Patient-facing digital tools for delivering genetic services: a systematic review
Bibliographic record
Abstract
This study systematically reviewed the literature on the impact of digital genetics tools on patient care and system efficiencies. MEDLINE and Embase were searched for articles published between January 2010 and March 2021. Studies evaluating the use of patient-facing digital tools in the context of genetic service delivery were included. Two reviewers screened and extracted patient-reported and system-focused outcomes from each study. Data were synthesised using a descriptive approach. Of 3226 unique studies identified, 87 were included. A total of 70 unique digital tools were identified. As a result of using digital tools, 84% of studies reported a positive outcome in at least one of the following patient outcomes: knowledge, psychosocial well-being, behavioural/management changes, family communication, decision-making or level of engagement. Digital tools improved workflow and efficiency for providers and reduced the amount of time they needed to spend with patients. However, we identified a misalignment between study purpose and patient-reported outcomes measured and a lack of tools that encompass the entire genetic counselling and testing trajectory. Given increased demand for genetic services and the shift towards virtual care, this review provides evidence that digital tools can be used to efficiently deliver patient-centred care. Future research should prioritise development, evaluation and implementation of digital tools that can support the entire patient trajectory across a range of clinical settings. PROSPERO registration numberCRD42020202862.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".