Beyond the COVID-19 Pandemic: Budget Impact Analysis of Remote Healthcare Delivery for Hypertension and Diabetes Mellitus Management in Thailand
Bibliographic record
Abstract
Purpose: The COVID-19 pandemic disrupted healthcare services globally, necessitating innovative care delivery models for non-communicable diseases. Remote healthcare pathways, including telehealth with pharmacy at home (PAH) and deferred care (DC), emerged as potential solutions for managing stable hypertension (HT) and diabetes mellitus (DM) patients. This study aims to estimate the budget impact of implementing PAH and DC compared to usual care (UC) for HT and DM patients in Thai tertiary care hospitals from the government perspective. Methods: A retrospective budget impact analysis was conducted using data from July-December 2021 (COVID-19 period) and July-December 2022 (new normal period). The study included stable patients from 35 tertiary care hospitals in Thailand. Direct medical costs were obtained from administrative databases and national costing studies. Multivariate log-linear regression models estimated conditional costs, controlling for patient characteristics. The analysis compared baseline scenario (UC only) versus alternative scenario (UC+PAH+DC). Sensitivity analyses were performed using 95% confidence intervals and ±20% population variations. Results: The alternative scenario demonstrated lower total budgets in both periods. During COVID-19, total costs were 12.23 versus 12.94 million USD (baseline), yielding 0.71 million USD in savings. In the new normal, costs were 11.93 versus 12.54 million USD (baseline), generating 0.61 million USD in savings. Cost-saving ratios were 0.06 USD and 0.05 USD per dollar allocated during the COVID-19 and new normal periods, respectively. Sensitivity analyses confirmed robustness across parameter variations. Conclusion: PAH and DC pathways represent economically advantageous alternatives, demonstrating cost savings from the government perspective. These findings support implementing remote healthcare delivery in resource-constrained settings, though comprehensive evaluations incorporating societal and patient perspectives are warranted. The findings are based on extrapolation-based results and should be interpreted with caution due to variability in parameters including adoption rates of PAH/DC, unit costs applied, patient numbers, retrospective design, bundled interventions, and the savings ratio.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".