Patterns and Perceptions of Smartphone Use Among Academic Neurologists in the United States: Questionnaire Survey
Notice bibliographique
Résumé
BACKGROUND: Smartphone technology is ubiquitous throughout neurologic practices, and numerous apps relevant to a neurologist's clinical practice are now available. Data from other medical specialties suggest high utilization of smartphones in routine clinical care. However, the ways in which these devices are used by neurologists for patient care-related activities are not well defined. OBJECTIVE: This paper aims to characterize current patterns of smartphone use and perceptions of the utility of smartphones for patient care-related activities among academic neurology trainees and attending physicians. We also seek to characterize areas of need for future app development. METHODS: We developed a 31-item electronic questionnaire to address these questions and invited neurology trainees and attendings of all residency programs based in the United States to participate. We summarized descriptive statistics for respondents and specifically compared responses between trainees and attending physicians. RESULTS: We received 213 responses, including 112 trainee and 87 attending neurologist responses. Neurology trainees reported more frequent use of their smartphone for patient care-related activities than attending neurologists (several times per day: 84/112, 75.0% of trainees; 52/87, 59.8% of attendings; P=.03). The most frequently reported activities were internet use, calendar use, communication with other physicians, personal education, and health care-specific app use. Both groups also reported regular smartphone use for the physical examination, with trainees again reporting more frequent usage compared with attendings (more than once per week: 35/96, 36.5% of trainees; 8/58, 13.8% of attendings; P=.03). Respondents used their devices most commonly for the vision, cranial nerve, and language portions of the neurologic examination. The majority of respondents in both groups reported their smartphones as "very useful" or "essential" for the completion of patient care-related activities (81/108, 75.0% of trainees; 50/83, 60.2% of attendings; P=.12). Neurology trainees reported a greater likelihood of using their smartphones in the future than attending neurologists ("very likely": 73/102, 71.6% of trainees; 40/82, 48.8% of attendings; P=.005). The groups differed in their frequencies of device usage for specific patient care-related activities, with trainees reporting higher usage for most activities. Despite high levels of use, only 12 of 184 (6.5%) respondents reported ever having had any training on how to use their device for clinical care. Regarding future app development, respondents rated vision, language, mental status, and cranial nerve testing as potentially being the most useful to aid in the performance of the neurologic examination. CONCLUSIONS: Smartphones are used frequently and are subjectively perceived to be highly useful by academic neurologists. Trainees tended to use their devices more frequently than attendings. Our results suggest specific avenues for future technological development to improve smartphone use for patient care-related activities. They also suggest an unmet need for education on effectively using smartphone technology for clinical care.
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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 | 0,002 | 0,005 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».