Supporting Patients’ Use of Digital Services in Primary Health Care in England: Synthesis of Evidence From a Mixed Methods Study of “Digital Facilitation”
Bibliographic record
Abstract
Background: General medical practitioners and other staff at primary care medical practices have an important role in facilitating patient access to online services in the National Health Service in England. These services range from online ordering of repeat prescriptions to conducting online consultations with health care professionals. We have defined "digital facilitation" as that range of processes, procedures, and personnel that seeks to support patients in their uptake and use of online services. Objective: We report how we have synthesized the evidence from a mixed methods study of digital facilitation in primary care in England. The study's objectives were to identify, characterize, and explore the benefits and challenges of different models of digital facilitation in general medical practices in England and to design a framework for evaluation of the effectiveness and costs of digital facilitation interventions. Methods: Our study comprised scoping review of literature, survey of staff in general practices, survey of patients, and ethnography at case study practices plus stakeholder interviews. We compiled a triangulation matrix of the findings from individual work packages through an iterative process whereby each work package's results were first analyzed separately and were then cumulatively combined across work packages in 3 successive workshops. From the resulting matrix, we developed a program theory and an implementation theory and constructed a framework for evaluations of digital facilitation in primary care. The final step of the synthesis process was to discuss the results with national and regional National Health Service stakeholders. Results: Triangulation yielded a combined set of findings summarized within 11 thematic groupings: 3 setting the scene within which digital facilitation takes place, and 8 related to different types of digital facilitation, their implementation, and effectiveness. Some thematic groupings were evident in the findings of all 4 of the research work packages; others were not addressed in all the work packages but were evident from those where they were addressed. Throughout the synthesis, there were no instances where findings from one work package contradicted the findings of another. Findings either reinforced each other or offered complementary or additional insights. The discussion at the stakeholder meeting held at the end of the study resulted in the research team clarifying some findings but not changing any of them. Conclusions: Digital facilitation can take many forms, though much of what is currently done in primary care practices in England is reactive and passive. Clear lines of responsibility, digital tools and platforms that work well for patients and practice staff, and investment in staff time and training are all needed if digital facilitation is to deliver on its promise. We propose a framework for future evaluations of the effectiveness and costs of digital facilitation interventions.
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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 | 0.127 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".