MEDICAL INFORMATION PORTALS: AN EMPIRICAL STUDY
OF PERSONALIZED SEARCH MECHANISMS AND SEARCH
INTERFACES
Andrea Andrenucci
Department of Computer and System Sciences, Stockholm University/ Royal Institute of Technology,
Forum 100, SE-16440, Kista, Sweden
Keywords: Internet HCI, User Modelling, Information Retrieval, User Needs.
Abstract: The World Wide Web has become, since its creation, one of most popular tools for accessing and
distributing medical information. The purpose of this paper is to provide indications about how users search
for health-related information and how medical portals should be implemented to fit users’ needs. The
results are mainly based on the evaluation of a prototype that tailors the retrieval of documents from the
Web4health portal to users’ characteristics and information needs with the help of a user model. The
evaluation is conducted through a user empirical study based on user observation and in-depth interviews.
1 INTRODUCTION
The World Wide Web has become, since its
creation, one of most popular tools for accessing and
distributing medical information (Eysenbach, Sa &
Diepgen, 1999). However Web-mediated medical
portals are rather limited since their search engines
do not allow for personalized searching facilities and
deliver the same generic medical information to all
users (Moon & Burstein, 2005).
The purpose of this paper is to provide
indications about how users search for health-related
information and how medical portals should be
implemented to fit users’ needs at best. The
indications cover search mechanisms, content
presentation and search interfaces. The results are
mainly based on the evaluation of a prototype that
tailors the retrieval of documents from the
Web4health portal to users’ characteristics and
information needs with the help of a user model
(UM). The evaluation is conducted through a user
empirical study based on user observation and in-
depth interviews. This study is, to our knowledge,
the first observational study carried out to
investigate the retrieval strategies of people
searching for health information on a medical portal
where different techniques for personalized search
were implemented. Since most of studies covering
Information Retrieval (IR) systems implementing
personalized search either focus on evaluating the
quality of the retrieved information (Boyle &
Encarnacion, 1994) or the interaction with the user
interface (Brajnik, Mizzaro & Tasso, 1996), we try
to evaluate both those aspects with the help of users’
point of view.
The paper is structured as follows: section two
describes related research in the fields of user
modelling, information retrieval and medicine.
Section three describes the portal and the prototype
utilized in this research. Section four and five
present the empirical study and its results. The paper
is concluded with a discussion (section six) and the
paper conclusions (section seven).
2 UM IN IR AND MEDICINE
Information retrieval (IR) and Information filtering
(IF) are two of the research areas where UM
techniques have been used most frequently. The
corpus can be limited to a certain information
domain or open to the entire Web. Among the most
recent systems belonging to the first group it is
worth mentioning Kavanah (Santos et al., 2003).
Kavanah provides search assistance in matters of
health and medical information. User queries are
dynamically changed or constructed considering not
only the user input but also information covering the
user preferences, knowledge and interests. This
95
Andrenucci A. (2006).
MEDICAL INFORMATION PORTALS: AN EMPIRICAL STUDY OF PERSONALIZED SEARCH MECHANISMS AND SEARCH INTERFACES.
In Proceedings of the Eighth International Conference on Enterprise Information Systems - HCI, pages 95-102
DOI: 10.5220/0002486900950102
Copyright
c
SciTePress
information is explicitly encoded through an
ontology network that is constantly updated.
The WIFS system (Micarelli & Sciarrone, 2004)
is an IR system that retrieves information from the
Web’s open corpus. It uses a sophisticated user
model to adaptively filter and sort search results
returned by the AltaVista search engine. WIFS uses
explicit user ranking of search results to keep the
user model updated.
One of the most popular areas for usage of UM
techniques in health applications is patient education
(Lyons et al., 1982), i.e. services aimed to supply
people without medical expertise with specific
information in order to make them understand their
situation better and reduce costs in health care.
