Non-Conventional Applications
6.3 Evaluation of the Proposed Framework
6.3.2 Usage Scenario 2: Functional Endoscopic Sinus Surgery
12 18 24 30
A B C D E F G H I J K L M N
SurgeryID
Occurrence
(a) Total number of bone ablation steps (Query III)
0 16 32 48 64 80
A B C D E F G H I J K L M N
SurgeryID
Timespan (minutes)
(b) Total timespan of bone ablation phase (Query IV)
Figure 6.15: Occurrence and duration of bone ablation steps in discectomy interventions
Query IV.For each intervention of type discectomy, calculate the total time span between the begin of the first and the end of the last ‘bone ablating’ activity.
The query is answered by defining a new composite measureTimespanasMAX(End) - MIN(Start) in fact tableACTIVITY. The aggregates are computed as a roll-up ofTimespanbySurgerywith a selection condition onAction(‘bone ablation’). A bar chart with the results of this query is shown in Figure 6.15b.
The above queries describe a real-world example from the field of medical engineering. The aggregates obtained in the above queries describe the usage pattern for bone ablating instruments and provide crucial information for predicting the success of a new surgical instrument in this field [130]. This new system is a power driven milling system, whose evolution speed is controlled by its spatial position in relation to the patient’s body [71]. The system is intended to replace the conventional bone ablating instruments and to enable the surgeon to perform the entire removal procedure in a single work step.
6.3.2 Usage Scenario 2: Functional Endoscopic Sinus Surgery
The second usage scenario presents the experimental results of recording three cases offunctional endoscopic sinus surgery(FESS) in the disciplineENT surgery. Ten workflow versions of each case were generated by respectively ten recorders with a similar level of training. The task was limited to storing only the data of type WORKSTEPand its dimensions. Each surgical case was executed by one surgeon and one assistant. Table 6.1 shows further statistics about the obtained data for each of the three cases. Incompleteness of data records was caused primarily by missing values forTreated Structure, presumably due to insufficient visibility or lack of time to enter the values asTreated Structureappears as the last input field in the recording software.
Figure 6.16 shows an example of using line-charts to validate the quality of the obtained data flows. Both line-charts show three recordings (versions) of the same intervention. TheX-axis corresponds to thestart timedimension ofACTIVITY, normalized to display simple auto-incremented timestamps (activity number
128 Chapter 6 : Data Warehouse Design for Non-Conventional Applications
Table 6.1: Statistical overview of the acquired data
Parameter Case 1 Case 2 Case 3
Total number of records 590 848 651
Portion of incomplete records 5.9% 4.2% 4.3%
Minimum number of records per workflow 57 77 61
Maximum number of records per workflow 63 89 68
Number of distinct action types 14 12 20
Number of instruments 11 11 13
Number of treated body structures 6 6 5
Average activity duration (seconds) 37.5 25.3 34.9
Minimum activity duration (seconds) 12.9 6.6 9.8
Maximum activity duration (seconds) 81.3 106 135.2
1 10 100 1000
0 10 20 30 40 50 60 70 80 90
activity number
Duration (seconds)
version 1 version2 version 3
1 10 100 1000
0 10 20 30 40 50 60 70 80 90
activity number
Duration (seconds)
version 1 version2 version 3
(a) Three versions of a workflow with a synchronization error
1 10 100 1000
0 10 20 30 40 50 60 70 80 90
activity number
Duration (seconds)
version 1 version2 version 3
1 10 100 1000
0 10 20 30 40 50 60 70 80 90
activity number
Duration (seconds)
version 1 version2 version 3
(b) Three versions of a workflow with correct synchronization
Figure 6.16: Synchronizing multiple workflow versions of the same surgical intervention
6.3 : Evaluation of the Proposed Framework 129
Measure frequency duration (minutes)
Dimension Surgery Surgery
Instrument 1 2 3 all 1 2 3 all
Blakesley forcep 16 29 16 61 10.5 6.9 7.4 24.8
Camera 2 2 3 7 0.8 0.2 0.5 1.5
Curette, sharp 3 3 2.4 2.4
Elevator, doble-ended 2 2 3 7 0.7 0.9 1.1 2.7
Forceps 2 2 4 0.7 0.4 1.1
Nasal speculum 2 2 2 6 0.7 0.8 0.5 2.0
Optics + Endoscope 14 14 15 43 18.0 17.9 18.8 54.6
Sterile towels 1 1 2 0.3 0.1 0.4
Suction unit 19 35 16 70 4.3 5.6 4.1 836
Syringe 2 2 2 6 0.7 0.6 0.3 1.5
Swab 2 2 2 6 0.7 0.7 0.4 1.8
Tamponades 2 2 2 6 0.5 0.4 0.4 1.3
Telephone 3 3 0.9 0.9
Total 61 93 70 224 36.8 34.9 37.2 108.9
Figure 6.17: Pivot table view of the instrument usage statistics
1, 2, . . . ,n) instead of absolute time values. TheY-axis shows the duration of each step. As expected, the trajectories of the resulting graphs are very similar. However, in the original data shown in Figure 6.16a, an anomaly is evident: while versions 1 and 3 appear well synchronized, version 2 seems shifted in time. The chart in Figure 6.16b shows the same data, with version 2 shifted backwards by two steps. Now, all versions appear correctly synchronized. Similar visualizations can be employed to instantaneously detect and resolve recording inconsistencies that may otherwise be extremely tedious to identify.
Once the “cleansed” data has been written to the data warehouse in form of final workflow versions, it is available for analysis and exploration. Figure 6.17 shows a pivot table with the results of the following query:
Query I. For each FESS intervention, calculate the frequency of using each instrument as well as the total duration of each instrument’s usage throughout the intervention (i.e., the number and the duration of the respective work steps).
A two-dimensional view of the instrument usage numbers with totals and subtotals in Figure 6.17 reveals valuable insights to surgeons and medical engineers. For instance, it confirms the expectation of similar usage patterns for most of the instruments across multiple instances of the same surgery type. Also, one can notice that two instruments were used only in the third case, which could be an indication of an exception in the workflow. Combination of frequency and duration in the same query is helpful for analyzing the intensity of usage. For instance, Blakesley forcep andSuction unit were used with similar frequency (61 and 70, respectively), however, with immense difference in duration (24.8 versus 836 minutes).
Availability of the multidimensional perspective of fine-grain workflow data enables effortless retrieval of relevant measures by the end-users, whereas advanced OLAP frontends can be employed for more sophis-ticated analysis tasks, such as pattern recognition, anomaly detection, etc. In Chapter 8 we continue using the SWA usage scenario for demonstrating advanced visual exploration options, such as ad hoc specification of user-defined measures.