Improving Safety in Postoperative Handoff Communication with Telemedicine and Machine Learning

Poor postoperative handoffs can risk patient safety

Effective communication during postoperative handoffs is crucial to patient safety. Currently, nearly one-third of medical errors and adverse events in healthcare can be tied to miscommunication and care coordination gaps during handoffs and care transitions. While postoperative handoffs have been standardized using communication checklists and process guiding protocols, such “one-size-fits-all” communication frameworks often don’t work and are not sustainable in routine care. These standardized checklists and protocols do not adequately support the collaborative and interactive features of effective communication practices, do not align well with the handoff care teams workflows and—lastly and most importantly—do not meet the needs of delivering the best, person-centered information in an effective and efficient manner.

Dr. Joanna Abraham and her team at Washington University recognize that care transition failures can lead to potentially unsafe and high-risk situations for surgical patients being transferred from the operating room (OR) to intensive care units (ICU). The team has identified the need to build a “flexible” yet standardized approach to support smart precision handoffs that enhance the care team’s resilience to communication errors. This project, EnhanCed HandOffs (ECHO), attempts to provide a handoff intervention that is both standardized and flexible, informed by a sociotechnical model that reflects the interactions of people and technologies in a complex healthcare system.

Using telemedicine and machine learning may improve postoperative handoff safety

With an extensive background in anesthesiology workflows and biomedical informatics, Dr. Abraham is implementing an integrated, sociotechnical intervention for OR-ICU handoffs called the intelligent interactive care continuity (INTERACT) bundle. The bundle includes two main components: a telemedicine-augmented handoff process and a machine learning–augmented handoff report. The telemedicine-augmented handoff process promotes a fresh-eye perspective during patient handoffs from telemedicine teams for the OR and ICU, whereas the machine learning–augmented report promotes the identification and sharing of patient risks to postoperative complications and anticipatory management plans during handoffs. Together, the INTERACT bundle components aim to personalize patient handoffs, thereby promoting intelligent and interactive communication of patient risks and risk mitigation strategies.

“We can do better when it comes to optimizing surgical patient experiences and patient safety and reducing healthcare costs associated with postoperative complications. By harnessing state of-the-art machine learning and telemedicine innovations, I am confident that our proposed cutting-edge intervention will transform the practice of postoperative handoffs from a purely information transactional process to a more patient-centered, thoughtful, interactive, and collaborative team process.” – Dr. Joanna Abraham

Reducing risks can increase safety of postoperative handoffs

Customizing postoperative handoffs allows for patient-specific insights to be shared across teams for promoting effective and efficient handoffs, while providing a safety net against handoff errors. The team will evaluate the effectiveness and feasibility of implementing the INTERACT bundle using mixed methods. This project is the first to leverage and implement these advanced technologies for increasing safety during handoffs.