Paving the COWpath

Display Omitted We propose a practice-based clinical pathway development process using EHR data.We represent EHR data as one-dimensional visit sequences using novel data modeling constructs.We cluster patients using hierarchical clustering with longest common subsequences (LCS).We characterize clinical pathways as Markov chains and elicit dominant transitions.We find pathways with potential guideline-consistent practices and sustainable improvements. ObjectiveClinical pathways translate best available evidence into practice, indicating the most widely applicable order of treatment interventions for particular treatment goals. We propose a practice-based clinical pathway development process and a data-driven methodology for extracting common clinical pathways from electronic health record (EHR) data that is patient-centered, consistent with clinical workflow, and facilitates evidence-based care. Materials and methodsVisit data of 1,576 chronic kidney disease (CKD) patients who developed acute kidney injury (AKI) from 2009 to 2013 are extracted from the EHR. We model each patients multi-dimensional clinical records into one-dimensional sequences using novel constructs designed to capture information on each visits purpose, procedures, medications and diagnoses. Analysis and clustering on visit sequences identify distinct types of patient subgroups. Characterizing visit sequences as Markov chains, significant transitions are extracted and visualized into clinical pathways across subgroups. ResultsWe identified 31 patient subgroups whose extracted clinical pathways provide insights on how patients conditions and medication prescriptions may progress over time. We identify pathways that show typical disease progression, practices that are consistent with guidelines, and sustainable improvements in patients health conditions. Visualization of pathways depicts the likelihood and direction of disease progression under varied contexts. Discussion and conclusionsAccuracy of EHR data and diversity in patients conditions and practice patterns are critical challenges in learning insightful practice-based clinical pathways. Learning and visualizing clinical pathways from actual practice data captured in the EHR may facilitate efficient practice review by healthcare providers and support patient engagement in shared decision making.