A110 PATIENT-GUIDED COMPLICATION TRACKING SYSTEM (PACTS): BUILDING ALLIANCES WITH PATIENTS FOR THE CONTINUED IMPROVEMENT OF POST-PROCEDURAL OUTCOMES.
Bibliographic record
Abstract
Abstract Background In Healthcare, interventions using Short Message Service (SMS) are growing as more patients have mobile phones. To date, studies have investigated using SMS to remind patients of upcoming appointments and provide preventative medical care. Although SMS interventions exist, little is known about their potential as a post-procedural follow-up tool. SMS follow-up systems present a unique opportunity for clinics to provide support to patients having unplanned post-procedural events. Moreover, the identification of these cases promotes the adoption of preventative measures. Before SMS follow-up programs can be integrated in clinics, proof-of-concept research needs to be conducted to assess the feasibility of this intervention. Aims This study aims to determine intervention design elements to maximize the response rate of a novel follow-up program implemented at St. Paul’s Hospital in Vancouver, BC. Methods An iterative prospective study was conducted to assess the effects of various design features on the response rate of an SMS follow-up system. Outpatients having a colonoscopy and/or gastroscopy at St. Paul’s Hospital between 11/19-03/20 were considered for inclusion in this pilot. Patients were asked to participate if they understood Grade 10-level English and had a mobile phone. For this pilot, a PAtient-guided Complication Tracking System (PACTS) was designed to send SMS to patients one week post-procedure. During each program round, adjustments were made to PACTS with the goal of increasing the response rate. The design changes made to the pilot were cumulative. One-way ANOVA and Tukey’s Honestly Significant Difference tests were completed to assess response rate differences between rounds. Results A total of 1829 patients met the inclusion criteria and consented to participate in the pilot. The overall median response rate was 93%. ANOVA test revealed a statistically significant difference in response rates between rounds, F(6, 196) = 3.369, p = 0.0035. Only the mean response rates between Rounds 1 and 7 yielded a significant pairwise difference (p < 0.001). Conclusions The PACTS pilot demonstrates that high response rates are achievable by SMS follow-up systems. This study identified several design elements to optimize SMS intervention response rates. These features included: sending a 1st SMS that explains the program’s purpose, sending a 2nd SMS to participants that did not respond to the first, and providing pilot information to patients upon admission and discharge. Future research on SMS follow-up systems should explore designing a program that can be integrated in clinics with minimal staff involvement. Funding Agencies None
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".