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Title page for ETD etd-03272017-150059

Type of Document Dissertation
Author Harrell, Morgan Rachel
Author's Email Address morgan.r.harrell@gmail.com
URN etd-03272017-150059
Title Learning the State of Patient Care and Opportunities for Improvement from Electronic Health Record Data with Applications in Breast Cancer Patients
Degree PhD
Department Biomedical Informatics
Advisory Committee
Advisor Name Title
Daniel Fabbri Committee Chair
Mark Frisse Committee Member
Mia Levy Committee Member
Robert Johnson Committee Member
Thomas Lasko Committee Member
  • breast cancer
  • electronic health records
  • data mining
  • machine learning
Date of Defense 2017-03-09
Availability unrestricted
Patient care is complex and imperfect. Understanding and improving patient care requires clinical datasets and scientific methodology. We designed a set of methods to characterize the state of patient care and identify opportunities for improvement from electronic health record (EHR) data. The state of patient care is the distribution of patients throughout a clinical workflow. An opportunity for improvement is a means to shift patient distribution away from suboptimal states. We tested our methods within Vanderbilt University Medical Center’s (VUMC) EHR system and the adjuvant endocrine therapy domain.

Our methods divide into three aims: 1) Determine sufficiency of the data, 2) Characterize the state of care, and 3) Identify opportunities for improvement. Data sufficiency is the rise and persistence of data in an EHR system. We built metrics for data sufficiency that can be used in cohort and data selection. We find that despite inconsistent and missing data, we can leverage EHR data for studies on patient care.

To characterize the state of patient care, we built a state diagram for adjuvant endocrine therapy at VUMC, and used EHR data to determine the distribution of patient across states. We measured drug choice frequencies, rates of adverse events, and recurrence rates. We also determined the extent to which EHR data can characterize complete patient care.

To identify an opportunity for patient care improvement, we identified a suboptimal state (failure to follow-up) among VUMC adjuvant endocrine therapy patients and framed a classification problem using EHR data. We used supervised machine learning to predict follow-up and identify significant predictors that may inform on improvement. Patients that fail to follow-up may receive the majority of their care outside of VUMC. Follow-up could be improved by 1) referral to VUMC primary care provider or 2) documenting where patients follow-up to reduce ambiguity of care.

These methods characterized the state of patient care and opportunities for improvement among an adjuvant endocrine therapy patient population using VUMC’s EHR data. We believe these methods are extensible to other EHR systems and other healthcare domains. These methods are valuable for drawing new clinical knowledge from clinical datasets.

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