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Applications of Agentsin Healthcare

Robert PuckettUniversity of Hawai`i at Manoa

March 18, 2010

OutlineWhat are Agents?What are Multi-Agent Systems?The Legacy of AIAgent Applications for

− Hospital AdministrationP ti t M it i− Patient Monitoring

− Community Outreach− Continuing Education− Integrated Medical Systems

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What are agents?

Autonomous software entity working on your behalfFormerly known as “Distributed AI”Wide range of agent properties:

− Deliberative, Pro-activeCommunicative / Social− Communicative / Social

− Observant & Reactive− Mobile

What are Multi-Agent Systems?The Agents

− Heterogeneous vs. homogeneous− Cooperative vs. Competitive− Role-based vs. Task-based

Environment− Observable by the agentsObservable by the agents− Access to equipment, databases,

sensors− Interfaces with people, experts

Rules that define interactions, goals

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Challenges in Healthcare

Security, Trust, Accuracy, PrivacySocial InertiaTime-sensitiveSpecialized medical legacy equipmentRapidly changing knowledge base

− Prescriptions, medical procedures, drug interactions, treatment options

Distributed medical knowledge

The AI Legacy“AI applications in Medicine failed to achieve a widespread distribution in the pclinical practice despite the outstanding performance shown by many of them” [1]

− Free-standing, isolated systems"Practical influence of [AI in medicine] in real-world settings will depend on the g pdevelopment of integrated environments" ... "the notion of stand-alone consultation systems had been well debunked by the late 1980s" [2]

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Agents in Healthcare

“I'm sorry Dave, but I don't think you need this insulin.”

Photos from: 2001: A Space Odyssey (1968)

From: http://www.doc.ic.ac.uk/~hkulatun/talks/Control_in_Healthcare.pdfFrom: http://www.doc.ic.ac.uk/~hkulatun/talks/Control_in_Healthcare.pdf

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Hospital AdministrationMonitoring medical protocol adherence [4]Scheduling of operating rooms, bedsg p g ,Cost management

− Antibiotics for restricted use (ARU) [3]Organ transplant coordination [7]Simulation of emergency departments [5], bio terrorism response [6]bio-terrorism response [6]

− Gauge resource/staff utilization− Identify bottlenecks

Link: http://www.youtube.com/watch?v=ilLylU1u0iQ

Antibiotics for Restricted Use (ARU) MonitoringMAS decision support system to revise and propose alternative antibiotics therapiesARU's expensive, pathology specific, aggressivePharmacology assistant program study showed

− 12.5% of ARU treatments warranted an intervention

− 92% of them were accepted− significant decrease in total antibiotic

expenditures

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PharmacyAssistant

ARU System

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ARU AgentsGuardian angel

R t ti t h hi di l i f− Represents patient, has his medical info− Interacts with other agents to review and

revise medical orders for ARU'sPhysician secretary

− Provides access to physician− Knows physicians work hours,

preferencesLaboratory manager

− Manages analysis requests, delivers results

ARU Agents

Pharmacy expert− Suggests antibiotic revisions based upon

patient data and lab analysisNurse

− Collects medical orders for patients when requested by human nurse

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Patient MonitoringGuardian: ICU patient monitor [18]

− Reasoning and context-based skillsPrepares short latency contingency− Prepares short-latency contingency reactions

Intelligent Monitor Agents (IM-Agents) [20]− Cooperating agents for specialized

monitoring and diagnostic tasks− Prototype of decision making for emergency yp g g y

trauma− Sort/analyze complex and dynamic

information− Provide diagnostics, warnings, intervention

advice

IM-Agent Architecture

DDM: Dynamic Decision Module

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Community Outreach

Hospital search and appointment system [8]Health reminder/alerts (R2Do2) [10]Explaining medical terminology [9]

− Low health literacy -> liking the agentHome care management systems

− 'K4Care' general system [11]− 'Super-Assist' (Diabetes) [12]

Empathic comforting agents [13]

Continuing Education

Agent assisted web search and filtering − “a 97% decrease in information overload

and an 85% increase in information relevancy over existing meta-search tools (with even larger gains over standard search engines).” [14]

Amplia: Agent-based medical training [15]

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AMPLIA

A medical diagnostic learning environment− hypothetical model construction− diagnostic reasoning

1. Learner specifies her knowledgemodel via probabilistic networks

LearnerAgent maintains model− LearnerAgent maintains model− System asks her about decisions− Assumes physicians implicitly

perform probabilistic reasoning

AMPLIA

2. Feedback and information provided to user

− Qualitative diagnosis strategy training− MediatorAgent decides educational

strategy for user3. Negotiation and educational review of her knowledge model

− DomainAgent determines degree user model differs from built-in model

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AMPLIA: Architecture

AMPLIA: Built-in Model

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AMPLIA: User Interface

Integrated Medical SystemsE-medicine: integrates information, communication, human-machine interfaces with health and medical technologies [16]with health and medical technologies [16]Salsa: Ambient Intelligence [19]

− Context-aware, ubiquitous technology− Adaptive, reacting to context and user

behavior− Agents act on behalf of users, share

information, represent and activate services, serve as wrapper for sensitive information

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Agent.Hospital

Testbed for healthcare agent information systems [17]development and evaluation for modeling and implementationintegrates models of numerous interdependent supply chainsp pp y

Agent.Hospital

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Conclusions

Great diversity of ways to apply agents− Simulators, Solvers, Collaboration

systemsAgents provide a logical abstraction to complexity of tasksDon't promise HAL and deliver Elizap

References[1] G. Lanzola, L. Gatti, S. Falasconi, and M. Stefanelli, “A framework for building cooperative software agents in medical applications,” g p g pp ,Artificial Intelligence in Medicine, vol. 16, Jul. 1999, pp. 223-249.

