Evidence map›Paper›PMID 41928231›Full record

ArticleBMC medical informatics and decision making2026

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

Ratnalekha V N Viswanadham, Yuhan Betty Cui, Priyanka Solanki, Nicole Redfern, Amelia Shunk, Angela Mastrianni, Defne L Levine, Devin M Mann, Safiya I Richardson

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Ratnalekha V N ViswanadhamDepartment of Population Health, NYU Grossman School of Medicine, 227 E. 30th St, Sixth Floor, New York, NY, 10016, USA.
Yuhan Betty CuiDepartment of Population Health, NYU Grossman School of Medicine, 227 E. 30th St, Sixth Floor, New York, NY, 10016, USA.
Priyanka SolankiDepartment of Medicine, NYU Langone Health, New York, NY, 10016, USA.
Nicole RedfernDepartment of Population Health, NYU Grossman School of Medicine, 227 E. 30th St, Sixth Floor, New York, NY, 10016, USA.
Amelia ShunkTulane University School of Medicine, New Orleans, LA, USA.
Angela MastrianniDepartment of Population Health, NYU Grossman School of Medicine, 227 E. 30th St, Sixth Floor, New York, NY, 10016, USA.
Defne L LevineDepartment of Population Health, NYU Grossman School of Medicine, 227 E. 30th St, Sixth Floor, New York, NY, 10016, USA.
Devin M MannDepartment of Population Health, NYU Grossman School of Medicine, 227 E. 30th St, Sixth Floor, New York, NY, 10016, USA.
Safiya I RichardsonDepartment of Population Health, NYU Grossman School of Medicine, 227 E. 30th St, Sixth Floor, New York, NY, 10016, USA. safiya.richardson@nyulangone.org.

Funding

National Institutes of Health's Heart, Lung, and Blood Institute 1R01HL171292-01
6 · The paper itself

Abstract

introductionLinking 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.

methodsWe 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.

resultsLonger 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.

Indexed as

CognitionElectronic Health RecordsGuideline AdherenceHydroxymethylglutaryl-CoA Reductase InhibitorsPrimary Health CareAgedFemaleHumansMaleMiddle AgedRetrospective StudiesHydroxymethylglutaryl-CoA Reductase InhibitorsAudit log dataClinical decision supportCognitive loadEHRMachine learningProcess miningStatin

Identifiers

PMID41928231
PMCPMC13170284

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.