Facilitating Equitable Access to Genomic Testing for Advanced Cancer: A Combined Intuition and Theory-Informed Approach to Intervention Development and Deployment Open Access

Estimates from the Australian Institute of Health and Welfare (AIHW) suggest that in 2023 alone around 165,000 Australians will be diagnosed with cancer and 51,000 will die, with around 3 in every 10 deaths resulting from cancer [1]. Modelling research from 2020 to 2050 indicates an estimated global economic cost of cancer to be $25.2 trillion [2], while the most recent estimates on health system expenditure on cancer in Australia are reported as $10.1 billion in 2015–16 [3].

Cancer arises primarily through changes in genes that allow a cell to escape growth controls and multiply to form tumours [4]. Genomic analysis of a tumour reveals tumour suppressor or oncogenic variants in the cancerous tissue. Tumour-specific variants can provide diagnostic, prognostic, and predictive information. Genomics can have a transformative role in informing tailored treatment decisions in oncology, enabled by increasingly sophisticated profiling methods [5]. Deep sequencing capabilities, at rapidly decreasing costs, have created unprecedented opportunities for researchers to gather insights into the biology of cancer initiation and progression. Treatment decisions are increasingly based on the results of molecular profiling, which has replaced the historical approach of prescribing standard chemotherapy based upon the tumour’s organ of origin, histology, and stage [6, 7].

The results of personalised cancer therapies, if implemented effectively, include “increased response rates, more durable responses, deeper responses, and decreased number of therapy-related side effects” ([6], p16). This is supported by the success of treatments targeting specific driver mutations and the recent FDA approval of tissue-agnostic cancer drugs [4]. With genomic-led treatment, increasingly leading to more optimal treatment pathways and efficient use of healthcare resources, national initiatives aimed at introducing genomic sequencing into routine diagnostics have already commenced in the USA [4] and the UK [8]. Despite acknowledgement of access to services being a key feature of high-performing healthcare systems [9] and healthcare utilisation [10‒12], it is evident that health systems have been slow to implement recognised rapid advances in genomics [13]. As a result, equitable access to the associated benefits of genomics care for patients is increasingly problematic [13].

Cancer care in Australia has significant health inequities with disparities for people across varying levels of social advantage [12]. In Australia, people living in the most socioeconomically disadvantaged areas (i.e., classified by sociodemographic factors such as income, education, and unemployment) have a 5 per cent higher cancer incidence rate, a 20 per cent lower survival rate, and a mortality rate over 40 per cent higher compared to people living in the least socioeconomically disadvantaged areas [14]. Ensuring equitable access to health services is key to maximising the benefits of genomic medicine for cancer patients [13, 15]. This is reflected in the nationally commissioned Genomic Medicine Service recently established in the National Health Service, UK, initiated with the aim of simplifying patient pathways, and reducing social and regional inequalities [16].

The use of somatic genomics in cancer care relies on a well-trained cancer workforce skilled in understanding and applying genomic information in clinical settings [17]. In Australia, management of cancer predisposition genes falls within the remit of the familial cancer clinic. These clinics are well established to provide counselling, medical advice, and information to support their patients and loved ones. In contrast, somatic genomic sequencing is undertaken by medical oncologists, most of whom have had no additional training in clinical genomics [18]. As genomic science advances and molecular cancer treatments grow, the oncology workforce faces increasing uncertainty in interpreting and applying genomic data in clinical practice [19]. This is further exacerbated due to the move away from testing single gene and small gene panels, to using of complex genomic profiling (CGP), where most gene panels comprise of hundreds of genes, to guide cancer treatment.

Stark variation exists in levels of oncologists’ genomic medicine awareness, knowledge, and skills, which is compounded by insufficient resources to embed CGP into standard workflows and inadequate guidelines for incorporating into oncology practice [8]. Consequently, the benefits of CGP may not reach patients who do not have access to quaternary centres of excellence in specialist clinical care. If equitable access to CGP is to be achieved, the genetic literacy support needs among the oncology workforce must be addressed [20]. Additionally, patients’ decision-making around undertaking testing is influenced by psychosocial and structural factors including trust in the institution and associated service provider(s), and proximal geographical access to the service [21]. Proximal access to the service, by extension, requires access to a skilled workforce across diverse locations. To support oncologists without genetics expertise transition to providing CGP services, strategies like partnerships between genetics and non-genetics providers, tele-genetic consultations [22], and continued education [23] have been identified to enhance skills, knowledge, and care quality. Key indicators of successful CGP integration include proficiency in pretest counselling, obtaining consent, delivering results, and using findings for clinical decisions, all essential steps in the clinical pathway [24].

