Evidence map›Paper›PMID 37566415›Full record

ArticleJAMA network open2023

Anesthesia Clinical Workload Estimated From Electronic Health Record Documentation vs Billed Relative Value Units.

Sunny S Lou, Laura R Baratta, Daphne Lew, Derek Harford, Michael S Avidan, Thomas Kannampallil

Abstract read
In one paragraph

Article in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sunny S LouDepartment of Anesthesiology, Washington University School of Medicine, St Louis, Missouri.
Laura R BarattaInstitute for Informatics, Washington University School of Medicine, St Louis, Missouri.
Daphne LewDivision of Biostatistics, Washington University School of Medicine, St Louis, Missouri.
Derek HarfordDepartment of Anesthesiology, Washington University School of Medicine, St Louis, Missouri.
Michael S AvidanDepartment of Anesthesiology, Washington University School of Medicine, St Louis, Missouri.
Thomas KannampallilDepartment of Anesthesiology, Washington University School of Medicine, St Louis, Missouri.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Accurate measurements of clinical workload are needed to inform health care policy. Existing methods for measuring clinical workload rely on surveys or time-motion studies, which are labor-intensive to collect and subject to biases. Objective: To compare anesthesia clinical workload estimated from electronic health record (EHR) audit log data vs billed relative value units. Design, Setting, and Participants: This cross-sectional study of anesthetic encounters occurring between August 26, 2019, and February 9, 2020, used data from 8 academic hospitals, community hospitals, and surgical centers across Missouri and Illinois. Clinicians who provided anesthetic services for at least 1 surgical encounter were included. Data were analyzed from January 2022 to January 2023. Exposure: Anesthetic encounters associated with a surgical procedure were included. Encounters associated with labor analgesia and endoscopy were excluded. Main Outcomes and Measures: For each encounter, EHR-derived clinical workload was estimated as the sum of all EHR actions recorded in the audit log by anesthesia clinicians who provided care. Billing-derived clinical workload was measured as the total number of units billed for the encounter. A linear mixed-effects model was used to estimate the relative contribution of patient complexity (American Society of Anesthesiology [ASA] physical status modifier), procedure complexity (ASA base unit value for the procedure), and anesthetic duration (time units) to EHR-derived and billing-derived workload. The resulting β coefficients were interpreted as the expected effect of a 1-unit change in each independent variable on the standardized workload outcome. The analysis plan was developed after the data were obtained. Results: A total of 405 clinicians who provided anesthesia for 31 688 encounters were included in the study. A total of 8 288 132 audit log actions corresponding to 39 131 hours of EHR use were used to measure EHR-derived workload. The contributions of patient complexity, procedural complexity, and anesthesia duration to EHR-derived workload differed significantly from their contributions to billing-derived workload. The contribution of patient complexity toward EHR-derived workload (β = 0.162; 95% CI, 0.153-0.171) was more than 50% greater than its contribution toward billing-derived workload (β = 0.106; 95% CI, 0.097-0.116; P < .001). In contrast, the contribution of procedure complexity toward EHR-derived workload (β = 0.033; 95% CI, 0.031-0.035) was approximately one-third its contribution toward billing-derived workload (β = 0.106; 95% CI, 0.104-0.108; P < .001). Conclusions and Relevance: In this cross-sectional study of 8 hospitals, reimbursement for anesthesiology services overcompensated for procedural complexity and undercompensated for patient complexity. This method for measuring clinical workload could be used to improve reimbursement valuations for anesthesia and other specialties.

Indexed as

AnesthesiaAnesthesiologyAnestheticsCross-Sectional StudiesDocumentationElectronic Health RecordsHumansWorkloadAnesthetics

Identifiers

PMID37566415
PMCPMC10422189

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