(PS5-D77) Promoting Efficiency: The Role of Supervisors and a Clinical Decision-making System

Clinical supervision promotes high quality mental health (MH) services.1 Because supervision is often time-limited,2 supervisory efficiency is important. In this study, we defined efficiency as identifying decisions, organizing information, making plans, and reviewing plans in a swift and organized manner. Little is known about the role supervisors or structured decision systems play in facilitating efficiency. This study examined how much variance in supervisory efficiency was explained by supervisors. This study also tested whether a set of clinical decision-making resources increased supervisory efficiency compared with supervision without that system. Used in supervision, these resources included screening measures, worksheets, and guides. We predicted that such resources could increase the decision-making/organizational quality of supervision and decrease the effort/time needed in supervision, which would in turn overall enhance supervisory efficiency. 

Data were obtained from 124 MH professionals (MHPs), including 95 providers and 29 supervisors treating a total of 221 cases as part of a cluster randomized effectiveness trial for enhancing treatment engagement. MHPs were notified of cases who had participated in 4 to 6 treatment sessions and were at risk for poor engagement based on a screening. In the first supervision session following the notification, MHPs’ discussions of these cases were audio-recorded, transcribed, and rated on a scale of 1 (low) to 5 (high) overall efficiency. Multi-level modeling (MLM) was used to examine the intraclass correlation coefficients (ICC) for efficiency scores with cases nested within providers and supervisors. An MLM with nesting at the supervisor level was then run to examine whether use of the decision-making system was associated with efficiency scores.    

Efficiency scores clustered weakly by provider (ICC=.031) and strongly by supervisor (ICC=.521). A chi-square test of model fit comparing two models, one with clustering by provider and supervisor and one with clustering by supervisor, revealed that the models did not significantly differ (χ2(1) =.543, p=.461). Results of an MLM clustering by supervisor revealed that the use of the decision-making system significantly predicted supervisory efficiency: Cases in which MHWs used the system had greater expected efficiency scores (3.97) than those who did not (2.03), (b0=2.03, t (27.3) =14.3, p< .001; b1=1.95, t (27.3) =10.1, p< .001).  

This study demonstrates that a set of resources can lead to significantly better efficiency in supervision, as noted by ratings from trained observational coders. Future directions include examining whether these findings were due to decreased effort/time used in supervision, increased organization and/or decision-making quality, or some combination of both. Research may also investigate whether supervisory efficiency leads to better implementation outcomes in subsequent treatment sessions and greater sustainability of the treatment approach overall in a demanding clinical context.  

References:    

 1 Bearman (2021). The Clinical Supervisor, 40(1), 1-7. 2 Dorsey et al. (2017). Administration and Policy in Mental Health and Mental Health Services Research, 41(3), 353-359.