“The Doctor Needs to Know”: Acceptability of Smartphone Location Tracking for Care Coordination
Notice bibliographique
Résumé
BACKGROUND: Care coordination can be highly challenging to carry out. When care is fragmented across health systems and providers, there is an increased likelihood of hospital readmissions and wasteful health care spending. During and after care transitions, smartphones have the potential to bolster information transfer and care coordination. However, little research has examined patients' perceptions of using smartphones to coordinate care. OBJECTIVE: This study's primary objective was to explore patient acceptability of a smartphone app that could facilitate care coordination in a safety net setting. Our secondary objective was to identify how clinicians and other members of primary care teams could use this app to coordinate care. METHODS: This qualitative study was conducted at a federally qualified health center in metropolitan Chicago, IL. We conducted four focus groups (two in English, two in Spanish) with high-risk adults who owned a smartphone and received services from an organizational care management program. We also conducted structured interviews with clinicians and a group interview with care managers. Focus groups elicited patients' perceptions of a smartphone app designed to: (1) identify emergency department (ED) visits and inpatient stays using real-time location data; (2) send automated notifications (ie, alerts) to users' phones, asking whether they were a patient in the hospital; and (3) send automated messages to primary care teams to notify them about patients' confirmed ED visits and inpatient stays. Focus group transcripts were coded based on emergent themes. Clinicians and care managers were asked about messages they would like to receive from the app. RESULTS: Five main themes emerged in patient focus group discussions. First, participants expressed a high degree of willingness to use the proposed app during inpatient stays. Second, participants expressed varying degrees of willingness to use the app during ED visits, particularly for low acuity ED visits. Third, participants stated their willingness to have their location tracked by the proposed app due to its perceived benefits. Fourth, the most frequently mentioned barriers to acceptability were inconveniences such as "false alarm" notifications and smartphone battery drainage. Finally, there was some tension between how to maximize usability without unnecessarily increasing user burden. Both clinicians and care managers expressed interest in receiving messages from the app at the time of hospital arrival and at discharge. Clinicians were particularly interested in conducting outreach during ED visits and inpatient stays, while care managers expressed more interest in coordinating postdischarge care. CONCLUSIONS: High-risk primary care patients in a safety net setting reported a willingness to utilize smartphone location tracking technology to facilitate care coordination. Further research is needed on the development and implementation of new smartphone-based approaches to care coordination.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».