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ACKTUS - A Platform for Developing Personalized SupportSystems in the Health Domain
Helena LindgrenDepartment of Computing Science,
Umeå UniversitySE-901 87 Umeå
Chunli YanDepartment of Computing Science,
Umeå UniversitySE-901 87 Umeå
ABSTRACTThis paper presents ACKTUS, a semantic web platform formodeling and managing knowledge integrated in supportsystems for health care, and for designing the interactionwith the end user applications. A key purpose is to allowthe domain experts to collaboratively model the knowledgecontent and tailor interaction to users. Therefore, the devel-opment has been done in a process of participatory action re-search where domain experts have contributed to the designand re-design of ACKTUS while they have been modelingthe content and behavior of end-user applications. The on-tology that serves as the knowledge structure in the systemintegrates the user model, modality values, clinical prac-tice guidelines and preferences, in the form of schemes andscheme-nodes (arguments) in an argumentation framework,partly by integrating the argument interchange format. Userstudies have shown that ACKTUS can be used for the in-tended purpose by domain professionals not familiar withknowledge engineering tasks. Moreover, the platform func-tions as a research infrastructure for health researchers intheir development and evaluation of new ICT-based inter-ventions targeting improved health.
Categories and Subject DescriptorsH.5 [Information Interfaces and Presentation]: Groupand Organization Interfaces; I.2 [Artificial Intelligence]:Miscellaneous
KeywordsCooperative design; Knowledge engineering; Interaction de-sign; User modelling; Personalisation; Ontology; SemanticWeb; Decision-support systems; e-health
1. INTRODUCTIONTransforming medical knowledge residing in individual ex-
perts and in sources of evidence-based medical studies to
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formal structures to be integrated in intelligent support sys-tems is a challenging task in the development of clinicaldecision support systems (CDSS) [30]. There is a trade-off between designing a knowledge building and knowledgemaintenance environment in which the clinician can easilyprovide expert knowledge, and a formalization environmentusing advanced knowledge structures for capturing modal-ities, handling negations, ambiguities, lack of information,etc. Using semiformal or formal structures for knowledgeacquisition often requires that the medical personnel needto be educated in using the systems and assigned significantamount of time to spend on entering the necessary knowl-edge (i.e., the knowledge acquisition bottleneck) [25, 27, 26,5].
The problem is similar when experience-based knowledgeis to be communicated by e.g., construction and miningworkers, older adults or adolescences in a participatory de-sign process. There is a growing understanding that thefuture user group of an application needs to be actively in-volved in the development process, partly, for increasing themotivation for using the resulting application. This is partic-ularly essential when behavior change systems are developedaiming at improving people’s health and daily routines [21,6, 3, 20].
In order to provide an individual appropriate support foraccomplishing a task, which the person values as importantand possibly difficult to accomplish, the personal factorssuch as the individual’s needs, preferences, ability, knowl-edge, etc. are vital to take into consideration. Methods forpersonalization and adaptation are useful for both a domainprofessional in daily work situations and an older adult inan ambient assisted home environment [10, 8, 24].
The complexity of a medical domain such as the the de-mentia knowledge domain creates a need for assessing thepatient’s difficulties from different viewpoints and using dif-ferent professional competences. The individual users of adecision-support system integrated in this use context havedifferent professional backgrounds, different preferences re-garding, for example, which guidelines to use and differentneeds for individually tailored support [11]. It is importantto create structures that allow the end user in clinical prac-tice to adjust the support based on preferences, local poli-cies and local work routines [11]. This adaptation includesfunctionalities such as which sources to base reasoning on,which assessment instruments to use, allow deviations fromguidelines in patient cases but also provide means to addmotivations for alternative interpretations.
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In order to address the need for new tools, which are easyand flexible to use by professionals who are not necessar-ily familiar with knowledge engineering or interaction designtasks, we present in this paper ACKTUS (Activity-CenteredKnowledge and interaction modeling Tailored to USers), aweb-based platform for developing knowledge-based appli-cations.
