EHR use & audit-log metadata

Liem M Nguyen, Stefanie S Sebok-Syer +11

Can EHR audit logs reliably identify which clinicians make up a patient's care team? Researchers observed rounds for 1931 patient days (678 development, 1253 validation) across pediatric, neonatal, and cardiovascular ICUs at a quaternary children's hospital, documenting daytime teams, then compared two audit-log algorithms: clinically informed heuristics and a Longitudinal Contribution Score (LCS). In the PICU development cohort, LCS outperformed heuristics for nurses (92.9% vs 67.6% accuracy), frontline clinicians (83.3% vs 77.9%), and attendings (66.5% vs 61.9%). In validation, only the nurse advantage replicated (91.2% vs 74.2%); frontline and attending accuracy were comparable (roughly 83% and 69-70%).

Pooja Reddy Spector, Anne Grauer +12

How can EHR audit log data be turned into automated measures of ordering errors? This methods paper lays out a framework for conceptualizing, implementing, validating, and optimizing measures based on the Retract-and-Reorder (RAR) approach, which flags orders that are placed, retracted, and then reordered as signals of potential error. Using illustrative examples, the authors describe the theoretical model underlying RAR detection, validation steps, and applications to studying error epidemiology, root causes, and intervention evaluation in near-real time. The abstract reports no empirical performance estimates or effect sizes.

Matthew J Mulligan, Molly B Conroy +6

Can standardized care-team protocols shift patient portal messages away from primary care clinicians? This quasi-experimental pre/post study evaluated a cost-neutral quality improvement initiative embedding EHR-based protocols in medical assistant and nurse workflows for patient medical advice requests across 11 primary care clinics in a large academic health system, using multivariable mixed-effects regression adjusting for clustering by clinician. The share of requests routed to a PCP fell from 61.6% to 57.6% (p<0.001; adjusted OR 0.83, 95% CI 0.82-0.84), a 4.6% reduction in adjusted mean percentage. PCPs still received more than half of all patient messages.

Bayley Bennett, Brittany Brennan +9

Can a vendor-derived "order friction" metric guide targeted redesign of pediatric medication ordering? This quasi-experimental pre-post analysis used Epic-supplied order friction data on all inpatient medication orders across a three-hospital pediatric system during 2024. A multidisciplinary workgroup built a hospital medicine preference list prepopulating dosing, frequency, and as-needed indications for 15 medications (45 variants, 25 orderables). Among 22 paired orderables, median changes per order fell from 3.20 (IQR 2.41-3.68) to 0.39 (IQR 0.18-1.04; p < 0.001). System-level volume-weighted changes per order fell from 3.06 to 2.72 as preference-list adoption rose from 9.6% to 15.8%. The design was uncontrolled.

Zachary Boggs, Heather Frazier +2

Can individualized EHR analytics paired with structured coaching improve resident inbasket performance? Investigators at a large Mid-Atlantic academic center conducted a stratified longitudinal analysis of EHR metrics with pre- and post-intervention surveys among PGY-2 and PGY-3 internal medicine residents and continuity clinic attendings, delivering personalized efficiency and quality reports in one-on-one R2C2-model feedback sessions across two training periods. Turnaround time for patient calls fell by 2.2 days (p<0.001), and mean time in patient calls declined from 0.99±0.53 to 0.74±0.38 minutes/day (p=0.03). Self-reported burnout (p=0.09), confidence (p=0.18), and perceived efficiency (p=0.56) were unchanged; 6 of 7 faculty found the analytics at least moderately useful.

Yang, C.-H., Salvatore, M. +9

What drives whether a patient shows up (informative presence) versus whether a lab is actually ordered at that visit (informative observation)? The authors model the two stages separately using a stochastic recurrent-event model for outpatient visits and a visit-weighted GEE for per-visit biomarker recording, across three EHR-linked cohorts: All of Us (n=599,423), Yale New Haven Health System (n=319,666), and Michigan Genomics Initiative (n=82,372), covering 68 biomarkers with detailed analysis of ten. Median outpatient visits ranged from 1.7 to 6.1 per year over 4.4-7.2 years of follow-up, while the median within-person share of visits containing a given biomarker ranged from 0.4% to 19.5%. Chronic disease burden and recent visits predicted higher visit rates consistently; race, ethnicity, and neighborhood income associations varied by cohort, and covariate effects on measurement varied by biomarker (prior cancer predicted more blood counts, fewer lipids).

