Figure 6.8: The best model of case study 1 selected by the strict optimisation
The strict model, which is the model that is selected by the strict optimisation, is presented in Figure 6.8 and demonstrates the mainstream pathway of breast cancer who had chemotherapy sessions as adjuvant treatment between 2004 - 2013. This model shows the healthcare process for multiple visits of breast cancer patients. It should be noted that, the number on the edges linked to the Start and End nodes represent case frequency, which shows how many number of patients were on that edge, however, the number of the edges in between states represent the absolute frequency that reflects how many times this transition has happened in the data. The state number is random and does not hold any meaning also, loop on same state is removed to simplify the model. Moreover, very low frequency events of less than 5 occurrence might not appear in the graph node.
The majority of the patients, n=652, have started their healthcare journey through admission either elective or emergency as shown in state1. Interestingly, the transition from (admis-
117 6.7. Optimisation
sions(s1) → Discharge(s2)) means that, most of the patients have been discharged directly after admission where no care event was recorded in between. A possible hypothesis for inter- preting this pattern is that it might be an indication to the primary treatment that was given to the patients before starting chemotherapy treatment, which is not our focus on extraction this case study since the patients here have adjuvant chemotherapy that is given post a primary treatment. Another possible interpretation of the pattern; admission then discharge without care events in between, is that the incompleteness of PPM data extract that was given to us. Digging deeper to the data has confirmed our first hypothesis, this pattern of care, admission followed directly by discharge, has mostly happened in the beginning of patients records where the primary treatment has occurred.
Figure 6.9: Active and passive time for breast cancer process in PPM
Figure 6.9 illustrates that the process of breast cancer patients go through active time and passive time. Active time is when the treatment starts usually by getting chemotherapy ses- sions for this case study in particular, to the end of the chemotherapy course which is mostly 6 cycles. Passive time is the time when a patient admitted to the hospital for any other reason except chemotherapy session. The absolute frequency of the passive time pattern, which is the transition from state 1 to state 2, in this case study is 6412 and this pattern could happen before starting the treatment. Time gap between passive time and active time may vary where it might be months or weeks before chemotherapy.
On the other hand, the pattern (Discharge(s2) → admission(s1)) is an expected sequence of the multiple visits for patients. After the admissions state, a patient moves to chemotherapy sessions which include cyclel, cycle2, cycle3, cycle4, cycle5 and cycle6. Once a patient finishes their chemotherapy session they will be discharged. A blood test may be taken at the begin- ning of the healthcare process and/or for the upcoming cycles before taking their chemotherapy session to avoid an acute event such as neutropenia sepsis.
Interestingly, the model has allocated a particular state (s4) for highly different patterns of chemotherapy process. In order to have a deeper insight of the process inside each state we can use any process mining tool and explore the processes of each states individually.
For instance, patterns of care inside state 4 in our model can be investigated using the Dotted chart in ProM as illustrated in Figure 6.10. This Figure shows vertically the number of patients
and horizontally the process instances where care events are ordered by the time when a case started and coloured by states numbers. It can be clearly seen that, two main different patterns represent two different groups of patients.
Using the Traces explorer plugin in ProM, the first group, which is the top pattern A, are patients who have started their treatment by chemotherapy cycles directly where no admis- sions event are recorded and they represent 44 cases. The second group, the bottom pattern B, are patients who had blood test to check if they may experienced an acute event such as neutropenia sepsis and they represent 43 cases while 284 cases have blood test in the middle of their treatment. This state represents 50% of cases.
Figure 6.10: Dotted chart for examining patterns inside state 4 combined with two main patterns generated from Traces explorer
By analysing the healthcare model and investigating the distinct events of each state we could relabel the states initially based on the main events that contribute in forming the states, see Figure 6.11 which shows the model with initial states labels.
119 6.7. Optimisation
Figure 6.11: Initial labelled states of breast cancer process in case study 1
2- Soft optimisation for models’ candidate space
Selecting the best model with flexibility toward state importance can be done using our soft optimisation (Equation 5.5). Our method has optimised the space of candidate models for this case study and the criteria are calculated as displayed in Table 6.5.
Table 6.5: Criteria calculation in case study 1 (soft optimisation) Model 1 2 3 4 5 6 7 8 9 10 11 12 States 2s 3s 4s 5s 6s 7s 8s 9s 10s 11s 12s 13s Linearity 0.99 0.66 0.73 0.62 0.66 0.69 0.55 0.74 0.63 0.60 0.57 0.60 Compactness 0.172 0.034 0.022 0.017 0.014 0.014 0.009 0.009 0.008 0.013 0.014 0.012 Cross simi- larity 0.835 0.687 0.433 0.419 0.368 0.345 0.311 0.298 0.287 0.337 0.338 0.306 Soft opti- misation 1.078 0.635 1.023 0.820 0.955 1.031 0.800 1.183 0.973 0.861 0.810 0.895
The best model is a model of 9 states, which is presented in Figure 6.13, where it has the maximum score of the optimisation function which is 1.183. The worst model is model number 2 which is the model of 3 state where it has 0.635 score as plotted in Figure 6.12
Figure 6.13: The b est mo del of case study 1 selected b y soft optimisation
123 6.8. Discussion