P621 Secure Electronic Document Signing Uptake in Biologic Prescribing for Immune Mediate Diseases
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic drastically impacted workflows in gastroenterology practice. Physicians managing immune-mediated diseases (IMDs) must complete special authorization (SA) and prescription renewal (Rx) forms for patients on biologic therapy. This adds significant administrative burden potentially leading to delays in therapy initiation and care continuity. Historically, document completion has largely been paper-based, with forms faxed between patient support programs (PSPs) and physician offices. The use of secure electronic document signing (SEDS) platforms during the pandemic has allowed physicians to receive and complete documents digitally. The aim of the project was to evaluate the impact of SEDS-based biologic documentation on clinical practice in order to 1) Determine if the use of SEDS platforms increased timeliness of document returns compared to traditional workflows 2) Assess whether SEDS usage is acceptable and sustainable and 3) Assess MD satisfaction with SEDS platforms. Methods This was a retrospective audit of SEDS and paper-based biologic document workflows from a single PSP (Abbvie Care). Outcomes of interest were the number of documents completed monthly using SEDS, new monthly users, and the number of active monthly users between April 1, 2020-March 31, 2021. Time (days) to SEDS completion (vs. paper process) was determined by reviewing timepoint data for SA and Rx documents from May 2019-January 2020 (‘pre- SEDS’) and for SEDS documents from May 2020-January 2021(‘SEDS’). The return time (RT) was defined as the time between date sent to a physician’s office by the PSP to the date returned to the PSP. Documents in the pre-SEDS cohort with a RT exceeding 30 days were excluded. Results In total, 5573 SA and Rx documents were completed by 383 physicians using the SEDS platform from April 2020-March 2021. A mean of 14.6 (sd 21.8) documents were signed per physician. The number of monthly electronic documents processed increased from 104 in April 2020 to 800 in March 2021. Active monthly users increased from 24 in April 2020 to 213 in March 2021 (31 new users monthly). A total of 19,387 paper documents were processed during the ‘pre-SEDS’ period and 3,317 electronic documents processed in the ‘SEDS’ period. The mean RT in the ‘pre-SEDS’ period was 8.03 days (sd 8.2) and the ‘SEDS’ period was 1.11 days (sd 2.6). Conclusion This data demonstrates acceptability, appropriateness, and improved processing efficiency of an SEDS platform improving timeliness of patient care. Next steps in this research include surveying physicians to understand the work-flow impact of SEDS, functionality, long-term sustainability, satisfaction and impacts on disease related outcomes.
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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 | 0.024 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".