Implementing clinical information systems: a multiple-case study within a US hospital
Bibliographic record
Abstract
The rapid movement of information technologies into health care organizations has raised managerial concern regarding the capability of today's institutions to satisfactorily manage their introduction. Indeed, several health care institutions have consumed huge amounts of money and frustrated countless people in wasted information systems implementation efforts. Unfortunately, there are no easy answers as to why so many health informatics projects are not more successful. The aim of this study is to provide a deeper understanding of clinical information systems implementation. The research reported in this paper focuses on building a theory of the dynamic nature of the implementation process, that is, the how and why of what happened. The general approach taken was inspired by the work of Eisenhardt (1989) on building theories from case study research. We examined the implementation process, use and consequences of three distinct clinical information systems at a large tertiary care teaching hospital. A series of four research propositions reflecting the dynamic nature of the implementation process are offered as each of the three cases are analyzed. Findings add a number of new perspectives and empirical insights to the existing body of knowledge in the fields of IT implementation and medical informatics.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".