Use of Agile Project Methodology in Health Care IT Implementations: A Scoping Review
Bibliographic record
Abstract
Health care organizations are investing in system solutions that can be leveraged across the continuum of care (i.e. electronic medical records (EMR's); electronic health records (EHR's); health information exchanges (HIE's) and patient portals. The importance of these systems and how they have evolved over the past 30 years has been well researched. The value and benefits of these systems are therefore well known; however, it is estimated that most projects are typically 100% over budget and a year behind schedule [1, p. 2]. In this paper the authors examine what literature is available on agile project management methodologies in health care settings. A scoping review of the literature available specifically on agile methods use in implementing systems within health care was undertaken. Findings revealed there is very little literature available on agile project management methodologies used in health care IT systems implementations. The authors identify there is a strong need for research to look into project management methodologies and identify areas in the project lifecycle, where change is needed to increase clinical systems adoption.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".