RESUMEN
Objective: To identify change management (CM) strategies for implementing novel artificial intelligence and similar novel technologies in operating rooms and create a new CM model for future trials and applications inspired by the abovementioned strategies and established models. Methods: Key phases of technology implementation were defined, and strategies for transformational CM were created and applied in a recent CM experience at our institution between October 15, 2020 and October 15, 2021. We appraised existing CM models and propose the newly created model. Results: The key phases of the technology implementation were as follows: (1) team assembly; (2) committee approvals; (3) CM; and (4) system installation and go-live. Key strategies were (1) assemble team with necessary expertise; (2) anticipate potential institutional cultural and regulatory hurdles; (3) add agility to project planning and execution; (4) accommodate institutional culture and regulations; (5) early clinical partner buy-in and stakeholder engagement; and (6) consistent communication, all of which contributed to the new CM model creation. Conclusion: Key CM strategies and a new CM model addressing the unique needs and characteristics of operating room novel technology implementation were identified and created. The new model may be customized and tested for individual institution and project's needs and characteristics.
RESUMEN
OBJECTIVE: To assess how staff attitudes before, during, and after implementation of a real-time location system (RTLS) that uses radio-frequency identification tags on staff and patient identification badges and on equipment affected staff's intention to use and actual use of an RTLS. PARTICIPANTS AND METHODS: A series of 3 online surveys were sent to staff at an emergency department with plans to implement an RTLS between June 1, 2015, and November 29, 2016. Each survey corresponded with a different phase of implementation: preimplementation, midimplementation, and postimplementation. Multiple logistic regression with backward elimination was used to assess the relationship between demographic variables, attitudes about RTLSs, and intention to use or actual use of an RTLS. RESULTS: Demographic variables were not associated with intention to use or actual use of the RTLS. Before implementation, poor perceptions about the technology's usefulness and lack of trust in how employers would use tracking data were associated with weaker intentions to use the RTLS. During and after implementation, attitudes about the technology's use, not issues related to autonomy and privacy, were associated with less use of the technology. CONCLUSION: Real-time location systems have the potential to assess patterns of health care delivery that could be modified to reduce costs and improve the quality of care. Successful implementation, however, may hinge on how staff weighs attitudes and concerns about their autonomy and personal privacy with organizational goals. With the large investments required for new technology, serious consideration should be given to address staff attitudes about privacy and technology in order to assure successful implementation.
