinion. There is already a powerful array
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204 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
of technological support for this, from image standardization software to
computer-aided detection. The possibility of remote second opinion is also
considered valuable if it does not take up too much time. If the patient is
referred, the oncologist wants to know the history as succinctly as possible
in order to review the diagnosis and begin with assessment and staging. If
the patient needs to undergo surgery, the images from the diagnostic stage
can be used in planning. In other cancers, radiotherapy planning may be
assisted by review of imaging (Warren et al., 2007).
A powerful influence over the direction of these early projects was
the Bioinfomed study which established a now familiar picture of the
correspondences between biosocial organization (molecule–cell–organ–
individual–community), pathologies and disciplines with different kinds
of informatics (molecular modeling–imaging of cells and organs–electronic
patient records–public health informatics) (Martin-Sanchez et al., 2004). It
challenged the community to bring together information at these different
levels into a coherent model. One of its most obvious successors is the Vir-
tual Physiological Human, a program that seeks to provide a framework for
the integration of different partial models of the human body, on different
scales, toward an aggregate systemic study of human physiology.
HealthGrid and SHARE
HealthGrid was an EU-inspired initiative to support projects in the use
of grid technology in health care and biomedical research. Incorporated as
a not-for-profit organization in France, this collaboration edited a white
paper setting out for senior decision makers the concept, benefits, and op-
portunities offered by healthgrids (Vincent et al., 2005). Starting from these
conclusions, the EU funded the SHARE project aimed at identifying the
important milestones toward wide deployment and adoption of healthgrids
in Europe, perhaps as part of an action plan for a “European e-Health
Area” (SHARE Collaboration, 2008). The project had to assess the status
quo and set targets; identify key gaps, barriers, and opportunities; establish
short- and long-term objectives; propose key developments; and suggest the
actors needed to achieve the vision. The road map had to encompass issues
regarding networks; infrastructure deployment; “middleware”; services to
end users; standards; security; ethical, legal, and regulatory developments;
social adjustments; and economic investments.
A draft road map was filtered through a number of “use cases” includ-
ing drug discovery, large-scale public health emergency, imaging-based
screening, and management of chronic conditions. The requirements arising
from these different case studies led to differentiation between the devel-
opment of (1) data, (2) computational, and (3) collaboration healthgrids.
Indeed, the third category crystallized in the course of the project. The
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
ultimate goal of a “knowledge grid” was then seen to emerge from the
interaction of these three subparadigms, rather than to be an enhancement
of the data grid, as had previously been thought.
Ethical, legal, social, and economic issues assumed increasing impor-
tance in the course of the project. The project mapped the legal and ethical
landscape, identifying barriers to the wide adoption of healthgrids. Aspects
of the law and emphasis on ethical requirements were initially considered
to be inert constraints but were subsequently treated as parallel dynamic
developments capable of being influenced by policy. These were therefore
included in the road maps as areas in which fresh thinking and strategy
were necessary. A project since undertaken at University of the West of
England, Bristol, has demonstrated that it is possible for technology to
incorporate goals such as regulatory compliance even in the face of poten-
tially contradictory demands from different frameworks.
In relation to health care, SHARE identified evidence-based practice as
the core requirement. As such, much of the work is underlain by assump-
tions about the dynamic nature of the evidence base, the need for biomedi-
cal advances to be translated into medicine, and for gold standard evidence
to be interpreted in operational terms. Arguably, it paid less attention to the
business of health care, including “internal markets” and commissioning
(as in the United Kingdom) or actual markets (as in the United States). For
example, the possibility of patients owning their data in real rather than in
moral terms was considered but not fully explored. Developments in health-
care systems—including the halting progress of the English National Health
Service National Programme for IT—have led governments to consider the
role of cloud computing for the management of electronic patient records.
This is regarded as a positive development that should help close the gap
between healthgrids (for science and knowledge management) and clouds
(for management, compliance, and business issues).
