Implementing a Structured Head and Neck Cancer Care Pathway in an Electronic Health Record: Iterative Process and Effects on Data Quality

The digital availability of health data not only improves processes in primary care, but it also facilitates the evaluation of healthcare delivery. Nevertheless, the preprocessing of data for secondary use is still time-consuming and expensive, particularly in head and neck cancer (HNC), where patients undergo complex multidisciplinary treatment trajectories. Therefore, we have looked further into the effects on data quantity and quality following the implementation of structured care pathways. Leveraging data extracted from these care pathways, we assessed the potential of real-time quality-of-care evaluation through dashboards, incorporating indicators such as a proposed "textbook process" model.Our mixed methods study assessed the value of a newly implemented structured HNC pathway and its effect on data quantity and quality through three processes: (1) A qualitative assessment of current barriers, data registration processes, and data-interpretation discrepancies with in-house data managers. (2) A prospective pilot (<i>n</i> = 41) in which patient data is registered both manually and semi-automatically. (3) An evaluation of the patient journey through dashboards with real-time indicators 1 year after go-live.During the iterative implementation phase of the structured care pathway, data completeness and correctness averaged 84.8 and 88.4%, respectively. The new method reduced registration time by 3.7 minutes per patient. A majority of 87.8% followed all four defined time points of the structured care pathway. One year after implementation and in-house validation, time-to-treatment intervals could be tracked, and processes could be adapted accordingly.A structured care pathway, followed by early implementation guided by a multidisciplinary team, forms the foundation for sustainable data capturing for multiple purposes, including quality registries. In-house dashboards further enhance data quality and process improvement.