Some Hospitals Are Falling Behind In Meeting ‘Meaningful Use’ Criteria And Could Be Vulnerable To Penalties In 2015
Bibliographic record
Abstract
With nearly $30 billion in incentives available, it is critical to know to what extent US hospitals have been able to respond to those incentives by adopting electronic health record (EHR) systems that meet Medicare's criteria for their "meaningful use." Medicare has provided aggregate incentive payment data, but still missing is an understanding of how these payments are distributed across hospital types and years. Our analysis of Medicare data found a substantial increase in the percentage of hospitals receiving EHR incentive payments between 2011 (17.4 percent) and 2012 (36.8 percent). However, this increase was not uniform across all hospitals, and the overall proportion of hospitals receiving a payment for meaningful use was low. Critical-access, smaller, and publicly owned or nonprofit hospitals appeared to be at particular risk for failing to meet Medicare's meaningful-use criteria, and the overall proportion of hospitals receiving a payment for meaningful use was low. Starting in 2015, hospitals that fail to meet the criteria will be subject to financial penalties. To address the needs of institutions in danger of incurring these penalties, policy makers could implement targeted grant programs and provide additional information technology workforce support. In addition, the capacity of EHR system vendors should be carefully monitored to ensure that these institutions have access to the technology they need.
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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 | 0.027 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".