Agile Software Development in Healthcare: A Synthetic Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Even though software can be found everywhere, software development has encountered many problems, resulting in the emergence of new alternative development paradigms. Among them, agile approaches are the most popular. While much research has been published about agile software development (ASD) in general, there is a lack of documented knowledge about its use in healthcare. Consequently, it is not clear how ASD is used in healthcare, how it performs, and what the reasons are for not using it. To fill this gap, we performed a quantitative and qualitative knowledge synthesis of the research literature harvested from Scopus and Web of Science databases, employing the triangulation of bibliometrics and thematic analysis to answer the research question What is state of the art in using ASD in the healthcare sector? Results show that the research literature production trend is positive. The most productive countries are leading software development countries: the United States, China, the United Kingdom, Canada, and Germany. The research is mainly published in health informatics source titles. It is focused on improving the software process, quality of healthcare software, reduction of development resources, and general improvement of healthcare delivery. More research has to be done on scaling agile approaches to large-scale healthcare software development projects. Despite barriers, ASD can improve software development in healthcare settings and strengthen cooperation between healthcare and software development professionals. This could result in more successful digital health transformation and consequently more equitable access to expert-level healthcare, even on a global level.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it