Technology and Regulatory Compliance
It has already been observed that the grid paradigm is in some ways at
odds with the requirements of healthcare organizations. Although it fea-
tured significantly in subsequent research, security was not a top priority in
its initial development. However, the complexity of medical data, the risk
of disclosure through metadata, and the granularity of confidentiality are
not readily accommodated in a raw grid environment. Healthgrids would
have to take account of these constraints if they were ever to succeed in
biomedical research or healthcare. Yet, all advantage would be lost if the
very efficiency of grid computing was undermined by a constant need for
human regulatory intervention.
The situation is somewhat reminiscent of the history of the motor car.
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When the first motorized carriage was introduced in England in the mid-
1890s, it was a legal requirement that a man walk ahead of any motorized
vehicle with a red flag to warn pedestrians and to ensure that its speed did
not exceed 4 mph.1 It would be absurd to impose a restriction of that nature
on healthgrids. The very idea behind the concept was to make sharing and
exchange of data and workflows as smooth and uninterrupted as possible.
Our goal in subsequent research was to show that technology could at least
meet legal and ethical regulatory frameworks halfway. In doing so, tech-
nological innovation as well as ethical and legal policies would be framed
in ways that acknowledged each other’s legitimate concerns. Along with
proposals for the mutual education of technologists and policy makers, this
project was intended to be a demonstrator not only of technology applied
to regulation, but of technology developed in the light of a sometimes un-
certain and occasionally self-contradictory regulatory framework.
In the European Union, many areas of activity are controlled by what
are known as “directives.” For example, the European Working Time
Directive restricts the number of working hours for different kinds of
work. However, European directives are not legislation. Each directive
has to be “transposed” as national legislation separately by each member
state. Consequently, there is no guarantee of consistency. In our case, the
relevant directive is 95/46/EC Data Protection Directive (European Parlia-
ment and Council of the European Union, 2010). The definitions of relevant
terms (e.g., “personal data”) and restrictions on data disclosure vary from
country to country, even though all legislation is supposed to correspond
to 95/46/EC. At the heart of the project reported here, therefore, is an as-
sumption that text law is too complex to be interpreted by nonlegal expert
users of healthgrids—whether they are biomedical researchers, clinicians, or
technologists. Thus, we propose a twin-track approach: on one hand, the
system may offer advice and decision support; on the other, it can ensure
enforcement of privacy obligations at the process level (Figure 8-2).
At its most abstract, the initial question was this: given some legislation
that has been translated into some sort of declarative framework, could we
take that and map it to a deontic logic of permissions and obligations. In
other words, can we develop an operational logic that could function at
the infrastructure level? This begs the question: What sort of declarative
framework would be suitable to encode legislation? The problem factors in
a variety of ways. One of these is to distinguish between actionable advice
and operational permissions/obligations. More importantly, the problem
also factors into “preconditions for access to the data” and “postconditions
for the treatment of the data.” Finally, since much compliance checking is
1 See http://www.datchethistory.org.uk/Link%20Articles/Ellis/evelyn_ellis.htm (accessed
September 10, 2010).
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
Regulatory
Compliance
Human System Process/
Process Controls
Privacy Guidelines Privacy Aware
Access Control
Teaching and
Policies/Enforceable
Decision Support
Compliance
Auditing
Privacy Compliance
Testing/Assurance
FIGURE 8-2 Proposed regulatory framework for the HealthGrid.
Figure 8-2.eps
done through audit, we need to determine what to document, and how, in
order to provide evidence for audit.
A Proposed Ontology for Data Sharing
An approach through ontology allows us to (1) provide a semantic
map of the directive and its “transposition” into UK, French, and Italian
legislation; and (2) use the so-called Semantic Web Rule Language (SWRL)
to reason with the ontology.