Implementation science – the scientific study of methods and strategies which facilitate the uptake of evidence-based practice and research into regular use by practitioners and policymakers [25] – can enhance the design of interventions addressing the needs of the workforce and support equity of access and provision of quality care [15, 21]. The concept of co-design, defined as meaningful end user engagement across all stages of the research process [26], is a participatory approach to design, which seeks to ensure the results meet the users’ needs and are feasible [27]. The Social Care Institute for Excellence in the UK [27] identifies four critical principles which guide co-design as follows: equality, acknowledging everyone has equally important skills, abilities and contribution to make; diversity, which ensures the process is inclusive and represents all stakeholders; accessibility, which ensures everyone has an equal opportunity to participate as full as they can and in way that best suits them; and reciprocity, which ensures participants get something back for putting something into the process. By putting key stakeholders at the centre of the design process, co-design is perceived to have an enabling role in reducing the costs arising from health research which fails to translate into meaningful benefits for patients [26]. Further, it has strong alignment with and complements the goals of implementation science.

Implementation research conducted in the USA, UK, and Australia has taken a variety of approaches to explore and understand the following: (1) the enablers and challenges to embedding genomic testing in oncology [28‒31] and (2) the outcomes and experiences following implementation of an intervention (i.e., service model) to support the use of genomic testing in oncology. These studies are indicative of varied levels of effort and approaches to use implementation science guided by intuition and tacit knowledge [28, 29, 32, 33] and implementation science-informed theories, models, and frameworks (TMF) [30, 31, 33‒35]. Most commonly, these studies indicate that use of implementation science TMF assisted and guided the development of data collection tools, informed data analysis, and contributed to the identification of factors which impact on successful implementation. A recent scoping review indicated gaps in the use of implementation science theory to inform the development/implementation of interventions in clinical genomic settings and a lack of measures to assess implementation effectiveness [36].

Despite being informed by implementation science to varying levels, there was a noticeable absence of the systematic use of TMF in combination with clinician intuition (i.e., solutions developed by clinicians based on their tacit experiences and knowledge) to co-design strategies to address gaps in genomic literacy [25, 32, 35]. In a complex and rapidly evolving field, acknowledging the interplay between experience-based intuition and theory-informed implementation is crucial for generating behaviour changes needed for wide-scale implementation of genomic testing in cancer care [37]. Additionally, given the complexity involved in delivering CGP (i.e., the clinical intervention), when undertaking an implementation science-informed approach, it is important to clearly define ontologies (the antecedents, objects, strategies, mechanisms, and outcomes of implementation) and how they relate [38]. These principles guided our use of an Implementation Research Logic Model (IRLM) which is a diagrammatic representation that can be used to distil the complexity involved in implementation research into discrete components [39]. In the context of a complex evolving field which calls for rapid changes in practice, this study aimed to use a combined clinician intuition and implementation science theory-driven approach to (1) identify factors (both challenges and enablers) affecting the capacity of oncologists to utilise genomics across diverse clinical settings; (2) develop service models to support the implementation of CGP across clinical settings with varying levels of oncologist genomic literacy; and (3) use a user-centred co-design approach to inform the operationalisation of the service models supporting integration of CGP in cancer care.

Between 2016 and 2019, the Melbourne Genomics Health Alliance funded a series of “proof of concept” demonstration projects designed to provide information on feasibility, requirements for a scalable genomic testing service, and enhanced patient outcomes. This included a solid tumour project (iPREDICT) [5]. The iPREDICT study explored the feasibility and impact of using a 391 gene CGP test. This multi-centre, demonstration study aimed to provide personalised care options for patients via detection of variant/s which provided diagnostic, prognostic, germline, or predictive information. Informed consent for testing was obtained by oncologists from the study team. The CGP assay was an in-house 391 gene, tumour-normal comprehensive cancer DNA panel utilising stored FFPE tumour and a matched normal blood sample. Treatment recommendations were made after curation of results and multidisciplinary review at a weekly molecular tumour board. Participants were followed for a minimum of 12 months post-test to capture result-led treatment decisions and overall survival. The iPREDICT study intervention comprised a seven-step clinical pathway (shown in Fig. 1). Eligibility criteria were used to identify patients who would benefit from CGP in this study: those with an incurable malignancy, who have no further standard treatment options and would be a candidate for possible access to molecularly targeted therapies. Patients speaking any language were eligible, with appropriate interpreting services for consent forms and surveys provided.

