Evidence map›Paper›PMID 40909762›Full record

ArticleClinicoEconomics and outcomes research : CEOR2025

Beyond the COVID-19 Pandemic: Budget Impact Analysis of Remote Healthcare Delivery for Hypertension and Diabetes Mellitus Management in Thailand.

Jongkonnee Chongpornchai, Tuangrat Phodha, Thanawat Wongphan, Kamonwan Soonklang, Peter C Coyte

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Article in ClinicoEconomics and outcomes research : CEOR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jongkonnee ChongpornchaiDrug Information and Consumer Protection Center, Center of Excellence in Pharmacy Practice and Management Research, Faculty of Pharmacy, Thammasat University, Pathum Thani, Thailand.
Tuangrat PhodhaDrug Information and Consumer Protection Center, Center of Excellence in Pharmacy Practice and Management Research, Faculty of Pharmacy, Thammasat University, Pathum Thani, Thailand.
Thanawat WongphanNakhon Nayok Provincial Public Health Office, Nakhon Nayok, Thailand.
Kamonwan SoonklangPrincess Srisavangavadhana Faculty of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand.
Peter C CoyteInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

budget impact analysiscostCOVID-19non-communicable diseasesremote healthcare deliverytelehealth

Identifiers

PMID40909762
PMCPMC12406030

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.