The article is organized as follows. The methods for thedevelopment of ACKTUS (Section 2.1), modules of the ACK-TUS platform (Section 2.2), its interaction design and func-tionality and their evaluation (Section 2.3) are described thefollowing section. ACKTUS contributions are compared torelated work in Section 3 and the paper ends with someconclusions and directions for future work.
2. ACKTUSThe main requirements that ACKTUS needs to meet are
supporting collaborative knowledge building and maintenanceof knowledge, and supporting the development of personal-ized support, including support for learning, reasoning anddecision making. Consequently, essential requirements forACKTUS are simplifying the entering of knowledge with-out loosing significant characteristics in the knowledge; andfunctionality that take individual preferences, motives, ex-perience, physical complaints, professional roles and workenvironment into consideration for personalization and adap-tation purposes.
In the following sections the methods for developing ACK-TUS and the modules and functionalities of ACKTUS willbe further described.
2.1 MethodsA participatory action research process has been applied
in the development of ACKTUS, which has involved domainexperts and potential user groups from different applicationdomains (e.g., [1]). The application domains include:
• investigation and management of dementia diseases:two geriatricians and additional 25 physicians havebeen involved [11, 20],
• fall prevention in older adults: five physiotherapistsand 17 older adults have been involved [14],
• intelligent home environments for older adults: fouroccupational therapists have been involved [15, 16],
• risk assessments and work-related health in the min-ing and construction industries: two expert physicians,four healthcare personnel and 20 workers have been in-volved [12].
Due to the broad range of potential users and target do-mains, ACKTUS has been developed in an agile and incre-mental process in which common structures of the differ-ent application domains have been identified and modeled.These modules have been applied to the application domainsand evaluated, and the results have been fed into further de-velopment.
Since focus has been broadened from clinical decision sup-port systems for medical professionals’ needs, to decisionsupport and disease management support for individualswho suffer from a medical condition, two studies are ongo-ing where knowledge-based applications are being developed
for cooperative work between the healthcare professionaland their client. The two research projects are focussingthe domains of chronic obstructive pulmonary lung diseases(COPD) and incontinence respectively. Preliminary resultsare described in Section 2.3.5.
2.2 System ArchitectureACKTUS novelty lies in the combination of modeling knowl-
edge alongside with modeling the interaction to be takingplace with the knowledge. This means that the domain ex-pert reflects on how the knowledge will appear to the userand models the knowledge accordingly to achieve the in-tended goals of using the application. Likewise, the domainexpert takes a stand in the essentials of the knowledge tobe medicated to the user, and design the interaction accord-ingly. Thus, the domain professionals are modeling with thetarget user group in mind, and can test their prototype ap-plications on fictive use cases. This is reflected in the mod-ules of the ACKTUS platform (Figure 1). The ACKTUSarchitecture is composed by the following three layers:
Data layer: The primary aim of this layer is to structureand store the information modeled by the domain ex-perts. This layer will also keep the information whichis collected from the interaction layer and inferred fromthe intelligence layer in the end-user application. More-over, this layer also keeps the structure of the graphicaluser interface of the end-user application as designedby the domain expert. The data layer is managed us-ing semantic data repositories. This layer has been im-plemented using SESAME repositories1, consequently,the information is captured in terms of RDF data (theResource Description Framework (RDF)2) and OWLontologies (the Web Ontology Language (OWL)3). Theprimary semantic model which is kept in this layeris the ACKTUS semantic model which is managed asan OWL ontology (i.e., core ontology). This semanticmodel plays a key role for managing issues of data in-teroperability between the different components of anarchitecture including additional applications.
In addition to the information modeled by the domainexperts, there is functionality, which stores informa-tion about end-users’ behavior when using the targetedend-user application. Events are stored in an actorrepository, and in a repository for behavior patterns.
