Unmet Parenthood Goals, Health-Related Quality of Life and Apparent Irrationality: Understanding the Value of Treatments for Infertility

Skedgel, Chris; Cubí‐Mollá, Patricia; Mott, David; Gameiro, Sofia; Boivin, Jacky; Al‐Janabi, Hareth; Brazier, John; Markert, Marie; Andersson, Fredrik; Jofre‐Bonet, Mireia

doi:10.1007/s41669-023-00402-5

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Safe and efficacious, assisted reproductive technology (ART) has the potential to enable millions more people globally to have a family, yet it continues to be underutilized [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. For patients contemplating the use of in vitro fertilization (IVF) with their own eggs, IVF with donor eggs, intra-uterine insemination (IUI), or a wait-and-see approach, understanding realistic treatment success probabilities (e.g., excellent and poor) is expected to empower patient decision making [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

Yao,

Nguyen,

Retzloff

et al. 2024

JCM

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Objectives: In vitro fertilization (IVF) has the potential to give babies to millions more people globally, yet it continues to be underutilized. We established a globally applicable and locally adaptable IVF prognostics report and framework to support patient–provider counseling and enable validated, data-driven treatment decisions. This study investigates the IVF utilization rates associated with the usage of machine learning, center-specific (MLCS) prognostic reports (the Univfy® report) in provider-patient pre-treatment and IVF counseling. Methods: We used a retrospective cohort comprising 24,238 patients with new patient visits (NPV) from 2016 to 2022 across seven fertility centers in 17 locations in seven US states and Ontario, Canada. We tested the association of Univfy report usage and first intra-uterine insemination (IUI) and/or first IVF usage (a.k.a. conversion) within 180 days, 360 days, and “Ever” of NPV as primary outcomes. Results: Univfy report usage was associated with higher direct IVF conversion (without prior IUI), with odds ratios (OR) 3.13 (95% CI 2.83, 3.46), 2.89 (95% CI 2.63, 3.17), and 2.04 (95% CI 1.90, 2.20) and total IVF conversion (with or without prior IUI), OR 3.41 (95% CI 3.09, 3.75), 3.81 (95% CI 3.49, 4.16), and 2.78 (95% CI 2.59, 2.98) in 180-day, 360-day, and Ever analyses, respectively; p < 0.05. Among patients with Univfy report usage, after accounting for center as a factor, older age was a small yet independent predictor of IVF conversion. Conclusions: Usage of a patient-centric, MLCS-based prognostics report was associated with increased IVF conversion among new fertility patients. Further research to study factors influencing treatment decision making and real-world optimization of patient-centric workflows utilizing the MLCS reports is warranted.

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Section: Introductionmentioning

confidence: 99%

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

Yao,

Nguyen,

Retzloff

et al. 2024

JCM

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Food for thought: more explicit guidance for inclusion of caregiver perspectives in health technology assessment

Bourke,

Skedgel,

Martí-Gil

et al. 2024

Int J Technol Assess Health Care

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Caregivers can play an important role in supporting and caring for people with progressive, life-threatening, or debilitating conditions. However, this supportive role can expose caregivers to various detrimental financial, physical, and psychosocial issues. When evaluating medical technologies for reimbursement decisions, health technology assessment (HTA) agencies typically focus on the treatment’s impact on patients and ignore or downplay the impact on caregivers. Including caregiver impacts within a wider societal perspective may better enable health systems to maximize health benefits from available resources. However, the lack of clear guidance or methodological recommendations from decision makers on the inclusion of caregiver impacts limits the number of HTA submissions that consider these effects. We outline a conceptual framework based on intensity and duration of caregiving to guide researchers, industry, and decision makers when developing policies for the inclusion of caregiver outcomes and justify their inclusion based on expected caregiver burden in identified circumstances.

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Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Yao,

Jenkins,

Nguyen

et al. 2024

Semin Reprod Med

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Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.

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Unmet Parenthood Goals, Health-Related Quality of Life and Apparent Irrationality: Understanding the Value of Treatments for Infertility

Cited by 3 publications

References 47 publications

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

Food for thought: more explicit guidance for inclusion of caregiver perspectives in health technology assessment

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

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