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Applications of Agents
in Healthcare
Robert Puckett
University 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.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
Pharmacy
Assistant
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 knowledge
model 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
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[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. 561567.
Even More References

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[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. 225231.
Still More References
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[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



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[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, “Agentbased 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.