Healthc Inform Res Search

CLOSE


Healthc Inform Res > Volume 19(3); 2013 > Article
Mohammadzadeh, Safdari, and Rahimi: Multi-Agent System as a New Approach to Effective Chronic Heart Failure Management: Key Considerations

Abstract

Objectives

Given the importance of the follow-up of chronic heart failure (CHF) patients to reduce common causes of re-admission and deterioration of their status that lead to imposing spiritual and physical costs on patients and society, modern technology tools should be used to the best advantage. The aim of this article is to explain key points which should be considered in designing an appropriate multi-agent system to improve CHF management.

Methods

In this literature review articles were searched with keywords like multi-agent system, heart failure, chronic disease management in Science Direct, Google Scholar and PubMed databases without regard to the year of publications.

Results

Agents are an innovation in the field of artificial intelligence. Because agents are capable of solving complex and dynamic health problems, to take full advantage of e-Health, the healthcare system must take steps to make use of this technology. Key factors in CHF management through a multi-agent system approach must be considered such as organization, confidentiality in general aspects and design and architecture points in specific aspects.

Conclusions

Note that use of agent systems only with a technical view is associated with many problems. Hence, in delivering healthcare to CHF patients, considering social and human aspects is essential. It is obvious that identifying and resolving technical and non-technical challenges is vital in the successful implementation of this technology.

I. Introduction

In recent years, heart disease has been a major widespread public health problem in most countries [1]. According to Centers for Disease Control and Prevention reports heart failure is the primary cause of more than 55,000 deaths each year. One out of nine deaths in 2008 was due to heart failure. About half the patients who have heart failure die within 5 years of diagnosis [2], and 40% of patients with chronic heart failure (CHF) die after hospital admission or within one year after they are admitted [3]. CHF is costly for healthcare systems in many countries. In the United States, it is estimated that this disease has cost more than $20 billion [4]. The global cost of heart disease is estimated at $863 billion in 2010 and it is estimated to rise to $1,044 billion by 2030 [5]. In spite of pharmacologic advances and mechanical therapies, prevalence of CHF is rising [6]. However, studies have shown that delay in symptom diagnosis, nonconformity with diet and medication instructions, as well as lack of knowledge and skills of patients for self-management are common causes of re-hospitalization of CHF disease [7-9]. Obviously, improving the monitoring of these patients, the use of self-management programs by patients, providing appropriate treatment based on clinical guidelines, educating patients and helping them pursue their care plans lead to improved treatment and prevention of deterioration of the status of these patients, decreasing hospital re-admission and reducing the use of healthcare resources [10-12]. Given the importance of the follow-up of CHF patients to reduce common causes of re-admission and deterioration of their status that lead to imposing spiritual and physical pressures on patients, their families, healthcare centers, and society, modern technology tools should be used to the best advantage. Use of information technology tools would be helpful to improve the monitoring of patients through continuous assessment of symptoms and signs of disease and checking compliance with self-management programs, thus, improving treatment, and resource use [13]. One innovation in the field of information technology in healthcare is the application of artificial intelligence and intelligent systems to facilitate, accelerate, and improve health services, especially in home care and telemedicine. Agents are one of the strongest artificial intelligence areas, especially in the generation of practical solutions for real problems. An agent is a computer system situated in a dynamic and unpredictable environment that is capable of flexible autonomous action to meet its design objectives [14]. One characteristic of agents is mobility, which is suitable for telemedicine and e-Health systems [15]. Multi-agent systems (MASs) are computational systems in which a collection of loosely coupled autonomous agents interact in order to solve a given problem [14]. Surveys have demonstrated the benefits of using multi-agent technologies in healthcare systems. Agents can provide suitable infrastructure for follow-up data analysis and CHF management due to their many advantage, such as autonomy, proactiveness and social ability as well as their ability to manage information located in different places and to handle communication and cooperation between independent elements [16-19]. The aim of this article is to explain key points which should be considered in designing appropriate MAS to improve CHF management. In this literature review articles were searched with keywords like multi-agent system, heart failure, chronic disease management in Science Direct, Google Scholar and PubMed databases without regard to the year of publications.