Lidia Moura, Rebecca G Mishuris +7

Do ambient documentation tools differ from one another in real-world impact? This comparative-effectiveness study followed 163 primary care providers in a large integrated health system (January 2024–June 2025), analyzing 59 130 provider-days across a tablet-based virtual human-assisted tool (A, n=65), an EHR-integrated tool (B, n=68), and a standalone tool (C, n=17), using intention-to-treat and per-protocol models with provider-clustered SEs and month fixed effects. Versus Tool B, Tool A showed more after-hours EHR time (+0.022 hours/provider-day), more manual note composition (+0.046), and lower 48-hour visit closure (−0.120; 95% CI −0.126 to −0.115). Tool C cut after-hours work by 0.055 hours/provider-day (~30 hours annually) but also reduced timely closure (−0.028).

Bhatia, A., Lash, S. +2

Can EHR analyses adjust for how much documentation a patient accumulates, separate from how sick they are? The authors built the EHR Density Index (EDI) using records from 24,987 adult patients at UNC Health (2018-2024), characterizing quantity, depth, and breadth of data per patient-year by assigning a utilization cluster via a Gaussian Mixture Model and computing within-cluster residuals across four clinical domains. Four interpretable clusters emerged; the Charlson Comorbidity Index predicted cluster membership but was only weakly associated with within-cluster residuals, suggesting the EDI captures record dimensions distinct from disease burden. The abstract reports no effect sizes.</summary>}Wait — I must not include stray text. Let me re-emit cleanly.{

A Jay Holmgren, Nate C Apathy +2

How are patient-initiated portal messages distributed across patients and physicians? This cross-sectional analysis combined records for 487,442 patients at UCSF Health with national EHR metadata from 224,068 ambulatory care physicians, examining medical advice request messages. The distribution was highly skewed at both levels: the top 5 percent of UCSF patients generated 52.8 percent of all messages, and primary care physicians nationally received a median of 9.6 messages per week versus 53.3 in the top quartile. Physicians with higher visit volume received fewer messages per visit, and higher message volume was associated with more EHR work outside work hours.

Andrew J Henreid, Kimon L H Ioannides +5

How do emergency physicians actually allocate shift time, and how much of it goes to the computer? This cross-sectional observational time-motion study in a high-volume urban ED used the validated TimeCaT application to track 20 physicians across one 8- to 9-hour shift each, totaling more than 150 hours of real-time observation, supplemented by EHR event logs for after-shift work. Physicians spent a median 34.1% of shift minutes on the computer (156.5 min) versus 26.9% with patients (115.2 min), plus 15.9% on verbal communication with staff. EHR logs showed an additional median 1.3 hours of post-shift computer use, or 29.8 combined computer minutes per scheduled hour. Visualizations showed frequent task switching and variable fragmentation.

Emma McCord, Nate C Apathy +5

What clinician characteristics moderate use of a chronic pain clinical decision support tool? Using electronic health record data from a pragmatic randomized controlled trial (October 2019–May 2022) covering 69 primary care clinicians with access to the OneSheet CDS, investigators modeled tool access within three days of an encounter, with generalized linear models testing clinician gender and years in practice as moderators. OneSheet was used in 959 of 145,511 encounters (0.7%). Use was lower for new-patient encounters (−0.42 percentage points; 95% CI −0.65 to −0.19) and higher for chronic pain diagnoses (3.48 pp) and long-term opioid therapy (4.70 pp). Associations were strongest among female clinicians with over 16 years in practice; prior use did not predict future use.

Viswanadham, R. V. N., Jones, S. A. +2

Does provider engagement with clinical decision support alerts vary by patient race and sex? This retrospective study used EHR data on alert-based CDS during outpatient primary care at a New York City academic health system, applying logistic regression to model alert engagement by patient race and sex with adjustment for encounter and provider factors, and a generalized structural equation model to test mediation by alert type. Direct effects indicated differential provider response by patient demographics; indirect effects indicated unequal assignment of alert types across groups, so uneven exposure alone could yield inequitable outcomes. The abstract reports no effect sizes.</summary