Work performed in this study has been summarized in papers in leading journals, specifically Naveh and colleagues in Safety Science (2018), IEEE Transactions on Engineering Management (2017), and American Journal of Medical Quality (2018). Also, in the many presentations, professional development workshops, seminars, conferences presentations, new additions to courses, work with research students, and the creation of a new research community that elaborates on the topic of errors.
In this project, I showed how big data and machine learning approaches can assist in eliminating errors in hospitals. I designed and executed exploratory research to understand errors using big data as an initial test and the starting point of a roadmap to error elimination. I carried out this part of the research project with the Dana-Farber Cancer Institute in Boston, USA, one of the world’s leading cancer research institutes. The Dana-Farber Cancer Institute collects relevant structured data from measuring devices (sensors and other machine data), and also has unstructured data collected in media files.
This study will influence the area of errors in organizations and big data for years to come through the work that I did with regard to:
• Improving the theory behind medical error origins and consequences and the ability to predict errors and to eliminate and manage them.
• Providing meaningful practical insights for making hospitals safer based on data science.
• Suggesting a research roadmap for the realization of big data potential for dealing with the complex issue of medical treatment errors; and establishing a network for future study, development, and realization of big data’s potential for eliminating medical treatment
errors.
This study's deliverables and milestones refer to and make it possible to understand the promise of big data, explore its fundamental aspects, and contribute specifically to the understanding of:
• Simultaneously and mutually enabling of error elimination and learning from errors in organizations, and specifically in hospitals. Changing the paradigm of preventing errors from occurring while simultaneously investing in activities that “welcome” them as valuable
feedback. Prevailing practices in health care organizations convincingly argue for a ‘both-and’ approach to this goal, meaning that, in a perfect world, health care professionals may simultaneously eliminate and welcome (and thus learn from) errors. Yet health care
organizations that do well on both accounts are extraordinarily rare. We suggest an 'either-or' approach as a way to reduce errors. I believe that this is a significant aspect for better dealing with errors in organizations.
• Understanding the major role of standardization in error prevention and learning. I have shown that standardization is expected to reduce errors but not always does so. Employee choices explain the link between standardization and error reduction. Standardization
both weakens and strengthens employee choices.
• Identifying the role of human systems integration that explains how the technical and human components of a system interact to influence errors.
• Discovering the influence of operations management measures and methodologies on error occurrence. In this respect, we explored the influence of OM practices on error occurrence and also integrated operations management methodologies with the origination
research approach.
