Three essays on technology and healthcare
Bibliographic record
Abstract
Advances in medical technology have changed the standards of models of care, while also constituting a driving force in raising costs. This thesis addresses the various impacts of technology on healthcare through three chapters. The first chapter investigates to what extent medical technology drives healthcare expenditure for a panel of Canadian provinces and another of OECD countries. A careful examination of existing technology proxies is conducted, and three new proxies are added to the literature. A novel dynamic common correlated effect approach from the emerging field of panel time series is employed to explore the relationship between healthcare expenditure and technology. As expected, the variables are tied together by a long-run relationship, but the speed of adjustment varies depending on the technology proxy used and the level of aggregation. The second and third chapters study the effect of two forms of health information technology on a variety of healthcare and health outcomes. Specifically, two programs are investigated: telemedicine and electronic medical records (EMR). Both programs take place in the Canadian province of Manitoba. The linkable administrative data housed at the Manitoba Centre for Health Policy was used to create cohorts of users and non-users for each of the programs. The second chapter uses a propensity-weighted regression model to measure the impact of telemedicine on four indicators of healthcare use. Results point to increased use of healthcare services for telemedicine users. But for those patients who showed higher intensity of use, telemedicine seems to be a substitute for regular care, and not an addition to it. Finally, the third chapter explores the association, at a primary care level, between use of electronic medical records and quality of care measures. A set of indicators covering preventive care, chronic disease management, and healthcare utilization are investigated through a difference-in-differences approach with patient and time fixed effects, and an estimation strategy that uses the variation in timing of adoption. Results show that patients with diabetes in primary care practices using EMR’s show improved management indicators, while no evidence of changes in preventive care or hospitalizations for a set of ambulatory care sensitive conditions is found.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".