RESUMO
BACKGROUND: The transition away from written documentation and analog methods has opened up the possibility of leveraging data science and analytic techniques to improve health care. In the implementation of data science techniques and methodologies, high-acuity patients in the ICU can particularly benefit. The Principles of Automation for Patient Safety in Intensive Care (PASPIC) framework draws on Billings's principles of human-centered aviation (HCA) automation and helps in identifying the advantages, pitfalls, and unintended consequences of automation in health care. THE FRAMEWORK AND ITS KEY CHARACTERISTICS: Billings's HCA principles are based on the premise that human operators must remain "in command," so that they are continuously informed and actively involved in all aspects of system operations. In addition, automated systems need to be predictable, simple to train, to learn, and to operate, and must be able to monitor the human operators, and every intelligent system element must know the intent of other intelligent system elements. In applying Billings's HCA principles to the ICU setting, PAPSIC has three key characteristics: (1) integration and better interoperability, (2) multidimensional analysis, and (3) enhanced situation awareness. RECOMMENDATIONS: PAPSIC suggests that health care professionals reduce overreliance on automation and implement "cooperative automation" and that vendors reduce mode errors and embrace interoperability. CONCLUSION: Much can be learned from the aviation industry in automating the ICU. Because it combines "smart" technology with the necessary controls to withstand unintended consequences, PAPSIC could help ensure more informed decision making in the ICU and better patient care.
