Optimizing Tele-ICU Operational Efficiency Through Workflow Process Modeling and Restructuring

Abstract

Little is known on how to best prioritize various tele-ICU specific tasks and workflows to maximize operational efficiency. We set out to: 1) develop an operational model that accurately reflects tele-ICU workflows at baseline, 2) identify workflow changes that optimize operational efficiency through discrete-event simulation and multi-class priority queuing modeling, and 3) implement the predicted favorable workflow changes and validate the simulation model through prospective correlation of actual-to-predicted change in performance measures linked to patient outcomes.

Setting: Tele-ICU of a large healthcare system in New York State covering nine ICUs across the spectrum of adult critical care.

Patients: Seven-thousand three-hundred eighty-seven adult critically ill patients admitted to a system ICU (1,155 patients pre-intervention in 2016Q1 and 6,232 patients post-intervention 2016Q3 to 2017Q2).

Interventions: Change in tele-ICU workflow process structure and hierarchical process priority based on discrete-event simulation.

Measurements and main results: Our discrete-event simulation model accurately reflected the actual baseline average time to first video assessment by both the tele-ICU intensivist (simulated 132.8 ± 6.7 min vs 132 ± 12.2 min actual) and the tele-ICU nurse (simulated 128.4 ± 7.6 min vs 123 ± 9.8 min actual). For a simultaneous priority and process change, the model simulated a reduction in average TVFA to 51.3 ± 1.6 min (tele-ICU intensivist) and 50.7 ± 2.1 min (tele-ICU nurse), less than the added simulated reductions for each change alone, suggesting correlation of the changes to some degree. Subsequently implementing both changes simultaneously resulted in actual reductions in average time to first video assessment to values within the 95% CIs of the simulations (50 ± 5.5 min for tele-intensivists and 49 ± 3.9 min for tele-nurses).

Conclusions: Discrete-event simulation can accurately predict the effects of contemplated multidisciplinary tele-ICU workflow changes. The value of workflow process and task priority modeling is likely to increase with increasing operational complexities and interdependencies.

Keywords: modeling; operations research; outcomes; queueing theory; tele-intensive care unit; workflow efficiency.

Figure 1.

A, Admission process. B, Processes of intervention, monitoring, and best practice.

Figure 2.

A, Main effects of the four factors on average time to first video assessment (TFVA) by a tele-ICU intensivist (MD). B, Main effects of the four factors on average TFVA by a tele-ICU nurse.

Figure 3.

A, Average wait times when video length increases. The horizontal axis displays the amount of video length increase as a percentage of the baseline value of 120 s from 0% to 100% (i.e., from 120 s to 240 s). Each point represents the average value over 100 replications of our discrete-event simulation model. The reference lines of 122 and 130 min from our base dataset (2016Q1), display average time to first video assessment (TFVA) by a tele-nurse and by tele-intensivist, respectively, before any operational changes were made. The reference line of 60 min represents the evidence-based operational goal for TFVA. The top-most lines display the average wait times to perform best practice (BP) adherence required by tele-intensivists (MDs) and tele-registered nurses (RNs) after the operational changes were implemented. The average wait times for RNs to complete tasks remain essentially unchanged as the video length increases across all tasks measured in this study. For the MD, the data are not as homogenous. Average TFVA displays minor increases from approximately 57 min to 62 min as video length doubles (0% to 100%), and average wait times to perform BPs tasks by the MD shows more substantial increases of 4 hr to 5 hr as video length increases from 120 to 240 s. If the average video length remains at 120 s (baseline data) both the average TFVA by the MD and the RN meets the operational goal of less than 60 min. These reductions in TFVA are obtained at the expense of an increase in wait times for BPs adherence. This increase in time to complete BPs tasks is still well below the 12-hr threshold. These results suggest that tele-ICU management can encourage longer video lengths without significant concerns regarding impact on average wait times for other tasks. B, Average wait time to perform tasks required by MDs and RNs depending on the tolerated delay for proactive monitoring (proactive monitoring delay threshold). Each point represents an average over 100 discrete-event replications. The reference line of 60 min indicates the operational goal for TFVA by MDs. The wait times to complete proactive monitoring tasks by MDs and RNs are minimally affected as we vary the delay thresholds. However, the average wait times to perform BPs tasks by MDs and RNs are affected significantly. In particular, the average waiting time to perform BPs tasks by MDs increases from 4 hr to over 10 hr as the delay threshold decreases. The average wait time for the MDs to perform the first-video assessment also increases as the delay threshold decreases. The waiting time is projected to no longer meet the operational goal of 60 min when the proactive monitoring delay threshold is less than 25 min. According to our simulation results, when the threshold is 25 min, the number of crisis interventions increases by approximately 8% due to the interdependence of crisis interventions and proactive monitoring tasks compared with our previous model assuming exogenous crisis intervention arrivals. These results suggest that the operational goal of 60 min for TFVA can no longer be met if the number of crisis interventions increase by more than 8%.