A comparison of three recommender strategies for facilitating person-centered care in nursing homes

Nathan Martindale, Gerald C. Gannod, Katherine M. Abbott, Kimberly Van Haitsma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Preferences for Everyday Living Inventory (PELI) is a 72-question instrument used for helping nursing homes assess person-centered care. In particular, the approach allows residents to express their preferences for both care and activities in order to provide direct care workers with insights on how to best provide a high-quality living experience. Among the challenges of using the PELI is its length: 72 questions give rise to issues of survey fatigue while also creating a workflow bottleneck for those providing care. In this paper we explore and evaluate the use of three different recommender strategies that we have applied to the PELI. In particular, we present the use of both rule-based and neighborhoodbased collaborative filtering in order to make recommendations on which preference questions to present to a resident. We illustrate the approaches by providing a domain-specific example, and then compare the approaches across a number of performance and quality metrics.

Original languageEnglish (US)
Title of host publicationProceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019
EditorsRoman Bartak, Keith Brawner
PublisherThe AAAI Press
Pages299-304
Number of pages6
ISBN (Electronic)9781577358053
StatePublished - 2019
Event32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019 - Sarasota, United States
Duration: May 19 2019May 22 2019

Publication series

NameProceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019

Conference

Conference32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019
CountryUnited States
CitySarasota
Period5/19/195/22/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software

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