Patient education has been utilized in matters of
smoking cessation (Lennox et al., 2001), eating
habits improvement (Grasso, Cawsey & Jones,
2000), and management of illnesses such as cancer
(Bental et al., 2000).
3 THE PORTAL AND THE
PROTOTYPE
The medical portal utilized in this research
(Web4health.info) is well established among the
medical portals on the Web. It is Yahoo-listed and it
was developed within a EU-financed project called
KOM 2002 (http://web4health.info/KOM2002),
whose goal is to provide multilingual medical
information to improve the mental health of
European citizens. Psychiatrists and
psychotherapists from five different European
countries (Italy, Sweden, Holland, Greece and
Germany) use the portal to jointly develop a set of
semantically classified Web pages that answer
questions in matters of psychological and
psychotherapeutic advice. Users consult the
knowledge base submitting questions in natural
language, which are then matched against pre-stored
FAQ-files (Frequently Asked Questions) consisting
of question/answer pairs, where the question part has
a template created to match many different
variations of the same question (Template-Based
Question Answering, Sneiders 2002).
The user model in the prototype is based both on
an explicit and an implicit acquisition of knowledge
about the user (Kass & Finin, 1998). The practical
implementation of the profiling mechanisms is done
through a dialogue to be held at the beginning of the
interaction process, where the user can choose
between two options: either explicitly stating the
topics of personal interest (Direct Choice, DC), with
the help of a menu-based rating form, or implicitly
providing them (Indirect Choice, IC), letting the
system infer his/her interests by monitoring the
interaction with the system. Through the form, users
choose specific diseases, select up to three
categories of interest in the knowledge domain,
ranking them in order of relevance (1 as the most
relevant, 3 as the least relevant) and select the
objective of their search. Through the Indirect
Choice, the system learns from observation, i.e. it
monitors the questions submitted by the user and the
topics of the answers retrieved by the Question
Answering (QA) system in order to learn about the
interests and the objectives of the user. The system
computes the topics and the objectives that occur
more often among user questions and the retrieved
documents, and ranks them in descending order. The
three most occurring categories, diseases and search
objectives are then presented to users on a feedback
panel. Users can then discard, confirm partly or in its
entirety the inferred profile.
Figure 1 shows the different alternatives
provided by the prototype once the user profile is
created: the information enclosed in it is either
utilized as an input for retrieving documents,
without any further submission of information (we
called it Profiled Answers, PA) or as a tool to
change the order of relevance of the answers
retrieved by the Web4health QA system (Profiled
QA). In the first case, the retrieval algorithm
considers the chosen alternatives as keywords in a
query and retrieves the documents with a matching
combination of categories or diseases. In the second
case it boosts the ranking of the documents that
match topics and objectives chosen or biased by the
user. For the empirical study described in next
section, we presented lists of answers sorted with
and without the user profile in two different
columns, named A and B, so that users and
researchers could more easily compare them.
The matching process is based on a simple
algorithm that works similarly if the user profile is
utilized as a query (Profiled Answers) or as a tool to
boost the ranking of retrieved answers (Profiled
Direct Choice
Indirect
Choice
User
Model
QA
Profiled
QA
Ordinary
QA
Profiled
Answers
Figure 1: Paths of the user model.
ICEIS 2006 - HUMAN-COMPUTER INTERACTION
96
QA). Answers are scored and ranked according to
how well their combination of categories, diseases
and objectives match their counterparts in the user
profile. The retrieved answers are sorted in a
descending order: the closer to the top, the more
relevant the answer is.
The implemented prototype utilizes three-tier
architecture, based on HTTP communication
between a Java middleware and the QA system of
Web4health. The middleware is needed to bridge the
gap between the user and the QA system, which just
processes the submitted queries and returns the
entries from the database according to the template-
matching algorithm (Sneiders, 2002).
