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J Gen Intern Med. 2019 Dec;34(12):2818-2823. doi: 10.1007/s11606-019-05219-9. Epub 2019 Aug 8.

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

Author information

1
Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. ernecoff.natalie@pitt.edu.
2
Sheps Center for Health Services Research, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
3
Division of Geriatric Medicine & Palliative Care Program, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
4
North Carolina Translational and Clinical Sciences Institute, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
5
Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
6
Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA.

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.

PMID:
31396813
PMCID:
PMC6854193
[Available on 2020-12-01]
DOI:
10.1007/s11606-019-05219-9

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