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J Biomed Inform. 2015 Dec;58:186-197. doi: 10.1016/j.jbi.2015.09.009. Epub 2015 Sep 28.

Paving the COWpath: Learning and visualizing clinical pathways from electronic health record data.

Author information

1
School of Information Systems Management, The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, United States. Electronic address: yiyez@andrew.cmu.edu.
2
The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, United States. Electronic address: rpadman@andrew.cmu.edu.
3
Teredesai, McCann & Associates, P.C., Pittsburgh, PA, United States. Electronic address: ndpatel219@gmail.com.

Abstract

OBJECTIVE:

Clinical 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 METHODS:

Visit 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 patient's multi-dimensional clinical records into one-dimensional sequences using novel constructs designed to capture information on each visit's 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.

RESULTS:

We 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 CONCLUSIONS:

Accuracy 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.

KEYWORDS:

Chronic kidney disease; Clinical pathway; Clinical practice guideline; Visualization

PMID:
26419864
DOI:
10.1016/j.jbi.2015.09.009
[Indexed for MEDLINE]
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