4 THE EMPIRICAL STUDY
The purpose of our study was to evaluate the
acceptance of online information and personalized
search services by “lay persons”, i.e. persons
without medical expertise. Within this main
category, two subcategories of end users were
distinguished: 1) Individuals who have “real”
problems and need help to solve their problems 2)
Individuals who are healthy but search for answers
in the areas of psychology and psychotherapy in
order to satisfy their information needs. Two sample
groups representing the afore-mentioned categories
of users were randomly chosen and included in the
study: a group of ten patients, three men and seven
women between 23 and 58 years of age, undergoing
psychotherapy in a private practice, and a group of
ten people, four men and six women between 25 and
59, who saw themselves as healthy and had not been
in contact with psychotherapy before. From now on
we will call them the Therapy Group (TG) and the
Healthy Group (HG).
Since our goal was to evaluate our prototype
from a user perspective, we chose to collect data
through qualitative research methods based on user
observation and in-depth interviews. Nielsen (1993)
advocates the usage of qualitative methods for the
evaluation of information retrieval systems in
particular when it comes to measure user satisfaction
with user interfaces and retrieved information. This
approach was also utilized in studies aiming at
discover how users search medical information the
Web (Eysenbach & Köhler, 2002).
Each user session lasted about one hour and
users were encouraged to think aloud (Long &
Bourg, 1997) while using the system. The interviews
focused on exploring the following issues: (1) which
characteristics are considered crucial by end-users in
a medical portal (2) how the users experienced the
search interfaces (3) how the subjects experienced
the different User Profiling approaches. The retrieval
performance of the system was analysed with a
standard statistical measure for IR: precision (Salton
& McGill, 1983), however the relevance of the
answers retrieved was based on subjective
judgments of the test users. Users were also asked to
rate, on a four-level Lickert scale, how well the
retrieved information managed to satisfy their
information needs.
In order to measure whether adaptivity enhanced
QA we decided to present answers, sorted with and
without considering the UM, in two columns named
A and B. The participants selected the relevant
entries in both columns, without knowing which
column contained which sorting algorithm. This was
done in order not to bias the participants’ judgment
(blind experiment, Chin 2001, p. 182).
5 RESULTS OF THE STUDY
5.1 General Differences
During user observation the researchers noticed that
group therapy patients tried to find answers mainly
related to their own problems and lives, while the
“healthy” group users were mostly interested in
getting general information rather than seeking
advice applicable to their own situation. This
difference was reflected in the search process,
particularly when using the natural language (NL)
panel. In general informants from the HG treated the
system like an electronic version of a medical
encyclopaedia, and the majority of questions
submitted by the HG was quite generic, containing
distinct keywords of the topics the informants
wanted information about, e.g. “What is Bulimia?”.
On the other side it was more noticeable among TG
informants a tendency to write more personal and
vague questions (“How can I feel harmonic?”) or
very specific questions that showed a deeper insight
into a given topic (“Is it possible to overcome a
nocturnal eating disorder?”).
Through the “thinking aloud” method and the
user observation the researchers noticed that most of
the participants tried to satisfy their information
needs according to the following pattern: they first
read the list with the answer headings, containing the
title and a short description of each retrieved answer,
picking the ones that seemed most relevant, then
they followed the link to the bodies of the chosen
answers. One major drawback in this selection
MEDICAL INFORMATION PORTALS: AN EMPIRICAL STUDY OF PERSONALIZED SEARCH MECHANISMS
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97
process was the fact the participants found it
difficult to see how relevant an answer was, if its
title or short description did not literally contain
the topics they had explicitly asked information
about.
5.2 Search Interfaces
Several users appreciated the possibility to choose
between different navigation paths through the
FAQs: either jumping back and forth between the
list with the answer headings and their bodies, or
following the links to related entries at the bottom of
each answer. This second path was chosen when
highly relevant content was found in one of the
retrieved FAQs: it spurred the subjects to continue
until pages without links came up. Several HG users
criticized the usage of too technical terms defining
the diseases covered by the knowledge base.