Our study uses principles of implementation mapping [40] and a two-phase approach to data collection, combining clinician intuition and implementation theory to identify service models and implementation strategies to promote more equitable, high-quality access to CGP for patients with cancer. A mixed-methods, user-centred co-design methodology was employed. Figure 2 provides a detailed overview of our study design. We used a purposive sampling method for recruitment of two separate cohorts of oncologists across both phases. This was to ensure that a representative sample from diverse clinical settings provided input throughout the process. Phase 1 data collection involved semi-structured interviews, while phase 2 utilised focus groups. Conducting individual interviews allowed clinicians to share their authentic lived experiences without influence from others, providing essential insights into implementation challenges and barriers for scaling the service. In contrast, focus groups were used in phase 2 to capture oncologists’ interactive discussions about the service models and their shared perceptions on successful implementation. Oncologists were emailed a formal study invitation inviting them to a discussion on using CGP in oncology care and to contribute insights informing the design of tailored interventions for its effective implementation. All oncologists were required to provide verbal informed consent prior to their participation. Recruitment was undertaken by the study coordinator for the iPREDICT project.

We used deliberative sampling to obtain insights from those who had engaged with CGP across a range of practice settings. We approached 19 oncologists for interviews (n = 9 from quaternary, n = 5 from tertiary, and n = 5 from private sites), targeting those who had referred 4 or more patients from a range of practice settings (private, and various public hospitals including quaternary adult, quaternary paediatric, and tertiary), actively engaged in Molecular Tumour Boards, and all lead clinicians at participating hospital sites. We conducted semi-structured interviews over the phone or in person, based on preference. The study team interviewed the 11 who responded, exceeding the literature-supported minimum of six for a phenomenological approach, and confirmed data saturation after six interviews. Given the small sample of participants, we did not collect data on sub-specialties as this would be potentially identifiable information. Participants (N = 11 from quaternary [n = 4], tertiary [n = 4], and private [n = 3] sites) were invited to identify challenges associated with the steps in the CGP clinical pathway (shown in Fig. 1) and potential strategies to inform the design of future service models and optimise implementation of CGP in cancer care across diverse settings and workforce genomic literacy levels. Questions to clinicians included, e.g., “What was your experience of referring, consenting and returning results to patients?,” “Did you have any education or support needs? (if YES, what?),” “Where would you go if you had any information needs?,” “Do you have any suggestions for future education or support which could be provided to clinicians involved in providing this service?,” and “What are the most important elements you think need to be considered when designing a service for the future” (see online suppl. file 1 for interview guide; for all online suppl. material, see https://doi.org/10.1159/000544946). The interview guide was informed by the Experience Design Framework (EDF), to gain insights into the experience of using CGP with patients and to explore principles and requirements for hypothetical future service models [41]. The EDF is a framework used to guide service design through an iterative analytical, ideational, and evaluative feedback loop.

The qualitative data from clinician interviews were analysed thematically via a mixed deductive-inductive approach (shown in Fig. 2). Two implementation science frameworks – the Consolidated Framework for Implementing Research (CFIR) [42] and the Expert Recommendations for Implementing Change (ERIC) [43] – were used to identify factors affecting implementation of the CGP service and design service models. Our approach to developing CGP service models comprised the following steps: (1a) establish and clarify themes/subthemes, (1b) code/rank challenges to the CFIR, and (1c) code intuitive and theory-driven strategies to prioritised challenges.

