Experiences of General Practitioners and Practice Support Staff Using a Health and Lifestyle Screening App in Primary Health Care: Implementation Case Study
Bibliographic record
Abstract
BACKGROUND: Technology-based screening of young people for mental health disorders and health compromising behaviors in general practice increases the disclosure of sensitive health issues and improves patient-centered care. However, few studies investigate how general practitioners (GPs) and practice support staff (receptionists and practice managers) integrate screening technology into their routine work, including the problems that arise and how the staff surmount them. OBJECTIVE: The aim of this study was to investigate the implementation of a health and lifestyle screening app, Check Up GP, for young people aged 14 to 25 years attending an Australian general practice. METHODS: We conducted an in-depth implementation case study of Check Up GP in one general practice clinic, with methodology informed by action research. Semistructured interviews and focus groups were conducted with GPs and support staff at the end of the implementation period. Data were thematically analyzed and mapped to normalization process theory constructs. We also analyzed the number of times we supported staff, the location where young people completed Check Up GP, and whether they felt they had sufficient privacy and received a text messaging (short message service, SMS) link at the time of taking their appointment. RESULTS: A total of 4 GPs and 10 support staff at the clinic participated in the study, with all except 3 receptionists participating in the final interviews and focus groups. During the 2-month implementation period, the technology and administration of Check Up GP was iterated through 4 major quality improvement cycles in response to the needs of the staff. This resulted in a reduction in the average time taken to complete Check Up GP from 14 min to 10 min, improved SMS text messaging for young people, and a more consistent description of the app by receptionists to young people. In the first weeks of implementation, researchers needed to regularly support staff with the app's administration; however, this support decreased over time, even as usage rose slightly. The majority of young people (73/87, 84%) completed Check Up GP in the waiting room, with less than half (35/80, 44%) having received an SMS from the clinic with a link to the tool. Participating staff valued Check Up GP, particularly its facilitation of youth-friendly practice. However, there was at first a lack of organizational systems and capacity to implement the app and also initially a reliance on researchers to facilitate the process. CONCLUSIONS: The implementation of a screening app in the dynamic and time-restricted general practice setting presents a range of technical and administrative challenges. Successful implementation of a screening app is possible but requires adequate time and intensive facilitation. More resources, external to staff, are needed to drive and support sustainable technology innovation and implementation in general practice settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".