PalliaSys: agent-based proactive monitoring of palliative patients

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PalliaSys agent-based proactive monitoring of
palliative patients

• A.Moreno, A.Valls, D.Riaño
• Multi-Agent Systems Group (GruSMA)
• Research Group on Artificial Intelligence
• Computer Science and Maths Dep.
• University Rovira i Virgili (URV)
• Tarragona, Spain

4th Intern. Workshop on Practical Applications of
Agents and Multi-Agent Systems León, 2005
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Outline of the talk

• Introduction to the Palliasys project
• The PalliaSys prototype
• Architecture
• Information Collection and Access
• Monitoring using Alarms
• Conclusions and future work

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Introduction PalliaSys Project

• Integration of Information Technologies and
  Multi-Agent Systems to improve the care given to
  palliative patients.
• Spanish research project, 2004-05.
• Work conducted between the Research Group on
  Artificial Intelligence at URV and the Palliative
  Care Unit of the Hospital de la Santa Cruz y San
  Pablo of Barcelona.

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Introduction PalliaSys Project

• Palliative patients are in a very advanced stage
  of a fatal disease. The aim of their care is to
  ease their pain.
• These patients may be located in hospitals
  (Palliative Care Units-PCU, or other units of the
  hospital), specialised hospice centres or at
  their own homes.

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Aims of the PalliaSys project

• To improve the process of collecting information
  from the palliative patients.
• To improve the access to this information by
  patients and doctors.
• To monitor the state of the patients.
• To apply intelligent data analysis techniques on
  the data of the PCU.

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The PalliaSys prototype Architecture
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Information collection

• Patients have to send periodically non-technical
  information relative to their health state.
• Fill in a form with 10 items to be valued 0-10
• In the current prototype forms can be sent
• through a web page, or
• with a mobile phone via WAP (simulated).
• Other communication means (PDAs, e-mails, SMS
  messages) are being developed.

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Information access (I)

• All the data of the palliative patients is stored
  in a central Data Base at the PCU of the
  hospital.
• Personal information, family data,
  auto-evaluations, health record
• Patients and doctors can access it.
• Patient queries are made directly on the DB (via
  web or WAP-simulated interface).
• Doctor queries are made through agent
  communication (the Doctor Agent requesting the
  information from the DB Wrapper).

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Information access (II)

• There is an agent that controls the access to the
  Data Base (the DataBase Wrapper).
• The whole system includes security mechanisms to
  protect the privacy of the medical data.
• User authentication (private-public keys)
• Encrypted messages (SSL)
• Access through login/password
• Permissions associated to user types

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The PalliaSys prototype Architecture
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Patient agents

• There is a patient agent associated to each
  palliative patient.
• It has to continuously monitor the status of the
  patient, and send alarms to the doctor associated
  to the patient if something goes wrong.

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Doctor agents

• A doctor agent is an agent associated to each
  doctor of the PCU, which is running in the
  doctors desktop computer.
• It provides a graphical interface to help
• Add new patients.
• Request information about his patients.
• Define alarm situations.
• Receive alarm signals from patient agents.

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Classes of alarms

• General alarms
• They are defined by the PCU head (through his
  Doctor Agent), and they have to be applied to all
  the patients of the unit.
• Doctor-specific alarms
• A doctor can define personal alarms, and he can
  assign them
• to a single patient, or
• to all his patients.

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Patient auto-evaluation

• There are 10 different aspects in patients
  auto-evaluation forms (weakness, pain, anxiety,
  hunger, etc).
• Each of the aspects has to be evaluated by the
  patient with an integer number from 0 to 10.
• Each patient has to send an auto-evaluation form
  every 2-3 weeks.

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Alarm types Basic alarms

• Alarms defined on a single auto-evaluation
• (Weakness gt7) and (Pain gt 8) extreme_weakness
• (Hunger lt 3) and extreme_weakness
  dangerous_weakness
• Extreme_weakness gt patients 1, 3 and 4
• Dangerous_weakness gt patients 2, 3 and 7.
• They can be combined with and/or/not operators.
• Simple alarms can be used to define more complex
  alarms.

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Alarm types Evolution alarms

• Alarms defined on a sequence of auto-evaluations
• 2e D Weakness gt 2 fast_weakness_increase
• 4e D Pain gt 3 fast_pain_increase
• 60d D Pain gt 5 extreme_pain_increase
• These types of alarms may be defined on the last
  n evaluations or on the evaluations received in a
  certain period of time.
• The use of Boolean operators and the definition
  of complex alarm situations is also allowed.

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Alarm management (I)

• Definition
• Alarms are defined by doctors through their
• Doctor Agents.
• Storage
• When an alarm is defined, it is automatically
  sent to the corresponding Patient Agent (or set
  of agents).
• It is also stored in the DB for proper recovery
  if necessary.

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Alarm management (II)

• Check
• When a new auto-evaluation is stored on the DB,
  the associated Patient Agent gets a signal, and
  then it checks all the alarms associated to that
  patient.
• Raise
• If any alarm situation is detected, a message is
  sent to the Doctor Agent that defined it with an
  explanation of why the alarm has been activated.

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Conclusion - Main ideas

• Information technologies and Intelligent agents
  may be used to build useful systems in the Health
  Care domain.
• The PalliaSys project is an example of the use of
  those tools.
• Most of the ideas underlying this project might
  also be applied in elderly care or home care.
• Use of Information Technologies
• Automated patient monitoring
• Intelligent data analysis

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Future work

• Explore the use of mobile phones to receive/send
  information from/to home patients.
• Improve the data analysis algorithms.
• Deploy and test the prototype.

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PalliaSys agent-based proactive monitoring of
palliative patients

• A.Moreno, A.Valls, D.Riaño
• Multi-Agent Systems Group (GruSMA)
• Research Group on Artificial Intelligence
• Computer Science and Maths Dep.
• University Rovira i Virgili (URV)
• Tarragona, Spain

4th Intern. Workshop on Practical Applications of
Agents and Multi-Agent Systems León, 2005