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Enregistrement W2758459535 · doi:10.2196/iproc.8457

Engaging Heart Failure Patients with Interactive Voice Response Calls and Multimedia Programs as They Transition from Hospital to Home

2017· article· en· W2758459535 sur OpenAlexvenueno aff
Mark Mulert, Elizabeth Wolens, Joann Clough, Geri Lynn Baumblatt, Jason Gottlieb

Notice bibliographique

RevueIproceedings · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueHeart Failure Treatment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHeart failureTransition (genetics)Interactive voice responseMedicineMedical emergencyMultimediaPsychologyComputer scienceInternal medicineTelecommunications

Résumé

récupéré en direct d'OpenAlex

Background: When people with heart failure (HF) are discharged from hospitals, they need to manage their condition. Patients are overwhelmed and feel poorly. To avoid complications and readmissions, it’s essential they quickly engage in new behaviors, such as weighing themselves each day. Patients often do not start or maintain these behaviors. Objective: Researchers sought to measure the impact of a user-centered, interactive voice-response (IVR) phone call and multimedia program series (EmmiTransition®) to educate, and motivate patients to take self-care actions post-discharge. Methods: Researchers analyzed call records and conducted aggregate analysis from patients who interacted with the series between August 2013 and May 2016 at UAB Medicine and other healthcare organizations. The 45-day IVR series explains key concepts and behaviors and asks patients to report information like their daily weight. Short multimedia programs provided additional information. Call records from 4,503 patients who completed the series were analyzed. There were 3,615 people who answered and interacted with the calls. Interactions were analyzed to identify the impact on driving people to report their weight daily post-discharge. The percentage of patients who reported weighing themselves daily increased steadily over the first two weeks. After viewing a multimedia program, patients could take an optional survey from Emmi Solutions. Responses and comments were tabulated. Results: On day one of the IVR calls, 66% of UAB Medicine patients who answered the call reported their weight. On day 14 of the calls, 89% of UAB Medicine patients who answered the call reported their weight, comprising a 36% increase in reporting in two weeks. The behaviors seen over the first two weeks were sustained. For the remaining 30 days of the series, 93% of patients who answered calls continued to report their weight. There were 936 patients who opted to take the post-multimedia program survey. The survey findings were as follows: 66% showed increased confidence to ask questions; 75% were prepared to manage their health condition; 75% were more motivated to take their medications; 89% were more aware of how their lifestyle impacts health; 87% were willing to take new action to manage their health; and 88% indicated that they were motivated to change their lifestyle. Examples of patient comments follow: “Read labels and try to decrease processed foods and transfer to whole fruits and vegetables utilizing herbs for seasoning”; “making sure I have a calendar over the bathroom scales to keep track of weight instead of going my memory”; “was not aware diet soda contained high salt count. I will not drink diet sodas as often maybe a glass once or twice a week”; “I will get a flu and pneumonia shot every year. This is not something I did in the past.” Conclusions: Most patients who engaged in this series started a new behavior, regularly reporting they weighed themselves. Most people continued this behavior throughout the 45-day series. Most patients who viewed a multimedia program and completed the survey expressed plans to take specific actions or behavior changes based on information offered by the program about how to weigh themselves, reduce sodium, or manage their fluids.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,146
Score d'incertitude au seuil0,624

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,251
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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