An evaluation of health information technology outsourcing success.
Bibliographic record
Abstract
Outsourcing involves contracting out functions performed by an organization to another organization. Many healthcare organizations are exploring outsourcing as a way to address demands for health information technology (HIT). This study researches the success of outsourcing in the health informatics industry in Canada. The study is designed to help understand whether outsourcing four functions of HIT (i.e. development, implementation, operations, and maintenance) can prove successful for an organization. Findings demonstrate that outsourcing these four functions occurs in Canada; however, the research from the semi-structured interviews finds that operations and maintenance may be more commonly outsourced in Canada, over development and implementation functions. Despite this, findings from this research suggest that outsourcing development and implementation may offer more benefits and fewer challenges than outsourcing operations and maintenance. The research also finds that there can be benefits of outsourcing, such as gaining access to expertise and improving service levels. A weakness of outsourcing may be that internal knowledge is lost and having to manage the change required from outsourcing. The study proposes that there are many factors that need to be considered when outsourcing to ensure it is successful.
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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".