Figure 8-3 gives a diagrammatic representation of the Protégé ontology
for rules on data sharing. At its center is an event of proposed DataSharing,
which relates to certain data to be shared (SharedData) whose Privacy
Status (Anonymized, Encrypted, or Raw) is also known. The DataSharing
has a Sender and a Receiver, both of which, along with the SharedData,
belong to a MemberState. The DataSharing has a SharingPurpose. Based
on this information we can determine the ConsentNecessity (Necessary or
Unnecessary), ConsentSpecificity (Specific or Broad), ConsentExplicitness
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Report on the State of Evidence on Patient Safety
The report, Summary of the Evidence on Patient Safety: Implications
for Research, provides the most comprehensive picture of adverse events in
health care (Jha, 2008). The report aims to not only describe the scope of
challenges facing policy makers around patient safety, but also to provide
recommendations and priorities for research. Members of the working
group consisted of experts with multidisciplinary expertise in epidemiol-
ogy, qualitative methods, and human factors and were from developing,
transitional, and developed nations in all seven WHO regions.
Initially, the group identified the types of adverse events in health
care and their causes. From these efforts, a list 23 major harms and their
underlying causes was created (Table 8-1). Although these topics are not
comprehensive of all epidemiological and clinical metrics, they are among
the most important. The 23 patient safety topics were then categorized
TABLE 8-1 World Alliance for Patient Safety List of Common Adverse
Events in Health Care
No. Domain Patient Safety Topic
1 Structure Organizational determinants and latent failures
2 Structure Use of accreditation and regulation to advance patient safety
3 Structure Safety culture
4 Structure Inadequate training and education, manpower issues
5 Structure Stress and fatigue
6 Structure Production pressures
7 Structure Lack of appropriate knowledge, availability of knowledge,
transfer of knowledge
8 Structure Having measures of patient safety
9 Structure Devices, procedures without human factors engineering
10 Process Errors in care through misdiagnosis
11 Process Errors in care through poor test follow-up
12 Process Errors in care: counterfeit/substandard drugs
13 Process Errors in care: unsafe injection practices
14 Process Bringing patients’ voices into patient safety
15 Outcomes Adverse events and injuries due to medical devices
16 Outcomes Adverse events due to medications
17 Outcomes Adverse events due to surgical errors
18 Outcomes Adverse events due to healthcare-associated infections
19 Outcomes Adverse events due to unsafe blood products
20 Outcomes Patient safety among pregnant women and newborns
21 Outcomes Patient safety concerns among older adults
22 Outcomes Adverse events due to falls in the hospital
23 Outcomes Injury due to pressure sores and decubitus ulcers
SOURCE: Jha (2008).
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
into three groups: structural factors, processes of care, and outcomes. Lead
experts in each topic area described the basic epidemiology of the topic,
how the issue impacts patient care, and knowledge gaps to be addressed
through future research.
Findings from the work are striking and identify large gaps in current
data to inform priority setting. The overarching message of the evidence is
that unsafe medical care continues to cause substantial morbidity, mortality,
and years of life lost—particularly in the developing world. The majority
of work has examined hospital care in developed nations and found ad-
verse events rates of approximately 10% (Brennan et al., 2004; Davis et
al., 2002, 2003; Thomas et al., 2000; Vincent et al., 2001). While few data
exist on the care delivered in developing and transitional nations, these
epidemiological studies suggest similar rates of adverse events but higher
morbidity and mortality compared to developed nations (Jha, 2008). Thus,
the consequences of unsafe care in the developing world appear to be much
greater. Many of these events are not only preventable, but also expensive.
Yet, safety remains low on the policy agenda.
While there is strong evidence on poor clinical outcomes as a result
of unsafe care in developed nations and a small but growing number of
smaller studies in developing and transitional nations, knowledge on struc-
tural factors and processes in care is not nearly as robust. The findings of
the report underscore the need to fill the large gaps in data to inform the
design of solutions and track strategies for improvement. Notably, under-
standing how to best address safety in different settings, determining which
solutions are exportable among nations, and assessing the cost-effectiveness
of specific solutions will be critical to guide policy makers as they make im-
portant, difficult decisions on how to allocate limited resources to improve
health across the globe. Without more data, formulating effective solutions
will pose a substantial challenge.
