Bridging the digital readiness gap in palliative home care: A process to data approach.

INTRODUCTION

As the population increase and cancer becomes a leading cause of death, there is an increasing need for personalized, technologically aided, and cost-effective home care. Instead of adding more hospital beds, more patients may receive long-term, high-quality treatment at home by transitioning to digital home healthcare systems. The economic challenges in Jordan could significantly impact the availability of palliative healthcare services for cancer patients. Home care can be a useful and affordable solution to these problems. However, a critical analysis of the current process and its corresponding data structure is necessary to prepare for digital solutions.

OBJECTIVES

To comprehend the existing palliative home care process and its respective data for cancer patients in Jordan prior to its digital transformation.

METHODS

The design science research methodology has been applied within two increments of deliverables. A role-oriented business process modeling technique was used in the first increment to present and get a critical understanding of the current palliative home care process and the accompanying data consumed to narrow the digital readiness gap. A data model from the first increment was algorithmically derived in the second increment. The King Hussein Cancer Center in Jordan's case study on the palliative home care process is used to demonstrate and evaluate the work.

RESULTS

The work delivered 33 data entities and their relationships from role-oriented business process models that were not identified before this research.

CONCLUSION

The business process model and data model contribute to attaining a higher digital readiness before a digital transformation into a technology-supported palliative home care system. This has involved eliciting roles, associated responsibilities, interactions, and privileges regarding their activities and respective data for future development. However, still some limitations are identified.