ENVISIONING uHEALTH

An Ontological Framework

Arkalgud Ramaprasad and Shazia A. Sathar

University of Illinois at Chicago, Chicago, IL 60607, U.S.A.

Keywords: uHealth, Ontology, eHealth.

Abstract: Ubiquitous health care (uHealth) is becoming feasible today, more so than ever before, due to rapid

advances in information technology. We can glimpse its possibilities in the care of the wounded in war and

the diagnosis and treatment of diseases from a distance. However, the visions of uHealth are many and

partial, ill-defined and unclear. We present a set of four ontologies to envision uHealth systemically. The

ontologies deconstruct uHealth into spatial, temporal, and semiotic ubiquity. Each aspect of ubiquity is

further deconstructed into three components. These ontologies can be used to construct a comprehensive

natural-language narrative of uHealth. They can also be used to (a) map the states-of-the-potential, art, and -

practice of uHealth, and (b) systematically design the trajectory for the transformation to uHealth.

1 INTRODUCTION

Health care at its core is an interaction based on

extensive information transportation and translation

– from the patient to the provider, from the provider

to the patient, from within the patient to the

diagnostician (for example, endoscopy), from the

researcher to the clinician, from the provider to the

insurer, from the provider to the pharmacist, and so

on (Ramaprasad, Papagari, & Keeler, 2009;

Ramaprasad, Valenta, & Brooks, 2009). In

combination with the internet – a revolutionary

information transportation system, evolving into a

translation system with the semantic web – there

have emerged many new alternatives to traditional

face-to-face health care. The new visions of health

care seek to transform the processes and outcomes

of traditional health care.

Pervasive health care, for example, is defined as

“healthcare to anyone, anytime, and anywhere by

removing locational, time and other restraints while

increasing both the coverage and the quality. The

pervasive healthcare applications include pervasive

health monitoring, intelligent emergency

management system, pervasive health- care data

access, and ubiquitous mobile telemedicine.”

(Varshney, 2007, p. 113) In a similar vein u-Health

is defined as “ubiquitous health care, health

management and medical services anytime

anywhere.” (Kugsang, Eun-young, & Dong Kyun,

2009, p. 829) Earlier in the development of these

concepts eHealth was defined as “the use of

emerging information and communication

technology, especially the Internet, to improve or

enable health and health care.” (Eng, 2001) Another

variation of the concept is m-Health, the use of

“mobile computing, medical sensor, and

communications technologies for health-care.”

(Istepanian, Jovanov, & Zhang, 2004, p. 405).

Related to the above broad visions of health care

are a number of capabilities envisaged because of

advances in information technology. The following

are some examples. The Bank of Health wherein

“[t]hrough a health “ATM” system what would

work like banking ATMs, the consumer will have

secure, private, and global access to a healthcare

“checking account” containing information like

blood types, medications, and personal family

medical histories.” (Ball & Lillis, 2001, p. 6)

Bardram envisages “a context-awareness

infrastructure in place in a hospital that various

clinical applications can access and use to adapt to

the context in which they are currently running…a

context-aware Electronic Patient Record (EPR), a

context-aware pill container, and a context-aware

hospital bed.” (Bardram, 2004, p. 1574) Intelligent

Biomedical Clothing (IBC) could weave together

“textile fibers, biomedical sensors and wireless and

411

Ramaprasad A. and Sathar S..

ENVISIONING uHEALTH - An Ontological Framework.

DOI: 10.5220/0003290004110416

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 411-416

ISBN: 978-989-8425-34-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

mobile telecommunications” (Lymberis & Olsson,

2003, p. 379) Intelligent agents could actively sense

and gather “information across the [health care]

delivery network….” (Weagraff, 2005, p. 3) Further

they “could provide care themselves….act on behalf

of the enterprise to correct faults or provide

information in a proactive manner….” (Weagraff,

2005, p. 3).

Each of the many visions of future health care

represents a part of the potential. In the parable of

the six blind men and the elephant, each man

experiences a part of the elephant and infers an

incorrect image of the whole. The real elephant

remains invisible to all of them (Ramaprasad, 2009)

until a wise man integrates the image of the whole

from the parts. Similarly, today the ‘elephant’ called

uHealth remains invisible. The objective of this

paper is to make it visible. We present an

ontological method to envision the complexity of

uHealth concisely and comprehensively, using

natural English, and at different levels of

granularity. It is a method to envision the whole, the

parts, and the relationship between the two.