The lead analyst (RW) undertook a hybrid deductive-inductive thematic analysis of all interview transcripts, which was guided by the steps of the CGP pathway (shown in Fig. 1) and interview questions (informed by the EDF), to develop themes and code challenges. The lead analyst independently coded the interview transcripts to identify challenges across the testing pathway, conferring throughout the coding with the research team to ensure rigour. The challenges and intuitive suggestions were then coded to the domains (e.g., inner setting, intervention characteristics) and constructs (i.e., available resources, intervention complexity) of the CFIR [41] in NVivo 12. This coding was also performed by the lead analyst (RW), again involving ongoing review and discussion with the research team. Specifically, the alignment of barriers and strategies to the definitions of the constructs/strategies from the CFIR/ERIC frameworks was thoroughly reviewed in a meeting with an implementation scientist (NT). Challenges were mapped to the steps of the CGP clinical pathway (shown in Fig. 1) and then prioritised by the research team. From this initial thematic analysis, three service models emerged, including a centralised expert (CE) support, local super user (LSU), and point of care (PoC) resource.

The respective constructs for each prioritised challenge (identified in phase 1b) were inputted into the CFIR-ERIC matching tool (available online: https://cfirguide.org/choosing-strategies/) to yield a list of potentially relevant theory-driven strategies [44]. Implementation strategies that received a cumulative endorsement of >20% were then considered by the research team and selected based on perceived contextual appropriateness in addressing the prioritised challenges. Additionally, intuitive strategies (defined as “on the spot” solutions provided by clinicians) [25, 37], elicited through our approach using the EDF, were recorded in our analysis. The whole research team met twice (2 × 1-h meetings) between August and September 2020 to consider both theory-driven and intuitive strategies, as well as their broader experiences with the iPREDICT trial to formulate a consensus table (see results; online suppl. file 3). The consensus table summarised the challenges, three service models, and respective intuitive/evidence-based strategies, which informed the development of a focus group tool used in phase 2 (see online suppl. file 2). The tool was refined through additional out of meeting comments after September 2020 and finalised for the focus groups in November 2020.

An electronic invitation was sent to the lead research contact at each of the three metropolitan and three regional settings (i.e., both public and private hospitals) which made up the sample universe for phase 2. Invitations were forwarded on to prospective participants at the discretion of the lead contact. A total of 10 medical oncologists (representing private and public hospitals in regional and metropolitan settings) took part in one of 4 focus groups held between December 2021 and February 2022. Five of the six sites that will participate in iPREDICT 2 were represented. Nine of the 10 participants had experience with requesting CGP testing and 7 with interpreting and reporting results of CGP tests. Focus groups were delivered virtually and were facilitated using the focus group tool – PowerPoint slides (see online suppl. file 2), and a live survey (e.g., slides 10, 21–22, 28–29, 34–35, 41–42). For each focus group, the survey was focused on one specific challenge identified in phase 1 (e.g., “lack of” support to undertake patient consent discussions). Participants were also provided with a list of implementation strategies for each service model (i.e., CE, LSU, PoC resource) that aimed to address the specified challenge. Guided by previously mentioned principles of user-centred co-design (i.e., equality, diversity, accessibility, and reciprocity) [27], a mixed-methods approach was applied to (a) rank the challenges and service models which formed the output from phase 1 and (b) identify the determinants for operationalising the future service models which would be used to implement the clinical pathway described in Figure 1.

The same two frameworks (CFIR and ERIC) and analytical approach used in phase 1 also guided the identification of challenges and associated strategies to operationalise the service models (shown in Fig. 2). Our approach to operationalising CGP service models comprised the following steps: (2a) rank-prioritised challenges and service models, (2b) identify challenges, and (2c) co-design strategies to operationalising each service model.

During the phase 2 focus groups, participants were asked to rank, via the survey tool, the prioritised challenges associated with the steps in the CGP clinical pathway identified in phase 1 from most important to least important on a 5-point rating scale. Participants also rated the perceived priority of each service model, ranging from low (1), medium (2), to high (3) priority. All quantitative variables were analysed and presented using descriptive statistics (i.e., means and standard errors).

Similar to phase 1, a second lead analyst (JE) coded the transcripts to identify challenges and intuitive strategies. The challenges and intuitive strategies were inductively coded into themes by the two lead analysts (JE and RW) and presented to and endorsed by the broader research team. The challenges were thereafter deductively coded to the CFIR domains/constructs. Theory-driven strategies to address challenges were generated using the CFIR-ERIC tool. The approach described in phase 1c to identify theoretical strategies with high endorsement was repeated. To address the ranked challenges (in terms of low, medium, and high impact), selected theoretical strategies and aligned intuitive strategies underwent further contextualisation, to support the operationalisation of one or more of the service models.

