Applying Persuasive Design Techniques to Change Data Entry Behaviour in Primary Care (Preprint)
Bibliographic record
Abstract
BACKGROUND Persuasive design (PD) is an approach that seeks to change the behaviours of users by using design and social influence. In primary care, clinician behaviours and attitudes are important precursors to structured data entry, and there is an impact on overall data quality. This research hypothesizes that PD could change data entry behaviours in clinicians and improve data quality. OBJECTIVE Our objective was to use PD principles to change clinician data-entry behaviours in a primary care environment and to increase data quality within a registry system. METHODS We performed a detailed systems analysis of the data-entry task by using cognitive work analysis (CWA). We used the results of this analysis with the Persuasive Systems Design (PSD) framework to describe the persuasion context. We identified several PD principles to be introduced in a new summary screen, which became part of the data entry workflow. As part of our experimental design, we defined three data quality measures (same-day entry, record completeness, and data validity) to measure changes in data quality and entry behaviour. We measured the impacts of the new screen with a paired pre/post t-test and generated XmR charts to contextualize the results. RESULTS 53 users were shown the new screen during their data entry over the course of 10 weeks. Based on a pre-post analysis, the new summary screen successfully encouraged users to enter more of their data on the same day as their encounter. The percentage of same-day entries increased by 10.34% (P < 0.001). During the first month of the new screen, users compensated by sacrificing aspects of data completeness, before returning to normal in the second month. Improvements to record validity were marginal over the study period (P = 0.045). Statistical process control techniques allowed us to study the XmR charts to contextualize our results and understand trends throughout the study period. CONCLUSIONS By conducting a detailed systems analysis and introducing new PD elements into a data entry system, we demonstrated it was possible to change data-entry behavior and influence data quality in a reporting system. The results show that using PD concepts may be effective at influencing data entry behaviours in clinicians. There may be opportunities to continue improving this approach, and further work is required to perfect and test additional designs. Persuasive design is a viable approach to encourage clinician user change and could support better data capture in the field of medical informatics.
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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 | 0.037 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".