# Evaluating Cognitive Load in Clinical Workflows Highlights Leverage Points for Guideline-Concordant Statin Initiation

## TL;DR

This study uses EHR data to understand how cognitive load affects statin prescribing, identifying workflow adjustments that could improve guideline adherence.

## Contribution

The study introduces a novel method to measure cognitive load in EHR workflows and links it to statin initiation behavior.

## Key findings

- Longer encounter duration increases the likelihood of statin initiation.
- Non-linear effects were observed for loop count and distinct event count on statin initiation probability.
- Average time per EHR event was the strongest predictor of statin initiation.

## Abstract

Linking EHR use to care quality offers insights for interventions to improve guideline adherence and close care gaps. We examine how EHR metadata can measure cognitive load in primary care providers during statin prescribing and identify points of cognitive load in the EHR workflow.

EHR primary care encounter data from a large academic health system in 2024 were retrospectively extracted. We identified adult patients who met the criteria for statin initiation and calculated their ASCVD risk scores. Cognitive load metrics were derived from EHR metadata. Logistic regressions evaluate associations between cognitive load and statin initiation, adjusting for patient covariates and provider fixed effects. Gradient-boosted forests and SHAP values identified key EHR events and cognitive load associated with the initiation of statin therapy.

Longer encounter duration increased the likelihood of statin initiation, whereas more time spent per EHR event decreased it. Non-linear effects were observed for loop count and distinct event count: the probability of initiation decreased with increasing loop counts up to 93.9 loops, then increased beyond this threshold. For distinct events, the initiation probability increased up to approximately 18 events and declined at higher counts. In a gradient-boosted decision tree model, average time per event was the strongest predictor (72.2% relative contribution). Additional positive predictors included the time spent reviewing lab results and on suggested medication order sets. Modifying the order list and looping back to it were negatively associated with statin initiation.

EHR metadata can associate cognitive load with appropriate clinical behavior, finding nonlinear relations between cognitive load and statin initiation rates. This work highlights the need to optimize EHR systems to reduce cognitive burden and support clinical decision-making. Connecting cognitive load to prescribing behavior gives insight into how workflow adjustments and enhanced decision support can improve adherence to guidelines and patient care.