II. General Key Factors in CHF Disease Management through MAS Approach

1. Organization

There are many factors in an organization that can have an impact on multi-agent-based information systems, such as organizational culture, which is an important factor in the success of new technology implementation [20]. Leadership behavior and commitment and providing relationship between the information system and organization performance, and tasks are influence on moving towards technologies [21]. The implementation of a CHF disease management system based on an agent approach requires the organizational culture to accept the need to use this system and high-level management support in healthcare organizations.

2. Human Resource

Some of the important considerations in the design and implementation of an agent-based system in CHF management include the use of skilled personnel in the field of information technology at healthcare centers [22]; user perspective and user knowledge [23,24]; and user characteristics, such as age, economic, social, and educational status [25]. User training is important to increase user acceptance and to maximize the use of information systems [26]. Staff must be involved in all steps from planning to implementation. Also, clear and effective communication between managers and all personnel involved in the project, describing the advantages and challenges of agent technology and change management are important to increase user acceptance [27].

3. Confidentiality and Privacy

Because of increasing dependence on information and communication technology (ICT) at healthcare centers in gathering, transfer, storage and evaluation of health data, the application of security and privacy guidelines is essential [28]. Respecting confidentiality and integrity while preventing the manipulation of data in MASs is possible through the application of many security measures. These include encryption methods to protect access to data that are transmitted by agents, levels of authentication mechanisms, to use of your usernames and passwords for login, identification of algorithms used to protect licenses and to determine the date of validity of licenses, limiting the levels of access to resources for agents, preserving of wireless communication security and access to health information or update information by authorized users [29,30].

4. Legal, Ethical and Administrative Issues

To advantages of MASs enable them to be used beyond borders on local, national, and international levels [31]; thus, they can provide citizens with healthcare services in different areas, with speed, accuracy, and quality. Obviously, implementing an agent-based system is successful when it is done in accordance with the principles of legal, ethical, and social rules.

5. Appropriate ICT Infrastructure

Providing appropriate ICT infrastructure (especially information, technical, and communicational infrastructure) is important for the successful implementation of agent-based systems. Also, sufficient investment in the purchase, design, implementation, maintenance, and updating of information systems is necessary [32-34].

III. Special Key Factors in CHF Disease Management through MAS Approach

1. Design

Designing an agent-based system in CHF management must be aligned with organizational goals and compatible with health personnel tasks [35]. Some of the most important factors that have been identified in designing such a system include the following: defining e-Health service content exactly [36], responsibility in all parts identified [37], and designing based on standard methods in a way that satisfies user needs. The first step in designing an agent-based system is studying the scenario in the domain of the usage system carefully and identifying all of the actors and the main tasks. In the next phase, the role of each actor must be defined and their activities in the area must be studied to determine all of their interactions in the agent-based system [38]. Furthermore, defining a proper strategy to select the best actions is very important [39]. In designing an agent-based system, items, such as robustness, efficiency, and communication challenges in cooperation between agents must be considered and suitable plan for them must be provided [40].

2. Architecture

Several important points must be considered in the architecture of a CHF disease agent-based management system. Knowledge management based on MAS should be used to ensure knowledge currency, and a flexible, extensible, and open architecture platform should be developed [41]. The Java agent development framework is a middleware developed and standard platform for MASs. This software is based on Java and Foundation of Intelligent Physical Agent standards. It is easy to use and has advantages, such as the capability of interoperating with other agents, uniformity, and portability [42]. Use of this middleware as a standard platform can be useful. Another important element that should be considered in the architecture of agent-based systems in CHF management is the application of an appropriate ontology, such as GALEN, UMLS, or SNOMED, to facilitate and accelerate the retrieval and integration of health information and support effective communication in systems based on agent technology. Ontology is the official, exact, and complete description of implications and relationship between concepts and individuals (often but not always) that enables the generation of interoperability standards and provides a common vocabulary [43-45]. Standards for data exchange between MAS and related tools and system interoperability with Electronic Health Records and other IT tools are key factors [46,47]. Also, using Web services to interact with different systems and providing various needs of all users is necessary [17].

