Implementing standardized provider documentation in a tertiary epilepsy clinic
Bibliographic record
Abstract
OBJECTIVE: To incorporate standardized documentation into an epilepsy clinic and to use these standardized data to compare patients' perception of epilepsy diagnosis to provider documentation. METHODS: Using quality improvement methodology, we implemented interventions to increase documentation of epilepsy diagnosis, seizure frequency, and type from 49.8% to 70% of adult nonemployee patients seen by 6 providers over 5 months of routine clinical care. The main intervention consisted of an interactive SmartPhrase that mirrored a documentation template developed by the Epilepsy Learning Healthcare System. We assessed the weekly proportion of complete SmartPhrases among eligible patient encounters with a statistical process control chart. We used a subset of patients with established epilepsy care linked to existing patient-reported survey data to examine the proportion of patient-to-provider agreement on epilepsy diagnosis (yes vs no/unsure). We also examined sociodemographic and clinical characteristics of patients who disagreed vs agreed with provider's documentation of epilepsy diagnosis. RESULTS: The median SmartPhrase weekly completion rate was 78%. Established patients disagreed with providers with respect to epilepsy diagnosis in 18.5% of encounters (κ = 0.13), indicating that they did not have or were unsure if they had epilepsy despite having a provider-documented epilepsy diagnosis. Patients who disagreed with providers were similar to those who agreed with respect to age, sex, ethnicity, marital status, seizure frequency, type, and other quality-of-life measures. CONCLUSION: This project supports the feasibility of implementing standardized documentation of data relevant to epilepsy care in a tertiary epilepsy clinic and highlights an opportunity for improvement in patient-provider communication.
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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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".