Process Mining in Healthcare. Ronny Mans

Transcription

1 Process Mining in Healthcare Ronny Mans

2 Introduction This talk: Applicability of Process Mining in the healthcare domain Challenges -> Results from applying Process Mining in the AMC PAGE 1

3 Overview Introduction AMC AMC case study DBCs gynecological oncology DBCs GO + radiotherapy + chemotherapy Future work Conclusion Questions / Discussion PAGE 2

4 Academic Medical Center (AMC) University hospital, Amsterdam 1002 beds patients admitted day admissions outpatient clinic visits Patients Own region Outside region PAGE 3

5 AMC AND Healthcare processes are highly variable -> Not known what happens in a healthcare process Process improvement projects Time consuming to collect data PAGE 4

6 Data? Introduction Each department has their system Integration often difficult Payment system contains all events DBC: Diagnosis Treatment Combination Episode of care All the care steps (inpatient, outpatient, day, and after care) that may be delivered for a specific medical problem or condition. Each service delivered to a patient is linked to one DBC PAGE 5

7 Data? DBC code: Cervical cancer Patient: Sue Visit outpatient clinic Lab test X-ray CT MRI DBC code: Ovarian cancer Patient: Rose Visit outpatient clinic Pathology Lab test MRI PAGE 6

8 Overview Introduction AMC AMC case study DBCs gynecological oncology DBCs GO + radiotherapy + chemotherapy Conclusion Future work Questions / Discussion PAGE 7

9 Case AMC log Case 627 Gynaecological oncology patients 376 Events Audit trail entries Gynaecology, Nursing wards, Radiology, All care steps for GO patients PAGE 8

10 Data Only day timestamps PAGE 9

11 Data Single lab tests PAGE 10

12 Data Visit to one department PAGE 11

13 Log Preprocessing Filtering: Getting the right abstraction Remapping Aggregation PAGE 12

14 Filtering Representative R Keep R Remove others Remap Element Log PAGE 13

15 Example Remap Element Log Acceptance Lab Test Na Ka Xray Hg Acceptance Lab Test Xray Hg Na O2-sat PAGE 14

16 Filtering No Representative R Define R Remap all to R Remap Element Log Aggregate repetitions in trace Repetition to Activity lab representative lab representative lab representative lab representative lab representative lab representative lab representative PAGE 15

17 Example Remap Element Log Repetition to Activity lab NaR lab KaR Xray lab HgR Xray lab Hg R lab NaR lab O2-sat R PAGE 16

18 Result after filtering Case PAGE 17

19 Log Preprocessing (2) Case Clustering: Grouping similar behaviour Trace clustering plug-in PAGE 18

20 Case clusters Case PAGE 19

21 Case biggest cluster Case PAGE 20

22 Case Social Network Case Interaction with dietics department PAGE 21

23 Case Case Basic Performance Analysis Plug-in PAGE 22

24 Results so far Results Complex hospital logs can be mined Log pre-processing can be used to derive understandable models Filtering for getting the right abstraction Clustering for analysing common behaviour PAGE 23

25 Overview Motivation AMC case study DBCs gynecological oncology DBCs GO + radiotherapy + chemotherapy Future work Conclusion Questions PAGE 24

26 AMC data DBC: Diagnosis Treatment Combination Episode of care All the care steps (inpatient, outpatient, day, and after care) that may be delivered for a specific medical problem or condition. Each service delivered to a patient is linked to one DBC Whole care path of gynecological oncology Gynecological oncology Radiotherapy Internal medicine PAGE 25

27 DBCs DBC code: GO Ovarian cancer DBC code: GO Cervical cancer Patient: Rose Patient: Sue Visit outpatient clinic Visit outpatient clinic Pathology Lab test Lab test X-ray MRI CT DBC code: gynaecological tumors DBC code: IM Cervical cancer MRI Patient: Sue Patient: Sue Visit outpatient clinic Visit outpatient clinic Radiotherapy Lab test MRI Chemo therapy Radiotherapy Lab test MRI Chemo therapy PAGE 26

28 AMC data Resulting log: 682 Gynaecological oncology patients Audit trail entries Gynaecology, Nursing wards, Radiology, Radiotherapy, PAGE 27

29 Diagnostic + therapeutic process Visual insights Process Mining PAGE 28

30 Visual insights Visit Radiology Lab to the outpatient clinic PAGE 29

31 Visual insights PAGE 30

32 Visual insights First surgery PAGE 31 31

33 Process Mining Split log in two parts Diagnostic part Therapeutic part PAGE 32

34 Diagnostic process PAGE 33

35 Diagnostic process (HM) 0, ArtificialStartTask (complete) 100 0, Pathology (complete) 60 0, Focus on most important events Lab (complete) 215 0, , , OC Gyn Onc (complete) 148 0, Pharmacy Lab (complete) 76 0, , Fitness: 0,7 Performance related data Radiology (complete) 161 0, , Nursing Ward H5Z (complete) 263 0, Operating Rooms (complete) 100 0, , ArtificialEndTask (complete) 100 PAGE 34

36 Overview Introduction AMC AMC case study Future work Conclusion Questions / Discussion PAGE 35

37 Research Goal: Obtain understandable results for the process analyst and medical specialist (end-user) 1. Capturing flexible processes 2. Presentation of process related information PAGE 36

38 Flexible processes (Declarative PLs) PAGE 37

39 Flexible processes (Declarative PLs) PAGE 38

40 Flexible processes Develop mining techniques for less procedural and declarative languages PAGE 39

41 Process related information PAGE 40

42 Process related information Good maps? Navigation by PowerPoints? Traffic information? Where is the next fuel station? Who is in charge? Seamless zoom? Customizable views? When will the destination be reached? PAGE 41

43 Overview Introduction AMC AMC case study Future work Conclusion Questions / Discussion PAGE 42

44 Conclusions Results Insights into healthcare processes Remaining challenges Capturing flexible processes Presentation of process related information Only day timestamps PAGE 43

45 Questions? / Discussion PAGE 44