The Global Burden of Disease
Building on the work of the report, the World Alliance for Patient
Safety focused on quantifying the global burden of unsafe care. The global
burden of disease is the metric used by WHO, policy makers, and funders
to allocate global health resources. The fundamental ability to accurately
calculate the global burden of diseases is dependent on the types of data
available. These results have vast implications for how big of a priority
patient safety is deemed.
To calculate the global burden of disease, the 10 major types of pre-
ventable events that were identified in the report on global patient safety
were used (Table 8-2). Using existing data, the group then developed two
new analytical models: (1) health burden, measured by disability-adjusted
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TABLE 8-2 Adverse Event Conditions Used
by WHO to Calculate the Global Burden of
Disease
Condition
Adverse drug events
Venous thromboembolism complications
Decubitus ulcers
Falls in the healthcare setting
Unsafe maternal/pregnancy care
Hospital-acquired infections
Surgical complications
Adverse medical device events
Unsafe blood products
Unsafe injection practices
Counterfeit medications
SOURCE: Jha (2008).
life years (DALYs) lost (due to injury and mortality) and (2) economic
burden, measured by the financial impact (i.e., increased length of stay,
repeated surgeries) on healthcare systems and society. The models included
the number of people at risk, rate of hospitalization, average age at the
time of acquiring the condition, four clinical outcomes (death, short-term
disability followed by long-term disability, short-term disability then full re-
covery, no or minimal disability), average duration of the condition, average
direct costs related to care of condition per episode, and disability weights.
The findings were again powerful and indicate that unsafe care is one
of the major causes of disability and death in the world. Initial estimates
suggest that over 34 million adverse events in hospitals occur among the
conditions examined (over 60 percent from developing and transitional
countries), and that the global burden of unsafe care from these conditions
may account for as many as 20 million DALYs lost per year (approximately
60 percent of which are from developing and transitional countries). The
number of estimated DALYs lost due do unsafe care falls directly behind
top major global causes of disability and death, such as lower respiratory
infection (94.5 million DALYs), unipolar depression disorders (65.5 million
DALYs), ischemic heart disease (62.6 million DALYS), and cerebrovascu-
lar disease (46.6 millions DALYs) (WHO, 2008a). However, unlike these
conditions, much unsafe care is preventable. Furthermore, these results are
likely to be conservative since not all types of adverse events were included
in the calculations. Thus, designing and implementing successful interven-
tions to curb unsafe care may be an important area to prioritize global
health efforts.
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
While the models were based on the most current and comprehensive
data available, the research methodology further highlighted the reality that
there is a paucity of systematic data sources globally. Particularly in devel-
oping and transitional nations, there is extensive variability in the data. For
example, rates of hospitalization among these nations ranged from 8 per-
cent to 98 percent. While hospitalization estimates in developed nations
were between 113 and 147 million, the estimates were between 111 and
469 million in developing and transitional countries. The data, still qualified
in developing nations, only exists on the prevalence of injury (how often
patients are injured in the hospital). The global burden of disease models
requires more key data on patient demographics, the severity of disability,
and injury duration. Until we have these data elements and more robust
information infrastructure that facilitates the collection and analysis of these
data, precise estimations to inform policy makers will be a major challenge.
WHO Resource-Poor Setting Initiative
Given the acute need for better data to help policy makers make deci-
sions in poor, resource-lacking countries, WHO has begun thinking about
identifying the minimum dataset needed in the developing world. Imple-
menting comprehensive electronic health records and health information
exchange infrastructure in the developing world is not a realistic strategy
at the present date. Thus, WHO has convened an expert consensus group
to identify the major causative structural factors (i.e., lack of protocols or
systematic monitoring) that drive a few key patient safety issues and then
determine a systematic method to collect the data elements hospitals need
to overcome structural failings. This is an important initial step to obtaining
the basic information that will help paint a broader picture on the scope
of patient safety issues and understand how these issues may be resolved.