Intelligence layer: The main aim of this layer is to sup-port decision-making. The semantics underpinning thereasoning applied in ACKTUS is based on an outlineof a context-based argumentation framework incorpo-rating values [13]. The Argumentation InterchangeFormat (AIF) [4], developed for facilitating argumentexchange and visualization of argumentation over theWorld Wide Web is integrated into the core ontology.AIF was extended with concepts and values represent-ing uncertain information, and is the base for extract-ing rules and arguments in decision-making tasks. Forthis purpose two basic inference engines have been de-veloped, which follow what the domain professional
1http://www.openrdf.org/2http://www.w3.org/RDF/3http://www.w3.org/2001/sw/wiki/OWL
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APPLICATION / INTELLIGENCE LAYER
AP/AIF REASONERS
Argument and Information Exchange Service
INTERACTION LAYER Content and functionality mediated by web browsers
ACKTUS APPLICATIONS
ACTOR REPOSITORY
ACKTUS DOMAIN
REPOSITORIES ACKTUS DOMAIN
REPOSITORIES
DATA LAYER
LOG DATA REPOSITORY
Ontology editor
Interac<on Design
Knowledge management
End-‐user tes<ng
ACKTUS Content Manage-
ment System
AUTHENTICATION DATABASE
Authentication Service
INTERFACE GENERATOR
ACKTUS APPLICATIONS
Figure 1: ACKTUS modules
has modeled. The AP-reasoner executes the assess-ment protocols, which is used in the modeling tasks fortesting flows of interaction and support for reasoning(Example B in Figure 3). The AIF-reasoner executesa set of extracted rules, selected based on reasoningcontexts, as defined by the modeling domain expert(Example A in Figure 3). This is used for testing theknowledge content in patient cases. These two enginesare also used in different end-user applications (Exam-ple C in Figure 3).
Interaction layer: The main purposes of this layer are toallow the user model the content and interaction withthe targeted application. The user interacts with aweb application built on top of the RDF repositoriesusing Java. The web application can display knowl-edge in several different languages and has interac-tive visualizations of the knowledge structures by usingGraphviz and Ajax techniques. In ACKTUS, the visu-alization of the knowledge structure is created by usingGraphviz4 package that produces SVG (Scalable Vec-tor Graphics) embedded in XHTML (eXtansible Hy-perText Markup Language). In addition to (X)HTMLthe web application can also output JSON and XMLto support updates to the user interface done using thejQuery javascript library. Examples of graphical userinterfaces are shown in Figures 2 and 3.
In addition to these layers web services are provided forallowing third party applications utilizing ACKTUS. Themain services are the authentication service, and the argu-ment and information exchange service. In the followingsection the semantic model of ACKTUS will be further de-scribed.
4http://www.graphviz.org/
2.2.1 OntologiesThe ACKTUS core ontology consists of an actor and activ-
ity (AA) ontology and a reasoning and knowledge modeling(RKM) ontology.
In addition to these, an Actor repository is used for stor-ing the information specific for a user in an ontology basedon events, to which sets of information are associated. Theevents and information are extracted and put into a seman-tic context using the ACKTUS core ontologies when usermodels are built for reasoning and decision making.
The initial version of the ACKTUS core ontology was de-veloped using Protege5 [18]. ACKTUS includes an ontologyeditor that allows the domain professional to model the classhierarchy of domain-related items in the ontology, and bythis contributing to the structuring of a particular knowl-edge domain.
The AA ontology serves as the information model of theuser. Our goal is to provide methods for achieving holisticuser modeling in smart environments. In therapeutic terms,holistic means to build an interpretation of a client’s situa-tion, which covers a broad range of aspects related to phys-ical, cognitive, psychological, social factors, abilities andlimitations, situated in an environment. For achieving anefficient solution, we approach the task by using the broadACKTUS core ontology as a reference ontology and vocabu-lary for information about the user and a knowledge domain,while allowing the systems and system agents to extract theinformation, which is relevant to a situation based on whattopic is in focus in a certain situation. Similarly, the dif-ferent ambient systems utilize subsets of the core ontology,which are expanded in the different applications to supportthe more focussed purposes of the applications. As a conse-quence, a sub-set of the user model is instantiated in eachsituation, which is comprehensive and sufficient for the sit-uation and purpose for which it is used.
