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Value-based healthcare in fertility care using relevant outcome measures for the full cycle of care leading towards shared decision-making: a retrospective cohort study

OBJECTIVE: To determine if the introduction of value-based healthcare (VBHC) in fertility care can help to create realistic expectations in patients resulting in increased patient value, by demonstrating the relevance of defining outcome measures that truly matter to subfertile patients. DESIGN: Ret...

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Detalles Bibliográficos
Autores principales: Bensink, Milou, Volkerink, Joy, Teklenburg, Gijs, van Bavel, Casandra C A W, Kuchenbecker, Walter K H, Cohlen, Ben J, Curfs, Max H J M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496656/
https://www.ncbi.nlm.nih.gov/pubmed/37696630
http://dx.doi.org/10.1136/bmjopen-2023-074587
Descripción
Sumario:OBJECTIVE: To determine if the introduction of value-based healthcare (VBHC) in fertility care can help to create realistic expectations in patients resulting in increased patient value, by demonstrating the relevance of defining outcome measures that truly matter to subfertile patients. DESIGN: Retrospective cohort study. SETTING: Tertiary fertility centre. RESULTS: Time to pregnancy (TTP) and ongoing pregnancy rate (OPR), as a proxy for the live birth rate, for the full cycle of fertility care, regardless of which and how many treatment cycles performed, were identified as the most relevant medical outcome measures. Outcome measures were incorporated into a digital dashboard by using anonymised and validated patient data from the electronic patient file. We were able to present the TTP and OPR for the population as a whole as well as stratified for age, diagnosis, gravidity and type of gamete source used thereby resulting in a virtual ‘patient like me’ resembling the individual patient in the consultation room. CONCLUSION: We have shown that, by applying VBHC principles, relevant outcome measures can be generated and stratified for different patient characteristics, in order to develop a virtual ‘patient like me’. This virtual ‘patient like me’ can be used in the consulting room in the form of a digital dashboard, attributing to create realistic patient expectations. This facilitates healthcare providers and patients in shared decision-making.