Conclusion
In summary, we find that the much of the developing and transitional
world faces challenges similar to those of the United States and other high-
income countries: ensuring the delivery of high-quality, safe care in an efficient
way. While the issues of access to health care feel paramount to developing
nations, ensuring access to safe, effective care is critically important. Our
preliminary work suggests that millions of the world’s citizens—a majority
in developing countries—are injured or killed due to unsafe health care.
Information systems, whether they be rudimentary or advanced, are central
to helping resource-poor nations develop an approach to improving patient
safety, and building the trust of patients in the healthcare system in order to
ensure that all of the world’s citizens have access to safe, effective care.
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216 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
INFORMATICS AND THE FUTURE OF
INFECTIOUS DISEASE SURVEILLANCE
David L. Buckeridge, M.D., Ph.D., and John S. Brownstein, Ph.D.
McGill University
Advances in information technology are enabling dramatic changes
in domestic and global infectious disease surveillance. Understanding the
nature of these changes is critical to ensuring that existing and novel sur-
veillance systems contribute effectively to disease control. In this paper, we
describe how information technology is altering the surveillance landscape
and identify how public health should harness these changes for effective
disease control.
Traditional Domestic and International Surveillance Systems
Infectious disease surveillance has evolved over the last century to
exploit many sources of information, but even where capacity is sufficient,
systems based upon laboratory-confirmed diagnoses remain the preferred
approach (Van Beneden and Lyndfield, 2010). Recent epidemics and pan-
demics, however, have highlighted the limited sensitivity and timeliness of
laboratory-based systems. Since a case can be detected only if an infected
person seeks medial care, sensitivity is limited by patterns of healthcare
utilization. During the clinical encounter, sensitivity can be further reduced
if a clinician does not order a laboratory test that can identify the organism
under surveillance, or if a test is not routinely available.
The reporting of a laboratory-confirmed case of infection to a public
health department is usually a manual process, which can take a week or
longer to occur. Moreover, subsequent reporting between public health
jurisdictions tends to follow a hierarchical pattern: a local health depart-
ment informing a regional public health authority which then informs the
national public health authority, a process that often takes 2 to 3 weeks
(Birkhead et al., 1991; Jajosky and Groseclose, 2004; Jansson et al., 2004;
Yoo et al., 2009). Finally, the national public health authority may inform
the World Health Organization in accordance with the International Health
Regulations (WHO, 2008b).
In the context where lab resources are constrained, systems for public
health surveillance face similar limitations. Existing networks of traditional
surveillance efforts—managed by health ministries, public health institutes,
multinational agencies, and laboratory and institutional networks—have
wide gaps in geographic coverage, capacity, and training, often resulting in
poor and sometimes suppressed information flow.
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
Using Information Technology to Enhance Existing Systems
Advances in information technology are beginning to alter the land-
scape of infectious disease surveillance by addressing the limitations of
traditional surveillance approaches. For example, large-scale telephone
consultation lines that rely upon computerized decision algorithms (such as
National Health Service Direct in the UK) attempt to direct patients to the
appropriate level of clinical care (Snooks et al., 2009). Such streamlining of
care may benefit laboratory-based surveillance by increasing the likelihood
that those with diseases under surveillance seek care. Another application
of information technology that may enhance existing surveillance systems is
the use of decision support to prompt clinicians to order tests for conditions
under surveillance (Lurio et al., 2010).
One of the more concerted attempts to apply information technol-
ogy to modernize existing surveillance systems has aimed to automate
the reporting of positive results from laboratories to public health depart-
ments. Evidence suggests that such automation can enhance sensitivity
and improve the timeliness of reporting, reducing delays in initial reports
from laboratories by 4 to 7 days (Effler et al., 1999; Overhage et al., 2008;
Panackal et al., 2002; Ward et al., 2005). In the United States, considerable
resources are being directed toward the acquisition of clinical information
systems that support such electronic laboratory reporting (Blumenthal and
Tavenner, 2010).
