posted on 2024-10-17, 11:41authored bySiddharth Sinha, Simon C Williams, John Gerrard Hanrahan, William R Muirhead, James Booker, Sherif Khalil, Neil Kitchen, Nicola Newall, Rupert Obholzer, Shakeel R Saeed, Hani J Marcus, Patrick Grover
Background
The introduction of electronic health records (EHR) has improved the collection and storage of patient information, enhancing clinical communication and academia. However, EHRs remain limited by data quality and the time-consuming task of manual data extraction. This study aims to utilise process mapping to help identify critical data entry points within the clinical pathway for VS patients, ideal for structured data entry and automated data collection, in an effort to improve patient care and research.
Methods
A two-stage methodology was conducted at a neurosurgical unit. Process maps were developed using semi-structured interviews with stakeholders in the management of VS resection. Process maps were then retrospectively validated against EHR for patients admitted between August 2019 and December 2021, establishing critical data entry points.
Results
Twenty stakeholders were interviewed in the process map development. Process maps were validated against the EHR of 36 patients admitted for VS resection. Operation notes, surgical inpatient reviews (including ward rounds) and discharge summaries were present for all patients, representing critical data entry points. Areas for documentation improvement were present in the preoperative clinics (30/36, 83.3%), preoperative skull base multidisciplinary team (32/36, 88.9%), postoperative follow-up clinics (32/36, 88.9%), and the postoperative skull base multidisciplinary team meeting (29/36, 80.6%).
Conclusion
This is a first use of a two-stage methodology for process mapping the clinical pathway for patients undergoing VS resection. Our study identified critical data entry points which can be targeted for structured data entry and for automated data collection tools, positively impacting patient care and research.
Funding
Crick (Grant ID: CC2036, Grant title: Schaefer CC2036)