Applications of Agentsin Healthcare

Robert PuckettUniversity of Hawai`i at Manoa

April 24, 2010

Outline What are Agents? What are Multi-Agent Systems? The Legacy of AI Agent Applications for

Hospital Administration Patient Monitoring Community Outreach Continuing Education Integrated Medical Systems

What are agents?

Autonomous software entity working on your behalf

Formerly known as “Distributed AI” Wide range of agent properties:

Deliberative, Pro-active 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 agents Access to equipment, databases, sensors Interfaces with people, experts

Rules that define interactions, goals

Challenges in Healthcare

Security, Trust, Accuracy, Privacy Social Inertia Time-sensitive Specialized medical legacy equipment Rapidly 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 clinical 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 development of integrated environments" ... "the notion of stand-alone consultation systems had been well debunked by the late 1980s" [2]

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

Hospital Administration Monitoring medical protocol adherence [4] Scheduling of operating rooms, beds Cost management

Antibiotics for restricted use (ARU) [3] Organ transplant coordination [7] Simulation of emergency departments [5],

bio-terrorism response [6]

Gauge resource/staff utilization Identify bottlenecks

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

Antibiotics for Restricted Use (ARU) Monitoring MAS decision support system to revise and

propose alternative antibiotics therapies ARU's expensive, pathology specific,

aggressive Pharmacology assistant program study

showed 12.5% of ARU treatments warranted an

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

expenditures

PharmacyAssistant

ARU System

ARU Agents Guardian angel

Represents patient, has his medical info Interacts with other agents to review and

revise medical orders for ARU's Physician secretary

Provides access to physician Knows physicians work hours, preferences

Laboratory manager Manages analysis requests, delivers results

ARU Agents

Pharmacy expert Suggests antibiotic revisions based upon

patient data and lab analysis Nurse

Collects medical orders for patients when requested by human nurse

Patient Monitoring Guardian: ICU patient monitor [18]

Reasoning and context-based skills 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

trauma Sort/analyze complex and dynamic

information Provide diagnostics, warnings, intervention

advice

IM-Agent Architecture

DDM: Dynamic Decision Module

Community Outreach

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

Low health literacy -> liking the agent Home 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]

AMPLIA

A medical diagnostic learning environment hypothetical model construction diagnostic reasoning

1. Learner specifies her knowledgemodel via probabilistic networks

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 user 3. Negotiation and educational review of

her knowledge model DomainAgent determines degree user

model differs from built-in model

AMPLIA: Architecture

AMPLIA: Built-in Model

AMPLIA: User Interface

Integrated Medical Systems E-medicine: integrates information,

communication, human-machine interfaces 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

Agent.Hospital

Testbed for healthcare agent information systems [17]

development and evaluation for modeling and implementation

integrates models of numerous interdependent supply chains

Agent.Hospital

Conclusions

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

systems Agents provide a logical abstraction to

complexity of tasks Don't promise HAL and deliver Eliza

References [1] G. Lanzola, L. Gatti, S. Falasconi, and M. Stefanelli, “A framework

for building cooperative software agents in medical applications,” 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 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.

More References [5] L. Patvivatsiri, “A simulation model for bioterrorism preparedness

in an emergency room,” Proceedings of the 38th conference on Winter 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.”

[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. 183-196.

[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.

Still More References [13] T. Bickmore and D. Schulman, “Practical approaches to

comforting users with relational agents,” CHI '07 extended abstracts 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, 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 Engineering, 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. 201-216.

[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.