Barriers and Facilitators to Automated Self-Scheduling: Consensus from a Delphi Panel of Key Stakeholders.

Introduction: Automated self-scheduling may benefit healthcare organizations, yet uptake has been slow. The aim of this study was to develop a consensus statement regarding the organizational-level determinants of implementation success based on the collective knowledge of experts. A three-stage modified Delphi method was used to reach consensus on the top determinants of implementation of self-scheduling solutions by healthcare organizations. A panel of 53 experts representing 41 academic health systems identified barriers and facilitators involving the organization's inner and outing settings, as well as the characteristics of the intervention and the individuals engaged in the solution. Offering convenience for patients is the leading enabler for organizations to implement the technology. The consensus may aid healthcare organizations and suppliers engaged in adopting and developing self-scheduling technology to improve implementation success. Further research is recommended to diagnose and examine each barrier and facilitator and how these factors interact. Objective: The aim of this study was to develop a consensus statement regarding the determinants of implementation success based on the collective knowledge of experts working in the field.
Methods: A Delphi panel was constructed based on selected participants employed by academic health systems and experienced with self-scheduling implementation. Panelists were recruited based on participation in an educational event that featured the topic. Purposive and snowball sampling were used. Panelists participated in surveys collected over three rounds. An 80 percent agreement among panelists and interquartile range (IQR) <1 determined the barriers and facilitators. The top-10 determinants were presented in rank order.
Results: Between January 6, 2021, and May 26, 2021, 53 panelists representing 41 academic health systems participated in three rounds of surveys to reach consensus on the barriers and facilitators to implementation of self-scheduling by healthcare organizations in the United States. In round one, panelists documented 530 determinants. In round two, the determinants were grouped into 72 barriers and 85 facilitators, each of which participants rated on a five-point Likert scale. Fifteen determinants met the 80 percent threshold and 1.0 IQR. The final round concluded with a top-10, rank-ordered listing of determinants (seven facilitators and three barriers) that also incorporated a median rating score using five-point Likert scale.
Conclusion: A three-stage modified Delphi method was used to reach consensus on the top determinants of implementation of self-scheduling solutions by academic health systems. The consensus may aid healthcare organizations and suppliers engaged in adopting and developing self-scheduling technology to improve implementation success. Further research is recommended to diagnose and examine each barrier and facilitator and how these factors interact.