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Published in: Journal of General Internal Medicine 12/2019

01-12-2019 | Chronic Kidney Disease | Original Research

Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application

Authors: Natalie C. Ernecoff, PhD, MPH, Kathryn L. Wessell, BS, Laura C. Hanson, MD, MPH, Adam M. Lee, MBA, Christopher M. Shea, PhD, MA, MPA, Stacie B. Dusetzina, PhD, Morris Weinberger, PhD, MS, Antonia V. Bennett, PhD

Published in: Journal of General Internal Medicine | Issue 12/2019

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ABSTRACT

Background

Systematic identification of patients allows researchers and clinicians to test new models of care delivery. EHR phenotypes—structured algorithms based on clinical indicators from EHRs—can aid in such identification.

Objective

To develop EHR phenotypes to identify decedents with stage 4 solid-tumor cancer or stage 4–5 chronic kidney disease (CKD).

Design

We developed two EHR phenotypes. Each phenotype included International Classification of Diseases (ICD)-9 and ICD-10 codes. We used natural language processing (NLP) to further specify stage 4 cancer, and lab values for CKD.

Subjects

Decedents with cancer or CKD who had been admitted to an academic medical center in the last 6 months of life and died August 26, 2017–December 31, 2017.

Main Measure

We calculated positive predictive values (PPV), false discovery rates (FDR), false negative rates (FNR), and sensitivity. Phenotypes were validated by a comparison with manual chart review. We also compared the EHR phenotype results to those admitted to the oncology and nephrology inpatient services.

Key Results

The EHR phenotypes identified 271 decedents with cancer, of whom 186 had stage 4 disease; of 192 decedents with CKD, 89 had stage 4–5 disease. The EHR phenotype for stage 4 cancer had a PPV of 68.6%, FDR of 31.4%, FNR of 0.5%, and 99.5% sensitivity. The EHR phenotype for stage 4–5 CKD had a PPV of 46.4%, FDR of 53.7%, FNR of 0.0%, and 100% sensitivity.

Conclusions

EHR phenotypes efficiently identified patients who died with late-stage cancer or CKD. Future EHR phenotypes can prioritize specificity over sensitivity, and incorporate stratification of high- and low-palliative care need. EHR phenotypes are a promising method for identifying patients for research and clinical purposes, including equitable distribution of specialty palliative care.
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Metadata
Title
Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application
Authors
Natalie C. Ernecoff, PhD, MPH
Kathryn L. Wessell, BS
Laura C. Hanson, MD, MPH
Adam M. Lee, MBA
Christopher M. Shea, PhD, MA, MPA
Stacie B. Dusetzina, PhD
Morris Weinberger, PhD, MS
Antonia V. Bennett, PhD
Publication date
01-12-2019
Publisher
Springer US
Published in
Journal of General Internal Medicine / Issue 12/2019
Print ISSN: 0884-8734
Electronic ISSN: 1525-1497
DOI
https://doi.org/10.1007/s11606-019-05219-9

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