Let’s TOC Fertility: A stepped wedge cluster randomized controlled trial of the Telehealth Oncofertility Care (TOC) intervention in children, adolescent and young adult cancer survivors

Abstract

Introduction:

Children, adolescent, and young adult cancer survivors experience overall increased risks of infertility that are preventable through effective fertility preservation services prior to starting cancer treatment. Oncofertility care is the evidence-based practice of informing newly diagnosed cancer patients about their reproductive risks and supporting shared decision-making on fertility preservation services. Despite longstanding clinical guidelines, oncofertility care delivery continues to be limited and highly variable across adult and pediatric oncology settings.

Materials and Methods:

We describe the design of a stepped wedge cluster randomized clinical trial to evaluate the effectiveness of the multi-component Telehealth Oncofertility Care (TOC) intervention conducted in 20 adult and pediatric oncology clinics across three health systems in Southern California. Intervention components are: 1) electronic health record-based oncofertility needs screen and referral pathway to a virtual oncofertility hub; 2) telehealth oncofertility counseling through the hub; and 3) telehealth oncofertility financial navigation through the hub. We hypothesize the intervention condition will be associated with increased proportions of patients who engage in goal-concordant oncofertility care (i.e., engagement in reproductive risk counseling and fertility preservation services that meet the patient’s fertility goals) and improved patient-reported outcomes, compared to the usual care control condition. We will also evaluate intervention implementation in a mixed-methods study guided by implementation science frameworks.

Discussion:

Our overall goal is to speed implementation of a scalable oncofertility care intervention at cancer diagnosis for children, adolescent and young adult cancer patients to improve their future fertility and quality of life.

Trial registration:

Clinicaltrials.gov Identifier: NCT05443737

1. Introduction

Five percent of new cancers in the U.S. occur in children, adolescents and young adults (CAYA).¹ Cancer treatments pose differential infertility risks via accelerated ovarian aging,^2,3 impaired uterine function,⁴ loss of sperm production,^5–7 and disruption of the hypothalamic-pituitary-gonadal axis.⁸ CAYA cancer survivors are less likely to experience a live birth (hazard ratio 0.5-0.8 for females, 0.6 for males)^9–12 and more likely to be infertile (relative risk [RR] 1.5-3.8)^13–15 than controls. Absolute risks of infertility are as high as 46% in male survivors (versus 17% of siblings)¹⁵ and 27% in female survivors (versus 15% of controls).^13,14

Fertility is consistently a top unmet need identified by CAYA cancer patients.^16,17 Patients want to know about their reproductive risks and engage in risk-reducing services. Post-treatment survivors experience distress, depression, and poorer quality of life, not only related to infertility and childlessness, but also to reproductive concerns and related unmet informational needs.^18–24 At the intersection of oncology and fertility, oncofertility care (i.e., reproductive risk counseling and fertility preservation [FP] services) is an evidence-based practice recommended by clinical guidelines to inform newly diagnosed cancer patients about their reproductive risks and to support shared decision-making on FP services. As risks vary by treatment and age,^5,13,25,26 oncofertility care must be tailored to each individual patient. Current standard of care FP services are initiated before cancer therapy and are safe and effective in improving long-term fertility outcomes for CAYA cancer patients.^27–32

Oncofertility care has had limited uptake because multi-level barriers leave many patients unable to access this care. A consistent barrier across health systems is the need to systemize processes, such as electronic health record (EHR)-based interventions, for clinics and providers to improve routine patient engagement in oncofertility care.³³ Access to reproductive specialists is also variable across adult and pediatric oncology care settings³⁴ and exacerbated by geographic disparities. Financial costs of oncofertility care are high, e.g., $10,078 for oocyte or embryo freezing.^35–37 To date, 16 states including California passed laws requiring health insurance plans to cover standard FP services for individuals facing iatrogenic infertility,^38,39 but heterogeneous downstream implementation through state regulators, insurers and clinics pose barriers to patients accessing health insurance for oncofertility care, resulting in forgoing care or additional medical financial hardship.⁴⁰

The primary objective of this study is to evaluate the effectiveness of the multi-component intervention in delivering tailored oncofertility care to newly diagnosed young cancer patients across three hospital systems in Southern California, while also gathering data on implementation.⁴¹

2. Materials and Methods

2.1. Study Design

Let’s TOC Fertility is a hybrid type 1, stepped wedge cluster randomized trial (SW-CRT) to evaluate the effectiveness and implementation of a telehealth oncofertility care (TOC) intervention. In a hybrid type 1 design, effectiveness will be evaluated through a clinical trial, and implementation of the intervention will be explored.⁴¹ The institutional review board (IRB) at the University of California, San Diego (UCSD) has approved this study protocol.

