The Spillover Effects of Health IT Investments on Regional Healthcare Costs
Bibliographic record
Abstract
Electronic health records (EHR) are often presumed to reduce the significant and accelerating healthcare costs in the United States. However, evidence on the relationship between EHR adoption and costs is mixed, leading to skepticism about the effectiveness of EHR in decreasing costs. We argue that simply looking at the hospital-level effects can be misleading because the benefits of EHR can go beyond the adopting hospital by creating regional spillovers via information and patient sharing. When patients move between hospitals, timely and high-quality records received at one hospital can affect the costs of care at another hospital. We provide evidence that although EHR adoption increases the costs of the adopting hospital, it has significant spillover effects by reducing the costs of neighboring hospitals. We further show that these spillovers are linked to information and patient sharing. Specifically, the spillovers are stronger when more hospitals in the region are in health information exchange networks and in the same integrated delivery systems, which can share information more easily. Furthermore, utilizing regional characteristics that can affect the extent of patient sharing such as urban versus rural areas, population density, average distance between hospitals, and hospital density, we find that locations with higher patient and hospital concentration experience stronger regional spillovers. Additionally, spillovers are stronger after the HITECH (Health Information Technology for Economic and Clinical Health) Act that increased EHR adoption and use. Overall, our findings suggest that we need to take into account externalities to understand the benefits of health IT investments and form policy decisions. This paper was accepted by Anandhi Bharadwaj, information systems.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".