Patient Digital Health Technologies to Support Primary Care Across Clinical Contexts: Survey of Primary Care Providers, Behavioral Health Consultants, and Nurses
Notice bibliographique
Résumé
BACKGROUND: The acceptance of digital health technologies to support patient care for various clinical conditions among primary care providers and staff has not been explored. OBJECTIVE: The purpose of this study was to explore the extent of potential differences between major groups of providers and staff in primary care, including behavioral health consultants (BHCs; eg, psychologists, social workers, and counselors), primary care providers (PCPs; eg, physicians and nurse practitioners), and nurses (registered nurses and licensed practical nurses) in the acceptance of various health technologies (ie, mobile apps, wearables, live video, phone, email, instant chats, text messages, social media, and patient portals) to support patient care across a variety of clinical situations. METHODS: We surveyed 151 providers (51 BHCs, 52 PCPs, and 48 nurses) embedded in primary care clinics across the United States who volunteered to respond to a web-based survey distributed in December 2020 by a large health care market research company. Respondents indicated the technologies they consider appropriate to support patients' health care needs across the following clinical contexts: acute and chronic disease, medication management, health-promoting behaviors, sleep, substance use, and common and serious mental health conditions. We used descriptive statistics to summarize the distribution of demographic characteristics by provider type. We used contingency tables to compile summaries of the proportion of provider types endorsing each technology within and across clinical contexts. This study was exploratory in nature, with the intent to inform future research. RESULTS: Most of the respondents were from urban and suburban settings (125/151, 82.8%), with 12.6% (n=19) practicing in rural or frontier settings and 4.6% (n=7) practicing in rural-serving clinics. Respondents were dispersed across the United States, including the Northeast (31/151, 20.5%), Midwest (n=32, 21.2%), South (n=49, 32.5%), and West (n=39, 25.8%). The highest acceptance for technologies across clinical contexts was among BHCs (32/51, 63%) and PCPs (30/52, 58%) for live video and among nurses for mobile apps (30/48, 63%). A higher percentage of nurses accepted all other technologies relative to BHCs and PCPs. Similarly, relative to other groups, PCPs indicated lower levels of acceptance. Within clinical contexts, the highest acceptance rates were reported among 80% (41/51) of BHCs and 69% (36/52) of PCPs endorsing live video for common mental health conditions and 75% (36/48) of nurses endorsing mobile apps for health-promoting behaviors. The lowest acceptance across providers was for social media in the context of medication management (9.3% [14/151] endorsement across provider type). CONCLUSIONS: The survey suggests potential differences in the way primary care clinicians and staff envision using technologies to support patient care. Future work must attend to reasons for differences in the acceptance of various technologies across providers and clinical contexts. Such an understanding will help inform appropriate implementation strategies to increase acceptability and gain greater adoption of appropriate technologies across conditions and patient populations.
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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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».