2 ONTOLOGY OF uHEALTH

2.1 Ontology of eHealth

The proposed ontology of uHealth is based on the

ontology of eHealth (Ramaprasad, Papagari et al.,

2009) shown in Figure 1. It encapsulates the

definition of eHealth as ‘transporting information to

transform health care.’ The five axes of the ontology

are information, spatial transportation, temporal

transportation, semiotic transportation, and heath

care. Each axis is defined by a taxonomy (one- or

two-level) of attributes. The axes are presented left

to right in Figure 1 with connecting words/phrases

between the columns.

A natural language descriptor of eHealth can be

concatenated by combining a category from each

column with the connecting words/phrases

(Ramaprasad, Papagari et al., 2009). For example:

• Transporting personal health information intra-

enterprise locally in real time as data to transform

outcomes of wellness.

• Transporting medical research information inter-

enterprise nationally in advance as action to

transform outcomes of illness.

• Transporting business financial intra-enterprise

regionally in real time as data to transform

management of revenue.

Ubiquity in health care is a combination of spatial,

temporal, and semiotic ubiquity. We will extend the

eHealth ontology to uHealth by including the

corresponding axes and desconstructing them

further into three components.

2.2 Ontology of uHealth

A colloquial expression for ubiquity is ‘any-place,

any-time’; it connotes spatial and temporal ubiquity.

To these two commonly used axes of ubiquity we

add the third axes of semiotic ubiquity – ‘any-

information’. It connotes the complete semiotic

cycle (Ramaprasad & Rai, 1996) – the

morphologics, syntactic, semantics, and pragmatics

of the generation and application of information.

Thus, we define uHealth as ‘transporting

information ubiquitously – spatially, temporally, and

semiotically – to transform health care.’ The

corresponding ontology is shown in Figure 2. The

Information and Health Care axes are the same as in

the eHealth ontology; the third (middle) axis is

Ubiquity is new and includes the three categories of

ubiquity.

The following are six of the 36 basic

connotations of uHealth which can be derived from

the ontology, each with an example.

1. Transporting personal information spatially

ubiquitously to transform health care outcomes.

For example, having a person’s emergency

contact information available anywhere.

2. Transporting medical information temporally

ubiquitously to transform health care outcomes.

For example, having a person’s prescription

information available anytime.

3. Transporting business information temporally

ubiquitously to transform health care quality.

For example, knowing a clinic’s complete

billing history to determine potential fraud.

4. Transporting personal information semiotically

ubiquitously to transform health care quality.

For example, interpreting socio economic data

to tailor treatment plan.

5. Transporting medical information semiotically

ubiquitously to transform health care quality.

For example, interpreting genetic information to

tailor drug treatment individually (Eichelbaum,

Ingelman-Sundberg, & Evans, 2006).

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Figure 1: Ontology of eHealth.

6. Transporting personal information semiotically

ubiquitously to transform health care

knowledge. For example, recommending

actions based on knowledge of personal and

family demographic history.

We can refine the concepts of spatial, temporal, and

semiotic ubiquity further as discussed below.

Figure 2: uHealth Ontology.

2.3 Ontology of Spatial Ubiquity

The concept of spatial ubiquity can be deconstructed

into three components, namely: spatial distribution,

spatial range, and spatial locus. These three are

shown as separate axes in Figure 3. The figure

articulates the statement ‘transporting information

spatially ubiquitously to transform health care’. The

Information and Health care axes of the ontology are

the same as in Figures 1 and 2.

Spatial Distribution describes the density of the

ubiquity. At the lower extreme ubiquity may be

defined by the ability to transport information

from/to fixed locations. As long as the relevant fixed

locations are covered one could describe the system

as being minimally ubiquitous. For example, as long

as all the clinics of a health care provider are

covered, a system may be described as being

ubiquitous. At the upper extreme ubiquity may be

defined as the ability to transport information

from/to a continuous space. Thus, the requirement

for ubiquity may be anywhere in the geographical

region covered by the clinics. Between these two

extremes spatial distribution may be defined by

networked points or mobile points from/to where

information has to be transported.

Spatial Range describes the scale of the ubiquity.

The ubiquity may be at a very close range; for

example, within a hospital room. Or, it may be at a

remote range; for example, anywhere within a city,

region, or country.

Spatial Locus describes the focus or origin of

ubiquity. It may be any one or a combination of the

health care providers listed in Figure 3. It may also

be described differently in terms of the facilities

such as clinics, hospitals, etc.

