A Multiagent System Enhancing Home-Care Health ... - IEEE Xplore

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 9, NO. 4, DECEMBER 2005

A Multiagent System Enhancing Home-Care Health Services for Chronic Disease Management Vassilis G. Koutkias, Student Member, IEEE, Ioanna Chouvarda, and Nicos Maglaveras, Member, IEEE

Abstract—In this paper, a multiagent system (MAS) is presented, aiming to enhance monitoring, surveillance, and educational services of a generic medical contact center (MCC) for chronic disease management. In such a home-care scenario, a persistent need arises for efficiently monitoring the patient contacts and the MCC’s functionality, in order to effectively manage and interpret the large volume of medical data collected during the patient sessions with the system, and to assess the use of MCC resources. Software agents were adopted to provide the means to accomplish such real-time information-processing tasks, due to their autonomous, reactive and/or proactive nature, and their effectiveness in dynamic environments by incorporating coordination strategies. Specifically, the objective of the MAS is to monitor the MCC environment, detect important cases, and inform the healthcare and administrative personnel via alert messages, notifications, recommendations, and reports, prompting them for actions. The main aim of this paper is to present the overall design and implementation of a proposed MAS, emphasizing its functional model and architecture, as well as on the agent interactions and the knowledge-sharing mechanism incorporated, in the context of a generic MCC. Index Terms—Chronic disease management, home-care system, knowledge sharing, medical contact center (MCC), monitoring and administration, multiagent system (MAS), telemedicine.

I. INTRODUCTION

A

popular approach to modern information technology (IT)-based healthcare delivery is the use of home-care systems, and specifically, medical contact centers (MCCs), which act as mediators between the medical staff and the citizens seeking advice and/or therapy [1]. Typically, MCCs provide monitoring, surveillance, and educational services to patients suffering from chronic diseases, such as diabetes, asthma, or congestive heart failure (CHF), through several communication platforms [2]. In such a home-care scenario for chronic disease management, the medical personnel have to monitor the patients’ status, based on the frequent interaction of patients with the MCC, and accordingly regulate them with medical interventions [3]. Therefore, new requirements are emerging toward supporting the medical personnel of MCCs in the provision of 24-hour health services, due to the increased amount of data that have to be processed and interpreted, a rather typical case in home-based patient monitoring services. Manuscript received March 31, 2004; revised October 13, 2004 and January 17, 2005. This work was supported in part by the IST-1999-13352 Project, entitled “Distance Information Technologies for Home-Care-The Citizen Health System (CHS),” funded by the Commission of European Community (CEC). The authors are with the Lab of Medical Informatics, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece (e-mail: [email protected]; [email protected]; [email protected]). Digital Object Identifier 10.1109/TITB.2005.847511

Specifically, efficient information extraction and monitoring mechanisms are required, in order to effectively manage and automatically interpret the large volume of medical data collected during the patient sessions with the system, highlighting cases where urgent attention is potentially required [4]. In this paper, a multiagent system (MAS) is presented [5], aiming to add value to the home-care services offered by a generic MCC. The MAS was designed to continuously monitor patient contacts, as well as the MCC operation, and by incorporating knowledge defined by the MCC personnel, in terms of rules, to process data and provide in real time the significant information obtained to the relevant recipients. It is worth mentioning that the demand for automated medical data interpretation and filtering mechanisms to assist the personnel of MCCs was raised in the context of the “Citizen Health System (CHS)” European project, which developed a MCC that operated during an 18-month clinical trial [2]. The software-agent paradigm was adopted due to its autonomous, reactive and/or proactive nature [6], which comprises important features in real-time application deployment for dynamic systems like the one under consideration. Furthermore, software agents can incorporate coordination strategies, thus enabling them to operate in distributed environments and perform complex tasks [7]. Generally speaking, software-agent technology is considered an ideal platform for providing data sharing, personalized services, and pooled knowledge [8]. In the research literature, there are several agent-based applications reported in the healthcare domain [9]. In particular, in [10] and [11], the software-agent metaphor is adopted for automated monitoring and diagnosis in healthcare environments. A “smart home” environment for telemonitoring of patients is presented in [12], emphasizing in the embodiment of software agents in sensor devices within the patient’s house. The adoption of the architectural example of software agents in home-care services is considered innovative, contributing to the exploration of the most advantageous options for adding value to an MCC. This paper addresses the necessity of automated online mechanisms for monitoring the interactions among the actors that participate in the MCC services of a home-care system. In the system presented, the use of software agents is mainly profitable toward better handling of alert situations by sensing the environment’s dynamic parameters, workload reduction (in the sense of time and effort) for the medical personnel through automated medical data interpretation, and supporting the administration of the MCC. In the following sections, an MAS abstract functional model is presented in the context of a generic home-care setting,

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KOUTKIAS et al.: A MULTIAGENT SYSTEM ENHANCING HOME-CARE HEALTH SERVICES FOR CHRONIC DISEASE MANAGEMENT

