Does Health Information Exchange Improve Long-Term Care Service Quality? Evidence from the Panel Data Analysis of the U.S. Long-term Care Facilities
Bibliographic record
Abstract
This paper examines the impact of health information exchange (HIE) on the service quality of long-term care (LTC) facilities based on a five-year period (2013-2017) panel data of the U.S. LTC facilities. Our results show a reverse impact of the HIE adoption on the readmission rate of LTC facilities. The readmission rate of an LTC facility with an operational HIE is reduced by 2% on average as compared to the rate of a facility without operational HIE. We also estimate the heterogeneous effect of HIE by two innovative healthcare ITs (EHR and Telemedicine). We find that the applications of EHR and Telemedicine in LTC facilities are still at a very early stage. Our findings empirically demonstrate the importance of promoting effective data exchange in LTC facilities as well as improving the use of EHR and Telemedicine to increase the value that HIE can create.
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".