The ACKTUS actor and activity (AA) ontology was cre-ated based on models of human occupation (e.g., [9, 24]),the International Classification of Disability, Functioningand Health (ICF) provided by the World Health Organi-zation6, and other medical terminologies [18]. The AA on-tology forms the semantic model of the user, the user’s activ-ities and of the user’s environment. The ontology functionsas a generic core ontology for health-related domains, and isexpanded with more specific information for each knowledgedomain following the scope and necessary granularity of adomain.
The user of the system is represented through the AAontology as a human who acts as a user, apart from his or herroles in e.g. a work or other social environment. As a humanactor, the user has motives, interests, roles, habits, skills andknowledge in domains. Furthermore, the human has a bodywith functions and processes and performs activities in anenvironment. It should be noted, however, that we make adistinction between e.g., the user’s actual performance of anactivity or cognitive function, and the collected informationabout the performance of an activity or cognitive function.The information is represented as instances of information-nodes (i-nodes). This distinction enables the treatment ofconflicting or ambiguous information obtained from differentsources as defeasible information, i.e., information which can
5http://protege.stanford.edu/6http://www.who.int/classifications/icf/en/
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Figure 2: Editors for data capture: critical questions, scales and concepts.
be overruled, or defeated when new information is obtained.Activities are represented by the Activities and Participationclass, following the ICF categorization as basic structure.
The RKM ontology is based on Argument InterchangeFormat (AIF) [4], expanded with nodes capturing conceptsand e.g., possibilistic values. The information stored inthe AIF-based RDF-structures is extracted into differentlogic languages depending on the topic and field of reason-ing through an argument and information exchange service(Figure 1). In the exchange of information between systems,the concept system serves as terminology for the knowl-edge and reasoning nodes structured by the RKM ontol-ogy. These classes contain the three key nodes assessment-protocol, i-node and s-node, which are shared between theACKTUS knowledge bases and the applications utilizing ser-vices of the ACKTUS architecture, e.g, the dementia diag-nosis and management support system DMSS [32], the ALIsystem [7], Safe Step [14] and the ”smart” Kitchen As-A-Pal[16].
These ontologies play a core role as a common semanticmodel (i.e., vocabulary) for the software agents and humanactors participating in dialogues. To have a valid vocabularyin terms of ontologies helps to manage issues of data inter-operability between the different components of ACKTUSapplications.
2.3 Functionality and Interaction DesignACKTUS provides the following key functionalities:
I) editors for developing tools and structures for datacapture tasks,
II) editors for developing tools and structures for the pro-vision of information and instructions,
III) editors for developing knowledge and reasoning con-tent and structures, and
IV) testing functionality, partly in end user interfaces.
The editors aid the representation of semantically rich fac-tual and procedural knowledge, which will be described inthe following sections.
2.3.1 Modeling and Maintenance of FactualKnowledge
ACKTUS provides tools for creating, editing and updat-ing the knowledge of a domain. Knowledge expressed inclinical practice guidelines and other evidence-based medicalknowledge sources is modeled by the domain professional,in addition to other kinds of knowledge such as rules-of-thumb and best practice knowledge. There is a preferenceorder built into the underlying ontology between knowledgesources, which can be overridden by end user’s own prefer-ences at the point of e.g., reasoning about a diagnosis.
Key classes of the ACKTUS ontology that support datacapture and reasoning are the activity-protocol, scale, concept-system-node, and argumentation-node. The domain profes-sional uses the activity protocol class for composing struc-tured information templates for data collection (Figure 2).The purpose is to follow closely the expressions in medicalliterature. Therefore, the concept-system nodes are modeledto provide the conceptual knowledge with terms and defini-tions of phenomenon to assess. Each activity protocol be-comes associated to a concept at suitable level of specificity.The concepts, their terms and definitions are retrieved frominternational medical terminologies when available. The do-main professional can use the ontology editor for creatingmore specific concepts useful in their domain, organize con-cepts as a terminology model and translate terms to differentlanguages.
