Factors Influencing Outcomes of Clinical Information Systems Implementation
Bibliographic record
Abstract
Healthcare agencies spend significant resources to acquire or develop clinical information systems. However, implementation of clinical information systems often report significant failures. A systematic review of the research literature identified processes and outcomes of clinical information system implementation and factors that influenced success or failure. Of 124 original papers, 18 met the primary inclusion criteria-clinical systems implementation, healthcare facility, and outcome measures. Data extraction elements included study characteristics, outcomes, and implementation risk factors classified according to the Expanded Systems Life Cycle. The quality of each study was also assessed. Forty-nine outcomes of clinical information system implementation were identified. No single implementation strategy proved completely effective. The findings of this synthesis direct the attention of managers and decision makers to the importance of clinical context to successful implementation of clinical information systems. The highest number of factors influencing success or failure was reported during implementation and system "go-live." End-user support or lack thereof was the important factor in both successful and failed implementations, respectively. Following the Expanded Systems Life Cycle management model instead of a traditional project management approach may contribute to greater success over time, by paying particular attention to the underrecognized maintenance phase of implementation.
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 | 0.048 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".