Outcomes Measures for Chronic Healthcare:
A National Model for Virtual Town Doctors
Richard B. Berlin, Jr., MD (Health Alliance Medical Plans, Urbana, Illinois)
Bruce R. Schatz, PhD (Library & Information Science, University of Illinois)
CANIS Laboratory, 704 S. Sixth, Champaign, IL 61820
www.canis.uiuc.edu email@example.com April 5, 1999
Chronic illness is a dominant feature of healthcare. Its dominance will increase as the
historically large baby boomer population reaches retirement age in ten years. There is no current
infrastructure that can handle this scale of chronic healthcare. A model is proposed for a Virtual
Town Doctor, an information system distributed across the Internet, which can support chronic
healthcare by information collection and, ultimately, by lifestyle coaching. Such an infrastructure
can be supported on a national scale, by providing personalized interactions via computer that
generate detailed databases of patient records. The local interactions track the progress of chronic
conditions, while global analyses of collected individual records discover similar cases to guide
individual diagnosis and treatment.
The model is based upon health functionality and outcomes measures, derived from health
status and quality of life questionnaires, which ameliorate the variability of the patient-physician
interaction. To handle chronic conditions, the interactions are administered directly in patient’s
homes on a daily basis. The standard protocols within the Internet make it possible, for the first
time, to reach large segments of the population with daily interactions of health status and quality
of life questionnaires. Future Interspace technology will support concept navigation, which will
eliminate data variability via terminology mapping across individuals’ records. In the early
twenty-first century, providing a Virtual Town Doctor for every American will become an
Much has been written of the effect of the Baby Boomer generation on our societal and
economic systems. However, their effect on the American healthcare system has yet to be felt.
This historically large population is now 50, and will soon be 65 years old. If there is concern in
contemporary medicine about appropriate treatments and outcomes today, just wait until
tomorrow. How can we cope in a constrained financial market, when we face the largest number
of elderly ever? The impact on the provision of care, in a contracting medical marketplace and a
constrained economic environment, will be profound.
The infrastructure to provide care at this scale does not exist [Ginzberg,1998; Smith,1998]. In
2011, the first of 77 million baby boomers reach retirement age; the age when health care costs
start to escalate [Schneider,1999]. In 2000, Medicare will have an estimated 40 million
enrollees; in 2040, Medicare will have an estimated 81 million. It is clear that the economy will
not be able to support all treatments for all the people all the time. We will have to squarely face
the issue of appropriate outcomes for chronic diseases with chronic treatments.
The common thread connecting the problems of healthcare in the 21st century is a model of
outcomes: health status functionality, quality of life, and, ultimately, of health outcome
measures. Measuring outcomes gives the medical definition of “true health” for an individual or
a population [Donabedian,1980]. Yet there is little agreement as to how health and disease
outcomes are defined [Eddy,1996]. It was easier in medicine when treatment plans were focused
on the surgical management of acute illness. Then, measurements of survival from the surgery
and the incidence of post-operative complications were sufficient to determine quality of care
and treatment delivered, and therefore the health outcome, for the patient. Mortality and
morbidity are good and useful measures when an operation can cure the disease, but leave room
for improvement when chronic conditions and illness are discussed.
But what to do as the population is older and larger, with an increasing incidence of chronic
disease in aging Baby Boomers? The clear determinants for acute illness are less applicable for
chronic illness characterized by a plethora of physical, psychological, social, economic, and
personal factors [Wagner,1998; Applegate,1990]. How does one determine the effectiveness of
treatment for minor depression in a nursing home patient who is visited by a physician, at best,
once a month? How does one measure the outcome of arthritis medication if the patient has
personal and economic constraints that prohibit visiting a health care facility more frequently
than once a year? And how does one measure the incidence of chronic disease in the urban areas
of poverty or dispersed pockets of the 40 million Americans without health insurance?
