Patient Journey Optimization using a Multi-agent approach

1. Patient Journey Optimization using a Multi-agent approach Choi Chung Ho

2. Agenda • Introduction • Problem formulation • Scheduling framework • Agent coordination • Experiments • Conclusion

3. Introduction

4. Our goal • To improve patient journey by reducing undesired waiting time for patients

5. How to achieve our goal? • To schedule patients in such a way that medical resources could be utilized in a more efficient manner

6. 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

7. 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

8. Problem formulation

9. Seven cancer centers in Hong Kong C = {HKE, HKW, KC, KE, KW, NTE, NTW}

10. Treatment operations ( ) { Radiotherapy planning, Radiotherapy, Surgery, Chemotherapy } Treatment operations and medical resources Treatment plan Medical resources (A) { Radiotherapy planning unit, Radiotherapy unit, Operation unit, Chemotherapy unit }

11. Patient journey • We define Patient journey as: Duration from the date of admission to the date of the last treatment operation completed

12. Scheduling framework

13. Two types of agents • Patient agent • Resource agent

14. Patient agent • A patient agent (Pi) is used to represent one cancer patient • Each Pi stores the corresponding patient’s treatment plan Treatment plan

15. Center (HKE) Center (KW) Center (KC) Center (NTW) Radiotherapy planning unit Radiotherapy unit Operation unit Center (NTE) Center (HKW) Center (KE) Chemotherapy unit Resource agent • A resource agent is used to representone specific medical unit, denoted as Rab aA, bC

16. Pareto improvement Scheduling algorithm

17. Agent coordination

18. Coordination framework

19. 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

20. Earliest possible start date (EPS) (j – 1) th treatment operation

21. Latest possible start date (LPS) (j – 1) th treatment operation j th treatment operation 1 day

22. Coordination framework (cont.)

23. Coordination framework (cont.) • In order to compute the bid value, three binary variables were defined: • 1) Last • 2) Noti • 3) Temp

24. 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

25. 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

26. 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

27. Coordination framework (cont.) • For each target patient agent PG:

28. Coordination framework (cont.) Coordination process for eliminating unnecessary exchanges

29. Unnecessary exchanges

30. Experiments

31. 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

32. 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

33. 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

34. Experimental results Group A Group B

35. Experimental results (cont.) Group B

36. Conclusion

37. 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

38. 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

39. The end