The activity-protocols have typically one or more scalesassociated to them, functioning as structured alternativesfor valuing the presence and characteristics of phenomenon.The scales are also created by the domain professional, typ-
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A
B
C
Figure 3: Three examples from the dementia domain of how the domain expert can evaluate the results: (A)an overview of potential inferences and their grounds based on the rules and their schemes can be testedin a patient case. The result is visualized as an interactive graph; (B) the step-by-step execution of nestedassessment protocols is shown in Example B; and (C) the content, inference flows and conclusions can betested in the graphical user interface of the end user application. Here the decision-support system fordementia diagnosis and management DMSS-W is shown, which builds on the ACKTUS-Dementia repository.
ically based on knowledge sources. Some scales contain aset of ordered values that mirror the uncertainty expressedin the underlying medical literature, e.g., possible, probable,unlikely.
2.3.2 Modeling and Maintenance of ProceduralKnowledge
The procedural statement (s-node) editor (called ”rule ed-itor” in the program, Figure 3) allows the domain expert todefine patterns of reasoning, which are used by the systemagents to create formal rules and arguments. From obser-vations or data collection tasks in a particular case, newknowledge can be derived when combined with the genericdomain knowledge represented as s-nodes, such as conclu-sions about diagnoses, recommendations and advices, andsuggestions, or the initiation, of activities. Each premise orconclusion is given a label in natural language, which is usedin dialogues and for generating explanations for reasoningand decisions made.
Each s-node is associated to a scheme, which represents asemi-formal pattern of reasoning typically based on a clin-ical diagnostic criteria or similar. The default scheme isthe argumentation scheme argument from expert opinion de-fined by Walton [31], representing the domain professionalcurrently modeling the knowledge. In domains where theevidence-based knowledge is weak, this scheme is typicallyused.
For providing support to the potential end user in reason-ing about e.g., diagnosis, the domain professional can useassessment protocols (Figure 3) and reasoning contexts. Theassessment protocols are ordered collections of data capturetemplates and other assessment protocols, composed as pro-tocols for assessment focussing different domains at differentlevels of specificity.
While the assessment protocols aid the end user in ba-sic data capture activities, the reasoning contexts are de-signed to aid the end user in higher-level reasoning anddecision-making. They consist of unordered sets of schemes,composed for the purpose to provide the user the available
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sources of knowledge in reasoning about a topic (Figure 3).The reasoning contexts are defined and triggered by a criticalquestion, a type of data collection template. The primaryoutcome of applying a reasoning context is the answer tothe question. A procedural relationship between reasoningcontexts can be defined, as a proposal of how the reasoningprocess should be accomplished.
2.3.3 Adaptability and PersonalizationThe design of the support for reasoning is based on the dif-
ferent methods for clinical reasoning observed among medi-cal personnel (e.g., [22]). Depending on level of expertiseand situation the professional applies different strategies.Therefore, we distinguish between lower-level data captureactivities, which are highly structured for the lesser skilled,compared to higher-level reasoning, which is supported usingloosely coupled reasoning contexts without internal order.
The domain professional can tailor the flow of executionof assessment protocols based on user characteristics (e.g.,gender, work tasks, profession, time spent in different haz-ardous work environment, end user’s interest, level of ex-pertise in e.g. dementia diagnosis, etc.). This is done usingthe rule editor. The execution of the rule base when assess-ing e.g., diagnoses, can be tailored based on the end user’spreferences among diagnostic criteria.
2.3.4 Methodology for Validation of ContentThe design of the ACKTUS ontology and associated edi-
tors is aimed at achieving transparency in each phase of theknowledge modeling. For evaluating the content, the do-main professionals can review the underlying medical docu-ments, compare the content to the interpretations made inthe form of schemes. Ambiguities can be identified and dis-cussed with, e.g., authors of the underlying medical guide-lines [17]. A selection can be made among different pos-sible interpretations, and verified among domain experts.While the schemes represent semi-formal interpretations ofthe possible decisions that can be made based on the sourceand available information about a patient, the s-nodes (usedfor extracting rules) are designed to implement the schemesin actual patient cases based on information obtained in e.g.,observations or clinical interviews.
