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Data Liquidity in Health Information Systems



In 2001, the Institute of Medicine report Crossing the Quality Chasm and the National Committee on Vital and Health Statistics report Information for Health were released, and they provided the context for the development of information systems used to support health-supporting processes. Both had as their goals, implicit or explicit, to ensure the right data are provided to the right person at the right time, which is one definition of "data liquidity." This concept has had some traction in recent years as a shorthand way to express a system property for health information technology, but there is not a well-defined characterization of what properties of a system or of its components give it better or worse data liquidity. This article looks at some recent work that help to identify those properties and perhaps can help to ground the concept with metrics that are assessable.
Data Liquidity in Health Information Systems
Paul K. Courtney, MS
Abstract: In 2001, the Institute of Medicine report Crossing the
Quality Chasm and the National Committee on Vital and Health Sta-
tistics report Information for Health were released, and they provided
the context for the development of information systems used to support
health-supporting processes. Both had as their goals, implicit or explicit,
to ensure the right data are provided to the right person at the right time,
which is one definition of ‘‘data liquidity.’ This concept has had some
traction in recent years as a shorthand way to express a system property
for health information technology, but there is not a well-defined char-
acterization of what properties of a system or of its components give it
better or worse data liquidity. This article looks at some recent work that
help to identify those properties and perhaps can help to ground the
concept with metrics that are assessable.
Key Words: Medical informatics, knowledge management,
electronic health records, personal health record
(Cancer J 2011;17: 219Y221)
The Institute of Medicine (IOM) report, Crossing the
Quality Chasm,
called for nationwide changes in the ways that
health care was delivered to reduce medical errors and improve
the quality of health care. The committee proposed to focus on
6 qualities of care: safe, effective, patient-centered, timely, effi-
cient, and equitable. The IOM recognized that, to achieve the
goals, not only would the care processes need to be redesigned
but there would also need to be a large investment in health
information technology (Health IT) infrastructure. This invest-
ment was to be focused on supporting the care processes that
take place in a clinic or a hospital within the context of the rev-
olution in the use of the Internet to access health information by
all stakeholders.
Later the same year, the National Committee on Vital and
Health Statistics (NCVHS) published what might be called a
complementary report titled, Information for Health: A Strategy
for Building the National Health Information Infrastructure.
The committee brought perspective that our country needed to
create a knowledge-based systemVusing the National Health
Information Infrastructure as the conduitVto be able to pro-
vide information to all parties who might need to manage their
own health. In this report, the committee broke down the major
stakeholders in the health care system into 3 groups: consumers,
health care providers, and communities. The 3 groups then de-
fined the content, needs, and boundaries of 3 dimensions of the
infrastructure: personal health, health care provider, and popu-
lation health. A key insight of this committee was that each of
these groups uses the same information but for different pur-
poses. And where there is a shared interest or process, there is
a need to share information between these groups/dimensions.
The National Health Information Infrastructure is in place to
support those processes. As the report states, the purpose of this
new infrastructure is ‘to push information and knowledge to the
point where all these health decisions are made, so the right
decisions can be made at the right time.
Together, these 2 reports provided a road map going for-
ward on how our nation could improve both clinical care and
public health by investing in an information infrastructure spe-
cifically designed to support those improvements. And they each
provided a somewhat distinct viewpoint: IOM firmly rooted in
the care processes and seeing the value of Health IT to support
them; NCVHS advocating a new infrastructure to connect the
different systems and make information and knowledge available
where and when needed.
‘Data liquidity’’ as a concept has been defined somewhat
loosely in many different ways. One definition is that data
are liquid ‘‘when health [data] flows faster and more freely.’
Another is that, when one creates ‘‘more ways and more choices
for patients to own their computable health data thus enabling
patients to use their data to get help and advice,’’ this is known as
data liquidity.