TABLE I TYPICAL PATIENT SESSION TYPES IN A GENERIC MCC

according to which a specific application scenario was implemented. Based on this scenario, the MAS architecture, its underlying knowledge-sharing mechanism, and the interactions among the agents are thoroughly described. Finally, an assessment study is presented, aiming to demonstrate the technical soundness and validity of the MAS, its added value, and its functionality in an MCC for diabetes and CHF home-care management. II. A GENERIC HOME-CARE SYSTEM The home-care system and the corresponding MCC considered in this paper follow the generic requirements of the MCC for diabetic and CHF patients developed in the context of CHS [2]. According to the CHS specifications, patient interactions with an MCC are provided via various communication means, such as computer telephony, wireless technology, or worldwide web technology. Independently of the communication means used, all the data flowing to/from the MCC are stored in a computerized patient record (CPR), specifically designed to meet the functional requirements of the home-care system. In the following, a typical patient interaction with a generic MCC is described [13]. As soon as the patient connects to the service, (s)he can choose to initiate a session, i.e., submit his/her daily measurements, browse educational content, or leave a message for the medical personnel (Table I). The medical personnel involved with the MCC define the schedule and the content of session types for each patient, offering a service customized to the patient’s medical profile. During the measurements session, each patient may send the measurements of his/her vital parameters, which are taken at home using simple microdevices (e.g., a home glucometer). Complementary to the measurements can be a number of questions asked of the patients, related to current lifestyle and possible symptoms/signs. In educational sessions, content is typically structured in categories for each disease. Patients may access educational material along these categories, in terms of text and voice messages, as well as small advice tips, by following their personalized educational plan. Furthermore, depending on the communication media, text or voice messages can be exchanged between the patient and the medical personnel. Typically, many interactions with the MCC take place, which can provide both medical and administrative information, the manual inspection of which is time-consuming, and introduces not only significant workload, but also the possibility of losing the best opportunities for timely intervention. This is particularly crucial, in case the MCC is embedded within a healthcare organization, where the medical personnel are also assigned the

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daily tasks of healthcare provision. Extended data review is important only in case of problematic conditions, crucial changes in a patient’s status, or for retrospective analysis. Therefore, for routine use, an advanced mechanism which filters out trivial situations, identifies the significant ones, and automatically notifies the medical personnel, would save time and reduce workload. Moreover, the administrative staff of the MCC has to be notified with information related to the system’s operation, regarding specific events that take place (e.g., an invalid login), as soon as they occur, or to obtain scheduled reports concerning the functionality of the system for usability evaluation purposes. In the following, the functionality of the proposed MAS is presented, aiming to provide additional value to the services of MCCs and address the aforementioned issues. III. MAS FUNCTIONALITY According to the requirements for effective patient monitoring and surveillance in the generic home-care setting described above, an MAS was designed, which monitors patient interactions with the MCC and generates prompts for actions, targeted to the MCC personnel. Initially, the MAS was abstractly represented. Interactions with the environment (inputs, outputs), internal functionality, and memory were elaborated. This analysis was crucial in determining the roles and tasks among the agents of the MAS and, without loss of generality, facilitated the specific architecture and implementation presented in this paper. A. Abstract Functional Model The agents’ role in the MAS is to sense the environment of the MCC, in terms of patient contacts, medical values submitted in concurrent sessions, and specific medical interventions for the patients, percept about the environmental states, and act correspondingly. Perception involves medical data interpretation and contacts assessment, considering the history of environmental states. Taking into account current perception, previous MAS actions, and medical interventions, actions are generated, consisting of notifications provided to the medical and the administrative personnel of the MCC regarding the information obtained. Typically, a patient performs a sequence of interactions with the MCC. These interactions correspond to events which constitute the observed environment for the MAS (Fig. 1). For the event , the set of observable parameters takes the value of vector , where is the value of the th monitored parameter, and is the number of all observable pa. Then, the current state of the system rameters, with is defined as (1) where is a perception mechanism, performing interpretation of the observable parameters at event . Also, let be the vector of previous and consequent system states, including the current one. Then, is the outcome of perception mechanism that takes into account the current state of the system and its previous states, such that (2)

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Fig. 2.

Fig. 1.

Let that

Abstract representation of the agent-based functionality.

be the vector of

previous agent-based actions, such , with being the th action performed for the th event, and is the vector of previous medical interventions, with corresponding to the th intervention th event. Then, the current agent-based performed for the is given by action (3)

where is a mechanism to generate the current action, and is given by (2). The described model may be considered as a black-box system, where input is the set of observable parameters of the MCC and the output is an agent-based action, e.g., generation of alert, notification, report, or recommendation messages targeted to the MCC personnel, taking into account the history of MCC states, previous medical interventions, and previous agent actions. According to this abstract functional model, an application scenario follows, in the context of the CHS home-care system. B. Application Scenario Regarding patient monitoring, the MAS characterizes patient health condition and identifies potential transitions from normal to problematic status in medical parameters, and vice versa. As a first step, corresponding to in Fig. 1, measurement values are classified as normal or problematic, according to ranges defined by the medical personnel for each patient or group of patients. Likewise, when answers to the symptoms/signs questions do not bear the “default” defined value, they are classified correspondingly. In the second step, corresponding to in Fig. 1, the frequencies and/or trends of all medical parameter states (normal/problematic) within a time window are taken into account, besides their current states. Transitions from normal to problematic (0:1), or persistent problematic medical parameters (1:1) hold significant information, a problematic-to-normal transition (1:0) indicates improvement of the specific parameter, while a stably normal parameter (0:0) is a trivial situation (Fig. 2). The outcome of this procedure is an overall characterization of the situation and a qualitative score (very serious, serious,

