BackgroundPersuasive design is an approach that seeks to change the behaviors of users. In primary care, clinician behaviors and attitudes are important precursors to structured data entry, and there is an impact on overall data quality. We hypothesized that persuasive design changes data-entry behaviors in clinicians and thus improves data quality.ObjectiveThe objective of this study was to use persuasive design principles to change clinician data-entry behaviors in a primary care environment and to increase data quality of data held in a family health team’s reporting system.MethodsWe used the persuasive systems design framework to describe the persuasion context. Afterward, we designed and implemented new features into a summary screen that leveraged several persuasive design principles. We tested the influence of the new features by measuring its impact on 3 data quality measures (same-day entry, record completeness, and data validity). We also measured the impacts of the new features with a paired pre-post t test and generated XmR charts to contextualize the results. Survey responses were also collected from users.ResultsA total of 53 users used the updated system that incorporated the new features over the course of 8 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. On average, the percentage of same-day entries rose by 10.3% for each user (P<.001). During the first month of the postimplementation period, 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=.05). Statistical process control techniques allowed us to study the XmR charts to contextualize our results and understand trends throughout the study period.ConclusionsBy conducting a detailed systems analysis and introducing new persuasive design elements into a data-entry system, we demonstrated that it was possible to change data-entry behavior and influence data quality in a reporting system. The results show that using persuasive design concepts may be effective in influencing data-entry behaviors 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.
Background: Persuasive design (PD) is an approach that seeks to change the behaviors of users by using design and social influence. In primary care, clinician behaviors 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 behaviors in clinicians and improve data quality. Objective: Our objective was to use PD principles to change clinician data-entry behaviors 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 behavior. 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.
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