Objective:

This article describes a study protocol for the implementation of quality and traceability control in the hazardous medication circuit through an analysis of risks and the development and introduction of a Big Data-based software application aimed at performing a continuous and dynamic audit of the whole system.

Method:

A standardized graphical modeling tool called Business Process Model Notation will be used to generate a detailed description of each of the stages in the hazardous medication circuit with a view to ensuring full traceability of the system. The information on each stage will be collected in a flowchart, which will be used —together with each event’s likelihood of occurrence and severity— as a basis to calculate the criticality index of the different control points established and to determine any control measures that may be required. The flowcharts will also be used to develop the technological support needed to capture all such data as may be relevant to the model. Proper quality control of the process will be ensured by client software agents intended to allow automatic application of efficient data processing tools at the different phases. In addition, Big Data methodologies, in particular machine learning, will be used to develop algorithms based on the repository of generated data to come up with patterns capable of improving the protocols to be applied. Lastly, proper operation of the process will be ensured by means of clinicalpharmaceutical verification and a full technical-documentary review of control and registration systems.

Conclusions:

The development of a risk management system based on mobile technology will allow integration of hazardous drugs into a standardized system, ensuring the safety, quality, and traceability of the hazardous medication handling process.