Medical imaging consultation practices and challenges at public hospitals in the Amhara regional state, Northwest Ethiopia: a descriptive phenomenological study
Bibliographic record
Abstract
Abstract Background Medical imaging plays a vital role in the accurate diagnosis, treatment and outcome prediction of many diseases and injuries. However, in many African countries, deserving populations do not have access to the proper medical imaging specialists’ services. As a result, clinicians continue to struggle to provide medical imaging via consultation. However, little is known about conventional referral consultation practices and their challenges. This study, therefore, aimed to explore the practice and challenges of medical imaging service consultation among health professionals and patients in the context of the Ethiopian public healthcare delivery system. Methods Descriptive phenomenological study was employed to explore the practice of medical imaging service consultation among health professionals and patients in public hospitals of Amhara region from October 12, 2021 to December 29, 2021. Semi-structured interview guides were prepared separately for key-informant and in-depth interviews. A total of 21 participants (6 hospital managers, 4 medical directors, 4 department heads, 3 medical imaging coordinators and 4 patients) were selected using the maximum variation sampling technique. All interviews were audio-recorded, transcribed verbatim and subjected to inductive thematic analysis using Open Code 4.02 software. Results Six major themes emerged following the thematic analysis: (1) medical image service delivery practices; (2) medical imaging consultation modalities; (3) benefits and drawbacks of the consultation modalities; (4) challenges; (5) challenge mitigation strategies; and (6) future recommendations. Image films, compact disks, and telegram apps were the consultation modalities used by the referring clinicians to send the medical images to radiologists. Frequent failure of imaging machines, delayed equipment maintenance, inadequate infrastructure, shortage of budget, lack of radiologists, and low-quality of printed image films were among the challenges influencing the medical imaging consultation service. Conclusions This research explored onsite and referral imaging consultation practices. However, there are many challenges encountered by the referring clinicians and the radiologists during the consultation process. These challenges could potentially affect clinicians’ ability to provide timely diagnosis and treatment services which would ultimately affects patient health status and service delivery. Virtual consultation via teleradiology and enhancing clinicians’ competence through long-term and short-term trainings are recommended to improve the referral consultation practice.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".