Embedding and Integrating a Digital Patient Management Platform Into Everyday Primary Care Routines: Qualitative Case Study
Bibliographic record
Abstract
BACKGROUND: Traditional primary care is characterized by patient consultations via phone and physical visits. However, the current development in Swedish primary care is to blend digital solutions with traditional solutions. This paper addresses this development by examining the normalization of embedding and integrating a digital health care platform into everyday care routines in a primary care clinic. The digital health care platform enables both synchronous (video calls) and asynchronous (chat) communication, as well as self-registration of patient data using automated questions and forms requiring the patient's input. OBJECTIVE: This study aims to explore the work that health care professionals (HCPs) have to undertake to implement and sustain a digital health care platform as part of their everyday work practice. METHODS: HCPs were observed and interviewed to assess their individual and collective engagement and the mechanisms involved in the implementation of the digital platform and its effects on everyday work routines. The normalization process theory (NPT) was used to frame the data analysis. RESULTS: The analysis identified several themes related to the four NPT constructs: coherence, cognitive participation, collective action, and reflexive monitoring. The use of these constructs enabled the analysis to identify ways of supporting implementation. For example, it showed the benefits of having implementation champions and scheduling work hours for HCPs to use the platform. The analysis also revealed a theme of materiality that deviated from the NPT constructs, as NPT gives ontological priority to human actors and social structures. CONCLUSIONS: Digital health care platform implementation is a complex process. Our findings provide insights into how individual and collective actions can be supported to embed and integrate a digital platform into everyday care routines. Primary health care organizations need to involve HCPs throughout the implementation process by reorganizing work and providing frequent feedback loops. HCPs are more likely to engage with and commit to changing practices if they perceive the digital platform to be beneficial compared with the current practice. However, they also need resources (eg, time, training, and continuous support) to put the platform into practice. Patient engagement and appraisal are important elements in implementation. Unless patients are willing to use the platform, there is no motivation for HCPs to embed the digital platform into everyday care practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".