The behavior of the composed s-nodes can be tested bythe domain experts using patient cases, which can also becomposed by the domain professional for the purpose (Ex-ample A in Figure 3). The rules implementing the diagnosticreasoning can be tested using the patient case, providing theuser an overview of the execution of the rule base in the formof a graph, visualized following the composed reasoning con-texts. The graph shows the findings collected using the datacollection templates, the executed rules, and their schemes.In this way the domain professional can evaluate the reasons(arguments) for a conclusion proposed by the system.
The results can also be tested in two differently designedgraphical end-user interfaces (GUI). One is form-based, visu-alizing the data collection templates as composed and cat-egorized into assessment protocols (Example C in Figure3). In this GUI the application functions as a checklist forassessment, with rule execution functionality available forevaluating the findings. The other alternative is to test theresults using a GUI, designed for persons who may be lessfamiliar with computer use. The interaction design is sim-plified, showing the user one data capture template at the
time, allowing for testing information and assessment flowstailored to a user case (Example B in Figure 3).
2.3.5 Evaluation StudiesDifferent aspects have been in focus for qualitative studies
with domain experts participating in the modeling of knowl-edge and interaction. Different types of professionals par-ticipated in a study from four different knowledge domains(occupational health focussing two domains, dementia andrehabilitation of older adults) [19]. The results showed thatall within two sessions were able to compose a knowledge-based assessment instrument to be used by colleagues indaily practice.
Domain experts modeled the knowledge regarding demen-tia diagnosis and the results were verified by authors of oneof the guidelines in focus [17]. It was observed that thephysicians were able to identify ambiguities in the sourcedocuments, and they modeled the knowledge in a more strictway compared to when they relied on a knowledge engineerto implement what they formulate in an informal way.
A pilot study was made of a period of distributed collab-orative knowledge modeling with the goal to customize theclinical decision-support system for dementia diagnosis to lo-cal practice [20]. The customization was done by local med-ical professionals and evaluated with domain experts. Asa result, the local medical professionals introduced a shortversion for novices, while educating the professionals in de-mentia management before implementing the full version.The full version is currently being implemented in a largercommunity of primary care physicians, who need support forimproving diagnosis and management of dementia patients.
ACKTUS was used for transforming informal knowledgeobtained in a series of participatory design sessions withtwo groups of older adults and physiotherapists, into formalknowledge implemented in a mobile application for olderadults [14]. A feasibility study will be conducted within thenear future of the resulting evidence-based intervention forpreventing falls.
An ongoing study is investigating how cooperative carecan be accomplished, where supportive applications are be-ing developed using ACKTUS for individuals who sufferfrom chronic obstructive pulmonary disease (COPD) or in-continence. There resulting applications are aimed to sup-port both the individuals and their healthcare profession-als. The modeling domain experts are physiotherapists andphysicians respectively, who are not familiar with neitherknowledge engineering or interaction design. Preliminaryresults show that guidance is needed in the initial phase oftranslating informal domain expertise and ideas about de-sign into formal representations using the ACKTUS mod-ules. The naming of different objects in the user interfaceand guidance how these can be used for composing the con-tent and interaction flows can be improved.
3. RELATED WORKThere is a number of different modeling languages devel-
oped for the purpose to function as instruments in the trans-lation of clinical practice guidelines (CPGs) into computer-interpretable versions (CIGs) [23]. A few examples are As-bru [25, 26, 5], PROforma [28, 27] and GLIF3 [2]. ACK-TUS shares the aim with these, to provide an instrumentfor translating clinical practice guidelines into a computer-interpretable format for developing clinical decision support
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systems. However, ACKTUS differs from these initiatives inthat ACKTUS allows also the representation of best prac-tice, or ”rules of thumb”, which are knowledge not yet vali-dated in evidence-based medicine. Consequently, the trans-parency is essential, and the valuation of the sources ofknowledge, following an ontology of reliability. Besides anontology of reliability, this is accomplished using argumen-tation schemes, which further describes the source.