A new model of health status, functionality, quality of life and health outcomes, particularly
for chronic conditions, is needed. The model proposed here provides a feasible method whereby
patients as individuals can be continuously tracked and their health measured over time. Subtle
changes in health status will be detected as they cannot be today. Treatments can then be
continuously course-corrected, through interactions with physicians and other healthcare
providers, to provide individualized healthcare. In addition, the collection of health tracking
records will enable the detailed tracking of entire populations. Finally, this methodology will
approach measurements of population health on a national scale for the first time, and enable
treatment guidelines on a customized basis for each individual, similar cohorts, and populations
based upon similar cases within that population.
“Outcomes management is a technology of patient experience designed to help patients, payers, and providers
make rational medical care-related choices based on better insight into the effect of these choices on the patient’s
life. Outcomes management consists of a common patient-understood language of health outcomes; a national data
base containing information and analysis on clinical, financial, and health outcomes that estimates as best we can the
relation between medical interventions and health outcomes, as well as the relations between health outcomes and
money; and an opportunity for each decision-maker to have access to the analyses that are relevant to the choices
they must make.
Outcomes management would draw on four already rapidly maturing techniques. First, it would place greater
reliance on standards and guidelines that physicians can use in selecting appropriate interventions. Second, it would
routinely and systematically measure the functioning and well-being of patients, along with disease-specific clinical
outcomes, at appropriate time intervals. Third, it would pool clinical and outcome data on a massive scale. Fourth,
it would analyze and disseminate results from the segment of the database most appropriate to the concerns of each
decision maker. This should also allow the entire outcomes management system to be modified continuously and
improved with advances in medical science, changes in people’s expectations, and alteration in the availability of
resources.” -- Paul Ellwood, Shattuck Lecture, 1988 [Ellwood,1989,p1551]
Outcomes have traditionally been provided by physicians or other medical professionals to
describe a patient’s, or population’s, course of disease or response to therapy [Donabedian,1980;
Coker,1998]. This situation relies on physicians who perform a brief examination to determine
the patient status. Because most patients visit a healthcare provider rarely or randomly, a
medical outcome description, if encompassing health status functionality and quality of life,
relies on an interpretation by the provider, based on limited evidence.
Attempts to ameliorate this situation often take the form of questionnaires that the patient
completes during diagnosis or treatment. General-health questionnaires, such as SF-36
[Ware,1992; Hays,1993], provide accurate answers to the questions posed. These surveys
attempt to measure outcomes in an organized and standard fashion. Disease-specific
questionnaires, such as AIMS2 [Mennan,1982,1992] for arthritis, provide similar concrete
assessment at a more detailed level. However, problems with any of these questionnaire
instruments remain. Are the questions appropriate for the patient in their current situation? Are
they asked often enough? Are they sampling at an atypical period in the condition?
Health outcome data is currently not used in routine management of the patient
[Meadows,1998]. Structural constraints make the underlying reasons clear. The number of
questions in a standardized questionnaire is geared towards the attention span of a patient and the
practical time constraints of the patient-healthcare facility interaction. This often limits the
length of the questionnaire to 3 pages, yielding 30-40 questions. There is simply not enough
information for an accurate diagnosis. The status of elderly patients with chronic illness are
particularly poorly captured, since they commonly have multiple conditions whose interaction is
missed by a general-health questionnaire or a single disease-specific one. In addition,
questionnaires have become so numerous and complex that they might best be administered by
experts who know their range of effectiveness [Applegate,1990].
Most patient perceptions of their personal health can be described by 10 broad categories
[Evans,1994; Kindig,1998]. These categories encompass the major physical health factors,
including: disease, health care, health function, genetic endowment, physical environment, social
environment, individual response, behavior, biology, well-being, prosperity. Similarly, there are
only a few important broad categories that encompass the major mental health factors. A
standard list of 10 factors [Hersch,1996] includes: level of consciousness, emotional state,
orientation, attention, memory, language, calculations, praxis, visual-spatial function,
reasoning/abstractions. Individuals relate to the health of their bodies through the 10 physical
factors and the 10 mental factors.