Transition in the status of an observable medical parameter.

mildly problematic, positively stable, and improving), corregiven by (2), which is derived from a weighted sponding to combination of all medical parameters. In the above-mentioned situations, the medical personnel are accordingly notified by the MAS about the severity of the situation and the related factors ( mechanism of the abstract model). It has to be noted that the way each parameter is interpreted depends on its physiological meaning and way of variation, e.g., the value of a patient’s weight, in a specific instance, might not be so important in an absolute sense, as its evolution in a short time window; therefore, current weight characterization is based on the derivative estimation within a short time period. On the other hand, since, e.g., continuous pulse information is not available, pulse variation within days is not a relevant feature, but instead, the important feature for pulse characterization is the frequent detection of out-of-range pulse values within a short time period. Furthermore, the significance of each monitored parameter may be different, e.g., it might be considered more crucial to take into account whether the patient has reported dyspnea than that (s)he did not exercise. Therefore, regarding a quantitative expression of the overall patient characterization, different weights can be introduced for each parameter. Hence, based on the qualitative and quantitative description of patient-status monitoring methodology, provided by the clinicians cooperating within the CHS project, this information has been heuristically combined in a linear model, offering a simple quantitative expression of the patient’s medical status. In a rather simple application scenario with a small set of discrete-value monitoring parameters, like the one under consideration, such an approach has been considered adequate. In case of a persistent, very serious or serious patient-contact characterization, the MAS generates a recommendation to the medical personnel for adapting the educational plan of the patient, according to his/her problematic condition (part of mechanism also). For example, if the blood glucose of a diabetic patient kept increasing during two weeks, an educational recommendation would be generated, suggesting adaptation to the patient’s educational profile on “blood glucose control.” Following an educational plan adaptation, i.e., the medical personnel followed the educational plan recommendation, the MAS assesses the impact of this intervention and notifies them accordingly. This impact is measured with the same logic that initially triggered the change, e.g., if it was the high weight value of the last three consecutive contacts that generated the recommendation for “diet issues” educational messages, then the outcome is measured by the variation of weight during the following weeks.


TABLE II PATIENT CONTACTS CHARACTERIZATION ACCORDING TO SCHEDULE COMPLIANCE

In the same fashion, the MAS: •

monitors patients’ compliance with their predefined schedule and generates appropriate reports for the medical personnel about cases of missed or unexpected sessions that might require more attendance (Table II); • identifies whether a patient left a message for the medical personnel and generates a relevant notification. This is important when a patient has some questions, or wants to notify the medical personnel of a special situation; • identifies possible invalid logins that took place in the MCC. This is necessary both for security purposes and for usability evaluation. For example, some elderly people may have difficulty in keying in their login; therefore, technical personnel of the MCC should contact them and help them get familiar with the system; • generates administrative-usability reports regarding the system’s operation, such as the number of contacts done during the day, patients’ use of communication means available, etc. These reports may constitute a usability feedback and assessment mechanism for the MCC services. Agent activities are performed either in real time, or according to a predefined schedule. Depending on the nature of knowledge they have to obtain, agents perform their activities continuously, as event-driven actions, or in specific time intervals according to a schedule. Data processed by the software agents of the MAS are located at the CPR of the home-care system, i.e., relevant database system and log files generated during patient contacts. In the following, the architecture of the MAS is presented, which implements the application scenario described.

IV. MULTIAGENT SYSTEM ARCHITECTURE The proposed MAS was designed according to a coordination strategy based on task decomposition and distribution [7]. Task decomposition was based on the layout of the information resources and physical actors, as well as the expertise of available agents, while task distribution was based on an organizational structure, where agents have fixed responsibilities for particular tasks. Thus, each agent was delegated a specific and simple task to accomplish, avoiding the assignment of extreme computational burden. Accordingly, emphasis was given in a task-oriented coordination among the agents of the MAS, where agents work in parallel, contributing to a common goal, and cooperate sharing their experience [14]. Delegating simple tasks to each

