Outcomes From Health Information Exchange: Systematic Review and Future Research Needs
Bibliographic record
Abstract
BACKGROUND: Health information exchange (HIE), the electronic sharing of clinical information across the boundaries of health care organizations, has been promoted to improve the efficiency, cost-effectiveness, quality, and safety of health care delivery. OBJECTIVE: To systematically review the available research on HIE outcomes and analyze future research needs. METHODS: Data sources included citations from selected databases from January 1990 to February 2015. We included English-language studies of HIE in clinical or public health settings in any country. Data were extracted using dual review with adjudication of disagreements. RESULTS: We identified 34 studies on outcomes of HIE. No studies reported on clinical outcomes (eg, mortality and morbidity) or identified harms. Low-quality evidence generally finds that HIE reduces duplicative laboratory and radiology testing, emergency department costs, hospital admissions (less so for readmissions), and improves public health reporting, ambulatory quality of care, and disability claims processing. Most clinicians attributed positive changes in care coordination, communication, and knowledge about patients to HIE. CONCLUSIONS: Although the evidence supports benefits of HIE in reducing the use of specific resources and improving the quality of care, the full impact of HIE on clinical outcomes and potential harms are inadequately studied. Future studies must address comprehensive questions, use more rigorous designs, and employ a standard for describing types of HIE. TRIAL REGISTRATION: PROSPERO Registry No CRD42014013285; http://www.crd.york.ac.uk/PROSPERO/ display_record.asp?ID=CRD42014013285 (Archived by WebCite at http://www.webcitation.org/6dZhqDM8t).
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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 | 0.122 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".