Despite the fact that all the participants could
speak and write English fluently, they were not
native speakers, which made the question writing
process more complex. As one user commented:
“even if I do speak and write English well I cannot
master its nuances as I do in my own language. I
would like to be able to write in my own language”.
In some cases users had to reflect “loudly” over how
to spell certain words, which took them extra time to
type sentences.
When the subjects needed generic information
without digging into deep details the Menu-based
interface was considered optimal. The NL-panel
allowed users to be more honest in intimate matters
and to disclose more personal information, which
was of help to work off worries and or give vent to
feelings. This was a common opinion among the
group therapy patients: “I like the fact that you can
submit questions in NL. You can write exactly what
is on your mind” as one user put it. Users, who were
not aware of which topics they needed information
about, appreciated the overview provided by the
menu-based interface: “I like the menus better,
because I can see directly what is available in the
database” as one HG user put it.
Amazingly the NL-based interface was not the
one that received most preferences among TG
participants (see table 1). One reason behind this
result is the fact that several persons from this group
saw it more as a mean to ventilate rather than to
search for answers. Similarly the majority of HG
users liked the menu-based interface better (see table
1); they were more interested in generic information
and appreciated the insight into the knowledge base
given by the form.
Table 1: User preferences about interfaces.
5.3 Profiling Approaches
While observing the users it seemed clear that most
of the participants felt more comfortable providing
their own profile directly. This was also confirmed
in the interview, which unveiled a general
scepticism towards letting the system infer the user
profile. This scepticism was particularly evident
among TG users: a priori they could not understand
how a computer program can infer so important and
sensitive information just monitoring the submitted
questions. They were worried about losing control
and were concerned about being overlooked. HG
users seemed more influenced by technical rather
than emotional aspects, for instance the awareness of
which topics they were interested of: “Well the
explicit approach is better if you know what you are
interested of, but if you are not sure it is better to let
the system infer your profile and choose for you.
Personally I prefer the explicit approach because I
know what I am interested of” as one participant put
it, or simply because they could save time: “The
explicit approach was better, since it took me less
time to create the profile”.
Even if most users preferred submitting personal
information directly, it is interesting to point out that
a large majority of participants (90%) were satisfied
with IC accuracy in inferring their profile.
5.4 Profiled Answers and Profiled
QA
As stated in section 4, the information enclosed in
the user profile was either utilized as an input for
retrieving documents, without any further
submission of information (we called it Profiled
Answers, PA) or as a tool to change the order of
relevance of the answers retrieved by the QA system
(Profiled QA). While observing the users, the
researchers noticed that the informants appreciated
the idea of creating a profile that could be used as a
query for retrieving documents. This positive
impression was also confirmed by the statistical
results of precision (see table 2), as well as by the
user rate of the retrieved answers (see table 3).
Precision was calculated for the documents in the
top five and top ten positions.
Menu-based
interface
NL interface Both
HG 60% 30% 10%
TG 50% 40% 10%
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The precision results and the relevance rates of
HG users are quite similar for both PA and Profiled
QA. This depends on the fact that HG participants
submitted more often generic questions, containing
distinct keywords matching the general topics of the
database, e.g. “eating disorders”, “what is
Bulimia?”. The majority of this category of users
utilized the NL and the menu-based interfaces,
pretty much in the same manner; there were no
substantial differences in the information submitted,
due to the generality of the requests. This led to the
retrieval of rather exhaustive sets of relevant
documents in both cases.
Table 2: Precision results.
Answers Precision-
HG
Precision-TG
Profiled QA top 5 58 % 47%
Profiled QA top 10 52% 41%
Ordinary QA top 5 51% 44%
Ordinary QA top 10 50% 41%
PA top 5 60% 66%
PA top 10 57% 62%
Table 3: How users graded the statement “The retrieved
answers succeeded in satisfying my information needs” in
a four level Lickert scale.
