patient journey optimization using a multi-agent approach

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Patient Journey Optimization using a

Multi-agent approach

Choi Chung Ho

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Agenda

Introduction Problem formulation Scheduling framework Agent coordination Experiments Conclusion

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Introduction

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Our goal

To improve patient journey by reducing undesired waiting time for patients

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How to achieve our goal?

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?

Hospitals are found to have a decentralized structure

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

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.

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, E-Commerce, and E-Services, page 9, Washington, DC, USA, 2006. IEEE Computer Society.

The use of health state as an utility function has been challenged

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

Medical resources (A)

{ Radiotherapy planning unit, Radiotherapy unit, Operation unit, Chemotherapy unit }

Treatment operations ( )

{ Radiotherapy planning, Radiotherapy, Surgery, Chemotherapy }

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Patient journey

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

Patient agent Resource agent

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Patient agent

A patient agent (Pi) is used to represent one cancer patient

Each Pi stores the corresponding patient’s treatment plan

Treatment plan

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Resource agent

A resource agent is used to represent one specific medical unit, denoted as Rab a A,

b C

Center(HKE)

Center(HKW)

Center(KC)

Center(KE)

Center(KW)

Center(NTE)

Center(NTW)

Radiotherapy planning unit

Radiotherapy unit

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.)

For each request, it includes:

1) Earliest possible start date (EPS)It is the earliest date on which a treatment operation could start

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.)

In order to compute the bid value, three binary variables were defined:

1) Last 2) Noti 3) Temp

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Coordination framework (cont.)

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.)

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.)

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.)

For each target patient agent PG:

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Coordination framework (cont.)

Coordination process for eliminating u

nnecessary exchanges

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Unnecessary exchanges

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Experiments

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Data set

5819 cancer patients in Hong Kong, with an admission period of 6 months (1/7/2007 – 31/12/2007)

The average length of patient journey is 90.7 days before applying our framework

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Experiments (cont.)

Group A: The scheduled treatment plans in the dataset are used for the initial assignment

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 Setting 1) All patient agents are willing to exchange their

timeslots with others whenever there is a Pareto improvement

Setting 2) Only 20% of the patients of each center are allowed to exchange their timeslots

Setting 3) Patients are only be swapped to a nearby cancer center

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

A multi-agent framework has been proposed for patient scheduling

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.)

Experiments show that the average length of patient journey could be reduced by about a week’s time by using the proposed framework

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