EHR-derived cognitive load is associated with guideline-concordant statin initiation in primary care

INTRODUCTION: Linking electronic health record (EHR) use to care quality may offer insights into potential interventions improving guideline adherence and closing care gaps. We examine how EHR metadata can measure cognitive load in primary care providers during statin prescribing and identify cognitive load points in EHR workflows associated with guideline-concordant statin initiation. METHODS: We retrospectively extracted 2024 data from EHR primary care encounters from a large academic health system. We identified adult patients who met the criteria for statin initiation and calculated their atherosclerotic cardiovascular disease (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 Shapley Additive explanations (SHAP) values were used to identify key EHR events and cognitive load patterns associated with statin initiation. RESULTS: Longer encounter duration was associated with increased likelihood of statin initiation, whereas more time spent per EHR event was associated with a decreased likelihood. Nonlinear associations were observed for loop count and distinct event count: predicted initiation probability decreased with increasing loop count to 93.9 loops, then increased beyond this threshold. For distinct events, initiation probability increased up to approximately 18 events and declined at higher counts. In a gradient-boosted decision tree model, average event time was the strongest predictor (72.2% relative contribution). Additional positive predictors included time spent reviewing lab results and on suggested medication order sets. Order list modification and looping back to it were negatively associated with statin initiation. DISCUSSION: EHR metadata can associate cognitive load with appropriate clinical behavior, revealing nonlinear associations between cognitive load and statin initiation rates. This work suggests opportunities to optimize EHR systems to reduce cognitive burden and support clinical decision-making. Connecting cognitive load to prescribing behavior generates hypotheses about how workflow adjustments and enhanced decision support might improve guideline adherence and patient care through prospective evaluation.

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Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2026-04-02
DOI
https://doi.org/10.1186/s12911-026-03392-6
Primary Topic
Electronic Health Records Systems
Type
article
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article

EHR-derived cognitive load is associated with guideline-concordant statin initiation in primary care

Ratnalekha V N Viswanadham, D. Levine, Angela Mastrianni, Priyanka Solanki et al.
BMC Medical Informatics and Decision Making
Electronic Health Records Systems
article

EHR-derived cognitive load is associated with guideline-concordant statin initiation in primary care

Ratnalekha V N Viswanadham, D. Levine, Angela Mastrianni, Priyanka Solanki, Safiya Richardson, Amelia Shunk, Nicole Redfern, Devin M. Mann, Yuhan Betty Cui
article en

Abstract

INTRODUCTION: Linking electronic health record (EHR) use to care quality may offer insights into potential interventions improving guideline adherence and closing care gaps. We examine how EHR metadata can measure cognitive load in primary care providers during statin prescribing and identify cognitive load points in EHR workflows associated with guideline-concordant statin initiation. METHODS: We retrospectively extracted 2024 data from EHR primary care encounters from a large academic health system. We identified adult patients who met the criteria for statin initiation and calculated their atherosclerotic cardiovascular disease (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 Shapley Additive explanations (SHAP) values were used to identify key EHR events and cognitive load patterns associated with statin initiation. RESULTS: Longer encounter duration was associated with increased likelihood of statin initiation, whereas more time spent per EHR event was associated with a decreased likelihood. Nonlinear associations were observed for loop count and distinct event count: predicted initiation probability decreased with increasing loop count to 93.9 loops, then increased beyond this threshold. For distinct events, initiation probability increased up to approximately 18 events and declined at higher counts. In a gradient-boosted decision tree model, average event time was the strongest predictor (72.2% relative contribution). Additional positive predictors included time spent reviewing lab results and on suggested medication order sets. Order list modification and looping back to it were negatively associated with statin initiation. DISCUSSION: EHR metadata can associate cognitive load with appropriate clinical behavior, revealing nonlinear associations between cognitive load and statin initiation rates. This work suggests opportunities to optimize EHR systems to reduce cognitive burden and support clinical decision-making. Connecting cognitive load to prescribing behavior generates hypotheses about how workflow adjustments and enhanced decision support might improve guideline adherence and patient care through prospective evaluation.

BMC Medical Informatics and Decision Making
Tulane University (US), NYU Langone Health (US), New York University (US)
Openalex Percentile: Top 30%
Electronic Health Records Systems
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