Checklists are a type of cognitive aid used to guide task performance; they have been adopted as an important safety intervention throughout many high-risk industries. They have become an ubiquitous tool in many medical settings due to being easily accessible and perceived as easy to design and implement. However, there is a lack of understanding for when to use checklists and how to design them, leading to substandard use and suboptimal effectiveness of this intervention in medical settings. The design of a checklist must consider many factors including what types of errors it is intended to address, the experience and technical competencies of the targeted users, and the specific tools or equipment that will be used. Although several taxonomies have been proposed for classifying checklist types, there is, however, little guidance on selecting the most appropriate checklist type, nor how differences in user expertise can influence the design of the checklist. Therefore, we developed an algorithm to provide guidance on checklist use and design. The algorithm, intended to support conception and content/design decisions, was created based on the synthesis of the literature on checklists and our experience developing and observing the use of checklists in clinical environments. We then refined the algorithm iteratively based on subject matter experts’ feedback provided at each iteration. The final algorithm included two parts: the first part provided guidance on the system safety issues for which a checklist is best suited, and the second part provided guidance on which type of checklist should be developed with considerations of the end users’ expertise.