Factors Influencing Outcomes of Clinical Information Systems Implementation
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
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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