At the more detailed end of the spectrum, there is a very
cogent and detailed description of data liquidity in the supply
chain planning systems. According to this report, ‘‘By adding a
mechanism to facilitate the flow of data throughout the enter-
prise,’’ vital data that are locked away in tightly controlled data
systems could be freed up for use and analysis by nonpower
What this report goes on to identify as a major problem
is that the interfaces used to interact with these systems are diffi-
cult to learn and use without substantial investment in training.
Although that is not a health system, that problem will no doubt
sound familiar to most who use today’s clinical systems. There
are also some recent articles from the medical field that address
this concept and provide frameworks and detailed definitions that
make it more likely to have operational meaning.
This article will discuss how data liquidity can be used in a
more formal way to describe the flow of data and information
throughout a health information system.
What is ‘‘data liquidity’’? It has been variously used to
describe data that are no longer confined to databases or data
silos in supply chain management systems,
financial systems,
and health systems. Another attribute of liquid data is that it
flows to where it is needed and when it is needed. Although
Agency for Healthcare Research and Quality Director Carolyn
Clancy was not referring explicitly to data liquidity, her de-
scription of the role of health IT as being able to ‘‘bring this
information immediately to clinicians, patients, and others when
and where they need it’’ beautifully describes the qualities of
liquid data in the health system.
The Cancer Journal &Volume 17, Number 4, July/August 2011 219
From the Clinical Research Directorate/CMRP, SAIC-Frederick, Inc, NCI-
Frederick, Frederick, MD.
Conflicts of Interest and Sources of Funding: This project has been funded in
whole or in part with federal funds from the National Cancer Institute,
National Institutes of Health, under contract no. HHSN261200800001E.
The content of this publication does not necessarily reflect the views or
policies of theDepartment of Health and Human Services nor does mention
of trade names, commercial products, or organizations imply
endorsement by the U.S. Government.
Reprints: Paul K. Courtney, MS, 6116 Executive Blvd, MSC 8317, National
Cancer Institute, Rockville, MD 20852. E-mail:
Copyright *2011 by Lippincott Williams & Wilkins
ISSN: 1528-9117
Copyright © 2011 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
There are 2 important concepts that need to be fleshed to
understand the value of the concept of data liquidity as it relates
to health systems in general and oncology care in particular.
First is the relationship between data, information, and knowl-
edge, known in the knowledge management literature as the
knowledge hierarchy (KH).
Data can be seen as the smallest
and most granular unit of information, such as the weight or
height of an individual. Information is then represented by data
that are aggregated and organized in a configuration determined
by contextual cues. This context can be other data about how
or when the original data were collected. For instance, blood
pressure and heart rate would be expected to be quite different
if those data were obtained when an individual had been rest-
ing for 10 minutes as opposed to being collected during a
stress test. Operationally, this is best implemented if the data
element of blood pressure is not stored as ‘‘resting for 10 minutes
blood pressure’ because another measure of blood pressure
that is clinically relevant might be developed that is ‘‘resting
for 10 minutes while standing blood pressure.’’ Instead, these
contextual metadata can be stored separately but linked to the
data. In research, use of metadata in this very granular fashion
would be very useful to use to track consent in a research pro-
tocol. If a person’s consent for blood, tissue, genetic material,
and so on were to be stored in metadata linked directly to the
data that were collected then if a query were run to find and
return data for a specific research protocol, the metadata would
travel with the data and would allow the data to either be in-
cluded or filtered out.
Another kind of context could be provenance of the data
if they had been derived from other values. A trivial example of
this is body mass index, which is calculated from the height
and weight using a specific formula. So, by these examples, it is
clear that even the smallest unit of data will still need to have
some metadata attached to it so that it can be properly collected
with other data given the right context (Fig. 1).
Knowledge can be understood to be actionable informa-
tion or information in action. In either case, knowledge requires
a human mind to comprehend the information (data in context)
as presented and to synthesize information that comes from other
kinds of information. In the clinical context, this could be patient
history, family history, and influences from work and the social
milieu. The clinician combines these sources and their profes-
sional experience and identifies one or more possible actions to
suggest to the patient.
