PTU-139 The need to work ‘smart’ on acute wards: could mobile phone applications be the answer?
Notice bibliographique
Résumé
Introduction Due to rising service pressures there is a need for innovation to optimise efficiency on acute medical and gastroenterology wards. Using smartphone applications to achieve this is an attractive proposition, but the utility of such technologies in clinical practice is unclear. Here, we report the experience of multidisciplinary staff after a pilot of a smartphone application at our hospital. Methods Two acute medical wards (including a 24 bed gastroenterology ward), were selected for a two-week pilot trial of a smartphone application, Listrunner (Desma Health, Canada), during October 2017. Multidisciplinary team members including; doctors, pharmacists, and nurses, were given access to Listrunner via dedicated secure mobile devices. During daily consultant ward rounds and throughout the working day, all tasks were uploaded onto Listrunner. A Control Centre lead reviewed all uploaded tasks, identified non-medical tasks and either completed these or reassigned them to more appropriate team members. At the end of the pilot, staff provided feedback on their experience via a structured questionnaire. Results During the pilot, whilst a total of 1080 tasks were uploaded onto Listrunner, 20% of these were non-medical tasks managed by the Control Centre. The most common tasks managed by Control Centre were chasing specialty reviews (42%) and chasing investigations (33%). At the end of the pilot, staff from both wards (n=19; Junior Doctors n=9, Nurses n=4, Consultants n=2, Pharmacists n=2 and Occupational Therapists n=2), completed questionnaires. Most doctors (73%) found Listrunner easy to use and 56% of juniors felt that it improved the relevance of their work by reassigning non-medical tasks. Overall, 42% rated Listrunner as ‘useful’, whereas 21% did not find it useful and 56% felt it improved communication between team members. When asked how Listrunner affected the conduct of ward work, the most popular responses selected were; ‘it improved patient flow/discharges’ (n=8), ‘it speeded up allocation of tasks’ (n=7) and ‘it prolonged the ward round’ (n=6). Whilst 12/19 (63%) felt it would be worth adopting Listrunner, 8/19 (42%) expressed some reservations about using smartphones in front of patients. The main barrier to adopting this technology more widely (according to 58%) would be the staffing levels and related costs required to replicate the pilot experience. Conclusions Our data suggest that a large number of tasks currently performed by doctors on acute wards are non-medical tasks. Smartphone technology appears to have potential to improve efficiency and streamline clinical activities, and our early experience may help inform future adoption and further development of this technology.
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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 | 0,005 | 0,024 |
| 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 | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,050 | 0,013 |
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 ».