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agent and coordinating the agent activities results in a MAS that can perform advanced functionality. Based on their role, there are different types of agents participating in the MAS, namely, mediator, monitoring, information, and visualization agents. In Table III, the agents participating in the MAS are described in terms of their type, their role, their contribution to the abstract functional model described, their features, and their location. From a microscopic viewpoint, agents of the framework incorporate the following attributes (besides autonomy, which constitutes the fundamental agent attribute), according to their role. • Reactivity: Agents sense their environment and act under specific conditions [6]. • Cognition: Agents perform information processing and reasoning, based on their internal knowledge base, in terms of rules [15]. Specifically, rules apply to medical values and their evolution in concurrent patient contacts with the MCC, taking into account previous medical interventions and agent actions. • Communication: Agents participate in communication acts, interacting and sharing knowledge with other agents of the MAS [16]. • Proactiveness: Agents are capable of “taking the initiative,” which is applied in the specific system as recommendations generated regarding educational plan adaptation [6]. Macroscopically, the software agents under consideration are distributed in the framework of the healthcare organization’s Intranet, which provides home-care services for various types of chronic diseases (corresponding to specialized clinics), e.g., in the MCC’s computer systems, a clinic’s terminal, or an administrator’s terminal (Fig. 3). Restricting the functionality of the MAS within the home-care provider’s Intranet enables the adoption of security mechanisms required in sensitive applications, like telemedicine systems. Hence, an agent platform, supplying an appropriate agent-execution environment, is installed in every location where agents have to operate. As an agent construction and execution environment, the Java Agent DEvelopment framework (JADE) was adopted [17]. JADE is an open-source software framework, aiming to assist the development and execution of agent applications in compliance with the Foundation for Intelligent Physical Agents (FIPA) specifications for interoperable MASs [18]. Communications between different agent platforms, executed along the architecture of the home-care provider’s Intranet, is achieved through the FIPA-compliant Internet inter-ORB protocol (IIOP) that JADE supports [19]. Security mechanisms provided by JADE, such as a security policy at the platform level, the enforcement of delegable access rights to agents and related resources, and secure intraplatform communication, have been included. The reasoning scheme of the proposed MAS is designed as a rule base, implemented with Java Expert System Shell (JESS) [15]. JESS is a rule engine written in Java, hence, it was easily incorporated as a customizable module in JADE-based agents. Rules are based on medical parameter ranges, as well as concurrent transitions of medical parameters, both defined by the

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TABLE III DESCRIPTION OF THE SOFTWARE AGENTS PARTICIPATING IN THE MAS

The implemented interaction protocol among the agents of the MAS, as well as the underlying knowledge-sharing mechanism, follows. V. KNOWLEDGE SHARING AND AGENT INTERACTIONS

Fig. 3. Distributed architecture of the proposed MAS in the home-care provider’s Intranet.

medical personnel either for a patient group, or for each specific patient. Since the information agents do not reside at the MCC, where the monitoring agents are located (Fig. 3), each monitoring agent applies a search mechanism in order to dynamically locate the corresponding information agents, which are distributed in several hosts of the Intranet. This is accomplished by querying a specific software agent, built in with the agent platform used, that provides “yellow page” services for the agents located in its platform, called directory facilitator [18]. Thus, each information agent, as soon as it is instantiated, is registered with the directory facilitator of its local agent platform.

Acquired knowledge is shared among the agents of the MAS by adopting a hybrid mechanism that combines communication acts, i.e., message exchange among agents, with an information blackboard (IB), constituting a shared memory system for the agent activities [20]. Specifically, the IB provides data related to previous MCC environmental states, previous agent actions, , , and and previous medical interventions ( corresponding to the abstract functional model described in Section III-A). In order to provide a common understanding among agents, a specific application ontology was constructed in Protégé-2000 [21] and incorporated in the agent messages. A part of the ontology, related with the agent actions defined in the MAS, is illustrated in Fig. 4. As an agent-communication language, the FIPA ACL standard language was chosen to represent the messages exchanged among agents of the MAS, which sets out the encoding, the semantics, and the pragmatics of messages [16]. The IB constitutes a virtual space, where agents read/write information regarding their previous/current activities. Information that was obtained in previous agent activities is stored in the IB, instead of being reobtained by the agents. Such an approach minimizes interactions among agents, and reduces their computational effort. Furthermore, it constitutes a monitoring mechanism for the agent activities. The IB was implemented as a tuple-space by adopting the Tspaces system [22]. A tuple is a data structure, represented as a vector of fields, where each field


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Fig. 4. Frames and slots of the application ontology developed, related to the agent actions performed, which constitutes a common terminology in agent communication acts. Rectangles represent classes with their slots and include each slot’s value type. Arcs represent relations between classes.

Fig. 5.

Scenario of interactions among the agents of the proposed MAS represented in AUML.

contains a typed value, either primitive, e.g., integer, string, etc., or a tuple. Tuples “live” in spaces, which are collections of tuples. In the current system, tuples are represented as Event ID, Event Datetime, Event Description

(4)

where the last field of the above formula is another tuple, expressing in more detail the corresponding event. In Fig. 5, a scenario of interactions among the agents of the MAS is illustrated via an agent-based unified modeling language (AUML) collaboration diagram [23], consisting of the following steps (the directory facilitator referred to in Section IV is omitted for simplicity). 1) Suppose that the Contacts_Agent currently identifies a new MCC contact of a CHF patient. 2) The Contacts_Agent initiates the agent-interaction protocol by requesting the corresponding monitoring agents

to assess the contact (concurrent tasks 2.1:, 2.2:, 2.3:, and 2.4:). 3) Monitoring agents access contact records located in the CPR (concurrent tasks 3.1:, 3.3:, 3.5:, and 3.6:), read corresponding tuples from the IB (3.2:, 3.4:), and process contact-related data. 4) Scheduler_Agent and Administrator_Agent reply to the aforementioned request of the Contacts_Agent (4.1:, 4.3:) with their outcome, and write corresponding tuples to the IB (4.2:, 4.4:). Problematic/improved parameters in the specific contact are identified by the Problem_Indication_Agent and the Improvement_Indication_Agent, respectively, which also request an overall characterization of the patient status from the Overall_Assessment_Agent (4.5:, 4.6:) providing the information of their processing.