A total of 14 distinct CFIR constructs, 43 intuitive strategies, and 28 theory-driven strategies were identified across both phase 1 and 2 data analyses. Challenges were inductively organised into themes and deductively coded according to the domains and constructs of the CFIR (shown in Fig. 3).

Eleven medical oncologists participated in phase 1 interviews, representing a 58% response rate. Demographic data, such as sub-specialties, were not collected to avoid potential identification due to the small sample size. In phase 2, the invite was sent out to the lead research contact at each of the six sites invited to participate in the study, to forward on to prospective participants at their own discretion. Therefore, a response rate cannot be reported. Ten medical oncologists from public and private hospitals in metropolitan and regional areas participated across four focus groups. Their experience ranged from 0–5 years (n = 4), 6–10 years (n = 4), to over 11 years (n = 1). Seven had formal genomics training, four rated their genomics understanding as “fairly good” or “good,” and all had ordered molecular or tumour tests and delivered and/or interpreted results.

Challenges associated with the steps in the CGP clinical pathway that were reported by participants (n = 11) were organised into three major themes: (1) access, (2) indicators for test use, and (3) supporting use of results in clinical care. For theme 1 (access), participants highlighted the importance of ensuring access to appropriate genomic expertise; this could be done by centralising expertise but may introduce equity of access barriers by reducing the physical points of contact at which a patient can receive care.

For theme 2 (indicators for test use), participants reported uncertainty about identifying which patient would be suitable for and benefit from CGP testing.

For theme 3 – supporting use of results in clinical care, the key uncertainty reported by clinicians was feelings related to confidence and comfortability with discussing findings with patients. In particular, the theme highlighted the unmet support for oncologists in navigating discussions with patients about test results and communicating findings from germline and somatic testing.

Three service models (i.e., CE support, LSU, and PoC resources) emerged through the initial thematic analysis, which are defined in Table 1. In total, nine subthemes were identified, and five challenges were prioritised based on their level of complexity and impact as defined by the study team (see online suppl. file 3). Challenges that were assigned a high to medium level of complexity and priority were included in the next phase of analysis. The five prioritised challenges were coded to 11 distinct CFIR constructs and mapped by the study team to the relevant steps in the CGP clinical pathway (shown in Fig. 1). Specific quotes illustrating the challenges encountered by oncologists across the key steps are highlighted in Table 2.

A total of 19 intuitive strategies were reported by participants across the five prioritised challenges in addition to 21 theory-informed strategies generated from the CFIR-ERIC matching tool. The study team considered the theory and intuition-informed strategies from the interview data, as well as their broader experiences with the iPREDICT study to contextualise and bundle strategies into discrete interventions (referred to as service models). For example, regarding the challenge of discussing potential germline findings with patients (CFIR = knowledge and beliefs about the intervention, self-efficacy), potentially relevant ERIC strategies, which received a cumulative endorsement of >20%, included “identifying and preparing champions,” “developing educational materials,” and “conducting ongoing training.” Similarly, oncologists intuitively suggested having guidance available, detailing what to do and what to discuss with patients in the event of an actionable result. In the context of the iPREDICT study, consultants had also expressed an interest in acquiring further genomics skills (i.e., being trained as LSUs). Thus, an LSU service model was conceived through a combination of theory, oncologists’ intuition, and tacit experiences from the study team, as a service model to provide guidance on, conduct consultations with, and support oncologists through their discussions of findings with patients. In total, 27 theory- and intuition-informed strategies were used to design three service models: model 1 – CE support, model 2 – LSU, and model 3 – PoC resources (see online suppl. file 3).

The highest ranked challenge associated with the CGP clinical pathway, identified in phase 1, by oncologists was interpreting, disclosing, and using results in clinical decision-making (M = 4.78; SD = 0.44) (shown in Fig. 4a). Out of the three service models, the CE (M = 2.22; SD = 0.97) and LSU (M = 2.11; SD = 0.78) models were perceived by oncologists to be the highest priority for implementation (shown in Fig. 4b).

Participants identified a total of 11 overarching implementation challenges associated with operationalising one or more service models, which were organised under 7 distinct CFIR constructs. There were implementation challenges that were unique to each model, as well as those that were relevant to all (see online suppl. file 4). An implementation challenge unique to the CE model included anticipated difficulties ensuring effective communications with hospitals and services during referral and follow-up processes (CFIR = networks and communications).