IV. Conclusion

Chronic diseases like CHF leads to reduced productivity and increased healthcare costs. The prevalence of CHF is rising. Educating patients and helping them to use self-management programs can decrease hospital re-admission. The use of modern ICT tools is the best way to prevent CHF and thus provide, appropriate healthcare management, and increase people empowerment. The architecture of agent-based systems allows high interoperability, and efficient information management, and appropriate sharing data sharing. Clearly, identifying and resolving technical and non-technical challenges are vital to the successful implementation of this technology. Thus, in the design and implementation of agent-based systems many issues should be considered. Information confidentiality and privacy issues and reduced face-to-face communication between patients and doctors can lead to increased stress for in some CHF patients. System must be designed using an appropriate architecture and following communication standards and protocols. The mode of communication between agents, ethical and legal aspects, feedbacks, user attitudes, appropriate readiness, stakeholders supports to use technology, budgeting, motivations, coverage healthcare costs based on MASs by insurance company, hierarchy, cost afford, identification opportunities and barriers, designing and creating a database that is rich in order to provide support of various formats and training and tasks and backup of database.
It should be noted that the use of agent systems only with a technical view is associated with many problems, such as lack of user acceptance. Hence, in delivering healthcare to CHF patients, considering social and human aspects and the involvement of end users and consideration of their needs in all phases of a project is essential. Also, for users, especially elderly people, special attention should be paid to ease of use and user friendliness.

Notes

No potential conflict of interest relevant to this article was reported.