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5) The Overall_Assessment_Agent reads corresponding tuples from the IB and generates characterization of the CHF patient’s status, based on frequency and/or trend analysis of all measurement states. 6) Accordingly, the Overall_Assessment_Agent informs the Contacts_Agent about its outcome (6.1:), potentially requests the Educational_Agent to generate an educational plan recommendation, or to assess the impact of a potential previously followed educational adaptation (6.2:), and writes tuples to the IB regarding its activity (6.3:). 7) The Educational_Agent accesses the IB to take into account its previous activity regarding the specific patient, and compiles the recommendation for a potential educational plan adaptation, or assesses the impact of the educational plan adaptation followed, recorded as a previous medical intervention in the IB. 8) The Educational_Agent informs the Contacts_Agent about its outcome (8.1:), and writes tuples to the IB (8.2:). 9) The Contacts_Agent informs the CHF_Agents, located in cardiology clinic’s terminals, which are distributed along the home-care provider’s Intranet, about the information received by the monitoring agents (9.1:, , 9.N:). In the same fashion, it would also inform the Administrator_GUI_Agents with administrative information. 10) Each CHF_Agent gathers information from the Contacts_Agent and informs its local platform CHF_GUI_Agent to visualize the content to the cor, 10.N:). Hence, each responding terminal (10.1:, cardiologist or corresponding case manager is informed regarding his/her patients only. It has to be noted that communication among agents is asynchronous, i.e., the requester agent continues with its queued reasoning/acting tasks, according to its internal behavior. Thus, steps indicated above as concurrent, for simplicity in the description, may take place asynchronously.

VI. RESULTS The main aim of this paper is to propose a system architecture and its underlying methodology for enhancing healthcare services of an MCC, illustrating its value by means of an application scenario. Thus, following the abstract MAS functional model, an application scenario has been implemented, and relevant agents were developed, incorporating a reasoning scheme, to enhance home-care health services according to the medical protocols defined in the CHS project [2]. Hence, agent functionality was designated for two different user groups, namely, medical personnel, related to chronic disease patients interacting with the MCC, and administrators of the MCC. In addition, an assessment study was designed aiming to demonstrate the technical soundness, the accuracy of medical knowledge incorporation, and the validity of the implementation in real situations (Section VI-A), and illustrate the virtue of such a system and obtain clinician feedback regarding system usage (Section VI-B).

Accordingly, appropriate graphical user interfaces (GUIs) were implemented for visualization purposes. In each terminal (for medical personnel or administrator), an information and a visualization agent were launched, in order to provide the corresponding information. As already mentioned, the construction of the proposed MAS followed the requirements of the CHS MCC; hence, the material chosen for its assessment was the CHS CPR, including all patient contacts and measurements, which were already collected and clinically evaluated. Specifically, in the following tests, a particular database application was periodically sending patient data from the CHS CPR (the outcome of the clinical trials) to the MCC database of the application scenario (blueprint of the CHS one), preserving the actual date/time information showing that the contacts were made, while the MAS was sensing the MCC environment and assessing each contact in real time. A. System Demonstration In order to demonstrate the system’s operation and functionality in a real-time scenario, the MAS monitored the MCC database, where patient contacts occurred successively in a controlled manner, as explained above. Specifically, 68 consecutive patient contacts, already existing in the CHS medical database, were retrieved and reproduced in the CPR, corresponding to the contacts of a CHF patient, who went through the CHS clinical trial for a period of six months. The medical interventions that were registered during the trial for the specific patient were available for comparison. There were two reasons for the selection of the specific patient in this test: a) his/her high adherence during the clinical trial, (there were no “gaps” in the evolution of the monitored parameters, e.g., due to services misuse, hospitalizations, etc., therefore, safe conclusions could be drawn), and b) the fact that enough medical interventions were also available in this case. From a technical viewpoint, during this real-time test, the MAS was stable and exhibited coherent behavior, i.e., it operated effectively as a unit [7]. The mean execution duration of the agent interaction protocol for a patient contact was 286 ms, while standard deviation was 92 ms; thus, the MAS provided a real-time response, in an ordinary computer system (Pentium III processor at 1.5 GHz, 256 MB of RAM, Windows platform), where the test was executed. Analyzing the patient status characterization generated by the MAS, in comparison with the contacts and actual medical interventions, it was shown that for the contacts that raised the interventions, the MAS also generated severe alerts, either concurrent with, or slightly preceding them. Hence, the medical knowledge encoded in the MAS was found in accordance with clinicians’ medical practice. It is interesting to note that although the specific patient was in an unstable condition during the clinical trial, as reported by clinicians, 50% of the contacts were classified as having minor severity; hence, the MAS generated a “soft” notification, implying that there was no urgent need for clinician inspection. Moreover, 24% of the patient contacts suggested stability or improvement in the patient’s status. Another 16% of the contacts called for more attendance, and only 10% of the contacts generated very serious alerts. Therefore, the specific test indicates that


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Fig. 6. Screen captures of the GUIs illustrating the MAS outcome for: (a) the administrative personnel, providing MCC usage reports (contact and session features, i.e., patients’ preference on available communication media and educational themes, number of daily session types, as well as authentication-related information); and (b) the medical personnel, providing patient contact characterizations (a list of CHF patients’ daily contacts (left panel) and categorization of contacts, in terms of serious problem, mild problem, and improvement indications, while potential educational recommendations and patient-schedule compliance information are provided in corresponding panels).