The standard questionnaires today are largely concentrated on disease diagnosis and physical
manifestations. For example, arthritis questionnaires ask about ability to move joints and
limitations placed on daily activity. But they omit genetic background of the patient, which may
be more important to assessing health status. As an example, if both of the patient’s parents
were dehabilitated by arthritis at age 65, then the patient likely will be, no matter what the
patient’s joint status at age 50.
How many questions would be necessary to completely and accurately capture the health
status of a patient? There are roughly 1000 common diseases described in a diagnosis manual
for general practitioners [Woolliscroft,1998]. Each of the 10 physical and 10 mental factors may
require 5 to 10 subcategories to elicit adequate information about the status. For example, the
visual-spatial mental health factor has subcategories [Hersch,1996,Table 21-16-1] of:
“orientation impaired, can’t drive, can’t use tools, route-finding problems, dressing difficulty,
can’t copy figures, agnosia, apraxia”.
Coverage for the health factors would thus require 50-100 physical and 50-100 mental
subcategories. Accounting for the health factors for every disease in a large dataset would
require 1000 data items (one for each disease) times each of the 100 physical and mental
subcategories. A patient’s dataset would therefore include 100,000 data points at any one time.
Collecting data daily on a patient’s health status would yield some 36,500,000 data points in a
year. This dataset would be approximate answers to a SF-36M (where M is a Million).
This large calculation does not include data on the 1000 genetic illnesses, future individual
data input from the human genome project, or medical input from physicians concerning
evaluations, diagnoses, treatments, medications and the like. Additionally, data would be
collected over time yielding a cumulative dataset of like quantity per year. Although some
subcategories might require fewer datapoints and questions, additional questions would be
needed for interactions across categories. Thus, a full health status dataset would lead to an
interaction health status/quality of life questionnaire covering all category factors administered
on a regular, daily basis. This dataset would include millions of health status datapoints, and
millions of physician and healthcare data inputs.
The need to ask a million questions will cause a radical paradigm shift in how health
assessment is done. To eliminate the variation of the current patient-physician interaction in
obtaining health status/quality of life data, all data must be standardized and accurately entered
by patients and physicians alike. To enable a patient at home to provide correct answers about
health status within their attention span, questions must be asked on a situational basis,
personalized to the present condition and the past answers of the particular patient.
The logistics of economically providing situational questions and data input require
networked solutions via home computers. Since the patients are describing their health on a
daily basis at their convenience, the descriptions will be more detailed and comprehensive than
the current infrequent and rushed interactions with healthcare providers.
The proposed health status and quality of life outcomes model is based upon the continual
monitoring of the physical and mental health factors of the individual patient. These health
monitors are similar in function to a heart monitor, which enables an individual patient to track
their own cardiological functions and seek treatment when the functions fall outside safe
parameters [Carey,1995]. In the more general health case, the patient must interact with a
tracking system to record their various health parameters on a continual basis. The dynamic
descriptions of health parameters can then be used to provide individualized treatments. During
the course of a chronic illness, for example, communications with health care providers could
result in treatments that could be rapidly varied to match the episodic nature of chronic disease.
The Internet provides the technology, for the first time, to record continuously an individual’s
health, via direct interaction with each patient. The elderly in general and the baby boomers in
particular are a population that is comfortable with computers and with accessing information
over the Internet [Jeffrey,1998]. An interactive program would be used by each individual to
generate a daily record of their health parameters. This would make possible the charting
through the good days and the bad days of life, through the days when medicines are taken or
not, through the days when a physician is visited or not.
