Evaluating Cognitive Load in Clinical Workflows Highlights Leverage Points for Guideline-Concordant Statin Initiation Article Swipe
Ratnalekha V N Viswanadham
,
Yuhan Cui
,
Priyanka Solanki
,
Nicole Redfern
,
Amelia Shunk
,
Angela Mastrianni
,
David N. Levine
,
David Mann
,
Safiya Richardson
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7632374/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7632374/v1
Introduction 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. Methods 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. Results 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. Discussion 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.
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All OpenAlex metadata
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Evaluating Cognitive Load in Clinical Workflows Highlights Leverage Points for Guideline-Concordant Statin InitiationWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-10-27Full publication date if available
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Ratnalekha V N Viswanadham, Yuhan Cui, Priyanka Solanki, Nicole Redfern, Amelia Shunk, Angela Mastrianni, David N. Levine, David Mann, Safiya RichardsonList of authors in order
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https://doi.org/10.21203/rs.3.rs-7632374/v1Publisher landing page
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https://www.researchsquare.com/article/rs-7632374/latest.pdfDirect link to full text PDF
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| abstract_inverted_index.count: | 154 |
| abstract_inverted_index.counts | 163 |
| abstract_inverted_index.during | 33 |
| abstract_inverted_index.events | 114, 184 |
| abstract_inverted_index.health | 56 |
| abstract_inverted_index.higher | 188 |
| abstract_inverted_index.loops, | 167 |
| abstract_inverted_index.model, | 195 |
| abstract_inverted_index.offers | 8 |
| abstract_inverted_index.points | 38 |
| abstract_inverted_index.rates. | 258 |
| abstract_inverted_index.reduce | 269 |
| abstract_inverted_index.statin | 34, 72, 96, 123, 133, 236, 256 |
| abstract_inverted_index.system | 57 |
| abstract_inverted_index.values | 110 |
| abstract_inverted_index.Linking | 2 |
| abstract_inverted_index.average | 196 |
| abstract_inverted_index.between | 92, 252 |
| abstract_inverted_index.counts. | 189 |
| abstract_inverted_index.derived | 84 |
| abstract_inverted_index.effects | 145 |
| abstract_inverted_index.events, | 175 |
| abstract_inverted_index.examine | 21 |
| abstract_inverted_index.finding | 249 |
| abstract_inverted_index.forests | 107 |
| abstract_inverted_index.improve | 13, 293 |
| abstract_inverted_index.insight | 283 |
| abstract_inverted_index.looping | 228 |
| abstract_inverted_index.measure | 26 |
| abstract_inverted_index.metrics | 82 |
| abstract_inverted_index.patient | 100, 298 |
| abstract_inverted_index.primary | 30, 48 |
| abstract_inverted_index.quality | 7 |
| abstract_inverted_index.results | 216 |
| abstract_inverted_index.scores. | 79 |
| abstract_inverted_index.support | 273, 291 |
| abstract_inverted_index.systems | 267 |
| abstract_inverted_index.whereas | 135 |
| abstract_inverted_index.Logistic | 88 |
| abstract_inverted_index.academic | 55 |
| abstract_inverted_index.behavior | 281 |
| abstract_inverted_index.clinical | 247, 274 |
| abstract_inverted_index.criteria | 70 |
| abstract_inverted_index.decision | 193, 290 |
| abstract_inverted_index.declined | 186 |
| abstract_inverted_index.distinct | 152, 174 |
| abstract_inverted_index.duration | 128 |
| abstract_inverted_index.effects. | 105 |
| abstract_inverted_index.enhanced | 289 |
| abstract_inverted_index.evaluate | 90 |
| abstract_inverted_index.identify | 37 |
| abstract_inverted_index.included | 210 |
| abstract_inverted_index.insights | 9 |
| abstract_inverted_index.metadata | 24, 240 |
| abstract_inverted_index.observed | 147 |
| abstract_inverted_index.optimize | 265 |
| abstract_inverted_index.patients | 66 |
| abstract_inverted_index.positive | 208 |
| abstract_inverted_index.provider | 103 |
| abstract_inverted_index.relative | 205 |
| abstract_inverted_index.therapy. | 124 |
| abstract_inverted_index.workflow | 286 |
| abstract_inverted_index.Cognitive | 80 |
| abstract_inverted_index.Modifying | 223 |
| abstract_inverted_index.adherence | 15, 294 |
| abstract_inverted_index.adjusting | 98 |
| abstract_inverted_index.associate | 242 |
| abstract_inverted_index.behavior, | 248 |
| abstract_inverted_index.cognitive | 27, 40, 93, 116, 243, 253, 270, 277 |
| abstract_inverted_index.decreased | 142, 159 |
| abstract_inverted_index.encounter | 50, 127 |
| abstract_inverted_index.guideline | 14 |
| abstract_inverted_index.increased | 129, 169, 179 |
| abstract_inverted_index.metadata. | 87 |
| abstract_inverted_index.nonlinear | 250 |
| abstract_inverted_index.predictor | 203 |
| abstract_inverted_index.providers | 32 |
| abstract_inverted_index.relations | 251 |
| abstract_inverted_index.reviewing | 214 |
| abstract_inverted_index.strongest | 202 |
| abstract_inverted_index.suggested | 219 |
| abstract_inverted_index.workflow. | 45 |
| abstract_inverted_index.Additional | 207 |
| abstract_inverted_index.Connecting | 276 |
| abstract_inverted_index.Non-linear | 144 |
| abstract_inverted_index.associated | 118, 234 |
| abstract_inverted_index.calculated | 75 |
| abstract_inverted_index.covariates | 101 |
| abstract_inverted_index.extracted. | 62 |
| abstract_inverted_index.guidelines | 296 |
| abstract_inverted_index.highlights | 261 |
| abstract_inverted_index.identified | 64, 111 |
| abstract_inverted_index.increasing | 161 |
| abstract_inverted_index.initiation | 73, 121, 158, 177, 257 |
| abstract_inverted_index.likelihood | 131 |
| abstract_inverted_index.medication | 220 |
| abstract_inverted_index.negatively | 233 |
| abstract_inverted_index.predictors | 209 |
| abstract_inverted_index.threshold. | 172 |
| abstract_inverted_index.adjustments | 287 |
| abstract_inverted_index.appropriate | 246 |
| abstract_inverted_index.initiation, | 97, 134 |
| abstract_inverted_index.initiation. | 237 |
| abstract_inverted_index.prescribing | 35, 280 |
| abstract_inverted_index.probability | 156, 178 |
| abstract_inverted_index.regressions | 89 |
| abstract_inverted_index.associations | 91 |
| abstract_inverted_index.approximately | 182 |
| abstract_inverted_index.interventions | 11 |
| abstract_inverted_index.contribution). | 206 |
| abstract_inverted_index.retrospectively | 61 |
| abstract_inverted_index.Gradient-boosted | 106 |
| abstract_inverted_index.decision-making. | 275 |
| abstract_inverted_index.gradient-boosted | 192 |
| abstract_inverted_index.<bold>Methods</bold> | 46 |
| abstract_inverted_index.<bold>Results</bold> | 125 |
| abstract_inverted_index.<bold>Discussion</bold> | 238 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.<bold>Introduction</bold> | 1 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.4892909 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |