Easing hospitalist electronic health record burden through clinical workstation single sign-on
Bibliographic record
Abstract
Background: Computer workstation single sign-on (SSO) was implemented in 19 hospitals to reduce manual keyboard login and expedite access to the electronic health record (EHR) and clinical applications.Objective: To quantify hospitalists time liberated from EHR keyboard to focus on patient care, and estimate financial value of this time for hospitalists.Methods: Login duration prior to and after SSO implementation were compared in eight hospitals. Using national estimates of hospitalist hourly wage, dollar values of time liberated from keyboard were calculated, stratified by different levels of total EHR use.Results: Following SSO implementation, first of shift login decreased 5.3 seconds (15.3%), and reconnect duration decreased 20.4 seconds (69.9%). The volume of hospitalist EHR use among all physician end users comprises 70%-90% of all electronic documentation and clinical orders issued, yielding an annual range of 10,302 hours (or 858.5 12-hour shifts) to 13,245 hours (or 1,103.8 12-hour shifts) in hospitalist time liberated from keyboard for patient care, with recurrent annual value of $1,164,126 to $1,496,685.Conclusions: Hospitalists gained meaningful amounts of time for patient care from SSO implementation. This time accrued to substantial financial value. SSO eases the EHR burden of hospitalists, and facilities using hospitalists extensively should consider SSO implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".