JMIR Publications

ABSTRACT

Background:

Identifying caregiver availability, particularly for dementia patients or those with a disability, is critical to informing the appropriate care planning by the health systems, hospitals, and providers. This information is not readily available and there is a paucity of pragmatic approaches to identifying caregiver availability and type automatically.

Objective:

Our main objective was to use medical notes to assess caregiver availability and type for hospitalized patients with dementia. Our second objective was to identify whether the patient lived at home or resided at an institution.

Methods:

In this retrospective cohort study, we used 2016-2019 telephone-encounter medical notes from a single institution to develop a rule-based natural language processing (NLP) algorithm to identify the patient’s place of residence and caregiver availability. Using note-level data, we compared the results of the NLP algorithm with human-conducted chart abstraction for both training (n=749) and test sets (n=227) for a total of 223 adults aged 65 and older diagnosed with dementia. Our outcomes included determining whether the patients: (1) reside at home; (2) reside at an institution; (3) have a formal caregiver; and (4) have an informal caregiver.

Results:

Test set results indicated that our NLP algorithm had high levels of accuracy and reliability for identifying whether patients had an informal caregiver (F1=0.94; accuracy=0.95, sensitivity=0.97, and specificity=0.93), but was relatively less able to identify whether the patient lived at an institution (F1=0.64, accuracy=0.90, sensitivity=0.51, specificity=0.98). The most common explanations for NLP misclassifications across all categories were: (1) incomplete or misspelled facility names; (2) past/uncertain/undecided status; (3) uncommon abbreviations; and (4) irregular use of templates.

Conclusions:

This innovative work was the first to use medical notes to pragmatically determine caregiver availability. Our NLP algorithm was able to identify whether hospitalized dementia patients have a formal or informal caregiver and, to a lesser extent, whether they lived at home or in an institutional setting. There is merit in using NLP to identify caregivers. This study serves as a proof of concept. Future work can use other approaches and further identify caregivers and the extent of their availability.