Patient Journey Patient Journey Optimization using a Optimization using a Multi-agent ApproachMulti-agent Approach

Victor Choi

Supervisor: Dr. William CheungCo-supervisor: Prof. Jiming Liu

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AgendaAgendaIntroductionPatient scheduling problem in

Hong KongProposed scheduling frameworkExperimentsConclusions and future works

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INTRODUCTIONINTRODUCTION

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ObjectiveObjectiveTo improve patient journey by

reducing undesired waiting times for patients

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How to achieve our How to achieve our objectiveobjectiveWith limited medical resources,

we need to schedule patients in a way such that the resources could be utilized in a more efficient manner

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Reasons of using a multi-Reasons of using a multi-agent approachagent approachIt is found that hospitals have a

decentralized structure, a multi-agent approached is proposed since it favors geographically distributed entities to be coordinated

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Related works of using a Related works of using a multi-agent approach for multi-agent approach for patient schedulingpatient 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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PATIENT SCHEDULING PATIENT SCHEDULING PROBLEM IN HONG PROBLEM IN HONG KONGKONG

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Seven cancer clusters in Seven cancer clusters in Hong KongHong Kong

C = {HKE, HKW, KC, KE, KW, NTE, NTW}9

Treatment operations and Treatment operations and medical resourcesmedical resources

Treatment plan

Treatment operations

{ Radiotherapy, Surgery, Chemotherapy }

Medical resources (A)

{ Radiotherapy unit, Operation unit, Chemotherapy unit }

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Patient journeyPatient journeyWe define patient journey as the

duration between the date of diagnosis and the date of the last treatment completed

Patient journey

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PROPOSED PROPOSED SCHEDULING SCHEDULING FRAMEWORKFRAMEWORK

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Two types of agentsTwo types of agentsPatient agentResource agent

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Patient agentPatient agent

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Resource agentResource agent

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Resource agent (cont.)Resource agent (cont.)

Cluster(HKE)

Cluster(HKW)

Cluster(KC)

Cluster(KE)

Cluster(KW)

Cluster(NTE)

Cluster(NTW)

Radiotherapy unit

Operation unit

Chemotherapy unit

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Scheduling algorithmScheduling algorithm

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Coordination frameworkCoordination framework

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Coordination framework Coordination framework (cont.)(cont.)For each request, it includes:

Earliest Possible Start Date (EPS)◦The earliest date on which a treatment

operation could start

Latest Possible Start Date (LPS)◦The latest date on which a treatment

operation should start such that the treatment operation could be performed earlier

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Coordination framework Coordination framework (cont.)(cont.)

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Coordination framework Coordination framework (cont.)(cont.)For each Target patient agent PG :

Last = 0 if the involving treatment operation is not the last one for PG; otherwise

Temp = 0 if no temporal constraints are violated for PG; otherwise

Noti = 0 if there is a week’s time of notification for PG ;

otherwise

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EXPERIMENTSEXPERIMENTS

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DatasetDatasetA dataset provided by the Hospital

Authority in Hong Kong (containing 4720 cancer patient journeys) is used for performing the simulation

The diagnosis period of these 4720 patient journeys spanned across six months (1/7/2007 – 31/12/2007)

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4 experiment settings4 experiment settingsSetting 1: Patient agents are willing to

exchange timeslots with others whenever none of their overall schedules would be lengthened as a result

Setting 2: Only 20% of patients from each cancer cluster are allowed to exchange their timeslots

Setting 3: Patients are only be swapped to a nearby cancer cluster

Setting 4: Timeslots released by deceased patients are allocated to the patient agents with the longest patient journey

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ResultsResults

Average length of patient journey

Maximum length of patient journey

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Simulations revealing the Simulations revealing the impacts of varying the unit impacts of varying the unit capacitiescapacitiesTo study the cost-effectiveness of

increasing the capacities of medical units, 3 different timeslot allocation strategies were used:

1) 2 timeslots were added to each medical unit on a daily-basis

2) 14 timeslots were added to each medical unit on a weekly-basis

3) 60 timeslots were added to each medical unit on a monthly-basis

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Simulations revealing the Simulations revealing the impacts of varying the unit impacts of varying the unit capacities - Resultscapacities - Results

Average length of patient journey

Maximum length of patient journey

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CONCLUSIONS AND CONCLUSIONS AND FUTURE WORKSFUTURE WORKS

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Conclusions and future Conclusions and future worksworksA multi-agent framework had

been proposed for patient scheduling

While no temporal constraints are violated for any single patient, no patients will get a lengthened overall schedule

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Conclusions and future Conclusions and future works (cont.)works (cont.)Experiments showed that even with a

fixed amount of medical resources, the average length of patient journey could be shortened by about a week’s time

In the near future, rather than routinely allocate a fixed amount of additional timeslots to each cancer cluster, we are going to assess how resources (or timeslots) should be allocated to cancer clusters in a more sophisticated way such that the overall patient journey could be shortened in a greater extent.

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THE ENDTHE END

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Q & AQ & A

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