References

1. Stewart S, Jenkins A, Buchan S, McGuire A, Capewell S, McMurray JJ. The current cost of heart failure to the National Health Service in the UK. Eur J Heart Fail 2002;4(3):361-371. PMID: 12034163.
crossref pmid
2. Center for Disease Control and prevention. Heart failure fact sheet [Internet]. Atlanta (GA): Center for Disease Control and prevention; 2012. cited at 2013 July 21. Available from: http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/docs/fs_heart_failure.pdf
crossref
3. Lofvenmark C, Karlsson MR, Edner M, Billing E, Mattiasson AC. A group-based multi-professional education programme for family members of patients with chronic heart failure: effects on knowledge and patients' health care utilization. Patient Educ Couns 2011;85(2):e162-e168. PMID: 21050694.
crossref pmid
4. Konstam V, Gregory D, Chen J, Weintraub A, Patel A, Levine D, et al. Health-related quality of life in a multicenter randomized controlled comparison of telephonic disease management and automated home monitoring in patients recently hospitalized with heart failure: SPAN-CHF II trial. J Card Fail 2011;17(2):151-157. PMID: 21300305.
crossref pmid
5. Bloom DE, Cafiero ET, Jane-Llopis E, Abrahams-Gessel S, Bloom LR, Fathima S, et al. The global economic burden of non-communicable diseases: a report by the World Economic Forum and the Harvard School of Public Health [Internet]. Geneva: World Economic Forum; 2011. cited at 2013 July 23. Available from: http://www3.weforum.org/docs/WEF_Harvard_HE_GlobalEconomicBurdenNonCommunicableDiseases_2011.pdf
crossref
6. Weintraub A, Gregory D, Patel AR, Levine D, Venesy D, Perry K, et al. A multicenter randomized controlled evaluation of automated home monitoring and telephonic disease management in patients recently hospitalized for congestive heart failure: the SPAN-CHF II trial. J Card Fail 2010;16(4):285-292. PMID: 20350694.
crossref pmid
7. Dendale P, De Keulenaer G, Troisfontaines P, Weytjens C, Mullens W, Elegeert I, et al. Effect of a telemonitoring-facilitated collaboration between general practitioner and heart failure clinic on mortality and rehospitalization rates in severe heart failure: the TEMA-HF 1 (TElemonitoring in the MAnagement of Heart Failure) study. Eur J Heart Fail 2012;14(3):333-340. PMID: 22045925.
crossref pmid
8. Patel H, Shafazand M, Schaufelberger M, Ekman I. Reasons for seeking acute care in chronic heart failure. Eur J Heart Fail 2007;9(6-7):702-708. PMID: 17188930.
crossref pmid
9. Grady KL, Dracup K, Kennedy G, Moser DK, Piano M, Stevenson LW, et al. Team management of patients with heart failure: a statement for healthcare professionals from the Cardiovascular Nursing Council of the American Heart Association. Circulation 2000;102(19):2443-2456. PMID: 11067802.
crossref pmid
10. Finkelstein J, Khare R, Vora D. Home automated telemanagement (HAT) system to facilitate self-care of patients with chronic diseases. J Syst Cybern Inf 2003;1(3):78-82.
crossref
11. Cross RK, Finkelstein J. Feasibility and acceptance of a home telemanagement system in patients with inflammatory bowel disease: a 6-month pilot study. Dig Dis Sci 2007;52(2):357-364. PMID: 17211702.
crossref pmid
12. Finkelstein J, Cha E. Hypertension telemanagement in African Americans. Circ Cardiovasc Qual Outcomes 2009;2(3):272-278. PMID: 20031848.
crossref pmid pmc
13. Finkelstein J, Wood J. Delivering chronic heart failure telemanagement via multiple interactive platforms. J Syst Cybern Inf 2013;11(3):34-39.
crossref
14. Dunin-Keplicz B, Verbrugge R. Teamwork in multi-agent systems: a formal approach. Hoboken (NJ): John Wiley & Sons; 2010.
crossref
15. Annicchiarico R, Cortes U, Urdiales C. Agent technology and e-Health. Basel, Switzerland: Birkhauser; 2008.
crossref
16. Wooldridge MJ. An introduction to multiagent system. 2nd ed. Chichester, UK: John Wiley & Sons; 2009.
crossref
17. Isern D, Sanchez D, Moreno A. Agents applied in health care: a review. Int J Med Inform 2010;79(3):145-166. PMID: 20129820.
crossref pmid
18. Isern Alarcon D. Agent-based management of clinical guidelines [dissertation]. Barcelona, Spain: Universitat Politecnica de Catalunya; 2009.
crossref
19. Bichindaritz I, Vaidya S, Jain A, Jain LC. Computational intelligence in healthcare 4: advanced methodologies. Heidelberg, Germany: Springer; 2010.
crossref
20. Randell R, Dowding D. Organisational influences on nurses' use of clinical decision support systems. Int J Med Inform 2010;79(6):412-421. PMID: 20233670.
crossref pmid
21. Choi W, Rho MJ, Park J, Kim KJ, Kwon YD, Choi IY. Information system success model for customer relationship management system in health promotion centers. Healthc Inform Res 2013;19(2):110-120. PMID: 23882416.
crossref pmid pmc
22. Khoumbati K, Dwivedi Y, Srivastava A, Lal B. Handbook of research on advances in health informatics and electronic healthcare applications: global adoption and impact of information communication technologies. Hershey (PA): Medical Information Science Reference; 2010.