[2] V.L. Patel, E.H. Shortliffe, M. Stefanelli, P. Szolovits, M.R. Berthold, R. Bellazzi, and A. Abu-Hanna, “The coming of age of artificial intelligence in medicine,” Artificial Intelligence in Medicine, vol. 46, May. 2009, pp. 5-17.

[3] L. Godo, J. Puyol-Gruart, J. Sabater, V. Torra, P. Barrufet, and X. Fàbregas, “A multi-agent system approach for monitoring the prescription of restricted use antibiotics ” Artificial Intelligence inprescription of restricted use antibiotics, Artificial Intelligence in Medicine, vol. 27, Mar. 2003, pp. 259-282.

[4] T. Alsinet, R. Béjar, C. Fernanadez, and F. Manyà, “A Multi-agent system architecture for monitoring medical protocols,” Proceedings of the fourth international conference on Autonomous agents, Barcelona, Spain: ACM, 2000, pp. 499-505.

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More References[5] L. Patvivatsiri, “A simulation model for bioterrorism preparedness in an emergency room,” Proceedings of the 38th conference on g y , gWinter simulation, Monterey, California: Winter Simulation Conference, 2006, pp. 501-508.

[6] H. Stainsby, M. Taboada, and E. Luque, “Towards an Agent-Based Simulation of Hospital Emergency Departments,” Proceedings of the 2009 IEEE International Conference on Services Computing, IEEE Computer Society, 2009, pp. 536-539.

[7] J.B. Antonio, A. Moreno, and A. Valls, “Hospital Arrangements for a Transplant Operation using Agents ”a Transplant Operation using Agents.

[8] T. Edwards and S. Sankaranarayanan, “Intelligent agent based hospital search & appointment system,” Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, Seoul, Korea: ACM, 2009, pp. 561-567.

Even More References[9] T. Bickmore, L. Pfeifer, and M. Paasche-Orlow, “Health Document Explanation by Virtual Agents,” Intelligent Virtual Agents, 2007, pp. p y g , g g , , pp183-196.

[10] B.G. Silverman, C. Andonyadis, and A. Morales, “Web-based health care agents; the case of reminders and todos, too (R2Do2),” Artificial Intelligence in Medicine, vol. 14, Nov. 1998, pp. 295-316.

[11] D. Isern, A. Moreno, D. Sánchez, Á. Hajnal, G. Pedone, and L. Varga, “Agent-based execution of personalised home care treatments,” Applied Intelligence.

[12] G.D. Haan, O.B. Henkemans, and A. Aluwalia, “Personal assistants for healthcare treatment at home,” Proceedings of the 2005 annual conference on European association of cognitive ergonomics, Chania, Greece: University of Athens, 2005, pp. 225-231.

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Still More References[13] T. Bickmore and D. Schulman, “Practical approaches to comforting users with relational agents,” CHI '07 extended abstracts g g ,on Human factors in computing systems, San Jose, CA, USA: ACM, 2007, pp. 2291-2296.

[14] S. Walczak, “A multiagent architecture for developing medical information retrieval agents,” Journal of Medical Systems, vol. 27, Oct. 2003, pp. 479-498.

[15] R.M. Vicari, C.D. Flores, A.M. Silvestre, L.J. Seixas, M. Ladeira, and H. Coelho, “A multi-agent intelligent environment for medical knowledge ” Artificial Intelligence in Medicine vol 27 Mar 2003 ppknowledge, Artificial Intelligence in Medicine, vol. 27, Mar. 2003, pp. 335-366.

[16] J. Tian and H. Tianfield, “A Multi-agent Approach to the Design of an E-medicine System,” Multiagent System Technologies, 2003, pp. 1093-1094.

And Yet More References[17] S. Kirn, C. Anhalt, H. Krcmar, and A. Schweiger, “Agent.Hospital — Health Care Applications of Intelligent Agents,” Multiagent pp g g , gEngineering, 2006, pp. 199-220.

[18] B. Hayes-Roth, R. Washington, D. Ash, R. Hewett, A. Collinot, A. Vina, and A. Seiver, “Guardian: A prototype intelligent agent for intensive-care monitoring,” Artificial Intelligence in Medicine, vol. 4, Mar. 1992, pp. 165-185.

[19] M.D. Rodríguez, J. Favela, A. Preciado, and A. Vizcaíno, “Agent-based ambient intelligence for healthcare,” AI Commun., vol. 18, 2005 pp 201 2162005, pp. 201-216.

[20] S.L. Mabry, T. Schneringer, T. Etters, and N. Edwards, “Intelligent agents for patient monitoring and diagnostics,” Proceedings of the 2003 ACM symposium on Applied computing, Melbourne, Florida: ACM, 2003, pp. 257-262.