General remarks

At the start of this section we summarized the potential advantages of utilizing a stochastic forecasting model to improve the instrument sterilization cycle. When reviewing the outcomes of the current chapter, however, we can also highlight some significant drawbacks of the approach that we utilized for this thesis. In section 5.2 for example we found that the use of procedure probability to predict tray demand can become laborious when large amounts of procedures and corresponding trays are involved. Here we only tried to generate the model for a single specialism which only includes a fragment of all the procedures and instrument trays used at OLVG. In addition to the vastness of the work involved in generating the forecasting model, it can sometimes prove hard to determine which trays are used for which procedure by default. As a result, we could conclude in section 5.3 that the use of a forecasting model is not always inclusive for all tray types. This was especially highlighted be the results of the forecast for the tray “CYSTOSCOPIE 22.5 FR 12° 30° OK URO”.

We would therefore like to recommend that the generation of this forecasting model will be conducted in a more facile way, for example by using a more automated software environment. Another possibility for simpler demand forecasting can be achieved by applying the current stochastic approach directly to measured throughput data of the sterilization cycle as done for the MASE comparison. Although much easier to implement, such a direct forecasting model would have the obvious disadvantage of not providing insight in tray utilization per procedure. The most likely candidate to provide good quality throughput data at this point would be the T&T system employed by the CSSD facility. As of September of this year, Clinium CSSD has started to increase its focus on this type of forecasting and was therefore willing to provide a new dataset that can be readily adopted for Poisson forecasting. Before using such datasets in a clinical context at OLVG, however, the discrepancies between the monitoring systems observed in section 4.2.2 will have to be investigated and resolved. Up to this point it remains unclear why and how these discrepancies occur and how they should be resolved.

5.5.Conclusion

In chapter 4 we emphasized the necessity to radically improve the planning functions of the instrument sterilization cycle. To do so, we argued that the use of a stochastic forecasting model could offer an appropriate starting point. In sections 5.2 and 5.3 we proposed to build such a model as a proof of principle. This was done by coupling the demand for a selection of conducted procedures to the demand for the corresponding instrument trays. Based on the stochastic forecasting model we hoped to achieve an accurate estimate of the amount of instrument trays required to cover 97.5% of the possible instrument tray demand. Furthermore, we hoped that the generation of the model would provide some additional insight into the impact that adequate surgery order protocol compilation would have on the amount of instrument tray supplies required to service the sterilization cycle.

For the investigation in this thesis we chose to build the instrument tray demand forecasting model, based on the demand for 25 procedures out of the possible 93 procedures for the Urology department at OLVG. After discussing the results of section 5.3 we were able to conclude that this forecasting model does not offer a valid representation of the actually recorded instrument tray demand for all instrument tray types.

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By utilizing the top 25 procedures we were able to predict a demand for 29 out of the 84 possible tray types. Using the forecasts for these 29 tray types we were able to cover 82.38% of all possible tray demand opposed to the intended 97.5%. If we shift our attention to the forecasting accuracy, we found that our forecasting model based on past procedure demand was just as accurate as a forecast based directly on past tray throughput. This conclusion was reached based on a mean absolute scaled error (MASE) analysis, where a MASE of 1 indicates an equal ratio between a forecast error and a reference error. For our MASE analysis values of 0.98 +/- 0.26 were observed for the 29 predicted tray types whereas the MASE values averaged 0.63 +/- 0.55 over all 84 different tray types.

In section 5.4.1 we found that it is possible to improve the stochastic forecasting model by including more procedures to the forecasting model. Given the vastness of the work involved, however, it remains to be determined whether it is worth the effort to expand this model in its current form. In section 5.4.3 we therefore offered that a different software platform could be used to automate demand forecasting as it was done in this thesis. Furthermore, we offered that an easier forecasting method could be generated based directly on the instrument tray throughput data of the central sterilization services department (CSSD). This can only be done under the condition that the reliability of the CSSD data is verified.

What this model does offer is a way to look into order protocol composition. From the description of order protocols and instrument trays in section 3.1 one might argue that surgery order protocols provide a clear- cut directive for which tray subsets are to be used for a specific procedure. In practice, however, the relationship between a subset of trays and a procedure is not as unambiguous as one might hope. The uncertainty in this relationship can be reduced by looking to the procedure frequency versus tray request data as demonstrated in section 5.2. Here deviations in practice can be utilized to inform the medical staff which empowers them to make the right decisions for order protocol adjustment.

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