Profiled Answers HG rates TG rates
I totally disagree 0% 0%
I disagree 17% 13%
I agree 66% 54%
I totally agree 17% 33%
QA (Ordinary & Profiled) HG rates TG rates
I totally disagree 0% 0%
I disagree 20% 25%
I agree 65% 60%
I totally agree 15% 15%
The results of the TG participants present more
notable contrasts. Profiled Answers were judged
being about 19% more precise than answers
retrieved by ordinary and profiled QA (see table 2).
TG users also picked the “I totally agree” alternative
for 33% of PA answer sets and 15% of QA answer
sets (see table 3). The main reason behind this
noteworthy diversities lies on the different approach
to the NL-based interface by some TG users. Those
informants tended to see it more as a mean to
ventilate their feelings rather than search for
information and in some cases they tended to write
way too vague questions (e.g. “There is nothing I
can do”) that did not have any matching counterpart
in the manual classification of the FAQ templates.
Thus no matching documents could be found. In
some other extreme cases the sentences enclosed
several keywords that revealed a deeper insight into
medical issues, which leaded to two different
outcomes: in the cases where specific, matching
FAQs were found, the accuracy of the retrieved
answers was extremely high, otherwise no answers
could be provided, due to the physical absence of
any matching FAQs.
5.5 Profiled and Ordinary QA
During the user observation it appeared quite clear
that UM managed to produce better results when it
came to generic questions, i.e. questions that did not
provide a complete picture of the users’ information
needs, or questions that were within the topics
chosen by the user. The ordinary sorting proved to
be better when users submitted questions outside the
range of the topics in the profile, since the retrieval
algorithm of the QA focuses mainly on keyword
matching and do not consider other parameters.
When users submitted questions that were rich of
details, the amount of data provided was sufficient to
retrieve accurate answers and no extra information
was needed. Thus the information contained in the
UM was of no help.
Both HG and TG participants judgments proved
that the quality of the UM-enhanced ranking was
slightly better for the top five answers, with a
difference of 7% for the HG and 3% for TG (see
table 2). The differences tended to fade when it
came to larger sets of answers (e.g. the top ten
answers). The majority of users also explicitly
picked the sorting enhanced by UM as preferable in
the user interview.
5.6 Crucial Characteristics in a
Medical Portal
In order to define which distinctive parameters were
considered as most important in a medical portal, the
respondents were asked to choose from a given list
containing the following attributes: 1) Access portal
information quickly 2) Access portal information
anonymously 3) Find information retrieved from
several sources 4) Find easy, comprehensible
content 5) Find up-to-date information on a weekly
basis 6) Retrieve objective content, i.e. not
influenced by sources with business interests 7) Find
detailed information 8) Submit questions to a human
expert 9) Find information written by reliable and
established sources in medical science.
Users were invited to pick all the parameters that
they considered important and that, in their opinion,
discriminate a good medical portal. Both groups
agreed about the fact that information coming from
MEDICAL INFORMATION PORTALS: AN EMPIRICAL STUDY OF PERSONALIZED SEARCH MECHANISMS
AND SEARCH INTERFACES
99
reliable medical sources was the most important
characteristics. Both groups also valued
comprehensible and up-to-dated content as
important attributes when consulting a medical
portal. The biggest difference between the groups
concerned the possibility of asking a human expert
and the level of detail of the information provided.
Seven users from HG estimated detailed information
as salient, while only three users from TG
considered it an important factor. Furthermore only
three HG users were interested in asking a human
expert, unlike TG, where eight persons considered it
crucial. This difference can be explained considering
that the two groups searched for information with
two different purposes: the group-therapy patients
posed questions mainly related to their own
problems, seeking advice applicable to their own
situation, while the “healthy” group users were
mostly interested in getting information covering
medical areas from a popular, scientific point of
view, i.e. they consulted it as an encyclopaedia or a
medical book. Another remarkable difference
between the groups concerns the anonymity factor:
seven participants from TG agreed about its great
usefulness but only four from HG shared the same
opinion.