Data sets would be analyzed for an individual patient, reviewed by a nurse triage, and
forwarded to a physician if and when necessary. The individual databases would also be used in
merged fashion to develop a picture of population health. Continual recording of discrete
outcomes would approximate the true health of the national population. The unprecedented
detail in these interactions would begin to paint a picture not just of health, but of well-being,
with all of its complex physiological, social, and even spiritual dimensions.
“The Rand investigators made each sub-dimension of health, such as physical functioning, emotional well-being,
general well-being, and social and role functioning, into measurable characteristics of individuals and populations
through the development of specific assessment instruments. They showed how patients themselves could be
sources of valid and reliable information on their own functional status, and they explored relationships between
these patient-centered measurements and more classical medically-oriented measurements of physiological status
and function. …
Rand's SF-36 is a tool for measurement of general health status that is applicable across a wide variety of health
care conditions and types of encounter. Meanwhile, other researchers have developed more disease-specific
assessment instruments targeted at symptoms, outcomes, and experiences associated with particular diagnoses.
Important advances in disease-specific measurement have occurred, with applicability to such [chronic] conditions
as arthritis, coronary heart disease, depression, and neurological impairment. Altogether, today's health services
research has transformed the simple and elegant measurements of survival proposed by Codman in the early
twentieth century into a robust and elegant ‘tool kit’ for the measurement of quality in its many and subtle
dimensions.” -- Donald Berwick in New Rules [Brennan and Berwick, 1996, pp115-116].
Chronic illness is now the dominant feature of health care [Kane,1998]. Some 22% of
primary care patients report a major complaint of chronic pain, and this impact will grow with
the aging of the population [Gureje,1998]. Arthritis is a chronic illness typical of the aging baby
boomer population. Some 12% of the US population, some 30 million people, have arthritis
[Harvey,1988]. Over the age of 65, essentially everyone has arthritis.
Like other chronic illness, arthritis has acerbations and remissions – the intensity and location
of the pain changes over time [Schnitzer,1993]. It is known that the disease and symptoms wax
and wane within the day [Bellamy,1991], that disabilities one year may disappear the next, that
one joint involvement may become several, or alter in significance over time [Bellamy,1990].
There are excellent arthritis questionnaires. However, their static administration lacks the
regular interaction needed to provide a true outcome of treatment response and measure of ability
to function with the current state of the disease [Bellamy,1995]. A continual tracking of a
patient's functional status would monitor and measure ability to use joints and remain active.
Continual tracking would significantly enhance the utility of existing arthritis questionnaires
An information infrastructure is needed to focus clinicians’ attention on changes in patients’
status [Kane,1998]. Rather than returning to a physician's office only when a medical regime
fails, a patient could instead interact electronically with a personal database that would track
progress, including response to therapy and problems with disease. Frequent and reassuring
interaction would achieve a truer picture of the episodic nature of chronic disease and lead to a
more sophisticated treatment pattern personalized to the particular individual.
By daily interaction with variants of traditional questionnaires, monitoring can be done of an
individual patient’s varying ability over time to cope with their disease. For example, an
adaptation for daily interaction of [Clark,1998] and [Brandao,1998] might include: “Do you have
stiffness in your knees? Is it greater or lesser than yesterday?” “Could you do your errands in
the neighborhood today?” “Do you have stiffness in your hands? Is it greater or lesser than
yesterday?” “Could you do your housework without help today?”
From the point of view of the healthcare delivery system, the interactive data model will
produce an evolving picture of a patient at any given time, taking into account diagnoses and
treatments, successes and setbacks. The system would assess the current level of functionality
and interactively coach the patient to higher levels of functionality. The consistency of
administration via computer would eliminate much of the inaccuracy from the current random
interactions between patients and physicians.
Periodically, this data would be reviewed (determined by medical parameters and health plan
factors) by trained professionals adept at such data evaluation. Such a record of a patient's and
disease's condition would identify changes that might be of importance to a patient and early
intervention by a health care professional could be recommended.