2.1a. Effectiveness Trial

The trial will be conducted in 20 oncology clinics (clusters) based on the number of available clinics across three health systems (see Section 2.9 for sample size calculations based on cluster number). The SW-CRT design (Figure 1) allows the TOC intervention at the clinic level to begin at randomly assigned times, i.e., steps.^42,43 The time period between steps is eight months. All clusters begin in the usual care control condition and finish in the intervention condition. At each of four steps, 3 to 6 randomized clinics will cross from the control to the intervention condition until all clinics are exposed.

Figure 1.

Diagram of stepped wedge cluster randomized trial. At each step, 3 to 6 clinics cross from control (white boxes) to intervention condition (blue boxes). Each time period is 8 months.

2.1b. Implementation Evaluation

We will use mixed methods to evaluate intervention implementation guided by the Consolidated Framework for Implementation Research (CFIR)⁴⁴ and the Implementation Outcomes Framework.⁴⁵

2.2. Multi-Component Telehealth Oncofertility Care Intervention

Through our previous pilot project,⁴⁶ the multi-component Let’s TOC Fertility intervention was developed for a hub-and-spokes model (Figure 2). Oncology clinics in the intervention condition will have the following components activated:

Figure 2.

Let’s TOC Fertility intervention components within a hub and spokes model for oncofertility care delivery.

Component 1:

EHR-based oncofertility needs screen and referral pathway to a virtual oncofertility hub. This adaptive (rule-based patient selection) and smart (dynamically updated to prevent repeated alerts) component is a provider nudge at a new oncology patient visit that a) prompts providers that reproductive health counseling for newly diagnosed cancer patients is recommended; b) defaults to selecting a referral order for oncofertility consultation; c) captures reason for not referring (Figure 3); d) links to a referral order.⁴⁶ The referral order requires the planned treatment to help fertility specialists estimate infertility risk and plan treatment timing.

Figure 3.

Electronic health record-enabled oncofertility needs screen provider nudge.

Component 2:

Telehealth oncofertility counseling through the oncofertility hub is by fertility specialists in one of the participating health systems. Upon receipt of authorized referral, fertility clinic staff contacts patients for a new consultation which can occur in person or by telehealth. The consultation encompasses a) counseling on known and unknown risks of cancer and cancer treatments on fertility and pregnancy; b) discussion on the full range of indicated and up-to-date FP services; and c) shared decision-making on FP and contraception.

Component 3:

Telehealth financial navigation through the oncofertility hub is by social worker oncology navigators. Patients who seek FP services will be offered an initial FP financial navigation session (video or telephone call, average 1-2 hours) that assesses medical financial hardship, discusses strategies to meet material needs on FP and provides psychosocial support for distress. Additional sessions may be needed and are allowable within a pragmatic approach. Each visit will be documented in standardized case report forms.

Oncology clinics in the usual care control condition will not have the intervention activated. The referral order for oncofertility consults will be operational for all health systems but using it requires oncology providers to find the order and make the referral. At the oncofertility hub fertility clinic, all referrals will be handled in the same manner.

2.3. Participants

2.3a. Effectiveness Trial

We will observe ~2800 newly diagnosed cancer patients ages 0-42 for females and 0-50 for males. This number represents the estimated number of eligible patients seen across clusters over 40 months (trial duration). We anticipate 60% female and 40% male participants.

Among the 2800 participants, 400 cancer patients and/or parents/guardians of pediatric cancer patients ages 0-17 will be recruited to provide patient-reported outcomes. Enrollment began in January 2022, and study participant assessments are scheduled to be completed by April 2025.

2.3b. Implementation Evaluation

A subset of cancer patients and parents/guardians (n=60) and healthcare providers (n=36) will be recruited to participate in semi-structured interviews. Healthcare providers who attend educational sessions prior to and 16 weeks after crossover to the intervention condition will be recruited to complete a brief survey (n≈50).