6 DISCUSSION
The results of this study provide indications about
the parameters that users with different needs
consider relevant on medical portals, the role of UM
in the retrieval of medical information and how
users experience UM and different search interfaces
on medical portals.
6.1 Crucial Characteristics in a
Portal
Users in general, regardless of their background,
value information coming from several reliable
medical sources, up-to-dated and comprehensible
content. People with medical problems are more
concerned about reading information not influenced
by parts with business interests and appreciate the
possibility to submit questions to human experts.
They also appreciate to access portal information
anonymously. People without open medical
problems are more interested in detailed information
than asking questions to human experts or accessing
information anonymously. These results confirm
what Eysenbach and Köhler (2002) discovered in
their studies in matters of criteria for trustworthiness
of medical sites. The authors found out that users
prioritized readability, professional layout, and
updated content coming from authorities in the field.
6.2 The Role of UM in the Retrieval
of Medical Information
The results of our study have indicated that in
general UM can enhance the information seeking
process and the precision of the retrieved
documents. This works both for individuals who
need help with their problems and individuals, who
are healthy, but search for answers in order to satisfy
generic information needs. The results were more
evident for the answers listed in the top five
positions. Generic requests or requests that were not
rich of details benefited the most from the UM, since
the information contained in the user profile added
more specificity, or completed what was stated in
the NL sentences. This helped the system to
prioritize better among the retrieved answers,
improving its ranking mechanism. Unfortunately our
prototype did not implement any functionality to
automatically re-edit fuzzy NL sentences before
submitting them. When users (mostly patients)
submitted questions that were too vague, no
matching answers could be found, since the retrieval
algorithm of the QA system focused on keyword
matching. In order to avoid this problem, the
information contained in the UM should be used to
fill this gap, re-editing or complementing indefinite
sentences before submitting them. This solution
would also reduce the cognitive workload of the
users. This is quite important, considering the fact
that people suffering from medical problems,
because of their condition, are already subjected to
mental stress and they are not supposed to remember
all the details of their information need in order to
receive precise answers.
Another drawback that our study has revealed is
what we defined as the “lock-out” problem: users
that asked questions outside of the topics specified
in the profile did not receive any benefit at all in the
sorting process, since no retrieved entry could be
prioritized. This outcome evidences the need to
implement a more dynamic profiling mechanism
that takes into consideration user questions even
after his/her profile has been created. Another case,
where the information contained in the UM proved
to be unhelpful, was when very specific questions or
questions that were rich of details were submitted:
the amount of data provided was sufficient to
retrieve accurate answers and no extra information
was needed.
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Our user interview has shown that users with
different characteristics are interested in different
levels of details in the information provided: only
30% of users suffering from mental problems found
detailed information relevant, unlike “healthy” users,
where a clear majority, 70%, found it necessary.
This confirms that it is important to adapt the style
and the content of retrieved documents to users’
background and search goals.
6.3 Direct or Indirect UM?
Our study has revealed that people in general
users are sceptical towards letting computer
programs monitor their steps and indirectly infer
their profile. The reasons behind this scepticism
were mostly emotional and technical. The emotional
reasons reckoned with the fear of losing control and
being overlooked (mostly TG users). The technical
reasons regarded parameters such as savings of time
or avoidance of misunderstandings (mainly HG
users).
For what concerns the individual information
needs, people who know what they are interested of,
or what their problem is, usually tend to prefer the
explicit approach, since they can specifically choose
the topic or the disease they need information about.
On the other side, people who do not know in the
first place which kind of information they
necessitate, tend to prefer the implicit approach,
since it draws conclusions from their interaction
with the system and proposes topics that can be
relevant to their information needs.
In general we can conclude that the direct
profiling is the choice that is best accepted by users.
The creation of the profile is not subjected to risks of
wrong assumptions or misunderstandings that may
occur in the monitoring process. Those risks are
particularly evident in cases where the profile is
created tracking the NL input of the user. NL
sentences can be very ambiguous and can have
different meanings in different contexts. Those
difficulties become even more evident when NL-
input in a foreign language is required.
