Transcript Patient Journey Optimization using a Multi

Patient Journey
Optimization using a
Multi-agent approach
Choi Chung Ho
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Agenda
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Introduction
Problem formulation
Scheduling framework
Agent coordination
Experiments
Conclusion
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Introduction
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Our goal
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To improve patient journey by reducing
undesired waiting time for patients
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How to achieve our goal?
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To schedule patients in such a way that
medical resources could be utilized in a more
efficient manner
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Why using a multi-agent
approach?
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Hospitals are found to have a decentralized
structure
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 A multi-agent approach is proposed as it
favors the coordination between
geographically distributed entities
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Related works of using a multi-agent
approach for patient scheduling
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T. O. Paulussen, I. S. Dept, K. S. Decker, A. Heinzl, and N. R.
Jennings. Distributed patient scheduling in hospitals. In Coordination
and Agent Technology in Value Networks. GITO, pages 1224–1232.
Morgan Kaufmann, 2003.
The use of health state as an utility
function has been challenged
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I. Vermeulen, S. Bohte, K. Somefun, and H. La Poutre. Improving
patient activity schedules by multi-agent pareto appointment
exchanging. In CEC-EEE ’06: Proceedings of the The 8th IEEE
International Conference on E-Commerce Technology and The 3rd
IEEE International Conference on Enterprise Computing, ECommerce, and E-Services, page 9, Washington, DC, USA, 2006.
IEEE Computer Society.
Temporal constraints between
treatment operations are not
considered
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Problem formulation
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Seven cancer centers in Hong
Kong
C = {HKE, HKW, KC, KE, KW, NTE, NTW}
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Treatment operations and
medical resources
Treatment
plan
Treatment operations (
)
{ Radiotherapy planning, Radiotherapy, Surgery, Chemotherapy }
Medical resources (A)
{ Radiotherapy planning unit, Radiotherapy unit, Operation unit, Chemotherapy unit }
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Patient journey
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We define Patient journey as:
Duration from the date of admission to the
date of the last treatment operation
completed
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Scheduling framework
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Two types of agents
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Patient agent
Resource agent
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Patient agent
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A patient agent (Pi) is used to represent one
cancer patient
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Each Pi stores the corresponding patient’s
treatment plan
Treatment
plan
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Resource agent
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A resource agent is used to represent one
specific medical unit, denoted as Rab a A,
b C
Center
(HKE)
Center
(KC)
Center
(KW)
Center
(NTW)
Radiotherapy planning unit
Radiotherapy unit
Center
(HKW)
Center
(KE)
Center
(NTE)
Operation unit
Chemotherapy unit
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Scheduling algorithm
Pareto
improvement
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Agent coordination
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Coordination framework
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Coordination framework (cont.)
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For each request, it includes:
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1) Earliest possible start date (EPS)
It is the earliest date on which a treatment operation could start
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2) Latest possible start date (LPS)
It is the latest date on which a treatment operation should start such
that the treatment operation could be performed earlier
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Earliest possible start date
(EPS)
(j – 1) th treatment operation
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Latest possible start date (LPS)
(j – 1) th treatment operation
j th treatment operation
1 day
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Coordination framework (cont.)
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Coordination framework (cont.)
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In order to compute the bid value, three
binary variables were defined:
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1) Last
2) Noti
3) Temp
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Coordination framework (cont.)
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Last is a binary variable that specifies
whether the involving treatment operation is
the last one in PG’s treatment plan;
Last = 0 if it is not the last one;
otherwise
1 th treatment operation
2 nd treatment operation
3 rd treatment operation
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Coordination framework (cont.)
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Noti is a binary variable that specifies
whether there is a week’s time of notification
for the target patient agent regarding the
exchange;
Noti = 0 if there is a week’s time of
notification;
otherwise
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Coordination framework (cont.)
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Temp is a binary variable that specifies
whether the temporal constraints between
treatment operations are violated for the
target patient agent after the proposed
exchange;
Temp = 0 if no violation; otherwise
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Coordination framework (cont.)
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For each target patient agent PG:
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Coordination framework (cont.)
Coordination
process for
eliminating
unnecessary
exchanges
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Unnecessary exchanges
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Experiments
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Data set
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5819 cancer patients in Hong Kong, with an
admission period of 6 months (1/7/2007 –
31/12/2007)
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The average length of patient journey is 90.7
days before applying our framework
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Experiments (cont.)
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Group A: The scheduled treatment plans in
the dataset are used for the initial assignment
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Group B: Only the statistics of the scheduled
treatment plans and the capacities of medical
units are used for the initial assignment
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Experiment settings
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Setting 1) All patient agents are willing to exchange their
timeslots with others whenever there is a Pareto improvement
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Setting 2) Only 20% of the patients of each center are allowed to
exchange their timeslots
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Setting 3) Patients are only be swapped to a nearby cancer
center
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Setting 4) Timeslots released by deceased patients are allocated
to those who have the longest patient journey
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Experimental results
Group A
Group B
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Experimental results (cont.)
Group B
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Conclusion
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Conclusion and future works
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A multi-agent framework has been proposed
for patient scheduling
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In this framework, while no single patient will
get a lengthened patient journey, all the
temporal constraints between treatment
operations would not be violated
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Conclusion and future works
(cont.)
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Experiments show that the average length of
patient journey could be reduced by about a
week’s time by using the proposed
framework
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In the future, we are going to see how the
bids submitted by the target patient agent
could be defined in a more sophisticated way
such that the overall patient journey could be
shortened in greater extent
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The end
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