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Observational Study
. 2024 Jul;72(7):2140-2147.
doi: 10.1111/jgs.18841. Epub 2024 Feb 27.

Patient portal messages to support an age-friendly health system for persons with dementia

Affiliations
Observational Study

Patient portal messages to support an age-friendly health system for persons with dementia

Kelly T Gleason et al. J Am Geriatr Soc. 2024 Jul.

Abstract

Background: Patient portal secure messaging can support age-friendly dementia care, yet little is known about care partner use of the portal and how message concerns relate to age-friendly issues.

Methods: We conducted a two-part observational study. We first assessed the feasibility of automating care partner identification from patient portal messages by developing and testing a natural language processing (NLP) rule-based classification system from portal messages of 1973 unique patients 65 and older. Second, two independent reviewers manually coded a randomly selected sample of portal messages for 987 persons with dementia to identify the frequency of expressed needs from the 4M domains of an Age-Friendly Health System (medications, mentation, mobility, and what matters).

Results: A total of 267 (13.53%) of 1973 messages sent from older adults' portal accounts were identified through manual coding as sent by a nonpatient author. The NLP model performance to identify nonpatient authors demonstrated an AUC of 0.90. Most messages sent from the accounts of persons with dementia contained content relevant to the 4Ms (60%, 601/987), with the breakdown as follows: medications-36% (357/987), mobility-10% (101/987), mentation-16% (153/987), and what matters (aligning care with specific health goals and care preferences)-21%, 207/987.

Conclusions: Patient portal messaging offers an avenue to identify care partners and meet the informational needs of persons with dementia and their care partners.

Keywords: age‐friendly; dementia; patient portal.

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Conflict of interest statement

Conflict of Interest Statement

The authors have no conflicts of interest or competing interests to declare.

Figures

Figure 1.
Figure 1.
Receiver Operating Characteristic Curve for training, testing, and final natural language processing model

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