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Enregistrement W2051188965 · doi:10.1097/nnr.0000000000000052

Point-of-Care Research

2014· editorial· en· W2051188965 sur OpenAlexaboutno aff
Susan J. Henly

Notice bibliographique

RevueNursing Research · 2014
Typeeditorial
Langueen
DomaineHealth Professions
ThématiquePatient-Provider Communication in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPoint (geometry)Health careIntersection (aeronautics)Point of careMobile deviceAction (physics)Everyday lifePoliticsInternet privacyPsychologyNursingPublic relationsMedicineComputer sciencePolitical scienceWorld Wide WebEngineeringLaw

Résumé

récupéré en direct d'OpenAlex

What images come to mind when you hear the phrase “point of care”? Do you visualize a nurse advising a mother about her child’s minor ailment in the clinic or a nurse helping a patient get out of bed in the ICU? Whatever you envision, some intersection of patient, nurse, place, and time is likely part of the picture. In the past, the point of care was taken to be a setting—hospital, clinic, infirmary—designed specifically to provide health services at specific times by specific providers to specific people with specific conditions or concerns in specific ways. Now, radical changes in technology and information, consumer expectations, demographics, health economics, and political action are revolutionizing the way care is provided. This perfect storm is upending conventional notions about point of care and opening new opportunities and challenges for nursing research. The incredible expansion of mobile cellular subscriptions—projected to reach almost 7 billion by the end of this year (International Telecommunication Union, 2014)—may be the most significant factor allowing the point of care to extend from traditional settings to the point of living, wherever it may be. First smart handheld devices and now computers incorporated into the mundane stuff of everyday life like t-shirts have allowed generation of the quantified self-movement (Wolf, 2010). Self-collection of personal health data on-the-go was coemergent with ideas for novel patient-driven healthcare models (Swan, 2009), the potential of which has been little explored or utilized. The same handheld, interconnected, smart technology generated the move for m-health that allows “point of care in your pocket” for healthcare providers (van Heerden, Tomlinson, & Swartz, 2012). Try a quick search of MEDLINE using “point of care” as keyword; in over 10,000 hits, you’ll find that lab testing and use of devices at the bedside in hospitals, in long-term care units, in homes, and in the field are fast changing the way assessments are done and treatment decisions are made (Bier & Schumacher, 2013; Walia, 2013). Telehealth is extending the reach of place-bound providers to the technology-mediated point of care (Institute of Medicine, 2012). Evaluation of quality in point-of-care testing and telehealth interactions is needed, however. Wireless technology and the Internet of things (IEEE Standards Association, 2014) are creating smart environments for the point of care. Smart, patient-centered ICUs designed for healing and capitalizing on information provided by sensors and devices are envisioned (Halpern, 2014). Sensor and information capabilities have potential to inform on-going adaptation of assistive technology to enhance independence for those aging with disabilities (Agree, 2014). Safety is a critical component of point-of-care research in nursing. For example, barcode technology and information technology are used in nursing units, laboratories, and pharmacies of hospitals around the world to support safe medication administration and accurate handling of specimens (e.g., Agrawal, 2009; Miller, Akers, Magrin, Whitehead, & Davis, 2013). Still, challenges in implementation exist (Voshall, Piscotty, Lawrence, & Targosz, 2013), and research is needed to ensure that the best systems are developed, deployed, used properly, and used for system improvement. Infusion of technology, the possibly intrusive nature of information gathering, and privacy concerns raise questions about human factors for point-of-care research. Information technology has potential to change the nursing process at the point of care (Courtney, Demiris, & Alexander, 2005) and preservation of the caring environment amidst the technology at point of care is an ongoing concern (Buckner & Gregory, 2011). Sensors allowing real-time monitoring for safety may support independence for elderly people at home, but more knowledge is needed about the attitudes, acceptability, and rated usefulness of the systems (Cesta et al., 2011). The increase in funding opportunities for point-of-care research underscores the expectation that new knowledge is needed to understand what works at the point of care, for whom, where, and when. The National Institute of Biomedical Imaging and Bioengineering (n.d.) sponsors the Point-of-Care Technologies Research Network; the National Institute of Nursing Research (n.d.) asked about how point-of-care/self-monitoring diagnostic devices could significantly improve self-management to improve quality of life for individuals with chronic illness. A search for “point-of-care” on the Agency for Healthcare Research and Quality Web site returned almost 1,500 results across their research portfolios. The Bill and Melinda Gates Foundation and Grand Challenges Canada have partnered to fund innovative ideas for point-of-care diagnostics in the developing world (“Foundation and Grand Challenges Canada,” n.d.). Papers considered for the new, ongoing series should report findings from original point-of-care research studies. Topics include but are not limited to use of devices and information technology at the point of care, patient safety issues, m-health, telehealth, and system interoperability. Design and evaluation of “smart” environments across the health–illness continuum and research about learning health systems are relevant to the call. Findings from investigations of communication and decision-making in emerging technology-supported point-of-care settings are welcome. Papers may be enhanced to include video or interactive graphs using supplemental digital content. In advance of submission, queries to the Editor are encouraged but not required. Submissions may be regular full-length papers or research briefs. In the letter to the editor uploaded with submissions, please mention that the paper should be considered for the series. Consider point-of-care research. Contribute to new knowledge about the point of care in our digital, interconnected world.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,048
score de la tête « metaresearch » (Gemma)0,111
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,063
Score d'incertitude au seuil0,256

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0480,111
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0050,007
Études des sciences et des technologies0,0040,005
Communication savante0,0120,010
Science ouverte0,0060,007
Intégrité de la recherche0,0040,005
Charge utile insuffisante (le modèle a refusé de juger)0,0630,016

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.

Tête enseignante Opus0,500
Tête enseignante GPT0,638
Écart entre enseignants0,137 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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