The economics of health information technology in medication management: a systematic review of economic evaluations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review and synthesis of the evidence surrounding the cost-effectiveness of health information technology (HIT) in the medication process. MATERIALS AND METHODS: Peer-reviewed electronic databases and gray literature were searched to identify studies on HIT used to assist in the medication management process. Articles including an economic component were reviewed for further screening. For this review, full cost-effectiveness analyses, cost-utility analyses and cost-benefit analyses, as well as cost analyses, were eligible for inclusion and synthesis. RESULTS: The 31 studies included were heterogeneous with respect to the HIT evaluated, setting, and economic methods used. Thus the data could not be synthesized, and a narrative review was conducted. Most studies evaluated computer decision support systems in hospital settings in the USA, and only five of the studied performed full economic evaluations. DISCUSSION: Most studies merely provided cost data; however, useful economic data involves far more input. A full economic evaluation includes a full enumeration of the costs, synthesized with the outcomes of the intervention. CONCLUSION: The quality of the economic literature in this area is poor. A few studies found that HIT may offer cost advantages despite their increased acquisition costs. However, given the uncertainty that surrounds the costs and outcomes data, and limited study designs, it is difficult to reach any definitive conclusion as to whether the additional costs and benefits represent value for money. Sophisticated concurrent prospective economic evaluations need to be conducted to address whether HIT interventions in the medication management process are cost-effective.
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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 | 0.037 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it