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Record W7134036920 · doi:10.58837/chula.the.2019.1611

Development of reimbursement model forhome-based chemotherapy in Thailand

2019· dissertation· W7134036920 on OpenAlexaboutno aff
Nattanichcha Kulthanachairojana

Bibliographic record

Venuenot available
Typedissertation
Language
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsnot available
Fundersnot available
KeywordsReimbursementContext (archaeology)Health careAmbulatoryService (business)Health economicsStakeholder

Abstract

fetched live from OpenAlex

The objectives of the study were (1) to review on HC reimbursement model in other countries (2) to conduct economic evaluation comparing HC with inpatient chemotherapy and hospital-based chemotherapy treatment (IP) from a societal perspective (3) to develop national models and financing and reimbursing of HC services in Thailand. Literature review and semi-structured interviews as the mixed methods had been applied for data collection. The characteristic of HC and reimbursement policies for public payer and service delivery were obtained from eight countries: Australia, Canada (Ontario), England, France, Japan, Korea, Singapore and the United States (US) (Medicare Part B). A cost-utility analysis was conducted for stage III colon cancer patients after resection with a 6-month time horizon. Parameter inputs for the analysis had gathered data from Ramathibodi hospital and literature review. The measuring of health outcomes was in the form of Quality-adjusted life years (QALYs). Result of international review, Thai context literature review and economic evaluation were used to develop the HC reimbursement model in Thailand. A stakeholder meeting was proceeded to summarize opinions to the models. The budget impact analysis was done after the reimbursement model was developed. Comparators of the analysis were hospital-based chemotherapy with the current reimbursement model and HC with proposed reimbursement models. The result found that the HC characteristics are various among countries. There are three types of HC: Ambulatory infusion pump (AIP), Hospital in the home program (HITH) and Home health provider (HHP) where the different service structures are provided under healthcare system organization and governance. Funding mechanisms reflect country-specific context and local variations in care provision, where this depends on the reimbursement mechanism and compliance on the healthcare system of each country. The result of the economic analysis showed that HC was a dominant option that provided 52,968 baht of cost-saving per patient for the episode of treatment. QALYs of HC and IP were 0.3598 and 0.3362, respectively. We recommend the reimbursement models that comply with the Thai context. The models supported hospital management, central venous (CV) port, AIP and mini spill kit that were the important compositions of HC service. The stakeholder meeting was conducted with 31 participants from payers, hospitals and cancer organizations in Thailand. The stakeholders considered AIP HC that would be suitable for the organization, governance, and management of the hospital and payers would support HC management and equipment for more accessibility of the service. The budget impact analysis found total cost saving comparing HC and IP treatment from a societal perspective was 27.84 million baht for 100% patient accessibility rate. The study concluded that HC is a cost-saving strategy comparing with IP. In Thailand, moving chemotherapy into HC could help with cost-saving and increasing patient health outcomes. For HC development, healthcare providers, patients, and relatives must be trained to handle the process of treatment at home. The HC AIP service is optional for a cancer patient in Thailand. The implementation of the service would be supported by the new reimbursement model that appropriate with the Thai healthcare system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.313
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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