In the context of the LSU model, oncologists foresaw challenges in identifying LSUs, particularly for regional and rural sites (CFIR = readiness for implementation, available resources).

For the PoC model, participants felt that it would be difficult to maintain the currency of the resources (CFIR = readiness for implementation, available resources).

An implementation challenge that applied to operationalising all models was a lack of resources to address patient information and support needs (CFIR = patients’ needs and resources).

A total of 11 intuitive strategies were mapped to 10 service model implementation challenges, following in-depth context clarification, to ensure both direct and indirect strategies relevant to addressing a challenge were coded. A total of 46 potentially relevant implementation strategies received a cumulative endorsement score of at least 20% using the CFIR-ERIC matching tool across all three models. After in-depth discussions with the review team, 20 distinct ERIC strategies were identified as contextually appropriate and relevant to support the operationalisation of each service model (see online suppl. file 4). For instance, “promote network weaving” was an ERIC strategy endorsed to facilitate more effective communications between hospitals and services to the CE model. The strategy was operationalised by the study team as identifying individuals at local sites who would be responsible for liaising with the centre of excellence, keeping communication pathways open, and ensuring ongoing continuity of care for oncologists and their patients. Figure 5 presents a short-form IRLM [39] depicting the relationships between phase 1 determinants and service models, as well as phase 2 determinants, the operationalised ERIC and intuitive strategies, their theorised mechanisms of action, and the relevant implementation, service, and clinical outcomes to assess (see online suppl. file 5 for the full version of the IRLM).

Systematically identifying implementation determinants enables researchers to develop evidence-informed strategies to support clinicians navigating new technologies within a complex area of care [45]. The emergence of CGP as a critical element for optimal care for advanced cancer patients is one such area that required an in-depth consideration of perspectives on support needs, to foster successful implementation [46]. This study applied a clinician intuition- and theory-driven approach in combination with the principles of co-design to develop a suite of service models to support oncologists to use CGP and identify implementation strategies for their operationalisation. This evidence-based approach was undertaken with the long-term goal of providing the resources required to deliver a high-quality service to oncologists with varying pre-existing genomic expertise. This study had three overarching aims to use a combined clinician intuition and implementation science theory-driven approach to (1) identify factors (both challenges and enablers) affecting the capacity of oncologists to utilise genomics across diverse clinical settings; (2) develop service models to support the implementation of CGP across clinical settings with varying levels of oncologist genomic literacy; and (3) take a user-centred co-design approach to inform the operationalisation of the service models supporting integration of CGP in cancer care.

Australian oncologists delivering iPREDICT experienced challenges across the CGP clinical pathway (shown in Fig. 1). The most common CFIR barriers were “access to knowledge and information,” “knowledge and beliefs about the intervention,” and “self-efficacy.” Counselling patients regarding the decision to have CGP was a key area of uncertainty described by oncologists, in addition to receiving, disclosing, and acting upon test results which was ranked as having the highest priority in the phase 2 survey. These findings suggest that having easily accessible expertise in genomics testing or resources to guide clinical decision-making in this context would improve confidence and beliefs in applying the intervention (i.e., use of CGP) to the appropriate patient. Our findings are consistent with prior local [47, 48] and global [49] research, which suggests that despite oncologists recognising their role in the CGP clinical pathway, perceptions of discomfort and low levels of confidence persist as challenges faced when consenting [47], disclosing results [47, 48, 50, 51], and interpreting and managing findings in cancer [47, 52, 53]. Difficulties determining patient suitability for CGP testing which was highlighted as a challenge in terms of both determining patient eligibility for CGP and beliefs about who would benefit from the intervention were not specific areas explored in depth in previous research studies.

The overarching challenges reported by oncologists in relation to CGP consenting, disclosing, interpreting, and clinical decision-making are subsequently experienced by patients who receive poor and uninformative communication [54]. As a consequence, patients often leave their appointments unable to clearly articulate how the results they receive will inform their future treatment pathway [54]. Findings unearthed through our phase 1 interviews, which mirror clinician-perceived and patient-reported experiences of suboptimal care in this context [47, 48, 52‒55], provide clear justification for the approach undertaken in our study to co-design, with end users, the service models to address the support needs of oncologists utilising CGP in cancer care.

