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The American Journal of Managed Care December 2015
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Innovations in Chronic Care Delivery Using Data-Driven Clinical Pathways
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Innovations in Chronic Care Delivery Using Data-Driven Clinical Pathways

Yiye Zhang, MS; and Rema Padman, PhD
This paper demonstrates that data-driven clinical pathways can be developed using electronic health record data to facilitate innovations in practice-based care delivery for chronic disease management.
A crucial prerequisite for success in the application of advanced machine learning methods to healthcare delivery is data quality. It is not uncommon for computational scientists to spend significant effort in cleaning EHR data before analysis. In addition, even after months of processing, there are often still missing data and errors, some arising from the mismatch between actual work flows and process assumptions, subjecting the analytical results to bias. Such inefficiency can be minimized by careful observation and understanding of the care delivery context, and planning of the data storage with a range of options available depending on the data size.49 At the same time, methods have been developed, such as imputation and approximate inference algorithms, that can accommodate missing data. For example, in this paper, we used the EM algorithm to infer the parameters of HMM. Furthermore, diversity is innate to most healthcare data, and we found it to be one of the biggest challenges in accurately inferring clinical pathways, requiring large amounts of data and robust methods for analysis and inference. In this paper, we examined encounter type, diagnosis, medication prescriptions, and biochemical measurements, but our data representation is flexible with regard to the number of clinical factors of interest. Therefore, when sufficient curated data becomes available, factors such as medical expenses and behavioral information can also be incorporated to enrich the learned pathways and personalized predictions of health and cost outcomes.


This paper presents additional promising evidence of the potential of machine learning applications for clinical decision making. We develop and demonstrate a methodology to facilitate more targeted management of patients with complex chronic conditions using data-driven clinical pathways. Clinical pathways are learned from a healthcare organization’s EHR data by summarizing multidimensional clinical history as chronologically organized sequences, capturing information on the co-progression of encounter types, diagnoses, medications, and biochemical measurements. Further, we link clinical pathways to a few outcomes within subgroups of patients with reasonable accuracy using hierarchical clustering and HMM. Applying our methodology to relevant EHR data on 664 patients with CKD stage 3 and hypertension, we identify clinical pathways that may be compared with current CPG recommendations in future studies, and contribute to the development of shared-baseline within hospitals. These methods and broad findings from EHR data are generalizable and can be adapted to other clinical conditions to support efficient review of treatments and outcomes and to aid clinical professionals and patients in making more informed treatment and management decisions.


The authors are very grateful to the forward-thinking physicians and staff of the community nephrology practice, Teredesai, McCann & Associates, PC, in Western Pennsylvania, who generously provided detailed, de-identified data from their 20-year electronic health record for this study. We particularly thank Pradip Teredesai, MD, FACP; Qizhi Xie, MD, PhD; Nirav Patel, MD; and staff members Linda Smith and Audra Barletta, who gave us important clinical and technical information about the data and the key characteristics of CKD, AKI, and their treatments. This study was designated as Exempt by the Institutional Review Board at Carnegie Mellon University.

Author Affiliations: The H. John Heinz III College, Carnegie Mellon University (YZ, RP), Pittsburgh, PA.

Source of Funding: This study is part of a doctoral thesis at Carnegie Mellon University and has no funding source.

Author Disclosures: Dr Padman and Ms Zhang report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (YZ, RP); acquisition of data (YZ, RP); analysis and interpretation of data (YZ, RP); drafting of the manuscript (YZ); critical revision of the manuscript for important intellectual content (YZ, RP); statistical analysis (YZ); administrative, technical, or logistic support (RP); and supervision (RP).

Address correspondence to: Yiye Zhang, MS, The H. John Heinz III College, Carnegie Mellon University, 4800 Forbes Ave, Pittsburgh, PA 15213. E-mail:
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