So, if data are to be liquid and flow to where it is ‘‘needed,’
it would be clearer and more accurate to say that the data are
collected on the basis of contextual information contained in
the metadata to form a unit of information. The ability to break
down the information into the ‘‘atomic’’ data while keeping the
metadata linked means that the data are agile and available.
Given the KH model for the relationship between data,
information, and knowledge, what then are the necessary char-
acteristics of a health information system that will enable data
liquidity as a property of that system? William Stead has been
working with computers in medicine since 1972 and has recently
put together a reconceptualization of the electronic health record
uses the ideas of KH discussed above without explicitly stating
In addition, 2 of his 3 proposals address issues connected
with data liquidity. The first one is as follows:
‘‘Define interoperable data as data that can be
assembled and interpreted in the light of current
knowledge, and reinterpreted as knowledge evolves.
Reinterpretation requires access to an archive of
‘raw signal’ (voice, image, text, biometrics, etc).’’
This sounds quite like the discussion above about data and
metadata in the KH model. The raw signal is the deconstruction
of information into its component data elements with the meta-
data coming from the original information. This deconstruction
is what gives the data the agility to be reinterpreted into new
kinds of information. Stead’s next proposal explicitly deals with
data liquidity, as he has defined it: the separability of data from
applications. It speaks to the fact that clinical systems are often
built in a vertically integrated fashionVthink of how Apple
created the iPod to work with iTunes, and Microsoft created
Zune to work with their Media Player, but neither the software
nor the devices work well if at all outside their ‘‘homes.’’ These
technology companies do this to ensure that their music and their
devices keep making them money. In software systems in a
medical center, this can be the same paradigm at work with dif-
ferent vendors having created systems with components that work
with others from the same vendor but not at all well with com-
ponents from other vendors. This can even be the case when your
own developers create a system if they are not doing so with this
separation of data from application as part of their design strat-
egy (Table 1).
There is a December 2010 report from the President’s
Council of Advisors on Science and Technology (PCAST)
rethinking the paths taken up to now to realize the full poten-
tial of the health information infrastructure, the road map for
which was laid out by the IOM and NCVHS. There are some
striking similarities between some of the proposals contained
within this report and those put forth by Stead. Echoing Stead’s
first proposal is one in the PCAST report that calls ‘‘break data
down into the smallest individual pieces that make sense to
FIGURE 1. Knowledge Hierarchy model as modified for this health
system context. Data with contextual metadata provides the
foundation for the process of creating information. Knowledge
requires humans to act on the information.
Courtney The Cancer Journal &Volume 17, Number 4, July/August 2011
220 *2011 Lippincott Williams & Wilkins
Copyright © 2011 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
exchange or aggregate.’’ Perhaps most importantly, this report
points out that the path of development we have been on col-
lectively, using service-oriented architecture as the model, is
just not sustainable. As in other scientific disciplines, health, too,
is becoming data driven, and the information systems in place
will need to change to support that paradigm.
Data liquidity may have some utility in being able to assess,
in a qualitative manner, how efficiently the health information
system is handling its tasks. Although there is no one definition
of the term in the literature as yet, the different definitions found
in the literature all fit into a construct that would describe some
aspect of system fitness. We also identified some barriers to data
liquidity and listed possible mitigation to increase the liquidity in
the system.
The PCAST report and Stead’s reconceptualization of the
EHR both look at how far we have come in developing our health
information system but indicate where we have stalled and lay out
ways to move beyond.
Data liquidity is a term and a concept that has increased
in use during the last few years to describe how data flow in an
information system. This is probably the result of greater use of
information systems for managing our health and other areas of
life. In short, we may have seen the road map for the next decade
in the PCAST report and in Stead’s reconceptualization of the
EHR. We may even be seeing a healthy learning cycle in this
country if we can evaluate this new advice and forge ahead.