Collecting continual interaction with detailed patient status over the Internet will eventually
develop a comprehensive healthcare database. Such a database would allow a similarity match
between a patient’s clinical situation and a cohort of similarly described patients. This cohort
would conceivably be quite large for some diagnoses and permit the formation of unique
subgroups for analysis. A patient’s situation would be viewed in light of the cohort of similar
cases, rather than the current environment where clinical trials are performed on a small number
of patients and the results of such trials extrapolated to individuals [Liang,1997].
The national database would locate the appropriate cohort from which to draw conclusions for
an individual patient. This cohort is the patient sub-population relevant to the particular patient’s
demographics and conditions. This identification would give a realistic expectation of disease
progression and treatment outcome, as measured over the population at large.
Virtual Town Doctors
“Advances in telecommunications and computer technologies, unimaginable a generation ago, have become
routine. These technologies are changing the nature between individuals and health professionals. The following
analysis results from the efforts of the Science Panel on Interactive Communication and Health (SciPICH),
convened by the Office of Disease Prevention and Health Promotion of the US Department of Health and Human
Services. … The SciPICH is focusing its attention on interactive health communication (IHC), which is defined as
‘the interaction of an individual –consumer, patient, caregiver, or professional - with or through an electronic device
or communication technology to access or transmit health information or to receive guidance and support on a
health-related issue’. …
Potential advantages for health communication efforts include: Improved opportunity to find information
‘tailored’ to the specific needs or characteristics of individuals or groups of users, …, Increased access to
information and support on demand, because these resources can be used at any time and from numerous locations.
… Increased opportunity for users to interact with health professionals or to find support form others similarly
situated through the use of networking technologies, which enable direct communication between individuals
despite distance or structural barriers.” -- IHC report summarized in [Robinson,1998,pp1264-65]
The new technology for the Internet that has arisen in the past ten years has standardized
many necessary components for constructing a national patient database from individual patient
interactions [Weinberger,1997]. Existing patient record systems are largely physical rather than
digital. Even for the few hospital and clinics with electronic patient records [McDonald, 1992],
the political and sociological difficulties of federating across existing information systems has
proven an impossible obstacle to widespread patient databases.
The document protocols in the World-Wide Web, however, have standardized the data
formats traditionally handled by diverse management information systems [Schatz,1994]. PC-
based Web-forms to database servers are rapidly substituting for traditional MIS database entry
to mainframes in many applications, including patient record systems [McDonald, 1998].
The rise of the Internet has made it economically feasible to support national-scale health
monitors from home personal computers. Personal computers are now widely enough deployed
throughout the general public that millions of Americans routinely browse information on the
Web. According to national surveys, healthcare is the single most referenced topic, with some
two-thirds of users having accessed such information [Hafner,1998]. A Harris Poll in February
1999 estimated that 60 million Americans accessed health information on the Web in 1998, with
10% of the respondents searching for arthritis.
A virtual doctor in the convenience of your home would thus be widely popular. This
“doctor” would have built-in medical knowledge of common diseases and electronic access to
national databases of similar patients so that patients could access information if they desired.
Patients are eager to learn about potential treatments and share experiences with similar patients.
For example, experience with CHESS [Gustafson,1999] showed that patients with severe
diseases, such as AIDS, are quite willing to spend an hour a day on the computer, even if only to
read targeted brochures and chat with similar patients. Elderly women with breast cancer are
willing to carry out daily interactions over the Net [Gustafson,1998].
Research technology is available now that can support a functional Virtual Town Doctor
(VTD). Such a “doctor” will have both local knowledge of your particular situation and global
knowledge of similar situations nationally. Every person will interact daily, or periodically, to a
virtual doctor to input information to their personal health status dataset from the convenience of
their own home. This doctor would be like a personal trainer who would gather personal
information, assess your current level of performance, and, as coordinated by a physician, coach
you to higher levels of performance. Specialized questionnaires would drive the coaching for
each health factor, with the doctor inferring the status levels from the patient’s answers.
