Bibliographic record
Abstract
Though 20th century has seen life expectancy largely lengthened worldwide, aging population, chronic diseases, worsening food supply with deficit nutrition and environmental problems add to the burden of healthcare systems all around the world. Data analytics, which has been seen as a significant power in other industries, is expected to contribute to the improvement of efficiency and effectiveness in healthcare. This thesis aims to identify and promote more effective and efficient strategic, operations and clinical policies in healthcare systems through descriptive, predictive and prescriptive analytics. To this end, this thesis focuses on three essays, i.e. three data-driven problems based on medium to large size of real life datasets, on: i) design of financial incentive systems for maternity care; ii) design of specialist response policies and modified triage coding to reduce waiting times in emergency departments (EDs), and iii) design of observation units for hearth failure patients. The first essay focuses on strategic level and aims to design a two-level financial incentive mechanisms to reimburse physicians, in order to reduce unnecessary C-sections while retain it for those who need it, resulting in enhanced birth quality with alleviated economic burden for overall health care system. Contributing to clinical decision-making, we first cluster the patients according to their pregnancy complexities, and characterize a threshold between spontaneous birth and medically necessary planned C-section by analyzing 12.7 million annual birth records from National Bureau of Economics Research through statistical learning methods. Then we compare payment systems analytically vis-\'{a}-vis a variety of performance measures within two-level hierarchy, (i) mainstream payment models and (ii) compensation on the top of mainstream payment, and provide insights about the effectiveness of alternative payment models in the context of maternity care. Finally, we propose optimal payment for physicians to maximize the value for patients under the principal and agent framework, from the strategic perspective.The second paper focuses on operational level and targets to reduce the length of stay in EDs by designing a systematic response policy for various specialists depending on ED clinical demands. This work is motivated by and verified with 40,000 ED visits to a local community hospital in Montreal. We first identify a class of patients who are more likely to require specialist consultation based on their clinical information available at the triage stage through statistical analysis. Then we analyze several alternative policies for specialists' response to consultation requests using queuing models with non-homogeneous Poisson arrival rates. Moreover, we examine an integrated ED decision-making by incorporating specialist consultation requests in the triage system. Finally, our proposed optimal specialist response policy and associated modified triage coding are verified through a comprehensive simulation model. We provide a feasible guideline of integrated patient streamlining to shorten length of stay and alleviate overcrowding in ED. The third paper focuses on clinical level and propose a framework to design a dedicated observation unit for acute decomposition heart failure patients, in order to provide proper treatment and reduce unnecessary hospitalization and chance of post-discharge events. To this end, we, first, use multiple analytical models to figure out the proper number of bed for this observation unit based on historical patient arrival data from a local community hospital. Based on the confined range of analytical capacity, we use simulation models to analyze different discharge and admission policies. We propose an optimal discharge-admission criteria for this dedicated observation unit to realize cost-saving and quality enhancement of treating acute decomposition heart failure patients.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".