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TABLE 1. Example of Barriers to Data Liquidity in Health Information Systems
Barriers Example Mitigation
Siloed and isolated databases Patient data in EHR cannot be accessed or
viewed by patient
Provide means to export data to personal
health record
Localized or proprietary
representation of data in storage
A tissue storage system and a clinical system
use different IDs to identify patients
Either migrate both to one system or provide a
mapping between the two.
Data are stored as derived or
concatenated values
Application concatenates 2 ID numbers, one for
the physician and one for the service, to create
a provider number.
Externalize data sources that are common across
multiple applications and use a link between
a provider table and a service table to uniquely
identify the individual.
Mismatch between system
conceptual model and user’s
mental model of information
Patient attempting to read and understand a
laboratory report.
Reconceptualize and reformat report with visual
cues to indicate laboratory values that require
attention or action.
The last column gives examples of mitigating actions that could free up data in a potentially useful manner.
The Cancer Journal &Volume 17, Number 4, July/August 2011 Data Liquidity in Health Information Systems
*2011 Lippincott Williams & Wilkins 221
Copyright © 2011 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
... In this work, how blockchain innovation may encourage in a healthcare system through five approaches: Digital Access Rules [8], Data Aggregation [9], Data Immutability [10], Data Liquidity [11], and Patient Identity [12]. Here, we have demonstrated how SHA256 hash algorithm [13] utilized on above five attributes for securing the medical records in a blockchain-based healthcare communication system. ...
Protecting the digital medical data from cyber-attacks is essential in ahealthcare communication system. Blockchain is a distributed as well as sharedledger-based technology which can more readily encourage information security.Blockchain approach is commonly applied for the digital crypto-currency; however,it is awaited to have future conducts in health care. Many healthcare systems todayare still subordinated on the back-dated application for keeping healthcare records.Here, the main challenges faced to date are information security and interoperability.This study introduces a blockchain-based model for securing the Electronic MedicalRecords (EMR). Here, we have used SHA256 secure hash algorithm for generatinga unique and identical 256-bit or 32-byte hash value for a particular medical record.In this manuscript, we have focused on five mechanisms (Digital Access Rules, Data Aggregation, Data Immutability, Data Liquidity, and Patient Identity) of datatransition for securing the medical records at the proposed blockchain model.
... Thus, databases represent critical elements of the course of action that is applied in order to intervene in the reality of facts. As in any complex information system, if an element has a problem, it creates a bottleneck and the consequences can propagate and be amplified throughout the system, with different level of awareness by the user/decision maker (1,2). ...
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... Despite the importance of these factors to predict health outcomes, many databases including health encounters or claims do not include information on weight, tobacco and alcohol [2]. The concept that the right data need to be included in a database to answer questions that require that data for meaningful interpretation is called "data liquidity" [3,4]. One reason that databases do not include variables or indicators of weight, tobacco smoking and alcohol use is the failure to record these factors using standard clinical coding systems like The International Classification of Diseases, Clinical Modification. ...
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Background: The lack of adequate and standardized recording of leading risk factors for morbidity and mortality in medical records have downstream effects on research based on administrative databases. The measurement of healthcare is increasingly based on risk-adjusted outcomes derived from coded comorbidities in these databases. However inaccurate or haphazard assessment of risk factors for morbidity and mortality in medical record codes can have tremendous implications for quality improvement and healthcare reform. Objective: We aimed to compare the prevalence of obesity, overweight, tobacco use and alcohol abuse of a large administrative database with a direct data collection survey. Materials and methods: We used the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for four leading risk factors in the United States Nationwide Inpatient Sample (NIS) to compare them with a direct survey in the Behavioral Risk Factor Surveillance System (BRFSS) in 2011. After confirming normality of the risk factors, we calculated the national and state estimates and Pearson's correlation coefficient for obesity, overweight, tobacco use and alcohol abuse between NIS and BRFSS. Results: Compared with direct participant questioning in BRFSS, NIS reported substantially lower prevalence of obesity (p<0.01), overweight (p<0.01), and alcohol abuse (p<0.01), but not tobacco use (p = 0.18). The correlation between NIS and BRFSS was 0.27 for obesity (p = 0.06), 0.09 for overweight (p = 0.55), 0.62 for tobacco use (p<0.01) and 0.40 for alcohol abuse (p<0.01). Conclusions: The prevalence of obesity, overweight, tobacco smoking and alcohol abuse based on codes is not consistent with prevalence based on direct questioning. The accuracy of these important measures of health and morbidity in databases is critical for healthcare reform policies.