We developed three service models for supporting use of CGP and co-designed potential strategies for their operationalisation in phase 2. For service model operationalisation, we gathered input from a cross section of oncologists from both regional and metropolitan settings. This ensured that context-specific user needs were addressed during the design and implementation of a service model, supporting the use of CGP. For instance, in regional/rural areas, oncologists reported potential difficulties finding superusers (i.e., experts/champions) which, from their perspective, was a result of an already limited and overutilised workforce. This finding mapped directly to a domain in the CFIR known as the “inner setting” which describes the setting in which the intervention is implemented [42].

In the following step, we combined intuition with theory-driven strategies using the CFIR-ERIC matching tool to address challenges coded to various CFIR constructs [43]. The tool indicated that assessing for organisational readiness, which includes an assessment of barriers and facilitators, was one evidence-based approach to address implementation barriers within the “inner setting.” For example, assessing organisational readiness would highlight sites with a poor workforce capacity and allow for intervention tailoring. An example of a suggested intuitive strategy (which aligned with theory-driven solutions in the ERIC framework [56]) was training multiple LSUs simultaneously, so that workload could be distributed among several clinicians rather than centralised to one individual. By synthesising intuition and evidence-based strategies, we hoped to create a more user-centric, evidence-informed, strategic plan for implementing each service model. Combining principles of co-design, alongside the synthesis of clinician intuition and theory throughout the design phases was undertaken with the intention of improving the likelihood of intervention uptake in clinical settings more prone to implementation failure. This in turn should pave the way forward for the provision of equitable access to a scalable, high-quality CGP clinical oncology service, beyond a research setting.

The CE support and LSU models were ranked as having the highest priority for implementation, which may indicate that providers prefer discussing uncertainties with a clinician (i.e., a specialist or an individual with genomics expertise) as opposed to consulting a written resource. This could reflect physician-reported preference for involving a second physician or a genetic counsellor in result disclosure with patients [49] reported elsewhere. In operationalising one or more of the models, one of the most frequently cited barriers was coded as “available resources.” This may in part explain why having a genomic expert present in result disclosure appointments was not suggested by our study cohort. Oncologists were particularly conscious of ensuring that the models were designed with a plan for long-term resource sustainability and scalability, considering funding resources, workforce constraints, and maintaining the currency of the model, as potential challenges.

Our study incorporated evidence-based approaches to designing genomics service interventions underpinned by a novel formula. Initially, we demonstrate the value of synthesising clinician intuition with a theory-informed approach. Oncologists are at the forefront of implementing genomics and will naturally develop solutions through their tacit experiences [25]. However, despite its potential value, clinician intuition is not systematically incorporated in quality improvement and implementation science research [25]. By formally recognising and coding the strategies provided by oncologists in our study, we were able to determine the extent to which they aligned with theory, which in turn influenced our strategy selection process. Additionally, the multi-phase co-design methodology we employed, involved partitioning the prominent ontologies of implementation science and applying them to develop a robust, wrap-around implementation approach to support the integration of CGP into cancer care. This is, as far as we are aware, a methodologically novel approach to service model and implementation strategy development undertaken in Australia.

Finally, building on the work of Smith et al. [39] we used an IRLM to visualise implementation objects (i.e., the clinical intervention, service models, and implementation strategies) as antecedents of implementation, service, and clinical outcomes [38]. The introduction of the concept “service model” arose by considering the relationships between phase 1 and 2 determinants and the object (i.e., a clinical service integrating CGP into cancer care) being implemented [57]. The three service model interventions were clearly distinct from the clinical intervention, but were complex in nature, requiring another phase of consideration for how they would be implemented. We hypothesised that the three service interventions would also have a direct relationship to service outcomes such as improved equity, timeliness of CGP delivery, and patient-centredness resulting in our distinct term “service models”. This approach allowed us to distinguish between the clinical intervention (i.e., integration of CGP), the interventions (i.e., service models) aimed at improving the servicing of that clinical intervention (e.g., the CE), and the implementation strategies developed to support the integration of each service model in the relevant clinical settings [58].

Our CGP IRLM provides an explicit visualisation of the underlying theory for each service model. In a subsequent evaluation of the implemented service models, as part of the clinical intervention underway, we intend to test the relationships depicted in this model, specifically in terms of their effect on implementation (i.e., acceptability, feasibility, cost, and sustainability) and service outcomes (i.e., equity, effectiveness, and timeliness). By doing so, we will further validate our methodological approach and provide others in this area with a framework for adopting and evaluating these or similar models within their respective clinical settings.