the proposed MAS can save clinician workload, and implicitly time, by introducing an initial distinction among the contacts, i.e., by safely highlighting on time the serious cases that require urgent clinician attendance, and separating them from the ones indicating minor severity, stability, or improvement (74% in this example). It has to be noted that in the clinical procedure, the medical personnel will periodically go through all cases, but the crucial ones need more frequent and focused attention. The administrative functionality was similarly tested, using a single day’s patient contacts with the CHS MCC during its clinical trial phase. Fig. 6(a) illustrates the respective usage report, including the preference among communication means available, the session types accomplished, and other features.

were five measurements of vital signs (namely, systolic and diastolic blood pressure, pulse, weight, and temperature), and the answers to five lifestyle- and symptoms/signs-related questions. The test took place in favorable conditions (environment was quiet and clinicians were concentrated). Although the scenario was rather simple and the number of contacts was not large, interesting observations resulted from the test, including clinician feedback. •

B. System’s Added Value The added value of the proposed system, indicating that workload reduction is inherently provided by such an approach, was assessed by end users. Specifically, two clinicians were asked to perform manually initial patient screening (characterize patient status) without MAS use, by applying qualitatively the same reasoning principles that were incorporated in the MAS. The two clinicians were involved in the CHS clinical trials, and were familiar with the MCC concept and functionality. First, the clinicians had to identify patient contacts which required special attention, and distinguish them from trivial cases, by using a (familiar to them) legacy application for medical data browsing. Specifically, 58 contacts of 10 patients, that were performed during a six-day period in the actual trial, were concurrently presented to clinicians and had to be reviewed (there were three contacts per day on average, and only once were there nine contacts during the same day). This was a rather typical case for patient status review in the context of the CHS trial, but a larger number of contacts could be expected in a real home-care setting. The medical data under consideration in each contact

•

•

•

According to the review, 64% of patient contacts corresponded to normal situations or situations that did not require immediate attendance, 7% showed improvement in patients’ condition, and only 29% of contacts indicated a serious problematic situation. As a result, more than 70% of patient contacts did not require urgent attention (a percentage similar to the first test’s outcome, indicating the need to distinguish trivial cases from significant ones). It took about an hour for each clinician to go through all patient contacts and finish the initial screening procedure. If the whole MAS functionality had to be applied manually, e.g., schedule compliance monitoring, educational plan adaptations, etc., the workload would significantly increase. In case the number of monitored parameters increases, as new multisensor monitoring directions may require, the review procedure might become extremely demanding. One of the clinicians reported that he faced difficulty in correctly evaluating contacts revealing a moderately bad patient condition, if they were made infrequently or irregularly, and tended to underestimate their importance. Reaching the end of the procedure, both clinicians began to get tired and considered the possibility of losing their efficiency and lucidness, implying that if the number of contacts that ought to be evaluated increased significantly, the necessary attention of the clinician might fall, and lead to errors.

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Clinicians claimed that it would be beneficial to review contacts whenever this is necessarily required. In a clinical environment, e.g., in a hospital, where clinicians are constantly occupied with various tasks, they may choose to be informed mainly for important cases. In the following, the same clinicians were presented with the outcome of the MAS performing the same task that they did manually. Fig. 6(b) illustrates the patient status characterization generated by the MAS, according to this test. The MAS outcome was approved by clinicians. The experts commented that since the MAS performed automatic patient screening and identified cases that required urgent attention, they would have the opportunity to cope first and invest more time and effort in the most significant cases, which would add to their qualitative effectiveness. They also reported that reviewing contacts that were not made on a regular and frequent basis would be easier and more efficient, since the MAS easily incorporated in its reasoning previous patient states, no matter where or how often they occurred. Furthermore, clinicians indicated that it would be beneficial if the MAS could notify them of important cases via, e.g., short message services (SMSs) or their paging system. They also suggested that interoperability and potential integration between the MAS and the MCC legacy application for medical data review would be profitable, avoiding unnecessary computer interaction time. The assessment study presented in this section illustrates the role of the proposed system, contributing to the quality of service (QoS) of a generic home-care system, following the paradigm of the CHS system. Consequently, according to these preliminary results, by adopting the system presented, the clinician’s workload related to reviewing patient contacts in such a home-care setting is reduced and experts’ time is saved, while the quality of medical procedure is preserved. VII. DISCUSSION In today’s IT applications, data, knowledge, systems, and all other related resources are inherently heterogeneous and complex, making decentralized solutions a necessity in order to assure flexibility and scalability [5]. Toward this aim, the introduction of new construction tools, being able to demonstrate features such as autonomy, reasoning, knowledge encapsulation, and cooperation, is required [6]. A powerful solution, according to this consideration, is agent-based systems, which represent an alternative way of analyzing, designing, and implementing complex software systems [7]. In this paper, a MAS was presented which is incorporated within a generic MCC for home-care delivery, offering services for both medical and administrative personnel. The proposed MAS was designed by following an abstract functional model, in which the agents’ role was to sense the MCC environment, percept about the environmental states, and act correspondingly. The ultimate goals are to effectively manage and interpret the large volume of medical data collected during the patient sessions with the system, to provide a mechanism for evaluation of the educational process, and to report on the MCC services usability and resources. These aims are addressed by designing and applying a coordination strategy among a team of agents

