Perspectives on access to imaging digital health records in oncology: A mixed methods systematic review
Bibliographic record
Abstract
• Patient access to DHRs for imaging results in, the oncological setting is largely seen as a positive change in healthcare. • Patients want timely access to their imaging results, with the opportunity to discuss with clinicians. • DHR access to imaging records must address accessibility, usability, imaging resources and interaction between patients and HCP. • Further research is needed to convey reporting recommendations for imaging professionals in the oncology setting. Digital Health Records (DHR) have become essential for managing patient data, including radiology and nuclear medicine reports. The wider adoption of DHR globally presents an opportunity to improve patient engagement and empowerment through effective access and sharing of imaging investigations. This review aims to synthesize literature on views, experiences, expectations, and preferences of oncology patients and healthcare professionals (HCP) when accessing imaging via DHR. This review was conducted using recommended Cochrane Handbook databases (registration: CRD42021213808), focusing on English articles published from 2000 onwards. Three experienced reviewers critically appraised selected articles, thematic analysis and narrative synthesis were used to extract data. 493 unique articles were identified, with 451 excluded, resulting in 42 articles assessed for eligibility. Nine studies were included, eight from the USA, one from Canada, published between 2010 and 2020. Findings suggest patient portals can positively impact patient and HCP engagement, and patients desire access to their imaging reports. Factors such as timing of access, adequate consultation time, resources for HCP to discuss findings, and format of information are critical considerations that influence both patient and HCP perceptions and preferences. Oncology patients want timely and understandable access to their imaging records. To ensure this, it is crucial to explore the appropriate timing, format, and methods to discuss these findings with patients. By involving all stakeholders in the planning process, we can develop DHR systems that provide personalised support for patients to manage their complex imaging results.
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 | 0.044 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".