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Enregistrement W2580331578 · doi:10.18553/jmcp.2017.23.2.125

Payer Perspectives on Patient-Reported Outcomes in Health Care Decision Making: Oncology Examples

2017· article· en· W2580331578 sur OpenAlexaboutno aff
Andrew P. Brogan, Carla DeMuro, Amy Barrett, Denise D’Alessio, V Bal, S. Hogue

Notice bibliographique

RevueJournal of Managed Care & Specialty Pharmacy · 2017
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic and Financial Impacts of Cancer
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésReimbursementFamily medicineMedicineMEDLINEAgency (philosophy)Health carePolitical science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Health authorities and payers increasingly recognize the importance of patient perspectives and patient-reported outcomes (PROs) in health care decision making. However, given the broad variety of PRO endpoints included in clinical programs and variations in the timing of PRO data collection and country-specific needs, the role of PRO data in reimbursement decisions requires characterization. OBJECTIVES: To (a) determine the effect of PRO data on market access and reimbursement decisions for oncology products in multiple markets and (b) assess the effect of PRO data collected after clinical progression on payer decision making. METHODS: A 3-part assessment (targeted literature review, qualitative one-on-one interviews, and online survey) was undertaken. Published literature was identified through searches in PubMed/MEDLINE and Embase. In addition, a targeted search was conducted of health technology assessment (HTA) agency websites in the United States, the United Kingdom, France, and Germany. Qualitative one-on-one interviews were conducted with 16 payers from the RTI Health Solutions global advisory panel in 14 markets (Australia, Brazil, France, Germany, Italy, South Korea, Netherlands, Poland, Spain, Sweden, Taiwan, Turkey, the United Kingdom, and the United States [n = 3]). Of the 200 payers and payer advisors from the global advisory panel invited to participate in the online survey, 20 respondents (China, France, Germany, Spain [n = 2], Taiwan, the United Kingdom, and the United States [n = 13]) completed the survey, and 6 respondents (Australia, South Korea, and the United States [n = 4]) partially completed the survey. RESULTS: Reviews of the literature and publicly available HTAs and reimbursement decisions suggested that HTA bodies and payers have varying experience with and confidence in PRO data. Payers participating in the survey indicated that PRO data may be especially influential in oncology compared with other therapeutic areas. Payers surveyed offered little differentiation by cancer type in the importance of PRO data but felt that it was most important to collect PRO data in phase 3 and postmarketing studies. Payers surveyed also anticipated an increasing significance for PRO data over the next 5-10 years. Characteristics of PRO data that maximize influence on payer decision making were reported to be (a) quality, well-controlled, and transparent PRO evidence; (b) psychometric validation of the PRO measure in targeted populations; and (c) publication in peer-reviewed journals. In markets with decentralized health care decision making, PRO data currently have more influence at the local level. Inclusion of PRO data in cancer treatment guidelines is key for centralized markets. Payers surveyed generally considered collecting PRO data postprogression to be useful. Of the 16 interviewees, 11 indicated that it is worthwhile to collect PRO data postprogression and that positive PRO data may support continued therapy at the physician's discretion upon regulatory approval, even in progressive disease. CONCLUSIONS: PRO data may help to differentiate treatments, particularly after clinical progression in oncology. Payers worldwide recognize high-quality PRO data as a key component of their decision-making process and anticipate the growing importance of PRO data in the future. DISCLOSURES: This study and preparation of this article were funded by Novartis Pharmaceuticals. This research was performed under a research contract between RTI Health Solutions and Novartis Pharmaceuticals. Brogan, Hogue, Demuro, and Barrett are employees of RTI Health Solutions. D'Alessio and Bal are employees of Novartis Pharmaceuticals. Study concept and design were contributed by DeMuro, Barrett, Bal, and Hogue. Brogan and Hogue took the lead in data collection, assisted by DeMuro and Bal. Data interpretation was performed by Brogan and Hogue, assisted by the other authors. The manuscript was written by D'Alessio and Brogan, along with the other authors, and revised primarily by Brogan, along with Hogue and assisted by the other authors. The abstract for this article was presented as a research poster at the following meetings: Hogue SL, Brogan A P, De Muro C, D'Alessio D, Bal V. Patient-reported outcomes (PRO) in post-progression oncology: implications in health technology assessments and payer decision making. Poster presented at the ISPOR 18th Annual European Meeting; November 7-11, 2015. Milan, Italy. Hogue SL, Brogan AP, De Muro C, D'Alessio D, Bal V. Influence of patient-reported outcomes (PRO) on market access decisions in markets with centralized healthcare systems. Poster presented at the ISPOR 18th Annual European Meeting; November 7-11, 2015. Milan, Italy. Hogue SL, Brogan AP, De Muro C, Barrett A, D'Alessio D, Bal V. Influence of patient-reported outcomes on market access decisions in decentralized markets (Brazil, Italy, Spain and the United States). Poster presented at the ISPOR 20th Annual Meeting; May 16-20, 2015. Philadelphia PA. Hogue SL, Brogan A P, De Muro C, Barrett A, McLeod L, D'Alessio D, et al. Payer Perspectives of Patient-Reported Outcomes Data: An Online Assessment. Poster presented at the ISOQOL 22nd Annual Meeting; October 21-24, 2015. Vancouver, British Columbia, Canada.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,042
score de la tête « metaresearch » (Gemma)0,070
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,042
Score d'incertitude au seuil0,220

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0420,070
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,005
Études des sciences et des technologies0,0020,005
Communication savante0,0040,006
Science ouverte0,0010,003
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,060
Tête enseignante GPT0,363
Écart entre enseignants0,303 · 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 source (Gemma direct ou Codex distillé), 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

Citations53
Publié2017
Routes d'admission1
Résumé présentoui

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