Examining implementation outcomes in health information exchange systems: A scoping review
Bibliographic record
Abstract
BACKGROUND: Health information exchange (HIE) facilitates the secure exchange of digital health data across disparate health systems and settings. The implementation of information technology projects in healthcare is complex, further complicated by the fact that implementation success, through the measure of implementation outcomes, has been inconsistently defined and evaluated. There is no known scoping review examining implementation success through implementation outcomes in the field of HIE technologies. The aim of this scoping review was to provide a synthesis of studies related to reported implementation outcomes of HIE solutions (and related interoperability technologies) with a goal to inform the implementation of large-scale HIE projects in the future. METHODS: A scoping review, guided by the Arksey and O'Malley Framework, was conducted in four databases (Medline, Embase, CINAHL, and Web of Science), gathering studies from January 2010 to June 2023. Studies that described the implementation of a technology supporting interoperability or HIE across different organizations and/or across different healthcare settings and described the evaluation of one or more implementation outcomes from the Implementation Outcome Framework (IOF) were included. RESULTS: 37 studies were included in this review. The implementation outcome adoption was most frequently reported (n = 24). Fidelity and penetration were not reported. Few studies provided definitions for the outcomes being evaluated. Few studies provided details surrounding the stage of implementation as it relates to the outcome examined. No studies used the IOF or other similar implementation science evaluation frameworks. CONCLUSION: This review highlights the existing gaps in the field of HIE/interoperability solutions implementation studies. Future studies should employ theoretical frameworks to guide their research, standardize language used to describe implementation outcomes, and expand knowledge of salient outcomes at varying stages of implementation.
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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 | 0.120 | 0.373 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.033 | 0.041 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".