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Enregistrement W7105669722 · doi:10.47895/amp.v59i16.13853

The All-of-Society Approach to Evidence-informed Policymaking in the Implementation of the Universal Healthcare Act of 2019

2025· article· W7105669722 sur OpenAlexaff

Notice bibliographique

RevueActa Medica Philippina · 2025
Typearticle
Langue
DomaineMedicine
ThématiqueGlobal Cancer Incidence and Screening
Établissements canadiensHealth Care Foundation
Organismes subventionnairesnon disponible
Mots-clésHealth careTest (biology)Public healthProcess (computing)Activity-based costingBreast cancerHealth economicsQuality (philosophy)

Résumé

récupéré en direct d'OpenAlex

The article “Breast Cancer in the Philippines: A Financing Cost Assessment Study” by Valera et al. epitomizes the “research-to-policy” paradigm and is a good model for public health and clinical researchers to follow. The scientific process of identifying a health-related problem, asking the question as to why the problem exists, suggesting a hypothetical answer and then gathering data to be able to test this hypothesis in an objective and unbiased manner requires rigorous discipline expected from a clinical researcher or trialist. Public health research follows the same process of identifying a public health problem, hypothesizing as to the cause of the problem, gathering of data, and then recommending solutions to said problem. Public health leaders and governing bodies then create policy from the research findings, hence the terms “research-to-policy” or “evidence-informed policymaking.” In the Valera article, the problem presented is that breast cancer is the most diagnosed cancer in the Philippines, and accounts for 17.7% of all incident cancers annually in the country. This translates to 33,079 women diagnosed every year who are diagnosed usually in later stages of the disease when standard treatment requires not just surgery (which is sufficient treatment for stage 1 breast cancer) but surgery, radiation, and chemotherapy. Each form of treatment drives the cost of care up, making the benefits derived from such treatment, like cure, or extension of life, or improvement in quality of life – inaccessible to most and inequitable in distribution. As a basis for their costing exercise, the authors chose as their use case a triple-positive Stage IIB breast cancer patient who would likely die from her disease unless she undergoes treatment. With treatment, the 5-year survival rate of Stage IIB breast cancer is 99%. Standard treatment for Stage IIB triplepositive breast cancer includes surgery followed by radiation, followed by six months of chemotherapy overlapping with 12 months of immunotherapy, then 5 to 10 years’ worth of hormonal therapy. The total cost of the entire treatment package was estimated to be between 1.6 to 1.8 million pesos. The study authors also discussed some of the challenges they met in the conduct of this study, including the reluctance of hospitals to share their surgery and radiation cost data, the absence of approved Clinical Practice Guidelines for Breast Cancer at the start of the study, and the need to update the Relative Value System (RVS) and the Peso Conversion Factor (PCF) of Philhealth. The authors end with a clarion call, 1) for recognition that advancements in the diagnosis and treatment of breast cancer can only mean rising costs for the patients and their payors in the very near future, 2) that costing should be adjusted and expenditures lowered, 3) that expenditure can be lowered greatly by setting up a budget for early diagnosis through screening programs, 4) that the government should consider dividing cancer care into initial, continuing, and end-of-life phases to ensure quality of life and appropriate medical care for all breast cancer patients, and 5) for each breast cancer treatment facility to have their own integrated, interoperable, and comprehensive cost data library. This study started in the midst of COVID-19 in 2021, and was presented to the Department of Health in October, 2023. The entire process “from research-to-policy” took three years, and in March 1, 2024, the 12-year-old Philhealth Z Benefit Package increased from 100,000.00 or 6% of the actual cost of treatment for breast cancer stage I, II, or III to 1,400,000.00 or 80% of the actual cost of treatment, largely due to the results of this study. Additionally, Stage 4 breast cancer patients undergoing palliative chemotherapy or hormonal therapy can have their medicines covered by the updated Z Benefit Package for Breast Cancer of Philhealth. Implementation on the ground was not easy and continues to be a challenge. The excitement generated by the announcement in major dailies naturally led to high expectations from patients and eventual disappointment at not being able to find Philhealth-contracted hospitals offering the Z Benefit Package for Breast Cancer near their communities. Today, there is at least one contracted health facility for Breast Cancer Z Benefit Package in each of the 13 regions of the country. Majority of these hospitals are public hospitals or DOH hospitals, but some private hospitals have patients also seeking the benefits of this program and therefore are finding ways to work with Philhealth for this to happen. The public health research data lifecycle begins with an individual patient or small cohort of patients and ends with the bigger population of patients with the same health problem. Valera et al., in publishing this paper, have given an example of how a research team composed of clinical domain experts, public health specialists, health economists, biostatisticians, research managers, policymakers, and patients, taking the all-of-society team approach, using the Universal Healthcare Act as its marching orders, can contribute toward “achieving better health outcomes through an equitable, sustainable, and quality healthcare system.”

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,315
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,102
Tête enseignante GPT0,436
Écart entre enseignants0,334 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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