Diabetes, kidney disease and cardiovascular disease patients. Assessing care of complex patients using outpatient testing and visits: additional metrics by which to evaluate health care system functioning
Bibliographic record
Abstract
BACKGROUND: The triad of cardiovascular disease (CVD), chronic kidney disease (CKD) and diabetes mellitus (DM) share many fundamental disease pathways. Patients with these conditions contribute excessively to health care costs. Opportunities for system redesign require metrics by which to evaluate the impact. METHODS: Using a provincial comprehensive set of administrative billing databases (outpatient visits, laboratory tests, pharmacy and hospital inpatient services), we itemized the prevalence of each and combination of conditions, resource utilization associated with each condition and combinations, using ICD 9-10 billing codes and standard definitions. Three consecutive years (2003-2005) were used to establish stability of findings. RESULTS: CKD, CVD and DM diagnoses are found in 422 124 persons within a province of 4.3 million individuals (10%); 1.7% had all three conditions. The median age of each cohort varied significantly between those with multiple conditions (67-79 years) versus those with single condition (56-72 years). The median number of physician visits was 26 per patient year. Duplicate testing accounted for expenditures of $3 million/annum; 7.55% of patients accounted for 34.4% of duplicate tests. Those with DM or CKD had similar use of medications, physician visits and hospital days. Those with all conditions (CVD-CKD-DM) had a median of 6 in-hospital days/year. A significant proportion were not on ACE/ARB or statin medications (30 and 45%, respectively). CONCLUSION: Patients with chronic, complex conditions consume a large number of outpatient and inpatient resources. Documenting these allows identification of a set of metrics by which to design and measure health care system redesign initiatives. Potential targets to benchmark in designing more effective systems have been identified.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".