For each heart failure admission, registered nurses at Guy’s and St Thomas’ NHS Foundation Trust collected data from five different sources, and then filled out a 10-page form for each patient. Information from the forms was then manually entered into the National Institute for Cardiovascular Outcomes Research (NICOR) web portal. This manual process for data collection and reporting was not only time-consuming and resource-intensive—but was also highly susceptible to error. To address these challenges, the organization leveraged the Health Catalyst® Data Operating System (DOS™) to integrate the data from the five source systems and extract data for nearly all of the elements required for heart failure readmissions—streamlining the NICOR submission process and improving data quality and accuracy.
Guy’s and St Thomas’ NHS Foundation Trust
As part of its efforts to improve the timeliness of care for patients undergoing abdominal aortic aneurysm (AAA) repair, Guy’s and St Thomas’ NHS Foundation Trust needed to collect data to guide care redesign, help assess the impact of specific interventions, and gauge progress toward desired outcomes. Guy’s and St Thomas’ implemented the Health Catalyst® Data Operating System (DOS™) platform, including a Referral Pathway analytics application, allowing the organization to aggregate and standardize data across source systems. Improved data and analytics have enabled Guy’s and St Thomas’ to analyze, evaluate, and monitor outcomes for the entire AAA cohort and evaluate operational performance and associated patient outcomes.
Responsible for coding approximately 380,000 episodes annually, clinical coders at Guy’s and St Thomas’ NHS Foundation Trust review documentation across several systems. The overwhelming amount of data, burdensome manual review processes, and limited coding resources made reviewing all data unfeasible. To address its coding challenges, Guy’s and St Thomas’ leveraged its data platform to combine and standardise data across disparate source systems. The organization now has access to data and technology that can be used to augment coders’ work, automating data gathering to better identify patients whose diagnostic coding could be improved.