Visualising patient pathways and identifying data repositories in a UK neuroscience centre

Abstract

ABSTRACT Background: Health and clinical activity data are a vital resource for research, improving patient care and service efficiency. Healthcare data is inherently complex, and its acquisition, storage, retrieval, and subsequent analysis require a thorough understanding of the clinical pathways underpinning such data. Better use of healthcare data could lead to improvements in patient care and service delivery. However, this depends on the identification of relevant datasets. Objective: We aim to demonstrate the application of Business Process Modelling Notation to represent clinical pathways at a UK neurosciences centre and map clinical activity to corresponding data flows into electronic health records and other non-standard data repositories. Methods: We used Business Process Modelling Notation (BPMN) to map and visualise a patient journey and the subsequent movement and storage of patient data. After identifying several datasets which were being held outside of the approved applications. We collected information about these datasets using a questionnaire. Results: We identified 13 approved applications where neurology clinical activity was captured as part of the patient's electronic health record including applications and databases for managing referrals, outpatient activity, laboratory data, imaging, and clinic letters. We also identified 22 distinct datasets that were not approved by the hospital and were created and managed within the neurosciences department, either by individuals or teams. These were being used to deliver direct patient care and include datasets for tracking patient blood results, recording home visits, and tracking triage status. Conclusions: Mapping patient data flows and repositories allowed us to identify areas in which the current EHR is not fulfilling the needs of day-to-day patient care. Data that is being stored outside of approved applications represents a potential duplication in effort and risks being overlooked. Future work should identify unmet data needs to inform correct data capture and centralisation within appropriate data architectures. Clinical Trial: Not Applicabl