**5.5 Determining an intervention point**

There is no easy way to determine an intervention point based on the predictive model. The beauty of deploying an ML model based on the active method described above is that one will be able to set an intervention point on when to alert an end user (theoretically at 90% sensitivity and 90% specificity) and leave the decision to intervene with the clinical end user. The author's recommendation is to continually modify this inflection point guided by data from near-misses and mis-categorized patients. Collecting real-time feedback from end-users on alert accuracy is also crucial for a model to survive. In conclusion, an ML deterioration model to predict sepsis produces ample value in a healthcare organization if deployed in conjunction with human intervention and continuous prospective re-assessment.