Monitoring human-computer interaction on other
interface parts, e.g. mouse clicks on chosen links, in
order to find indicators of the user interests, is not
fully trustworthy either. Users may misunderstand
the available choices; links may be selected just for
curiosity or to confirm the user knowledge in a
certain topic (Kobsa, Koenemann & Pohl, 2001).
Considering the sensitiveness of the information
provided on medical portals, we want to avoid any
possible misunderstanding that can arise from wrong
assumptions; so direct profiling is preferable.
6.4 NL or Menu-Based Interface?
The affordance (Norman, 1999) of the menu-based
interface enables users to produce relatively short,
generic queries and does not provide much
flexibility in the search process, since the language
nuances cannot be exploited. This fits more the
search of users who utilize the information portal as
an electronic medical book and have rather generic
information needs (mainly users without health
problems). This technique fits also topics where
users tend to submit requests that can be
summarized to few standard queries; for instance
cancer (Bader & Theofanos, 2003).
Users who have troubles in formulating their
own information needs in NL sentences can also
benefit from the menu-based interface, since they
can choose from a list of pre-selected topics. Thus
the menu-based interface reduces the cognitive
workload and does not force users to come up with
questions matching their information needs.
On the other hand the NL-based interface is more
suitable: 1) In counselling or dialoguing matters, in
other words when users want to open up and submit
a problem for examination or discussion, or simply
just want to ventilate their feelings. The NL-
interface can better resemble the doctor/patient
verbal interaction and give users more control over
the input to be submitted 2) When users have an
explicit, specific and detailed question in their mind
or want to exploit the nuances of the human
language.
The level of expertise in the knowledge domain
can also determine the adequacy of the search
interface. Our study showed that users that were not
familiar with medical terms could not take full
advantage of the form-based search. Through NL-
based interfaces users can freely express themselves
in own words that correspond to their own
individual level of expertise in the domain. It is
though important to support multilingual input, so
that users can submit questions in their own
language.
7 CONCLUSIONS
The results of this study provide the following
indications to help developers in the implementation
of medical portals on the Web:
- Implement UM on medical portals: this
research has shown that UM can enhance the
information seeking process and the quality of the
retrieved information on medical portals. UM is also
MEDICAL INFORMATION PORTALS: AN EMPIRICAL STUDY OF PERSONALIZED SEARCH MECHANISMS
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101
useful when we do not want to burden the cognitive
workload of the users in the process of formulating
their information needs. Thus the “one-size fits all”
information delivery approach available on medical
portals should be changed.
The user model should evolve dynamically in
order to avoid what we have defined as the “lock-
out” problem. Since users seem sceptical towards
letting computer systems infer their profile, it is
preferable to let users create their own profile
explicitly.
- Implement different search interfaces: our
research has shown that menu-based and NL-based
interfaces fit different types of information needs
and allow different levels of specificity. Users
should be able to choose between both types of
interface.
- Adapt the description of retrieved documents:
as stated in section 5.1, users may sort out relevant
documents only because the headings do not
explicitly name the topics they ask information
about. Thus it is important to generate descriptions
in real time that explicitly link the content of the
documents to users’ information needs.
- Allow users to submit questions in their own
language: formulating information needs in NL is
not an easy task, especially when it comes to foreign
languages. In order to reduce misunderstandings and
fully exploit the nuances of NL, it is preferable to
implement search interfaces that support
multilingual input, so that users can submit
questions in their own language.
- Implement the “ask human experts”
functionality and allow anonymous information
access: our interview has revealed some differences
concerning what the two user groups prioritize on a
medical portal. Two of the biggest differences
concerned the possibility to submit questions to
human experts and to access portal information
anonymously. If the portal is aimed at helping
people with medical problems, then these
functionalities should be available.
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