2.4. Inclusion and Exclusion Criteria

2.4a. Effectiveness Trial

Cancer patients will be eligible for the effectiveness trial if they are English or Spanish speaking, aged 0-42 years if female or aged 0-50 years if male, and have a newly diagnosed cancer or new cancer relapse due to treatments that may pose infertility risks. Patients with Stage IV metastatic tumors (other than thyroid) will be excluded due to poor prognosis. Patients with non-melanoma skin cancer will also be excluded because usual treatment poses no infertility risk. Cancer patients will be eligible to provide patient-reported outcomes if able to read English or Spanish, aged 7-42 years if female or aged 7-50 years if male, and/or parent/guardian of pediatric patient aged 0-17 years.

2.4b. Implementation Evaluation

Effectiveness trial patients or parent/guardian of a pediatric cancer patient will be eligible if they have completed the patient-reported outcomes and are able to participate in semi-structured interviews in English or Spanish.

Organizational leaders, providers (e.g., oncologists, advanced practice providers, reproductive endocrinology providers, financial navigators) and staff (e.g., insurance authorization personnel, financial counselors) at the affiliated clinical service sites will be eligible for study surveys and interviews.

2.5. Procedures

2.5a. Cancer participants will be eligible for the study after their first oncology visit, following which outcomes will be measured in electronic health records for the entire study population. The follow up time for the primary outcome of engagement in goal-concordant care ends at the start of cancer treatment; this time frame was chosen to allow for oncofertility care decision making and completion of FP services, if needed. Subsets will be recruited to provide patient-reported outcomes via surveys and/or semi-structured interviews after their cancer treatment initiation.

We will conduct the following assessments of cancer participants:

1. Ascertainment of eligibility and measurement of outcome: The primary outcome is engagement in goal-concordant care, which trained study coordinators at each site will ascertain via review of medical records for patient eligibility and the extent to which eligible patients engaged in oncofertility services (1| an oncofertility needs screen, 2| referral to oncofertility consult, 3| oncofertility consult, and/or 4| FP services) that met their individual needs prior to cancer treatment initiation (Figure 3).

2. Following medical record review for eligibility, study coordinators will approach consecutive participants to complete a 30-minute self-report survey (in-person, online or via telephone) on their oncofertility care experience.

3. Study coordinators will recruit participants who completed surveys to complete a 30-minute semi-structured interview. Study coordinators will approach purposefully sampled patients to reflect diversity of health systems, cancer types and life stage.

4. Fertility specialists and financial navigator will document areas covered during consultations.

2.5b. Oncology team members will be asked to attend an educational session at two time points. In the first, study staff will present the intervention at a clinic team meeting prior to the clinic crossing over to the intervention condition. This 15-minute session will cover FP clinical guidelines and an overview of the intervention components and related processes. At a second educational session (16 weeks after crossover), study staff will provide education as above and audit results for their clinic.

We will conduct the following assessments with healthcare providers:

1. After the educational sessions, oncology providers will be asked to complete a brief survey to assess acceptability, feasibility and appropriateness of the intervention and provide open-ended feedback.

2. At the end of the trial, study staff will approach purposefully sampled providers, organizational leaders, and clinic staff who reflect diversity of health systems, specialties, gender, and age to complete 30-minute semi-structured interviews to assess intervention implementation.

2.6. Randomization and Blinding

Randomization will be stratified by health system to ensure balance on when a switch to treatment occurs. As all clinics within each of the three health systems have agreed to participate, clusters will be randomly assigned to one of four steps (3 to 6 clinics per step) at the time of crossover from control to intervention using a computer-generated list of random numbers, i.e., the order of switch-over is determined randomly for each group (health system) of clusters. Allocation and crossover date will be concealed from the clusters, with only the next clinic randomized for rollout being revealed immediately prior to crossover to the intervention condition.

To minimize bias, a statistician blinded to cluster identity will generate the allocation sequence. Reproductive endocrinologists and clinic support staff will be blinded to the intervention status of the cluster from which a patient is referred to minimize differences in counseling in patients whom they expect to receive financial navigation. Oncology providers, financial navigators, and research study staff will not be blinded.

2.7. Outcomes

2.7a. Effectiveness Trial Primary Outcome

The primary outcome is engagement in goal-concordant oncofertility care (Figure 4) after the first oncology visit and prior to cancer treatment initiation, as measured by EHR review for all eligible patients. For example, when an oncofertility needs screen identifies a patient at risk of infertility, this patient is referred for an oncofertility consult, the consultation occurs, the patient desires to undergo oocyte freezing, financial navigation takes place, and oocyte freezing is completed. A second example of concordant care is when an oncofertility needs screen identifies a patient who does not have fertility needs. Discordant care includes cases where no needs screen is documented, or there is a need, but the patient is not referred, does not undergo consultation, or does not undergo FP. Via an IRB approved waiver of consent and HIPAA, study staff will be trained to abstract the primary outcome of engagement in goal-concordant oncofertility care from patients’ medical records using standardized case report forms.