... 13 High-cost therapeutics for MS (the market currently exceeds $15 billion globally) are prescribed with very few data available to identify individuals who would be most likely to benefit, and thus those in whom the therapies will be cost-effective. 12 The introduction of "data liquidity" 14 with custom IT systems in neurology accelerates the translation of research to the clinic and beyond. It may even integrate data collected directly from patients. ...
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We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine – i.e. the application of information technology to medicine—has the potential to radically transform medical practice. We introduce three key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real-time use of the data to assist decision-making). Together these form the stepping-stones that are expected to accelerate standardization of data across platforms, promote evidence-based medicine, support shared decision-making, and ultimately lead to improved outcomes. ANN NEUROL 2014. © 2014 American Neurological Association
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After reading this chapter, you should know the answers to these questions: What is the central tenet of consumer health informatics? What are the issues that biomedical informatics research can address relating to consumer communication, decision-making, and information access needs? Although technology is a major focus of the discussion around consumer health informatics, we realize that any technology of interest today will be obsolete in the next 10 years. However, the issues this technology is trying to address will continue to be prevalent. With that in mind, what are the larger biomedical informatics topics this technology is being used to address? Define patient portals, personal health records, patient-controlled health records, and personal health applications.
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The explosive growth of biomedical complexity calls for a shift in the paradigm of medical decision making-from a focus on the power of an individual brain to the collective power of systems of brains. This shift alters professional roles and requires biomedical informatics and information technology (IT) infrastructure. The authors illustrate this future role of medical informatics with a vignette and summarize the evolving understanding of both beneficial and deleterious effects of informatics-rich environments on learning, clinical care, and research. The authors also provide a framework of core informatics competencies for health professionals of the future and conclude with broad steps for faculty development. They recommend that medical schools advance on four fronts to prepare their faculty to teach in a biomedical informatics-rich world: (1) create academic units in biomedical informatics; (2) adapt the IT infrastructure of academic health centers (AHCs) into testing laboratories; (3) introduce medical educators to biomedical informatics sufficiently for them to model its use; and (4) retrain AHC faculty to lead the transformation to health care based on a new systems approach enabled by biomedical informatics. The authors propose that embracing this collective and informatics-enhanced future of medicine will provide opportunities to advance education, patient care, and biomedical science.
We describe a future in which health and wellness are transformed by (1) the availability of definitive and unambiguous tests to prove or disprove each diagnosis, (2) new methods based in systems biology to help unravel the web of messages transmitted across cellular and subcellular networks, and (3) universal access to data that has been freed from data silos to produce true data liquidity for a constellation of purposes ranging from personal health management to population health research. We believe the resulting "connected health" environment will have a profound impact on every aspect of modern life.
A computer-based, self-administered, interactive questionnaire has been designed for interview and diagnosis of patients with functional headaches. Patients respond on a simple keyboard to a sequence of questions on a cathode-ray oscilloscope. The system selects pertinent questions depending on the patient's individual responses. The questionnaire included clinical symptoms, neurological manifestations, prior treatment, emotional factors, and personality problems. Upon completion, a computer-generated printed summary is presented for the clinical record. The system utilizes the acquired data to differentiate between common, classical, and cluster migraine, muscle contraction, and other types of headache. Computer diagnoses agreed with those of the physician in 36 of 50 patients. The automated interview saves physician's time, offers a data base for research, and provides a diagnostic aid for patients with functional headaches.
Data liquidity: practical approach to achieving strategic initiative results
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