**5.2 Institutional considerations for deployment of sepsis model at cedars-sinai**

The deployment of sepsis AI alerting systems can be categorized into two approaches - passive and active, each with distinct staffing models. The passive approach involves a central hub of trained personnel monitoring sepsis alerts at one location such as a command center.

Despite claims of successful implementation at some institutions, this approach has huge dependency on a small group of people and is much more expensive than the second approach which we will cover shortly.

Effective staffing of this passive model requires careful consideration of the number and distribution of generated alerts. The distribution of alerts over time must also be considered. Due to the workflow of data collection that feeds the alert, the distribution of alerts may be bimodal or trimodal. Most alerts may occur during specific times of the day such as when labs are reported, vital signs are entered by nursing or during changes of shift. To adequately manage the expected volume and timing of alerts, staffing requirements should be calculated. Specifically, the team should be capable of handling 30–40 alerts within a 3-hour period, with occasional alerts occurring during off-peak times. In practice, this will likely require the hiring of an additional three full-time equivalent (FTE) nurses for the day shift and 2

FTE nurses for the night shift, with float coverage provided during weekends and vacations. The active approach or the fractal-behavior model is one in which humans work collaboratively with the AI model. In this approach each nurse is responsible for managing their own 4 or 5 patients assigned to them during the shift. There are two phases to the management of a patient based on whether they have sepsis or not.

Phase 1—When the sepsis alert is prevented from firing because the nurse has proactively screened the patient for sepsis using a standardized rule-based ML algorithm that uses a multivariate decision tree—i.e., non-linear decision making. In this case each nurse is consistently evaluating every patient at shift change, or when they first have the patient assigned to them. This method captures data before it is readily available in the EHR (e.g., patient's mental status, clinical appearance, and subjective judgment around source of infection). If a patient screens positive for infection—more action can be taken at that time to implement a diagnostic or treatment workflow.

Phase 2—When the sepsis alert fires—the bedside provider activates a workflow that allows them to perform a secondary clinical evaluation (SCE) to evaluate the alert in the context of the patient's clinical status. Frequently the decentralized active approach is criticized for failing because bedside nurses and providers fail to respond to alerts due to alert fatigue [26–28]. However, this approach only fails when the institution is relying solely on the EHR to mobilize the alert.

Hospital systems should consider adopting a user-centered design (UCD) instead of relying on traditional EHR interfaces. UCD involves the development of an interface that is tailored to clinical workflows thereby maximizing efficiency. Ruminski et al. found that displaying a visual monitor significantly reduced the rate of sepsis [27]. Furthermore, studies have shown that color coding and screen positioning in the user's visual field can improve provider satisfaction and reduce sepsis rates by over 50%. It is vital to align clinical end-users with the facility's IT department to ensure that the product meets clinical expectations while remaining compatible with the EHR.

This approach establishes a highly reliable two-step method that when repeated by hundreds of nurses daily resembles a fractal that is made of repeated behaviors. It is independent of staffing and nursing ratios, does not require additional FTE hires and is more economically feasible the cost of several million dollars a year less in staff salaries to implement than the passive model.
