The Role of Electronic Medical Records in Reducing Unwarranted Clinical Variation in Acute Health Care: Systematic Review
Bibliographic record
Abstract
BACKGROUND: The use of electronic medical records (EMRs)/electronic health records (EHRs) provides potential to reduce unwarranted clinical variation and thereby improve patient health care outcomes. Minimization of unwarranted clinical variation may raise and refine the standard of patient care provided and satisfy the quadruple aim of health care. OBJECTIVE: A systematic review of the impact of EMRs and specific subcomponents (PowerPlans/SmartSets) on variation in clinical care processes in hospital settings was undertaken to summarize the existing literature on the effects of EMRs on clinical variation and patient outcomes. METHODS: Articles from January 2000 to November 2020 were identified through a comprehensive search that examined EMRs/EHRs and clinical variation or PowerPlans/SmartSets. Thirty-six articles met the inclusion criteria. Articles were examined for evidence for EMR-induced changes in variation and effects on health care outcomes and mapped to the quadruple aim of health care. RESULTS: Most of the studies reported positive effects of EMR-related interventions (30/36, 83%). All of the 36 included studies discussed clinical variation, but only half measured it (18/36, 50%). Those studies that measured variation generally examined how changes to variation affected individual patient care (11/36, 31%) or costs (9/36, 25%), while other outcomes (population health and clinician experience) were seldom studied. High-quality study designs were rare. CONCLUSIONS: The literature provides some evidence that EMRs can help reduce unwarranted clinical variation and thereby improve health care outcomes. However, the evidence is surprisingly thin because of insufficient attention to the measurement of clinical variation, and to the chain of evidence from EMRs to variation in clinical practices to health care outcomes.
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How this classification was reachedexpand
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 | 0.046 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.011 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".