A Town Doctor is an effective metaphor, indicating that the Net is an enabling technology for
the average person in the modern world, with its global scale and fast pace. The longing for the
“good old days” of small towns and friendly neighbors can be addressed by an always-available
always-knowledgeable virtual doctor. The town doctor was more effective than an HMO
physician because he knew what was happening with you and with your world.
A VTD will record the interactions, to create an accurate and current database, and coach the
patient to better health, based on general medical knowledge and specific database records. The
home setting will be particularly conducive to eliciting daily details of condition progress and the
life-style environment around the patient. The local advisor on the computer screen will be
backed up by expert analysts of the national patient database.
Initial users for VTD will likely be elderly patients, who already monitor their health
continually. Having a doctor always available in the natural setting of their homes will help
reassure them, which may in itself aid in their feeling better. The most similar current situation
is a triage nurse, who consults with patients over the telephone, using a heuristic computer
program covering the most common complaints [McKeon,1998].
A Virtual Town Doctor is better, in that the source has a much wider set of medical
knowledge available and is much more readily available (plenty of time to listen and plenty of
information to dispense). The information is far more detailed than a CD-ROM medical
encyclopedia and the information is far more personalized, due to the continual record. In time,
VTD will create a comprehensive national database as more patients come online and record
their complaints and their feelings, covering situational effectiveness of particular treatments.
Initial technologies for VTD will likely be interactive patient questionnaires, backed by
searchable medical manuals. The user interface will eventually develop into computer-generated
virtual doctors listening and talking in natural speech, using built-in references for standard
medical situations and recorded patient databases for custom medical situations. The session
transcripts will implicitly generate the individual values for the patient questionnaires. A
national grid of health monitors will create a patient database similar in statistical detail and
coverage to the house database created by electricity meters for the national power grid.
It will be technologically feasible in less than five years to generate realistic-looking people
on computer screens, whose talking heads can interact with patients in a natural fashion (in this
limited environment and situation). Patients can choose whoever is most reassuring to them, be
it their favorite grandfather or their favorite movie star, be it Andrew Weil or Marcus Welby.
The virtual doctor will react sympathetically to their facial emotions and provide personalized
interactions, based on the connotation as well as the denotation.
Living in the Interspace
“The Interspace is the world of the next century, where the visions of 1960 will finally be realized after 50 years.
It will again take 10 to 15 years for the research prototypes, just now demonstrating the first realizations of the
procognitive system [semantic correlations across disparate sources] envisioned in 1960, to reach widespread
commercial usage, perhaps by 2010. The first major revolution of the Net Millenium will come when the
information infrastructure supports routine vocabulary switching. Then scientists will be able to break the bondage
of their narrow specialties and effectively utilize the whole of scientific information in their research.”
-- Bruce Schatz in [Schatz,1997,p333]
Virtual Town Doctors will shortly become standard information infrastructure. Feasibility of
sophisticated user interfaces for VTDs is only part of the reason. More significantly, information
infrastructure is evolving to support concept search across heterogeneous sources. The lack of
concept search is the primary technological obstacle that has prevented federation of electronic
patient record systems in the past [Lincoln,1999]. Concept search can retrieve different records
discussing the same topic (concepts) but using different terminology (words).
In less than ten years, the Internet will have transformed into the Interspace, where users
navigate connections across concept spaces of information rather than transmit data across
packet networks of computers [Schatz,1997]. This will enable the federation of all the different
information sources generated by different healthcare stakeholders into a single uniform source
for search and navigation.
For example, a single search for “limited knee mobility due to osteoarthritis in elderly
women” should retrieve relevant items from VTD interactions across the country entered by
different patients with different terminologies and analyzed by different doctors for different
treatments. Vocabulary navigation across the Interspace will eliminate patient variability for
answers to questions, much as standardized questionnaires across the Internet will eliminate
physician variability in asking the questions initially.
