P-664 Optimizing IVF unit workload by balancing retrievals using artificial intelligence algorithm for trigger day decision
Authors: Hourvitz A , Reuvenny S , Luz A , Hourvitz R , Baum M , Youngster M , Maman E 
Published: June 2023

Abstract

Study question
Can an algorithm effectively reduce fluctuations in the number of retrievals per day by distributing them more evenly throughout the month without compromising clinical outcome?

Summary answer
A trigger day decision algorithm can evenly distribute retrievals throughout the month, significantly reducing the variations of retrievals per day, without compromising clinical outcome.

What is known already
Whether clinic workload can have an impact on IVF outcomes is controversial with some studies indicating that high procedure volume may lead to lower pregnancy rates. Furthermore, efficient workload management is crucial for fertility clinics to achieve optimal outcomes for both patients and staff. Overwhelming workloads can lead to staff burnout and decreased productivity. Clinics need to be cautious with the number of patients they treat due to the fluctuations in the number of retrievals. Reducing these fluctuations can help the clinic maintain stability in patient flow, reduce waiting lists, and enhance overall efficiency and productivity.

Study design, size, duration
A retrospective cohort study including data of 6,562 retrieval protocol cycles performed in a large center serving over 50 physicians, between November 2021 and October 2022.
The data includes 5,377 (81.94%) antagonist protocol cycles and 1,185 (18.06%) cycles of different protocols. The antagonist protocol cycles also include information on suggested trigger options: a specific single day, two options or three optional days, which were determined by a trigger management algorithm.

Participants/materials, setting, methods
An algorithm was developed to evenly distribute retrievals throughout the month. It divides the cycles into two groups: those with one suggested trigger day and fixed retrieval day (4,730 cycles 72.08%), and those with two/three suggested trigger day options and flexibility in choosing retrieval day (1,832 cycles 27.92%). The algorithm first allocates cycles with two options and then cycles with three options, aiming to minimize the variation in the number of retrievals between days.

Main results and the role of chance
The study showed that the implementation of a balancing algorithm on a clinic with an average of 18 retrievals per day resulted in a decrease of the standard deviation of the number of retrievals per day, from an average of 7.66 to 3.16, narrowing the range (average ± 2 standard deviation) of daily activity from 3-33 to 12-24 daily retrievals.
Furthermore, the study showed that the number of days with more than 150% or less than 50% retrievals than the average of the month, decreased significantly from an average of 8.4 days per month to 0.5 day per month when the balancing algorithm was applied.
Assuming the clinic has the capacity to handle the previous upper range of daily retrievals, the implementation of the balancing algorithm reduced the standard deviation and allowed for an increase in the average number of daily retrievals by approximately 31%, to an average of 23.6 cycles per day, with the clinic's capacity remaining unchanged.

Limitations, reasons for caution
This algorithm was developed specifically for antagonist cycles, which comprise approximately 80% of retrievals. Therefore, it cannot be applied to about 20% of cycles that are limited to a fixed trigger day but may have similar flexibility when an algorithm is available.

Wider implications of the findings
The use of a balancing algorithm has the potential to reduce workload and improve efficiency for medical and laboratory staff, thereby reducing errors and costs. Furthermore, it may enable clinics to treat more patients using the same facilities and resources, thus decreasing waitlists for treatments.

Trial registration number
HMC-0011-22