The Impact of Health Information Technology on the Quality of Medical and Health Care: A Systematic Review
Bibliographic record
Abstract
The aim of this study was to systematically review the published evidence of the impact of health information technology (HIT) or health information systems (HIS) on the quality of healthcare, focusing on clinicians's; adherence to evidence-based guidelines and the corresponding impact this had on patient clinical outcomes. The review covered the use of health information technologies and systems in both medical care (i.e. clinical and surgical) and other areas such as allied health and preventive services. Studies were included in the review if they examined the impact of Electronic Health Record (EHR), Computerised Provider Order-Entry (CPOE), or Decision Support System (DS); and if the primary outcomes of the studies were focused on the level of compliance with evidence-based guidelines among clinicians. Measurements considered relevant to the review were either of changes in clinical processes resulting from a change of the providers' behaviour, or of specific patient outcomes that demonstrated the effectiveness of a particular treatment given by providers. Of 23 studies included in the current review, 17 assessed the impact of HIT/HIS on health care practitioners' performance. A positive improvement, in relation to their compliance with evidence-based guidelines, was seen in 14 studies. Studies that included an assessment of patient outcomes, however, showed insufficient evidence of either clinically or statistically important improvements. Although the number of studies reviewed was relatively small, the findings demonstrated consistency with similar previous reviews of this nature in that wide scale use of HIT has been shown to increase clinician's adherence to guidelines.
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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 | 0.028 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".