Combining Spatial Distribution and Spatial

Range, spatial ubiquity may range from fixed points

at a very close range to continuous points at remote

range. Further combining it with Spatial Locus, the

technology required for transporting information

from/to fixed points at very close range from/to

patient would be different from that for transporting

information from/to continuous points at remote

range from/to patients. Thus, spatial distribution,

range, and locus can impose different requirements

on a uHealth system.

2.4 Ontology of Temporal Ubiquity

The concept of temporal ubiquity can be

deconstructed into three components, namely:

temporal interval, temporal range, and temporal

locus. These three are shown as separate axes in

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413

Figure 3: Ontology of spatial ubiquity.

Figure 4: Ontology of temporal ubiquity.

Figure 4. The figure articulates the statement

‘transporting information temporally ubiquitously to

transform health care’. The Information and Health

care axes of the ontology are the same as in Figures

1 and 2.

Temporal Interval describes the frequency of

transportation of information. It ranges from the

Scheduled through Periodic to Continuous.

Scheduled transportation would be the least

ubiquitous temporally, Continuous the most. The

temporal interval for transportation would be

determined by the temporal characteristics of the

information. Scheduled ubiquity may be adequate

for a low frequency or unchanging information such

as weight and height; Continuous ubiquity may be

necessary for high frequency information such as a

heart monitor from an ambulance.

Temporal Range describes the period over which

information has to be transported for uHealth. Real

time anchors the low end of the axis; Very long

periods, which may be as long as a person’s life time

or even a family’s lifetime (in the case of certain

genetically inherited diseases), anchors the high end

of the axis. Between the two anchors are three

categories labeled Immediate, Short, and Long. The

clock-time equivalents of these ranges may vary

between contexts. Real time range for lifestyle

change may be Very long range for chronic heart

failure.

Temporal Locus describes the location of the

temporal range relative to an encounter. It ranges

from Pre (before) the encounter to During, Post, and

the Lifecycle of the encounter. For transient events

such as a common infection the locus may be just

during the encounter; on the other hand, for a

chronic condition such as diabetes it would have to

be during the Lifecycle of the disease.

Combining Temporal Interval and Temporal

Range, temporal ubiquity may range from

Scheduled intervals in Real time range to

Continuous interval in Very long time range. Further

combining it with Temporal Locus, the technology

required for transporting information at scheduled

intervals in real time range Pre encounter would be

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Figure 5: Ontology of Semiotic Ubiquity.

different from that for transporting information at

Continuous interval in Very long time range for the

Lifecycle of the encounter. Thus temporal interval,

range, and locus can impose different requirements

on a uHealth system.

2.5 Ontology of Semiotic Ubiquity

The concept of semiotic ubiquity can be

deconstructed into three components, namely:

Semiotic Phase, Semiotic Stage, and Semiotic

Locus. These three are shown as separate axes in

Figure 5. The figure articulates the statement

‘transporting information semiotically ubiquitously

to transform health care’. The Information and

Health Care axes are the same as in Figures 1 and 2.

Semiotic Phase describes the two broad phases

of the semiotic cycle, Generation and Application of

information(Ramaprasad & Rai, 1996). Repeated

completion of both phases is necessary for

translating information into action and obtaining

information about action (Ramaprasad, Valenta et

al., 2009). Clinical research, for example, may focus

more on the Generation of information; clinical

practice, on the Application of information.

Similarly, continuous monitoring devices may focus

primarily on the ubiquitous Generation of

information with a small component of Application

to discover potential emergency alerts. An aspect of

semiotic ubiquity is the inclusion of both phases.

Semiotic Stage describes the four broad stages of

the semiotic cycle: Analysis, Interpretation,

Conclusion, and Action. The first three correspond

to the commonly used syntactics, semantics, and

pragmatics (Ramaprasad & Rai, 1996) categories.

The value of information is enhanced at each stage.

The full value of information is realized only when

the cycle is complete. An aspect of semiotic ubiquity

is the completion of all the four stages.

Semiotic Locus describes the location of the

semiotic process. It may be at the point of Need,

Care, Service, or Research. Other locations may be

added to the list. The Semiotic Locus may also

include multiple locations. Ideally, uHealth would

entail the ability to complete the semiotic cycle from

any locus – the ability to translate information at any

place and time.

Combining Semiotic Phase and Semiotic Stage,

Semiotic Ubiquity would include Generation and

Application of Analysis, Interpretation, Conclusion,

and Action. The integration of these at any Locus

would be the objective of Semiotic Ubiquity. The

practice of evidence based medicine, for example,

requires the ability to transport Medical information

for Generation/Application of Interpretation,

Conclusion, and Action at the point of Care.

