Information and Communication Technologies (ICTs) enabling integrated primary care for complex patients: a protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: An increasing number of individuals are living with multiple chronic conditions, often combined with psychosocial complexities. For these patients with complex conditions, an integrated primary care model that provides care coordination and a team-based approach can help manage their multiple needs. Information and communication technologies (ICTs) are recognized as a critical enabler of integrated primary care. A better understanding of the use of ICTs in an integrated care setting and how ICTs are being leveraged would be beneficial to identify knowledge gaps and could lead to successful implementation for ICT-based interventions. OBJECTIVE: This study will systematically scope the literature on the topic of ICT-enabled integrated healthcare delivery models for patients with complex care needs to identify which technologies have been used in integrated primary care settings. METHOD: This study protocol outlines a scoping review of the peer-reviewed literature, using Arksey and O'Malley's (enhanced by Levac et al.) scoping review methodology. Peer-reviewed literature will be identified using a multi-database search strategy. The results of the search will be screened, abstracted, and charted in duplicate by six research team members. DISCUSSION: The key findings of the study will be thematically analyzed to describe the implemented ICTs aimed for complex patients within the integrated primary care model. The finding will highlight what types of ICTs are being put in place to support these models, and how these ICTs are enabling care integration. This review will be the first step to formally identify how ICT is used to support integrated primary health care models. The results will be disseminated through peer-reviewed publications, conference presentations, and special interest groups.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".