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Comparative Study
. 2015 Apr;22(e1):e93-103.
doi: 10.1136/amiajnl-2014-002702. Epub 2014 Oct 16.

Quantifying care coordination using natural language processing and domain-specific ontology

Lori L Popejoy  1 , Mohammed A Khalilia  2 , Mihail Popescu  3 , Colleen Galambos  4 , Vanessa Lyons  1 , Marilyn Rantz  1 , Lanis Hicks  3 , Frank Stetzer  1
Affiliations
Comparative Study

Quantifying care coordination using natural language processing and domain-specific ontology

Lori L Popejoy et al. J Am Med Inform Assoc. 2015 Apr.

Abstract

Objective: This research identifies specific care coordination activities used by Aging in Place (AIP) nurse care coordinators and home healthcare (HHC) nurses when coordinating care for older community-dwelling adults and suggests a method to quantify care coordination.

Methods: A care coordination ontology was built based on activities extracted from 11,038 notes labeled with the Omaha Case management category. From the parsed narrative notes of every patient, we mapped the extracted activities to the ontology, from which we computed problem profiles and quantified care coordination for all patients.

Results: We compared two groups of patients: AIP who received enhanced care coordination (n=217) and HHC who received traditional care (n=691) using 128,135 narratives notes. Patients were tracked from the time they were admitted to AIP or HHC until they were discharged. We found that patients in AIP received a higher dose of care coordination than HHC in most Omaha problems, with larger doses being given in AIP than in HHC in all four Omaha categories.

Conclusions: 'Communicate' and 'manage' activities are widely used in care coordination. This confirmed the expert hypothesis that nurse care coordinators spent most of their time communicating about their patients and managing problems. Overall, nurses performed care coordination in both AIP and HHC, but the aggregated dose across Omaha problems and categories is larger in AIP.

Keywords: Omaha classification system; care coordination; natural language processing; ontology.

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Figures

Figure 1:
Figure 1:
Activity extraction and dose-calculation process.
Figure 2:
Figure 2:
Activities provide a new level of detail describing how nurses coordinated care within each Omaha problem.
Figure 3:
Figure 3:
Number of problem profiles representing each activity.
Figure 4:
Figure 4:
Top 20 most frequent activities in the Case management category and the occurrence percentage in every group.
Figure 5:
Figure 5:
Number of unique activities by Omaha problem in Case management category.
See this image and copyright information in PMC

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