Objectives

Chronic diseases are common, complex, and expensive health conditions that can benefit from innovations in healthcare service delivery enabled by information technology and advanced analytic methods. This paper proposes a data-driven approach, illustrated in the context of chronic kidney disease (CKD), to develop clinical pathways of care delivery from electronic health record (EHR) data.

Study design

We analyzed structured and de-identified EHR data from 2009 to 2013 of 664 CKD patients with multiple chronic conditions.

Methods

Machine learning algorithms were used to learn data-driven and practice-based clinical pathways that cluster patients into subgroups and model the co-progression of their encounter types, diagnoses, medications, and biochemical measurements. Given a pattern of biochemical measurements, our algorithm identifies the most probable clinical pathways, and makes predictions regarding future states, with and without temporal information. CKD stages, their complications, and common medications are included in the clinical pathways.

Results

Using the EHR data of 664 patients who were initially in CKD stage 3 and hypertensive, we identified 7 patient subgroups-each distinguished primarily by the type of complications suffered by the patients. Our algorithm demonstrates fair accuracy (up to 44% and 75%, respectively) in learning the most probable clinical pathways and predicting future states associated with temporal patterns of biochemical measurements and patient subgroups.

Conclusions

Data-driven clinical pathway learning summarizes multidimensional and longitudinal information from EHRs into clusters of common sequences of patient visits that may assist in the efficient review of current practices and identifying potential innovations in the care delivery process.