The CANIS (Community Architectures for Network Information Systems) Laboratory at the
University of Illinois at Urbana-Champaign is developing large-scale models of the Interspace
and deploying prototypes with medical information to practicing clinicians. Last year, we
generated concept spaces for all of MEDLINE, in the largest computation ever in information
science [Chung,1999; Alper,1998b]. The utility of these concept spaces is now being evaluated
by physicians in the HealthAlliance regional HMO, using a web interface from a home computer
to the CANIS research prototype of the Interspace.
An Interspace session with concept spaces on MEDLINE illustrates the power of concept
spaces in interactively translating vocabulary terminology across different stakeholders. For
example, an arthritis patient would interact with her VTD, using coaching expertise from arthritis
questionnaires and contextual expertise from previous interactions. The patient would state that
“my knee mobility is fine but I have stabbing pains in my knee” and “my medicine causes
stomach irritation”. The VTD would recommend switching to Tylenol instead of the current
prescription drug and notify the healthcare provider. The explanation is the situation has
changed from reducing the inflammation to reducing the pain.
Such an interactive session is feasible due to vocabulary navigation in the Interspace. A
physician had previously analyzed concept spaces for MEDLINE to develop treatment
guidelines for particular situations. GI (gastrointestinal) bleeding with NSAIDs (non-steroidal
anti-inflammatory drugs) was a common complaint. An article discussing “maintenance therapy
for rheumatoid arthritis” made clear that simple analgesics such as acetaminophen were the
preferred treatment when pain management was the primary goal. This article did not explicitly
mention “GI Bleeding” or “NSAIDs”. It was located using concept spaces to navigate within
the arthritis subspace from “GI Bleeding” to the related phrase “maintenance therapy”.
Similar vocabulary navigation produces the inferences in the interactive session for the
patient. The concept spaces for previous VTD interactions on arthritis interactively transform
“stomach irritation” into “gastrointestinal bleeding”. The previous patient records indicate “my
medicine” is an NSAID. So the established treatment guideline is used to suggest
“acetaminophen”, which is transformed by the Interspace into “Tylenol”, as a commonly
occurring related phrase.
Cross-correlation across information sources is the next wave of infrastructure in the Net.
This information infrastructure will be supported by navigating across concept spaces in the
Interspace. The indexing techniques to support text mining of patterns for large document
collections are already operational in research laboratories [Alper,1998a]. They are
computationally feasible, since they only record phrases that occur together frequently, which
can be utilized to navigate between related phrases. These transformations are simple
terminology variations, using the document context, rather than deep semantic inferences.
The next generation of personal computers will be powerful enough to compute concept
spaces for all the knowledge of a community-scale collection. Each clinic in each community
can collect their local patient interactions and perform their own analysis to identify local
patterns. These local patterns can be assembled into global patterns, using concept switching
technologies across community collections, which will generate a national patient database.
CANIS is developing models of virtual town doctors. Deployment of a medical Interspace
(MEDSPACE) is in stages: from 10 users in the laboratory to 1000 in the region. A thousand
users is the scale achieved in the Illinois Digital Library project for experimental systems with
new technology on engineering collections [Schatz,1999]. The MEDSPACE project
[www.canis.uiuc.edu] is building an experimental testbed for clinical medicine, by indexing and
integrating concept spaces on medical literature and patient records.
During the twenty-first century, the packet switching gateways of the Internet will transform
into the concept switching gateways of the Interspace. Patients can act locally by continually
monitoring their own health and reporting their conditions in their homes via Virtual Town
Doctors. Physicians can then think globally by analyzing the resultant national patient database,
containing detailed local information, and perform diagnosis by locating similar patients. The
enabling technology to Act Locally Think Globally will support the friendliness of small towns
with the reach of national healthcare plans. A revolution in the quality and economy of
healthcare infrastructure will arise from this revolution in information infrastructure.
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