Reducing Inappropriate Urinary Catheter Use by Involving Patients Through the Participatient App: Before-and-After Study
Bibliographic record
Abstract
BACKGROUND: The risk of urinary tract infections is increased by the inappropriate placement and unnecessary prolongation of the use of indwelling urinary catheters. Sustained behavior change in infection prevention could be promoted by empowering patients through a smartphone app. OBJECTIVE: The aim of this study is to assess the feasibility and efficacy of implementation actions on patients' use of the Participatient app on a clinical ward and to compare 3 survey methods for urinary catheter use. METHODS: Participatient was introduced for all admitted patients at the surgical nursing ward in a university hospital in the Netherlands. Over a period of 3 months, the number of new app users, days of use, and sessions were recorded. In a comparison of urinary catheter use before and after the implementation of the app, 3 methods for point prevalence surveys of catheter use were tested. Surveys were conducted through manual parsing of the text in patients' electronic medical records, parsing a survey of checkbox items, and parsing nursing notes. RESULTS: In all, 475 patients were admitted to the ward, 42 (8.8%) installed the app, with 1 to 5 new users per week. The actions with the most ensuing app use were the kick-off with the clinical lesson and recruiting of the intake nurse. Between the survey methods, there was considerable variation in catheter use prevalence. Therefore, we used the standard method of manual parsing in further analyses. Catheter use prevalence decreased from 38% (36/96) to 27% (23/86) after app introduction (OR 0.61, 95% CI 0.32-1.14). CONCLUSIONS: The clinical application of Participatient, the infection prevention app for patients, could be feasible when implementation actions are also used. For surveying indwelling urinary catheter use prevalence, manual parsing is the best approach.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".