Figure 4.

Through the Let’s TOC Fertility intervention, a newly diagnosed patient’s oncofertility care is tailored to their need. Component 1 is the EHR-based needs screen, followed by a linked referral pathway activated for a patient with an oncofertility need. Component 2 is a telehealth oncofertility consult, which will determine if the patient has a need for fertility preservation (FP) services. Component 3 is the telehealth financial navigation activated for the patient with a FP service need. The primary outcome of goal-concordant oncofertility care (green boxes) will be assessed for each cancer patient. Patients who do not undergo the screening, referral, consult, financial navigation, and/or fertility preservation services that met their needs will be considered to receive goal-discordant care (red boxes).

2.7b. Effectiveness Trial Patient-Reported Outcomes

The primary patient-reported outcome is decisional conflict for engaging FP services, as this decision is complex and occurs rapidly following cancer diagnosis. The 16-item Decisional Conflict Scale (DCS) will be used to measure patients’ decisional conflict for engaging in FP services.⁴⁷ The DCS consists of 16 statements with 5 response options (ranging from strongly agree to strongly disagree) that measure the patient’s perceptions of uncertainty in choosing options, modifiable factors contributing to uncertainty, and effective decision-making. For minor cancer patients aged 13-17 years, the adolescent’s and parents’ decisional conflict will be measured. For minor cancer patients aged 12 years and younger, only the parents’ decisional conflict will be measured.

Adult cancer participants and parent/guardian participants aged 18 and older will complete patient-reported outcomes including shared decision-making,⁴⁸ health-related quality of life using the PROMIS-Global Health,⁴⁹ and financial hardship using the 15-item Economic Strain and Resilience in Cancer tool.⁵⁰ We will identify pregnancies, pregnancy intentions, fertility assessments and treatments, and contraceptive practices using questions derived from the National Survey of Family Growth.⁵¹ We will also collect data on oncofertility services and associated costs, clinical outcomes, reproductive risk knowledge, and demographic information. Minor cancer patients aged 13-17 years will self-report their shared decision making, reproductive risk knowledge, desires for future fertility, and health-related quality of life using PROMIS-Global Health.⁴⁹ Minor cancer patients aged 7-12 years will self-report their desires for future fertility and health-related quality of life using PROMIS-Pediatric Global Health.⁴⁹

2.7c. Implementation Evaluation Outcomes

CFIR domains will guide systematic assessment of determinants of implementation: intervention characteristics (complexity, adaptability), inner setting (clinic-level compatibility, available implementation resources), outer setting (insurance benefit mandate, quality metrics), individuals (knowledge and beliefs about the intervention, self-efficacy), and process.⁴⁴ Conceptualized by the Implementation Outcomes Framework,⁴⁵ measured implementation outcomes include:

1. Fidelity, i.e., was intervention delivered as planned? EHR audit reports and review will summarize provider actions in response to the nudge and whether referrals were made and consults and/or navigation visits were completed. Fidelity checklists will quantify 1) whether the oncofertility counseling session addressed known and unknown risks of cancer and cancer treatments on fertility/pregnancy, the full range of relevant FP procedures, and a shared decision-making on next steps and 2) whether the financial navigation session addressed the domains of medical financial hardship and presented strategies to meet material needs on FP and psychosocial support for distress.

2. Acceptability, feasibility, and appropriateness from healthcare provider surveys: We will measure quantitative outcomes relevant to implementation using the Feasibility of Intervention Measure, Intervention Appropriateness Measure, and Acceptability of Intervention Measure⁵² for education sessions, EHR needs screen/referral, oncofertility consult, and telehealth navigation.

3. Intervention costs from a health system perspective (development and maintenance of the EHR needs screen and referral pathway, EHR reporting, navigator time) will be collected.

