JMIR Publications

ABSTRACT

Background:

The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literature, it has been reported that people die annually due to adverse events, and various methods exist for investigating and measuring adverse events. However, some methods have a limited scope, limited data extraction and the need for data standardization. For these reasons, researchers have recently proposed the use of automated triggers to overcome the limitations of the manual method.

Objective:

The current study aims to develop automated triggers to predict the potential risk of harm to patients at the outpatient level.

Methods:

A mixed methods research will be conducted within a Design Thinking framework and the principles will be applied in creating the automated triggers, following these stages: (1) Empathize and define the problem, involving observations and inquiries to comprehend both the user and the challenge at hand; (2) Ideation, where various solutions to the problem are generated; (3) Prototyping, involving the construction of a minimal representation of the best solutions; (4) Testing, where user feedback is obtained to refine the solution; and (5) Implementation, where the refined solution is tested, changes are assessed, and scaling is considered. Furthermore, machine learning methods will be adopted to develop automated triggers, tailored to the local context in collaboration with an expert in the field.

Results:

The research is currently in its initial phases, with the institution's organization showing support for the project. Organization members have been briefly informed, and initial meetings have been conducted. Currently, the study is in the final stages of approval before submission to the Ethical Board for review and approval, enabling the commencement of data gathering in the ambulatory care unit.

Conclusions:

After the development of automated triggers in the outpatient setting, it will be possible to prevent and identify potential risks of adverse events more promptly, providing valuable information. This will enable healthcare professionals to adopt evidence-based preventive measures, enhancing productivity in outpatient care and contributing to the safety, quality, and effectiveness of the provided care.