Background The adaptability of existing recommendations on shared care implementation to Asian settings is unknown. This qualitative study aims to elicit public- and private-sectors primary care practitioners’ (PCPs) perspectives on the sustainable implementation of a shared care model among breast cancer survivors in Singapore. Methods Purposive sampling was employed to engage 70 PCPs from SingHealth Polyclinics, National University Polyclinics, National Healthcare Group Polyclinics, and private practice. Eleven focus groups and six in-depth interviews were conducted between June to November 2018. All sessions were audio-recorded and transcribed verbatim. Guided by the RE-AIM framework, we performed deductive thematic analysis in QSR NVivo 12. Results PCPs identified low-risk breast cancer survivors who demonstrated clear acceptability of PCPs’ involvement in follow-up as suitable candidates for shared care. Engagement with institution stakeholders as early adopters is crucial with adequate support through PCP training, return pathways to oncologists, and survivorship care plans as communication tools. Implementation considerations differed across practices. Selection of participating PCPs could consider seniority and interest for public and private practice, respectively. Proposed adoption incentives included increased renumeration for private PCPs and work recognition for public PCPs. Public PCPs further proposed integrating shared care elements to their existing family medicine clinics. Conclusions PCPs perceived shared care favorably as it echoed principles of primary care to provide holistic and well-coordinated care. Contextual factors should be considered when adapting implementation recommendations to Asian settings like Singapore. With limited competitive pressure, the government is then pivotal in empowering primary care participation in survivorship shared care delivery.

Background The predominant oncologist-led model in many countries is unsustainable to meet the needs of a growing cohort of breast cancer survivors (BCS). Despite available alternative models, adoption rates have been poor. To help BCS navigate survivorship care, we aimed to systematically develop a decision aid (DA) to guide their choice of follow-up care model and evaluate its acceptability and usability among BCS and health care providers (HCPs). Methods We recruited BCS aged ≥ 21 years who have completed primary treatment and understand English. BCS receiving palliative care or with cognitive impairment were excluded. HCPs who routinely discussed post-treatment care with BCS were purposively sampled based on disciplines. Each participant reviewed the DA during a semi-structured interview using the ‘think aloud’ approach and completed an acceptability questionnaire. Descriptive statistics and directed content analysis were used. Results We conducted three rounds of alpha testing with 15 BCS and 8 HCPs. All BCS found the final DA prototype easy to navigate with sufficient interactivity. The information imbalance favouring the shared care option perceived by 60% of BCS in early rounds was rectified. The length of DA was optimized to be ‘just right’. Key revisions made included (1) presenting care options side-by-side to improve perceived information balance, (2) creating dedicated sections explaining HCPs’ care roles to address gaps in health system contextual knowledge, and (3) employing a multicriteria decision analysis method for preference clarification exercise to reflect the user’s openness towards shared care. Most BCS (73%) found the DA useful for decision-making, and 93% were willing to discuss the DA with their HCPs. Most HCPs (88%) agreed that the DA was a reliable tool and would be easily integrated into routine care. Conclusions Our experience highlighted the need to provide contextual information on the health care system for decisions related to care delivery. Developers should address potential variability within the care model and clarify inherent biases, such as low confidence levels in primary care. Future work could expand on the developed DA’s informational structure to apply to other care models and leverage artificial intelligence to optimize information delivery.

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