The patient-centered medical home in the Veterans Health Administration.
Bibliographic record
Abstract
BACKGROUND: The Veterans Health Administration (VHA) is the largest integrated US health system to implement the patient-centered medical home. The Patient Aligned Care Team (PACT) initiative (implemented 2010-2014) aims to achieve team based care, improved access, and care management for more than 5 million primary care patients nationwide. OBJECTIVES: To describe PACT and evaluate interim changes in PACT-related care processes. STUDY DESIGN: Data from the VHA Corporate Data Warehouse were obtained from April 2009 (pre- PACT) to September 2012. All patients assigned to a primary care provider (PCP) at all VHA facilities were included. METHODS: Nonparametric tests of trend across time points. RESULTS: VHA increased primary care staff levels from April 2010 to December 2011 (2.3 to 3.0 staff per PCP full-time equivalent). In-person PCP visit rates slightly decreased from April 2009 to April 2012 (53 to 43 per 100 patients per calendar quarter; P < .01), while in-person nurse encounter rates remained steady. Large increases were seen in phone encounters (2.7 to 28.8 per 100 patients per quarter; P < .01), enhanced personal health record use (3% to 13% of patients enrolled), and electronic messaging to providers (0.01% to 2.3% of patients per quarter). Post hospitalization follow-up improved (6.6% to 61% of VA hospital discharges), but home telemonitoring (0.8% to 1.4% of patients) and group visits (0.2 to 0.65 per 100 patients per quarter; P < .01) grew slowly. CONCLUSIONS: Thirty months into PACT, primary care staff levels and phone and electronic encounters have greatly increased; other changes have been positive but slower.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".