The impact of alternative care pathways adopted during the COVID-19 pandemic on the management of non-communicable diseases at a tertiary care hospital in Thailand
Bibliographic record
Abstract
Background: Management of non-communicable diseases (NCDs), including hypertension (HT) and diabetes mellitus (DM), was significantly impacted by the COVID-19 pandemic. Many institutions adopted alternative care pathways, e.g. pharmacy at home (PAH), and the deferred care (DC). While PAH has been studied for clinical outcomes, evaluation of the DC remains limited. Consequently, this study evaluates both the clinical and economic outcomes of the PAH and DC as alternatives to usual care. Method: A retrospective study was conducted at a tertiary care hospital in Thailand from 1 July 2021, to 30 June 2023. Data from outpatients with HT and DM were classified into PAH, DC, or discharged home with follow-up at the hospital. Clinical outcomes included changes in systolic blood pressure (SBP), diastolic blood pressure (DBP), and fasting blood sugar (FBS), calculated from baseline to follow-up. Economic outcome was the cost of illness (COI) per patient visit. Multivariate multilevel mixed-effects linear regression assessed clinical outcomes, while log-linear regression evaluated economic outcome. Results: < 0.001). During the pandemic period, COI reductions were 32.3% in PAH and 93.5% in DC compared to usual care. Similar trends were observed in the post pandemic period, with COI reductions of 40.0% for PAH and 96.1% for DC. Conclusion: PAH and DC pathways did not worsen the clinical outcomes and reduced costs during and following the pandemic. As a result, these two pathways, developed during the COVID-19 pandemic, can be adapted for regular use. When these pathways are integrated into regular use, they can be promptly and fully reactivated in future emergencies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".