This study employed an explanatory sequential mixed-methods design, integrating both quantitative and qualitative data collection. The approach was selected to provide a comprehensive understanding of the research problem by initially collecting and analysing quantitative data, followed by qualitative data to explain and expand on the findings (Creswell 2014; Schoonenboom & Johnson 2017). The mixed-methods design is underpinned by pragmatism, which emphasises problem-solving through the interplay of knowledge and action (Allemang, Humphries & Barber 2022; Okesina 2020).

The quantitative phase involved a structured survey using the Press Ganey Inpatient Survey (PGIS) to measure family satisfaction with paediatric oncology care. The qualitative phase included semi-structured interviews with family members to gain deeper insights into their experiences. The integration of data followed a three-stage process:

The study was conducted at a newly commissioned central and tertiary care hospital in Madinah, Saudi Arabia, which serves a population of approximately 1.5 million people. The hospital specialises in oncology care and provides advanced treatment modalities, including chemotherapy, immunotherapy and targeted therapy.

The facility includes an 18-bed infusion area for outpatient chemotherapy, with six beds dedicated to paediatric oncology patients, a 16-bed inpatient paediatric oncology and haematology unit, admitting children diagnosed with leukaemia, lymphomas and solid tumours, and a dedicated paediatric oncology outpatient clinic for follow-up care post-discharge.

Data collection was conducted in three phases, integrating quantitative and qualitative methods to ensure a comprehensive understanding of family experiences in paediatric oncology care (Table 1).

Participants provided informed consent before data collection, with confidentiality and anonymity ensured throughout the study.

This study employed an explanatory sequential mixed-methods design, integrating both quantitative and qualitative approaches to provide a comprehensive analysis. The quantitative data were collected using the PGIS and analysed with IBM SPSS Version 27.0. Descriptive statistics, including frequencies, percentages, means and standard deviations, were used to summarise key variables. To examine associations between socio-demographic factors and satisfaction scores across different stages of the paediatric oncology journey, Pearson’s chi-square test was conducted, with statistical significance set at p < 0.05.

For the qualitative phase, semi-structured interviews were conducted, transcribed verbatim and translated into English when necessary. The data were analysed using manifest content analysis, guided by Creswell’s (2014) framework. The analysis followed a structured process, beginning with familiarisation, where transcripts were reviewed multiple times to ensure accuracy and completeness. This was followed by the coding phase, where descriptive labels were assigned to significant statements. The identified codes were then categorised into broader themes and subthemes. Finally, an interpretative phase was conducted to explore emerging insights, contradictions and unique perspectives within the data.

To enhance the depth and validity of the study, a comparative approach was used to integrate the quantitative and qualitative findings. The connection between the two datasets was established by using the quantitative results to guide the selection of qualitative interview participants. Additionally, the qualitative insights were utilised to explain unexpected findings from the quantitative analysis, ensuring a more nuanced understanding of the data. Finally, both datasets were merged to identify converging and diverging themes, strengthening the study’s conclusions and providing a more holistic depiction of the paediatric oncology care experience. This multi-step analysis ensured methodological rigour and enhanced the reliability and validity of the study’s findings.

Rigour in this mixed-methods study was ensured through methodological and analytical strategies applied to both the quantitative and qualitative phases. For the quantitative phase, the PGIS, a widely validated instrument, was used to collect data. The tool demonstrated excellent internal consistency, with a reported Cronbach’s alpha of 0.98. A pilot study conducted with 10 participants further supported its reliability, yielding a Cronbach’s alpha of 0.89. In the qualitative phase, rigour was maintained by employing strategies to ensure credibility, dependability, transferability and confirmability. Credibility was enhanced through data saturation, member checking and verbatim transcription. Dependability was established via ethical approvals from two independent committees and consistency in data collection. Transferability was achieved by providing rich, contextual descriptions of the setting, participants and study design. Confirmability was upheld by maintaining objectivity and supporting findings with direct participant quotations. The integration of both datasets through triangulation further strengthened the trustworthiness and validity of the study’s conclusions.

The study adhered to strict ethical protocols to protect participants’ rights and ensure compliance with research ethics. Ethical approval was obtained from the UKZN Biomedical Research Ethics Committee (BREC), approval number BREC/000046791/2022, and from the Hospital and Research Centre Institutional Review Board (IRB) in the Kingdom of Saudi Arabia, approval number RAC/M2022-0004. Gatekeeper permission was also secured from the hospital management before participant recruitment.

Participation in the study was entirely voluntary, with informed consent obtained from all participants. To ensure accessibility, all study documents, including consent forms, were provided in both English and Arabic. Participants were informed of their right to withdraw from the study at any stage without consequences. Anonymity was maintained by assigning unique numerical identifiers to survey responses and interview transcripts.

Recognising the potential emotional impact of discussing paediatric oncology experiences, the study implemented protective measures for vulnerable participants. A hospital psychologist was made available for referrals in case of distress, and participants had the option to pause or withdraw from interviews.

To ensure data security, all research data were stored in password-protected digital files and securely archived hard copies. In accordance with institutional guidelines, research data will be retained for 5 years before being permanently deleted or destroyed.

The journey map was constructed by integrating survey responses from caregivers (quantitative phase) and in-depth interviews (qualitative phase). This process ensured that both statistical trends and personal experiences were captured.

To complement the survey results, nine in-depth interviews were conducted with caregivers, revealing six major themes:

Table 5 summarises the key qualitative themes.

By merging the quantitative satisfaction scores with qualitative narratives, key areas for intervention emerged:

The finalised paediatric oncology patient journey map is a visual representation of a patient’s care experience, designed to improve clinical workflows by aligning care processes with patient needs, enhance caregiver support by providing clear guidance on what to expect at each stage and identify service gaps for targeted quality improvement initiatives.

This structured, finalised patient journey map serves as a tool for optimising paediatric oncology care, ensuring that healthcare providers, patients and caregivers are better informed and supported throughout the care continuum.

Patient journey mapping has been widely recognised as a tool for identifying service inefficiencies and improving patient-centred care (Arias et al., 2020). By systematically analysing both patient experiences, this study’s journey map serves as a strategic framework for optimising paediatric oncology services. The integration of evidence-based strategies within the map aligns with best practices in healthcare service improvement, as highlighted in studies by Halvorsrud, Kvale and Følstad (2016) and Sijm-Eeken, Zheng and Peute (2020).

A key strength of the developed journey map is its adaptive static models, as this map was iteratively refined through stakeholder feedback, ensuring alignment with real-world clinical workflows and patient needs. The involvement of healthcare providers in the mapping process facilitated the identification of logistical bottlenecks, such as drug shortages and administrative delays, which were subsequently addressed in the final version.

The findings of this study align with previous research emphasising the utility of PJM in oncology care. A study by Agarwal et al. (2023) mapped the care pathways of lung and breast cancer patients to identify unmet needs and gaps in service delivery. Similarly, Kushniruk, Borycki and Parush (2020) highlighted how journey mapping enhances service improvement efforts by visually depicting inefficiencies in care coordination.

Moreover, Philpot et al. (2019) demonstrated that institutions implementing frameworks observed significant improvements in patient satisfaction and clinical outcomes. This study’s findings support this assertion, as the journey map provided a structured approach to mitigating distress and enhancing patient experience at each stage of care.

The findings suggest several directions for practice and research. Incorporating structured communication at critical stages of care and strengthening psychosocial and post-discharge support would address gaps identified by families. The journey map developed here can serve as a framework for staff training, quality improvement initiatives and improved coordination of care. Journals and researchers are encouraged to further test and adapt this tool in different paediatric oncology settings, broadening its utility and transferability.