A Dynamic Model of Health Insurance Choices and Healthcare Consumption Decisions
Bibliographic record
Abstract
Chronic diseases, which account for 75% of healthcare expenditure, are of particular importance in trying to understand the rapid growth of healthcare costs over the last few decades. Individuals suffering from chronic diseases can consume three types of services: secondary preventive care, which includes diagnostic tests; primary preventive care, which consists of drugs that help prevent the illness from getting worse; and curative care, which includes surgeries and expensive drugs that provide a quantum boost to the patient’s health. Although the majority of cases can be managed by preventive care, most consumers opt for more expensive curative care that leads to a substantial increase in overall costs. To examine these inefficiencies, we build a model of consumers’ annual medical insurance plan decisions and periodic consumption decisions and apply it to a panel data set. Our results indicate that there exists a sizable segment of consumers who purchase more comprehensive plans than needed because of high uncertainty vis-à-vis their health status, and that once in the plan, they opt for curative care even when their illness could be managed through preventive care. We examine how changing cost-sharing characteristics of insurance plans and providing more accurate information to consumers via secondary preventive care can reduce these inefficiencies. Data and the Web appendix are available at https://doi.org/10.1287/mksc.2016.1021 .
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".