The Use of Case Studies in Systems Implementations Within Health Care Settings: A Scoping Review
Bibliographic record
Abstract
There is little evidence available in the research literature as to how to undertake an implementation process that ensures electronic medical record (EMR)/electronic health record (EHR) implementation success (i.e. high levels of clinician adoption). The research literature has documented the presence of a direct relationship between how systems are implemented and their level of adoption by clinicians after implementation. In order to develop recommendations for systems implementation to enhance the level of clinician adoption and to ensure EHR/EMR success, researchers need to analyze implementation failures (i.e. where there has been a low level of adoption among clinicians) and successes (i.e. where there has been a high level of clinician adoption). This paper examines EMR/EHR system implementation in the context of adoption success, by conducting a scoping review of the EMR/EHR case study literature. The paper attempts to answer the following: "How does the published, case study research literature provide insights into the success and/or failure of EMR/EHR implementations?" Case studies can provide insights that allow researchers to identify best practice approaches to EMR/EHR implementations that may turn the tide towards reducing the number failed EMR/EHR implementation projects.
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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 | 0.052 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".