3 CONCLUSIONS

Ubiquity is polymorphic. Thus the meaning of

uHealth can vary by the context and the

corresponding needs. The ability to access one’s

electronic medical records (EMR) in any physician’s

office in a geographical region may be the state-of-

the-need of ubiquity in a developed country. On the

other hand, the ability to access a physician within

half a day’s travel by foot may be the state-of-the-

need in a developing country. A physician practicing

evidence based medicine (EBM) may desire

semiotic ubiquity at the point of care through access

to online reviews, journals, and decision support

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415

tools. A physician practicing in a remote village may

be content to collect the data from the patient and

send it by snail-mail for analysis, interpretation, and

action to a specialized tertiary care hospital.

Effective strategies are systemic and systematic

responses to a problem. For uHealth, they have to be

based on the integrated image of the whole

‘elephant’ called uHealth, not on fragmented images

of its parts. If uHealth strategies are to be effective

in the new internet age, they have to be designed,

developed, and implemented systematically in the

context of the particular health care system. Ad hoc,

fragmented strategies will be ineffective.

The ontological framework for conceptualizing

uHealth discussed in this paper provides a language

and logic for designing and developing a coherent

uHealth strategy. The framework can be used to map

the states-of-the-art, -need, and –practice; and from

these maps to assess the gaps between the states and

determine strategies to bridge the gaps. The

ontologies can be adapted to a context by changing

the axes and taxonomies accordingly. Thus, one can

envision the trajectory of transformation from

traditional health to uHealth in the age of the new

internet – perhaps the age of the Übernet.

REFERENCES

Ball, M. J., & Lillis, J. (2001). E-health: transforming the

physician/patient relationship. International Journal of

Medical Informatics, 61(1), 1-10.

Bardram, J. (2004). Applications of context-aware

computing in hospital work: examples and design

principles. Paper presented at the SAC '04, Nicosia,

Cyprus.

Eichelbaum, M., Ingelman-Sundberg, M., & Evans, W.

(2006). Pharmacogenomics and individualized drug

therapy. Annual Review of Medicine, 57, 119-137.

Eng, T. R. (2001). The eHealth Landscape: A Terrain

Map of Emerging Information and Communication

Technologies in Health and Health Care: Princeton,

NJ: The Robert Wood Johnson Foundation.

Istepanian, R. S. H., Jovanov, E., & Zhang, Y. T. (2004).

Guest Editorial Introduction to the Special Section on

M-Health: Beyond Seamless Mobility and Global

Wireless Health-Care Connectivity. Information

Technology in Biomedicine, IEEE Transactions on,

8(4), 405-414.

Kugsang, J., Eun-young, J., & Dong Kyun, P. (2009).

Trend of wireless u-Health. Paper presented at the

ISCIT 2009, Incheon, Korea.

Lymberis, A., & Olsson, S. (2003). Intelligent biomedical

clothing for personal health and disease management:

state of the art and future vision. Telemedicine Journal

and e-Health, 9(4), 379-386.

Ramaprasad, A. (2009). Ubiquitous Learning: An

Ontology. Ubiquitous Learning: An International

Journal, 1(1), 57-65.

Ramaprasad, A., Papagari, S. S., & Keeler, J. (2009).

eHealth: Transporting Information to Transform

Health Care. In L. Azevedo & A. R. Londral (Eds.),

Proceedings of HEALTHINF 2009 – Second

International Conference on Health Informatics (pp.

344-350). Porto, Portugal: INSTICC Press.

Ramaprasad, A., & Rai, A. (1996). Envisioning

management of information. Omega-International

Journal of Management Science, 24(2), 179-193.

Ramaprasad, A., Valenta, A., & Brooks, I. (2009).

Clinical and Translational Science Informatics:

Translating Information to Transform Health Care. In

L. Azevedo & A. R. Londral (Eds.), Proceedings of

HEALTHINF 2009 – Second International Conference

on Health Informatics. Porto, Portugal: INSTICC

Press.

Varshney, U. (2007). Pervasive healthcare and wireless

health monitoring. Mobile Networks and Applications,

12(2), 113-127.

Weagraff, S. (2005). The Case for Intelligent Agents

Preparing for the future of care. Paper presented at

the, International Conference on Computational

Intelligence for Modelling, Control and Automation,

2005 and International Conference on Intelligent

Agents, Web Technologies and Internet Commerce.

HEALTHINF 2011 - International Conference on Health Informatics

416