Outcome and Resource Use of Patients with Liver Disease: Analysis of Chinese Electronic Medical Records
Bibliographic record
Abstract
Researches aiming to improve health care quality, such as the outcome studies of liver diseases, require large databases and rich case mix information to incorporate severity assessment and risk adjustment in order to generate robust results. Researchers worldwide have recognized the potential value of electronic medical record (EMR) and tremendous efforts are underway to advance outcome research using EMR. Chinese EMR provides us a unique chance for analysis of the outcome and resource use of liver disease given the high prevalence of liver disease and widely-used EMR in China. The aim of this thesis was to analyze the outcome and resource use of patients with liver disease through appropriate risk adjustment using information extracted from Chinese EMR. Three studies were conducted to achieve the aim of this thesis. The first study was to develop and validate an EMR data extraction method to define clinical conditions, including liver diseases, comorbidities, and treatments. The research from this initial study demonstrated that the data extraction method had high validity and had the potential to be applied to other Chinese EMRs following our framework. The second study aimed to compare statistical performance of the commonly used risk adjustment methods for liver diseases. The study concluded that liver-specific severity scoring methods outperformed the comorbidity methods in predicting in-hospital mortality using a large Chinese EMR database. Combining severity and comorbidity scoring assessments could improve the risk adjustment performance. The third study examined the impact of financial factors on outcome and healthcare resource use. We tested the effect of patient’s cost sharing (reimbursement ratio) on in-hospital mortality, hospital length of stay, total and medication costs in hospital, and use of major procedures after controlling for potential confounding variables. An association was found between the cost-sharing model and healthcare resource use and cost. This research has proposed a validated data extraction method for Chinese EMR, provided evidence for appropriate implementation of common risk adjustment methods in analysis of outcome for liver disease, and offered us insight into the impact of financial incentives on health utilization, cost, and outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".