with distinct roles, each one with a particular expertise in terms of a reasoning scheme, and a knowledge sharing mechanism combining message exchange with an IB. MASs are difficult to evaluate because there are no specific metrics widely accepted for the assessment of their architectural integrity and the performance of their implementation [24]. A formal MAS-evaluation methodology, assessing agent-oriented software engineering methodologies applied, as well as agent platforms, has not been standardized yet. The proposed MAS constitutes both a system of architectural complexity and a system incorporating reasoning in its operating logic, as well as in its functional model. Therefore, by use of a specific implementation scenario, the efficient encapsulation of medical knowledge, the real-time operation of the system, and its functionality were demonstrated. Furthermore, the value added in a home-care system was illustrated, indicating that such a system, by implicitly indicating priorities regarding patients’ status and MCC usage in general, certainly undertakes a workload, which would otherwise require a significant amount of time and effort by an expert. Therefore, urgent situations may be handled more effectively. Overall, the proposed system was found to be of significant merit, adding quality and efficiency to the review procedure, and as a whole to health services provided by the home-care system. Such a system could be further appreciated in a medical scenario where the set of monitored parameters is larger, as new multisensor monitoring directions require, for example, in a home-care setting, including continuous multiparametric recordings. In this case, the review procedure will become extremely demanding. Performing an extended clinical trial using the MAS in such a complex monitoring scenario is considered as a future work. In this context, more sophisticated information-processing models have to be incorporated in the MAS’s reasoning scheme. Various future directions are surveyed, toward the enhancement of the system’s functionality through the embodiment of intelligent reasoning capabilities; specifically, the delegation of more sophisticated tasks to some agents of the community, such as data-mining mechanisms, may enhance data interpretation. Enrichment of the mechanisms supporting knowledge management and fine-tuning of the personalized monitoring rules would be beneficial. The system’s modularity and reusability, facilitated by the agent metaphor and the ontological design adopted, enables the extension of the MAS’s functionality, even with services addressing more actors of the MCC than the medical personnel and the administrators (e.g., patients, other healthcare service providers, etc.), via the embodiment of additional agents. The expansion of possible interfaces to receive user notifications (e.g., on PDAs) is also an attractive perspective, as suggested by the clinicians. Finally, the agent metaphor facilitates a future development addressing interoperability with legacy applications, by incorporating in the MAS appropriate wrapper agents. VIII. CONCLUSION The effective and continuous operation of MCCs providing home-care services introduces a set of new requirements. From


a medical viewpoint, patient interactions with the MCC have to be reviewed, in terms of patient status characterization, patient adherence, etc. From a technical viewpoint, the MCC services have to be assessed, in order to evaluate and optimize resources use. The above-mentioned requirements are addressed by a MAS distributed in the Intranet environment of the homecare provider, aiming to monitor the operation of the system and notify the MCC personnel in real time with the information obtained. The proposed MAS was designed as a cooperative agent team, where agents share a common goal and each one adopts a request to do its share toward achieving the goal of the team. For the medical personnel, unnecessary data browsing is substituted by more compact information regarding patient status, delivered proactively. For the administrative personnel, an overview of the system’s operation and usage is timely provided. The proposed MAS constitutes an effort toward the design of intelligent, flexible, and integrated large-scale home-care telemedicine systems. REFERENCES [1] E. A. Balas and I. Iakovidis, “Distance technologies for patient monitoring,” British Med. J., vol. 319, p. 1309, Nov. 1999. [2] N. Maglaveras et al., “Home care delivery through the mobile telecommunications platform: The Citizen Health System (CHS) perspective,” Int. J. Med. Inf., vol. 68, pp. 99–111, Dec. 2002. [3] M. F. Collen, “Historical evolution of preventive medical informatics in the USA,” Methods Inf. Med., vol. 39, no. 3, pp. 204–207, 2000. [4] E. A. Balas et al., “Improving preventive care by prompting physicians,” Arch. Internal Med., vol. 160, no. 3, pp. 301–308, Feb. 2000. [5] K. Sycara, “Multiagent systems,” AI Mag., vol. 19, no. 2, pp. 79–92, Summer 1998. [6] N. Jennings, K. Sycara, and M. Wooldridge, “A roadmap of agent research and development,” Auton. Agents Multi-Agent Syst., vol. 1, no. 1, pp. 7–38, Jan. 1998. [7] G. Weiss, Multiagent Systems—A Modern Approach to Distributed Artificial Intelligence. Cambridge, MA: MIT Press, 2000, pp. 79–120. [8] S. Case, N. Azarmi, M. Thint, and T. Ohtani, “Enhancing e-communities with agent-based systems,” IEEE Computer, vol. 34, no. 7, pp. 64–69, Jul. 2001. [9] C. Mazzi, P. Ganguly, and M. Kidd, “Healthcare applications based on software agents,” in Proc. MEDINFO, 2001, pp. 136–140. [10] J. E. Larsson and B. Hayes-Roth, “Guardian: An intelligent autonomous agent for medical monitoring and diagnosis,” IEEE Intell. Syst., vol. 13, no. 1, pp. 58–64, Jan. 1998. [11] M. E. Hernando et al., “Multi-agent architecture for the provision of intelligent telemedicine services in diabetes management,” presented at the Workshop Intell. Adapt. Syst. Med., Prague, Czech Republic, 2003. [12] V. Rialle, J.-B. Lamy, N. Noury, and L. Bajolle, “Telemonitoring of patients at home: A software agent approach,” Comput. Meth. Prog. Biol., vol. 72, no. 3, pp. 257–268, Nov. 2003. [13] V. G. Koutkias, S. Meletiadis, and N. Maglaveras, “WAP-based personalised healthcare systems,” Health Informatics J., vol. 7, no. 3/4, pp. 183–189, Sep./Dec. 2001. [14] E. H. Durfee, “Scaling up agent coordination strategies,” IEEE Computer, vol. 34, no. 7, pp. 39–46, Jul. 2001. [15] E. Friedman-Hill, JESS in Action. Greenwich, CT: Manning, 2003, pp. 31–40. [16] Y. Labrou, T. Finin, and Y. Peng, “Agent communication languages: The current landscape,” IEEE Intell. Syst., vol. 14, no. 2, pp. 45–52, Mar./Apr. 1999. [17] F. Bellifimine, A. Poggi, and G. Rimassa, “JADE—A FIPA-compliant agent framework,” in Proc. 4th Int. Conf. Exhibition Practical Applicat. Intell. Agents, Multi-Agents, London, U.K., 1999, pp. 97–108. [18] FIPA Abstract Architecture Specification. Foundation of Intelligent Physical Agents, FIPA00001-2000. [Online]. Available: http://www.fipa.org [19] G. Coulouris, J. Dollimore, and T. Kindberg, Distributed Systems Concepts and Design. Reading, MA: Addison-Wesley, 2001, pp. 669–695. [20] V. Devedzic, “Knowledge modeling—State of the art,” Int. Comput.Aided E , vol. 8, no. 3, pp. 257–281, 2001.

