Impact of picture archiving communication systems on rates of duplicate imaging: a before-after study
Bibliographic record
Abstract
BACKGROUND: Electronic health information systems, such as picture archiving communication systems (PACS), are commonly believed to reduce the need for duplicate testing. However, empirical data to support this belief are not available. METHODS: Before-after study using administrative claims data from the Ontario Health Insurance Plan to determine whether the introduction of PACS at 10 hospitals in the Thames Valley region of southwestern Ontario, Canada between June 2004 and December 2005 reduced the frequency of duplicate imaging examinations. The imaging modalities studied were: chest and abdominal X-ray; computed tomography of the abdomen/pelvis, head, and chest. The frequency of duplicate testing was examined at 3 different time frames: 7 days, 30 days, and 60 days after a given index test. RESULTS: Overall frequencies of duplicate imaging were: 2.7% within 7 days of an index imaging test, 6.7% within 30 days, and 9.8% within 60 days. Comparing the 12 months before and 12 months after PACS, absolute reductions in the frequency of duplicate X-rays using 7-day, 30-day, and 60-day time frames were: 0.2% (P=0.01), 0.6% (P<0.001), and 0.9% (P<0.001), respectively. In contrast, there were absolute increases in the frequency of duplicate CT scans after PACS of 0.0% (P=0.92), 0.5% (P=0.01), and 0.5% (P=0.01), respectively. CONCLUSION: The frequency of duplicate imaging is relatively low and we did not find large reductions in duplicate imaging after the introduction of PACS. Independent evaluation of electronic medical systems should be conducted to confirm widely held beliefs of their potential benefits.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".