Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study.

Journal: PloS one

PMID: 31408458

Abstract

BACKGROUND: Older individuals receiving home assistance are at high risk for emergency visits and unplanned hospitalization. Anticipating their health difficulties could prevent these events. This study investigated the effectiveness of an at-home monitoring method using social workers' observations to predict risk for 7- and 14-day emergency department (ED) visits.

Authors

Jacques-Henri Veyron

Agence Nationale d'Appui à la performance (ANAP), Paris, France.
Patrick Friocourt

Service de médecine interne gériatrique, Hôpital Simone Veil, Blois, France.
Olivier Jeanjean

Pôle de gériatrie, SSR, Soins Palliatifs, Groupe hospitalier Nord-Essonne, Longjumeau, France.
Laurence Luquel

Hôpital privé gériatrique Les Magnolias, Ballainvilliers, France.
Nicolas Bonifas

Ecole Polytechnique, Palaiseau, France.
Fabrice Denis

Institut Inter-Regional de Cancérologie Jean Bernard, Le Mans, France.
Joël Belmin

Hôpital Charles Foix, Assistance Publique-Hôpitaux de Paris, Ivry-sur-Seine, France.

Keywords

Aged Aged, 80 and over Algorithms Emergency Service, Hospital Female Frail Elderly Geriatric Assessment Home Care Services Home Health Aides Hospitalization Humans Independent Living Machine Learning Male Predictive Value of Tests Proof of Concept Study Prospective Studies Surveys and Questionnaires

External Resources

View on PubMed Access via DOI PubMed (31408458)

Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study.

Abstract

Authors

Keywords

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