Direct-to-patient digital diagnostics in primary care: Opportunities, challenges, and conditions necessary for responsible digital diagnostics
Bibliographic record
Abstract
BACKGROUND: Diagnostics are increasingly shifting to patients' home environment, facilitated by new digital technologies. Digital diagnostics (diagnostic services enabled by digital technologies) can be a tool to better respond to the challenges faced by primary care systems while aligning with patients' and healthcare professionals' needs. However, it needs to be clarified how to determine the success of these interventions. OBJECTIVES: We aim to provide practical guidance to facilitate the adequate development and implementation of digital diagnostics. STRATEGY: Here, we propose the quadruple aim (better patient experiences, health outcomes and professional satisfaction at lower costs) as a framework to determine the contribution of digital diagnostics in primary care. Using this framework, we critically analyse the advantages and challenges of digital diagnostics in primary care using scientific literature and relevant casuistry. RESULTS: Two use cases address the development process and implementation in the Netherlands: a patient portal for reporting laboratory results and digital diagnostics as part of hybrid care, respectively. The third use case addresses digital diagnostics for sexually transmitted diseases from an international perspective. CONCLUSIONS: We conclude that although evidence is gathering, the often-expected value of digital diagnostics needs adequate scientific evidence. We propose striving for evidence-based 'responsible digital diagnostics' (sustainable, ethically acceptable, and socially desirable digital diagnostics). Finally, we provide a set of conditions necessary to achieve it. The analysis and actionable guidance provided can improve the chance of success of digital diagnostics interventions and overall, the positive impact of this rapidly developing field.
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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 | 0.109 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".