Selecting Electronic Health Record Systems: Development of a Framework for Testing Candidate Systems
Bibliographic record
Abstract
The process of selecting electronic health record systems is one of the most critical decisions in the journey towards automating and improving healthcare using information technology. However, there are a wide variety of problems associated with system selection and procurement processes in healthcare. Indeed the literature contains numerous examples of systems that were purchased and customized that failed to meet user needs, were implemented well behind schedule, which cost much more than expected and which in some cases failed completely. In this paper we describe a framework which we have developed and have begun to apply in considering key processes in the selection of electronic health records - i.e. the testing of potential candidate systems to determine if they meet user and institutional needs. The objective of our work is to improve our understanding of and the effectiveness of this critical decision making aspect of health informatics. In our work we consider system selection in terms of strength of evidence obtained from testing candidate systems.
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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 | 0.213 | 0.259 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.032 | 0.012 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| 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".