These applications of information technology have the potential to
improve existing surveillance systems but they cannot resolve some of the
most important limitations of surveillance. In resource-poor settings, they
cannot address the issue of laboratory testing capacity. Even where labora-
tory resources are sufficient, improving test ordering and reporting does
little to address the delays inherent in hierarchical reporting among public
health jurisdictions after initial reports are received from laboratories.
Using Information Technology to Disrupt the Traditional Approach
In addition to enhancing existing surveillance systems, advances in
information technology are also disrupting the traditional public health
surveillance model by enabling new approaches to data sharing. Data are
increasingly available from sources other than laboratories and these novel
types of surveillance data are often shared outside of traditional public
health channels. In contrast to the hierarchy that typifies reporting of
laboratory-confirmed cases, data are increasingly shared more broadly, with
decreased control over data sharing by governmental agencies.
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218 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
The DiSTRIBuTE project2 is one example of an innovative approach
to sharing surveillance data extracted from sources other than laboratories
(Buckeridge et al., 2011). This project builds on the growing adoption of
syndromic surveillance systems (Buehler et al., 2009), which allow public
health departments to follow the reasons for visits to emergency depart-
ments (EDs) in their jurisdictions (Mandl et al., 2004). Although these ED
data lack the specificity of laboratory-confirmed reports, they are sensi-
tive, available immediately, and have been shown to correlate well with
laboratory-confirmed reports for diseases such as influenza (Marsden-Haug
et al., 2007). The DiSTRIBuTE project allows health departments with
syndromic surveillance systems to rapidly share information from their
systems. Over one-third of ED visits in the United States are now captured
by the DiSTRIBuTE system, and information extracted from these data
to support influenza surveillance are made publicly available with a delay
of less that 72 hours for the majority of participating health departments
(Buckeridge et al., 2011).
HealthMap is another example of using information technology to
expand the scope of surveillance sources and free the flow of surveillance
information. HealthMap harnesses and organizes the enormous amount
of valuable epidemic intelligence found in web-accessible sources such as
discussion sites, disease reporting networks, and news outlets (Freifeld et
al., 2008). These resources provide current, highly local information about
outbreaks—even from areas relatively invisible to traditional global public
health efforts. These web-based data sources not only facilitate early out-
break detection, but also support increasing public awareness of disease
outbreaks prior to their formal recognition (Brownstein et al., 2010).
A Renewed Science of Surveillance on the
Road to Effective Disease Control
Applications of information technology are enhancing existing systems
and disrupting current surveillance models to make more information about
infectious diseases available with less delay. Although some applications of
information technology that influence infectious disease surveillance are
under the control of the public health system, many are not. This reality is
both exciting and challenging for the future of public health surveillance.
It points to a future where disease information is available broadly and
quickly, but raises the questions of how, and by whom, this information
will be used to further effective disease control.
Public health workers use surveillance data to assess population health
status and project the likely evolution of that status in the face of available
2 See http://www.ISDSDistribute.org (accessed January 14, 2011).
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FOSTERING THE GLOBAL DIMENSION OF THE HEALTH DATA TRUST
interventions (Buehler et al., 2009). To accomplish these tasks, data from
different surveillance sources must be combined (Khan et al., 2010). Such
combination could make the most of highly specific laboratory data, when
available, and more sensitive and timely data from other sources. Combin-
ing data to support decision making, however, requires an understanding of
the nature and quality of the data, something that is not always available
for novel data sources.
While concern about the nature and quality of data is appropriate,
public health authorities cannot and should not avoid novel sources of
data and rest complacent with traditional models of surveillance. Instead,
public health surveillance as a discipline must extend its theoretical and
practical foundations to embrace the opportunities presented by informa-
tion technology. In other words, a renewed science of disease surveillance is
needed; one that starts from public health principles and embraces informa-
tion technology enhancements as well as disruptive changes on the road to
improved disease control (Thacker et al., 1989).
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