crossref
23. Cresswell K, Sheikh A. Organizational issues in the implementation and adoption of health information technology innovations: an interpretative review. Int J Med Inform 2013;82(5):e73-e86. PMID: 23146626.
crossref pmid
24. Venkatesh V, Thong JY, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 2012;36(1):157-178.
crossref
25. Hardiker NR, Grant MJ. Factors that influence public engagement with eHealth: a literature review. Int J Med Inform 2011;80(1):1-12. PMID: 21112244.
crossref pmid
26. Chae YM, Yoo KB, Kim ES, Chae H. The adoption of electronic medical records and decision support systems in Korea. Healthc Inform Res 2011;17(3):172-177. PMID: 22084812.
crossref pmid pmc
27. Safdari R, Mohammadzadeh N. Patient health monitoring through mobile health systems International Conference on Electronic Health (ICEH 2012); 2012 Nov 29-30. Tehran, Iran.
crossref
28. Safdari R, Mohammadzadeh N. Multi-agent systems and health information management 2nd e-Hospital and Telemedicine Symposium; 2011 Nov 8-9. Tehran, Iran.
crossref
29. Bellifemine F, Rimassa G, Poggi A. Developing multi-agent systems with a FIPA-compliant agent framework. Softw Pract Exp 2001;31(2):103-128.
crossref
30. Quaglini S, Stefanelli M, Cavallini A, Micieli G, Fassino C, Mossa C. Guideline-based careflow systems. Artif Intell Med 2000;20(1):5-22. PMID: 11185420.
crossref pmid
31. EU-Project K4CARE! [Internet]. Tarragona, Spain: K4CARE; 2008. cited at 2013 Sep 1. Available from: http://www.k4care.net/
crossref
32. Cripps H, Standing C. The implementation of electronic health records: a case study of bush computing the Ngaanyatjarra lands. Int J Med Inform 2011;80(12):841-848. PMID: 22001067.
crossref pmid
33. Lucas H. Information and communications technology for future health systems in developing countries. Soc Sci Med 2008;66(10):2122-2132. PMID: 18343005.
crossref pmid
34. Haux R. Individualization, globalization and health: about sustainable information technologies and the aim of medical informatics. Int J Med Inform 2006;75(12):795-808. PMID: 16846748.
crossref pmid
35. Viitanen J, Hypponen H, Laaveri T, Vanska J, Reponen J, Winblad I. National questionnaire study on clinical ICT systems proofs: physicians suffer from poor usability. Int J Med Inform 2011;80(10):708-725. PMID: 21784701.
crossref pmid
36. Hardiker NR, Grant MJ. Factors that influence public engagement with eHealth: a literature review. Int J Med Inform 2011;80(1):1-12. PMID: 21112244.
crossref pmid
37. Rigby M. The management and policy challenges of the globalisation effect of informatics and telemedicine. Health Policy 1999;46(2):97-103. PMID: 10346288.
crossref pmid
38. Lin H. Architectural design of multi-agent systems: technologies and techniques. Hershey (PA): Information Science Reference; 2007.
crossref
39. Alonso E, Kudenko D, Kazakov D. Adaptive agents and multi-agent systems: adaptation and multi-agent learning. Heidelberg, Germany: Springer; 2003.
crossref
40. Kostiadis K, Hunter M, Hu H. The use of design patterns for the development of multi-agent systems Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics; 2000 Oct 8-11. Nashville, TN; p. 280-285.
crossref
41. Bhat S, Wahid A. Multi agent based knowledge management: issues and challenges. Int J Comput Sci Commun 2010;1(2):293-297.
crossref
42. Han BM, Song SJ, Lee KM, Jang KS, Shin DR. Multi agent system based efficient healthcare service Proceedings of the 8th International Conference on Advanced Communication Technology; 2006 Feb 20-22. Pyeong Chang, Korea.
crossref
43. OpenGALEN [Internet]. OpenGALEN Foundation; cited at 2013 July 10. Available from: http://www.opengalen.org
crossref
44. Volot F, Joubert M, Fieschi M, Fieschi D. A UMLS-based method for integrating information databases into an Intranet. Proc AMIA Annu Fall Symp 1997;1997:495-499. PMID: 9357675.
crossref
45. Safdari R, Partovipour E, Maidani Z. Unified medical language the infrastructure of EHR. Tehran, Iran: Jaffari; 2009.
crossref
46. Nealon JL, Moreno A. Agent-based health care systems [Internet]. Tarragona, Spain: Universitat Rovira i Virgili; 2003. cited at 2013 Sep 1. Available from: http://deim.urv.cat/~itaka/Publicacions/eulat03a.pdf
crossref
47. Lawler EK, Hedge A, Pavlovic-Veselinovi S. Cognitive ergonomics, socio-technical systems, and the impact of healthcare information technologies. Int J Ind Ergon 2011;41(4):336-344.
crossref
TOOLS
Share :
Facebook Twitter Linked In Google+ Line it
METRICS Graph View
  • 10 Crossref
  • 13   Scopus
  • 3,509 View
  • 32 Download
Related articles in Healthc Inform Res


ABOUT
ARTICLE CATEGORY

Browse all articles >

BROWSE ARTICLES
FOR CONTRIBUTORS
Editorial Office
1618 Kyungheegung Achim Bldg 3, 34, Sajik-ro 8-gil, Jongno-gu, Seoul 03174, Korea
Tel: +82-2-733-7637, +82-2-734-7637    E-mail: hir@kosmi.org                

Copyright © 2024 by Korean Society of Medical Informatics.

Developed in M2community

Close layer
prev next