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[21] N. F. Noy et al., “Creating semantic web contents with Protege 2000,” IEEE Intell. Syst., vol. 16, no. 2, pp. 60–71, Mar. 2001. [22] T. J. Lehman et al., “Hitting the distributed computing sweet spot with TSpaces,” Comput. Netw., vol. 35, no. 4, pp. 457–472, Mar. 2001. [23] J. Odell, H. Van Dyke Parunak, and B. Bauer, “Representing agent interaction protocols in UML,” in Agent-Oriented Software Engineering II, M. Wooldridge, G. Weiss, and P. Ciancarini, Eds. Berlin, Germany: Springer-Verlag, 2001, vol. 2222, pp. 121–140. [24] O. Shehory and A. Sturm, “Evaluation of modeling techniques for agentbased systems,” in Proc. 5th Int. Conf. Auton. Agents, Montreal, QC, Canada, 2001, pp. 624–631.

Vassilis G. Koutkias (S’04) received the diploma in electrical and computer engineering in 1998, the M.Sc. degree in medical informatics in 2001, and the Ph.D. degree in 2005, all from the Aristotle University of Thessaloniki (A.U.Th.), Thessaloniki, Greece. He is currently working with the Lab of Medical Informatics, A.U.Th. His main research interests include multiagent systems in healthcare and bioinformatics, grid technologies, telemedicine systems, pervasive healthcare, as well as medical imaging. He is involved in R&D projects in the field of home-care telemedicine systems, and he is lecturing part-time at A.U.Th. and at the Technological Education Institute of Thessaloniki. Mr. Koutkias has been a member of the Technical Chamber of Greece since 1999.

Ioanna Chouvarda studied electrical engineering at the Aristotle University of Thessaloniki (A.U.Th.), Thessaloniki, Greece, and received the Ph.D. degree in medical informatics in 2001. She has been involved in various projects in the area of Medical Informatics. During her Ph.D. studies, she worked on signal processing, and especially on wavelet analysis of cardiac signals. Her main areas of interest are time-frequency analysis and modeling of signals, basically of cardiovascular origin, applications of artificial intelligence for medical information management and processing, and telemedicine systems. Currently, she is with the Lab of Medical Informatics, A.U.Th., working on R&D, and she is lecturing part-time at A.U.Th. and at the Technological Education Institute of Thessaloniki. Dr. Chouvarda has been a member of the Technical Chamber of Greece since 1993.

Nicos Maglaveras (S’80–M’87) received the diploma in electrical engineering from the Aristotle University of Thessaloniki (A.U.Th.), Greece, in 1982, and the M.Sc. and Ph.D. degrees in electrical engineering with an emphasis in biomedical engineering from Northwestern University, Evanston, IL, in 1985 and 1988, respectively. He is currently an Associate Professor with the Lab of Medical Informatics, A.U.Th. His current research interests include nonlinear biological systems simulation, cardiac electrophysiology, medical expert systems, ECG analysis, medical imaging, and neural networks. He has published more than 140 papers in referred international journals and conference proceedings. He has developed graduate and undergraduate courses in the areas of medical informatics, biomedical signal processing, and biological systems simulation. He has served as a Reviewer in CEC AIM technical reviews and in a number of international journals, and participated as Coordinator or Core Partner in national and CEC-funded research projects. Dr. Maglaveras is a member of the Greek Technical Chamber, the New York Academy of Sciences, CEN/TC251, and Eta Kappa Nu.