A96 IMPROVING AUTOMATED TELEHEALTH SERVICES TO MEET THE NEEDS OF PATIENTS AND HEALTHCARE PROFESSIONALS
Notice bibliographique
Résumé
Abstract Background There is a growing demand for automated telehealth programs in medicine to improve health-related behaviours such as adherence to treatment schedules. In addition, these services are a promising alternative to increase access to medical care for rural and marginalized patients. Although promising, telehealth programs need to be evaluated based on patient responsiveness in order to tailor automated services to their target population and identify factors that minimize response rates. Purpose To evaluate the effect of socioeconomic, health and structural factors on the response rate of an automated follow-up program implemented at St. Paul’s Hospital in Vancouver, BC. Method A retrospective chart review was conducted of patients who did not respond when contacted by a PAtient-Guided Complication Tracking System (PACTS). PACTS sent Short Message Service (SMS) to outpatients having a flexible sigmoidoscopy, gastroscopy and/or colonoscopy one week post-procedure. Patients who received PACTS SMS between 03/21-08/21 were included in this study. Individuals were considered non-responsive if they failed to reply after receiving an initial SMS and a second reminder text message (sent 24 hours after the first SMS). Socioeconomic factors including: age, sex and personal annual income were assessed. Income was analyzed using postal code census data. To study health factors, patient comorbidity was evaluated using the Charlson Comorbidity Index (CCI) where CCI > 2 was considered high. Finally, access to a general practitioner (GP) was investigated to study structural factors influencing responsiveness to PACTS. Result(s) Of the 200 people studied, 109 of these individuals were male (54%) and 91 were female (46%). The mean age of non-respondents was 60.2 ± 16.4. Postal codes were reported for 144 patients (72%) and the mean annual income of these individuals was $48 928 ± $9710. The mean Charlson Comorbidity Index was 2.1 ± 1.7. 109 non-respondents (54%) had a family doctor listed in their chart and 91 non-respondents (46%) did not. Conclusion(s) Based on the age, sex, personal annual income and comorbidity results, socioeconomic and health factors do not impact response rate. The large number of non-respondents without GPs indicate that structural factors influence responsiveness. The high proportion of non-respondents lacking GPs may represent a subgroup of individuals that under-use healthcare services. Further evaluation of non-respondents and comparative analysis with a large group of respondents are pending and will likely support these conclusions. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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 | 0,004 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».