Despite efforts to undertake a methodologically robust research process, our study is not without some limitations. Double coding of transcripts was not performed due to resource and time constraints. We acknowledge this as a limitation to assessing the reliability of our deductive coding. In phase 1, we contacted oncologists for an interview that had referred patients into the study or who were actively engaged with the study, as we needed to interview clinicians who had used CGP as part of delivering oncology care. Thus, the sample interviewed had some associated bias, as these were the enthusiasts who had already participated in the pilot clinical study. When evaluating the current trial underway, it will be important to identify those who are not using CGP and explore the underlying reasons in this group to inform future efforts to scale the clinical intervention and enable equitable patient access to it. However, noting that despite our best efforts to understand barriers to using CGP in the current intervention versus a hypothetical scenario, the results may not be relevant to other sites. In phase 2 focus groups, we deliberately recruited participants from sites engaging in the planned trial of the models identified in phase 1. Thus, the views represented those working in sites which were contemplating or are currently engaged to some extent, in delivering cancer genomic profiling to their patients. Therefore, mirroring the previously flagged limitation, the perspectives which informed the co-design of strategies to operationalise the service models may not represent how such an intervention might be operationalised at other sites. The co-design of the service models in phase 2 did not include patient perspectives; as these are models with which patients do not have experience, it was felt to be premature to ask for patient input at this stage. This is instead being addressed in the current phase of the study (iPREDICT 2), in which the service models are being implemented and evaluated. Patient perspectives on the models are being collected using patient surveys. The project has also established a patient panel to provide input into implementation of the models and patient resources.

Our study provides a robust justification for the development of three equity-centred and evidence-informed service models to support the oncologist workforce to implement CGP in cancer care settings across Australia. We have also generated an implementation science logic model to test the relationships between the service models developed, and their impact on patient, service, and implementation outcomes. A multi-centre implementation study where a comparative evaluation of implementation outcomes (feasibility, effectiveness, acceptability, sustainability, and scalability) across the three different models of care implemented in regional and metropolitan healthcare settings in VIC is currently underway. Our future research will be focused on validating and evaluating the proposed models, while continuing to refine our approach to support the integration and long-term sustainability of an oncology-centred CGP service.

Phase 1 of this study (interviews) was reviewed and approved by Melbourne Health Human Research Ethics Committee, Approval No. 13/MH/326. Phase 2 of this study (focus groups) was reviewed and approved by the Royal Children’s Hospital Human Research Ethics Committee, Approval No. HREC/67812/RCHM-2020. Ethical Approval for interviews was granted by the Melbourne Health Human Research Ethics Committee (13/MH/326). Ethical Approval for focus groups was granted by the Royal Children’s Hospital Human Research Ethics Committee (HREC/67812/RCHM-2020). All participants in this study were required to provide verbal consent prior to the commencement of interviews and focus groups. This consent procedure was approved by the Ethics Committees.

Dr. Natalie Taylor was a member of the journal’s Editorial Board at the time of submission.

This study was funded by the State Government of Victoria (Department of Health) and the 10 member organisations of the Melbourne Genomics Health Alliance. Work undertaken at the Murdoch Children’s Research Institute was supported by the Victorian Government’s Operational Infrastructure Support Program. JE is a recipient of the Australian Governments Research Training Program (RTP) Scholarship Award and is supported by a Medical Research Future Fund, Brain Cancer Survivorship Top-up Scholarship (MRFBC000002).

N.T., M.M., C.G., S.O.H., K.S.,. R.W., and J.D. all contributed to the conceptualisation and design of this study. R.W. facilitated phase 1 interviews, and N.T. facilitated phase 2 focus groups. R.T., M.M., N.T., S.O.H., K.S., and J.D. all contributed to development of the FG tool. R.W., M.M., N.T., and J.E. (for phase 2) led the data analysis, and findings were presented to the broader team for endorsement. R.W. and J.E. drafted and finalised the manuscript which received input from by N.T., M.M., C.G., S.O.H., K.S., and J.D.

Additional data supporting the findings of this study can be found in online supplementary files 3 and 4. The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.