ACKTUS also differs from these in that ACKTUS is aim-ing at allowing healthcare professionals, who are not expe-rienced in knowledge engineering, to model knowledge andto construct their envisioned supportive applications. Theplatform is designed to be used by medical or health pro-fessionals who are not necessarily experienced in knowledgeengineering or interaction design.
Another difference from the guideline representation lan-guages, is that the targeted users of applications developedusing ACKTUS may be the patient, or client who may besuffering from a medical condition, or at risk for injuries, andnot necessarily a medical professional. Since ACKTUS alsoallows the domain expert model knowledge in an interactiondesign context, the expert can aim for optimal presentationof information seemed as crucial for the patient/client to re-ceive. Aspects regarding motivational factors become essen-tial, e.g., for promoting a change of behavior, and methodsfor personalization.
ACKTUS shares similarities with PROforma in that bothuse arguments, which may be defeasible [28]. A differenceis that PROforma restricts the defeasible arguments to beassigned same confidence level, and the number of argumentsin favor and against a statement is counted and compared.In ACKTUS different levels of confidence can be assignedan argument, which was considered necessary for managingthe uncertainty in the dementia domain.
At a superficial level, ACKTUS can be seen as a contentmanagement system (CMS). However, the ontologies under-lying the platform, and the potentials in using inferencesto generate support and conclusions, advance ACKTUS be-yond traditional CMSs.
4. CONCLUSIONS AND FUTURE WORKThis paper introduces ACKTUS, which is a web-based
platform for developing knowledge-based applications. Theplatform is designed to be used by professionals such as med-ical domain experts who are not necessarily experienced inknowledge engineering or interaction design. ACKTUS nov-elty lies in the combination of modeling knowledge alongsidewith modeling the interaction to be taking place with theknowledge. Thus, the domain professionals are modelingwith the target user group in mind, and can test their pro-totype applications on fictive use cases. Moreover, the re-sults can immediately be tested in an end-user view, whichenable rapid prototyping and cooperative design, involvingalso citizens who are potential end users. Researchers indifferent medical and health domains are able to developnew evidence-based interventions and conduct studies withclients.
The end user may be a colleague, a group of patients orother persons. The aim is providing personalized supportto users such as non-expert clinicians at the point of care,or construction and mining workers in monitoring and pre-venting work related injuries. The potential end-users ofthe knowledge-based support applications developed so far
range from different kinds of health professionals, miningand construction workers, older persons in their homes, ado-lescences, individuals with cognitive or mental deficiencies.
Three of the research projects are now investigating howto integrate information from wearable sensors or sensors inthe environment into support applications developed usingACKTUS. As a consequence, the information and argumentinterchange services in ACKTUS will be further developed.
Future work will aim to improve the interaction designof ACKTUS based on the results from the ongoing stud-ies, and focus on scalability and interoperability. One goalis to develop methods for mapping content to other plat-forms for interventions, e.g., national platforms for serviceprovision in healthcare. ACKTUS is currently functioningas a research and innovation infrastructure for the researchgroups, which are using the platform for developing pro-totypes for evaluating interventions with end users. In thisprocess, ACKTUS is being used as an instrument for dissem-inating research results from evidence-based medical studiesto clinical practice or individuals’ daily living. Consideringthe broad variety of application domains so far, it is ex-pected that ACKTUS can function as the bridge betweenresearch and clinical practice/everyday living of individualswith health conditions also in additional medical and healthknowledge domains in the future.
5. ACKNOWLEDGMENTSThe authors are grateful to the participating domain pro-
fessionals and end user groups. The project has been partlyfunded by VINNOVA (The Swedish Governmental Agencyfor Innovation Systems), the Swedish Brain Power, the Emiland Wera Cornell foundation and AFA Insurance.
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