4. Adaptation, i.e., deliberate alteration of intervention design or delivery to improve its fit in a given context: Adaptation of each intervention component will be tracked from open-ended feedback (patient and provider surveys, interviews and provider education sessions) and summarized using Stirman et al.’s Expanded Framework for Reporting Adaptations and Modifications to Evidence-Based Interventions.⁵³

In semi-structured interviews of patient and clinic participants, we will assess: i) quality of, satisfaction with, fit, and adaptability of each intervention component (Innovation factors); ii) roles of navigators and formal and informal processes that facilitated or prevented interactions among the outer context, inner context, and intervention (Bridging factors); (iii) clinical pathways, workflows, and provider support (Inner context); and iv) policies and funding (Outer context). We will also solicit open feedback on participant engagement with the intervention and oncofertility care.

2.8. Analytic Approach

2.8a. Effectiveness Trial

Intervention effectiveness in increasing engagement in goal-concordant care:

The primary analysis will be intention-to-treat. The primary outcome is a binary indicator (yes/no) of engagement in goal-concordant care prior to cancer treatment initiation measured at the level of individual patients. We will fit a generalized linear mixed model (GLMM) with a logit link function for the binary outcome with logit(P(y_ict=1) = β₀ + β_t + δX_ct + b_c , where i is patient, c represents cluster, t represents period, y_ict is the outcome value (1 or 0 for yes or no respectively), X_ct = 1 if cluster c is exposed to the intervention during period t (and 0 otherwise); β₀ is the intercept, β_t estimates period effect, and δ the intervention effect, and b_c is a cluster specific random effect to account for within-cluster correlation.⁵⁴ Standard REML approaches will be used for parameter estimation, and 95% CIs will be calculated for the intervention effect. As sensitivity analysis, we will consider extensions of the above model to allow for cluster-period interactions, varying correlation structures (exchangeable, exponential decay), inclusion of a random intercept for health system, and varying intervention effects across clusters or period.⁵⁴ To assess stability of our findings, we will implement robust inference methods for SW-CRT studies.⁵⁵ For the primary analysis, all eligible patients will be included.

Intervention effectiveness in improving patient-reported outcomes:

The primary analysis will be intention-to-treat. The primary outcome is high decisional conflict on engaging in FP services. This score will be dichotomized at 37.5 (scale 0–100; > 37.5 indicates high decisional conflict).⁴⁷ Secondary outcomes are binary and continuous measures of psychosocial well-being, reproductive risk knowledge, intention to undergo oncofertility services, oncofertility care satisfaction, oncofertility care costs to the patient, and financial distress. Outcomes will be compared using a mixed model similar to that described above, with an identity link for continuous variables (e.g., reproductive knowledge), and a logit link for binary outcomes (i.e., decisional conflict).

Qualitative data:

We will analyze qualitative data (open-ended feedback from surveys, interviews) in MaxQDA software using thematic analysis.⁵⁶ We will identify inductive themes related to patient perspectives on oncofertility care decisions and goals, psychosocial, health behavior, and financial hardship experiences. Two independent coders will (1) read transcripts, familiarize with the text, and develop initial codes, (2) code three or more transcripts iteratively to establish inter-rater agreement (goal 80% agreement) and refine codebook, (3) finalize codebook by consensus, (4) code data, (5) summarize data by themes and compare categories (e.g., identifying theme variations, note differences between individuals and sub-groups, explore nuance), and (6) develop an overall interpretation.⁵⁷

Integration of quantitative and qualitative data:

Following the taxonomy of mixed methods designs,⁵⁸ the structure of the data is parallel mixed methods design, in which qualitative and quantitative (QUAL + QUAN) data are collected and analyzed separately; the data are combined at the interpretive level, while each data set remains analytically separate.⁵⁹

2.8b. Implementation Evaluation

Fidelity, acceptability, feasibility, appropriateness, intervention costs from the patient and health system perspectives, and adaptation will be evaluated through a mixed methods approach.

Quantitative data:

We will summarize the quantitative outcomes via summary statistics and visualization tools. Scores for each four-question measure will be averaged—higher scores indicating greater acceptability, appropriateness, or feasibility.⁵² Cost data will be summarized to describe feasibility of interventions, patient burden, potential for scale-up, and replication across care settings. To account for clustering (by oncology clinic), we will use GLMM for binary and linear mixed models for continuous variables to compare outcome proportions, scores, and costs. We will assess differential effects on outcomes by health system, clinic characteristics (e.g., specialty, volume), provider characteristics (e.g., gender, age, specialty), and patient characteristics (e.g., treatment, insurance).

Qualitative data:

We will analyze qualitative data (open-ended feedback from surveys, interviews, telehealth consultation notes, and navigation logs) using similar methods as described above. In thematic analysis, along with deductive themes (CFIR constructs^44,60), we will identify inductive themes.

