Journey mapping as a novel approach to healthcare: a qualitative mixed methods study in palliative care - Stephanie Ly; Fiona Runacres; Peter Poon (BMC, 2021)
Abstract Background Journey mapping involves the creation of visual narrative timelines depicting the multidimensional relationship between a consumer and a service. The use of journey maps in medical research is a novel and innovative approach to understanding patient healthcare encounters. Objectives To determine possible applications of journey mapping in medical research and the clinical setting. Specialist palliative care services were selected as the model to evaluate this paradigm, as there are numerous evidence gaps and inconsistencies in the delivery of care that may be addressed using this tool. Methods A purposive convenience sample of specialist palliative care providers from the Supportive and Palliative Care unit of a major Australian tertiary health service were invited to evaluate journey maps illustrating the final year of life of inpatient palliative care patients. Sixteen maps were purposively selected from a sample of 104 consecutive patients. This study utilised a qualitative mixed-methods approach, incorporating a modified Delphi technique and thematic analysis in an online questionnaire. Results Our thematic and Delphi analyses were congruent, with consensus findings consistent with emerging themes. Journey maps provided a holistic patient-centred perspective of care that characterised healthcare interactions within a longitudinal trajectory. Through these journey maps, participants were able to identify barriers to effective palliative care and opportunities to improve care delivery by observing patterns of patient function and healthcare encounters over multiple settings. Conclusions This unique qualitative study noted many promising applications of the journey mapping suitable for extrapolation outside of the palliative care setting as a review and audit tool, or a mechanism for providing proactive patient-centred care. This is particularly significant as machine learning and big data is increasingly applied to healthcare.