Integration of quantitative and qualitative data:

Using similar methods as described above, we will triangulate QUAL + QUAN data to explain the process by which intervention component was delivered with fidelity, is effective, feasible, appropriate, acceptable, and/or cost-effective for clinical oncofertility care delivery.

2.9. Power Analysis and Sample Size

2.9a. Effectiveness Trial

The number of clusters across the three hospitals is fixed at 20. At two of the three health systems, the baseline proportion of newly diagnosed male and female cancer survivors undergoing goal-concordant care is 30%. Preliminary data from three intervened-upon clusters (not part of the proposed trial) showed increase to 57% goal-concordant care (27% absolute difference) and an intraclass correlation coefficient (ICC) of 0.3.⁶¹ Assuming four steps and the minimum sample size per period to be 12, we would have 90% power to detect a 30% to 45% difference in outcome at a two-sided significance level of 5%. We anticipate a total of 2800 participants over the trial period. Thus, with modest cluster sample sizes, we are well-powered to detect a clinically significant difference in outcome.

For patient-reported outcomes, we powered the sample size on the DCS.⁴⁷ A prior cohort study of newly diagnosed breast cancer survivors reported 66% prevalence of high decisional conflict.⁶² We showed AYA cancer survivors who did not undergo oncofertility care were 1.25 times more likely to experience high decisional conflict.⁶³ To detect an absolute difference from 66% to 50% (25% relative difference), assuming four steps and ICC of 0.1 and an cluster auto-correlation (CAC) between 0 and 1, power 80%, and two-sided significance level of 5%, the minimum sample size per period per cluster will be 4.^64–67 Thus, we will aim to recruit a total of 400 patients/parents/guardians.

2.9b. Implementation Evaluation

Qualitative sample size estimates are based on sampling to reflect diversity of health systems, patients, parents/guardians, providers and organizational leaders, and points of data saturation in our prior studies with survivors and providers.^68–71 As samples of 9-12 for interviews are appropriate per stratum for saturation of homogeneous samples,^68,71,72 to reflect the heterogeneity of our population, we set the sample size of 36 oncology, fertility and navigation providers, and 60 patients/parents/guardians. If saturation is not reached, we will increase sample size.

3. Discussion

Barriers including limited knowledge on reproductive risks, FP service costs, and lack of fertility specialist access are known to prevent young cancer patients from engaging in oncofertility care. Through conducting a SW-RCT, we hypothesize that implementation of the Let’s TOC Fertility intervention will result in increased patient engagement in goal-concordant oncofertility care. Using a mixed-methods approach, we will explain how the intervention was effective, acceptable, appropriate, and delivered with fidelity. Finally, we will identify potential barriers, facilitators, and adaptations to the intervention to improve implementation and scale up of the intervention to various adult and pediatric oncology settings.

Since this SW-RCT includes a heterogeneity of cancer types and treatments, ages, and life stages, a limitation is that the degree of engagement with oncofertility care may differ by cancer type, reproductive risk, and life stage. Nevertheless, the fundamental decision of whether to undergo FP is faced by newly diagnosed young cancer patients, thus informing inclusion of the broad population of cancer patients aged 0-42 years for females and 0-50 years for males. Another potential source of error is misclassification of the outcome of goal-concordant oncofertility care. To limit this, research staff will be trained to abstract the EHR outcomes, and the site PI will check a subset of outcomes for quality control and adjudicate unclear cases. To address generalizability, we developed the intervention across ten academic/community, adult/pediatric, and rural/urban clinical settings with significant stakeholder engagement for intervention fit. For example, EHR-based telehealth interventions are generalizable to health systems with EHR; however, intervention components, such as the oncofertility needs screening and referral, can be adapted to paper-based processes in low-resource settings.⁷³

Despite these limitations, we anticipate the intervention will reduce inequities in access to oncofertility care and its associated financial hardship, improve patient and clinician interactions, and result in more young cancer patients engaging in goal-concordant oncofertility care that preserves their family-building goals and improves life after cancer. The mixed-methods data collected on the intervention will inform reach and delivery of other cancer specialty care. We will learn how and why implementation is successful and what adaptations and strategies are required to enable scaling up the intervention to diverse oncology, fertility and patient navigation settings.

Data Availability

The datasets generated during and/or analyzed during the current study will be available from the principal investigator (H. Irene Su) on reasonable request.