The Utilization of Wireless Handheld Computers with MEDLINE is an Effective Mechanism for Answering Clinical Questions at the Point of Care
Notice bibliographique
Résumé
A Review of: 
 Hauser, Susan E., Dina Demner-Fushman, Joshua L. Jacobs, Susanne M. Humphrey, Glenn Ford, and George R. Thoma. “Using Wireless Handheld Computers to Seek Information at the Point of Care: An Evaluation by Clinicians.” Journal of the American Medical Informatics Association 14.6 (Nov./Dec. 2007): 807-15.
 
 
 Abstract
 
 Objective – To assess the effectiveness of wireless handheld computers (HHCs) for information retrieval in clinical environments and the role of MEDLINE in answering clinical questions at the point of care.
 
 Design – A prospective single-cohort study.
 
 Setting – Teaching rounds in the intensive care units and general medicine wards in two hospitals associated with a university’s school of medicine in the United States. 
 
 Subjects – Five internal medicine residents with training in evidence-based practice. 
 
 Methods – While accompanying medical teams on teaching rounds for approximately four consecutive weeks, each resident used MD on Tap (an application for handheld computers) on a TreoTM 650 PDA/cell phone to find answers in real time, to questions that were raised by members of the medical teams. Using a special version of MD on Tap, each resident initialized a UserID. Serving as evaluators, the residents described and categorized clinical scenarios and recognized questions. They also formulated search terms, searched MEDLINE and identified citations determined to be useful for answering the questions. An intermediate server collected details of all MEDLINE search query transactions, including system response time, the user (based on UserIDs), citations selected for viewing, the saving of citations to HHC memory, as well as use of the Linkout and Notes features. In addition evaluators submitted daily summaries. These summaries included information on the scenarios, clinical questions, evidence-based methodology (EBM) category, the team member who was the source of the question, the PubMed Identifiers (PMIDs) of relevant citations, and comments. At the end of the data collection period, each evaluator submitted a summary report consisting of a qualitative and quantitative evaluation of his experience using MEDLINE via the handheld device to find relevant evidence based information at the point of care. The report also focused on the usefulness of MD on Tap features, along with suggestions for additional features. 
 
 Data analysis encompassed matching the text of daily summaries to transaction records in order to identify sessions (containing a scenario, clinical question, one or more search queries, citation fetches and selected PMIDs). A senior medical librarian/expert indexer reviewed all the citations selected by evaluators and graded each citation as A (useful for answering the question), B (provided a partial answer) or C (not useful for answering the question). Only those graded A were regarded as “relevant.” For the purpose of analysis a session was deemed to be successful “if at least one of the citations selected by the evaluator as relevant was also classified as Relevant” (810) by the expert indexer. Similarly, an individual query was successful “if at least one of the citations among the results of the query was Relevant, that citation was viewed by the evaluator during rounds, and it addressed the clinical question as recorded in the daily summary” (810). Various types of relationships were analyzed including the characteristics of clinical questions vis-a-vis successful sessions, search strategies in relation to successful queries, and the association between MD on Tap features and successful queries. SAS/SUDAAN version 9.1 was used for statistical analysis. 
 
 Main Results – Evaluators answered 68% (246 of 363) clinical questions during rounding sessions. They identified 478 “relevant” citations, an average of 1.9 per successful session and 1.3 for each successful question. Session lengths averaged 3 minutes and 41 seconds. Characteristics of the evaluator (training, interest, experience and expertise) were a significant predictor of a session’s success. The significant determinants of query success were “the number of search terms that could be mapped to Medical Subject Headings (MeSH)” (812), the number of citations that were found for a query, and the use of MD on Tap’s auto-spellcheck feature. Narrative comments from the evaluators indicated that using MEDLINE on a HHC at the point of care contributed positively to the practice of evidence -based medicine. 
 
 Conclusion – Wireless handheld computers are useful for retrieving information in clinical environments. The application of several MeSH terms in a query facilitates the retrieval of MEDLINE citations that provide answers to clinical questions. The MD on Tap program is a valuable interface to MEDLINE at the point of care.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,043 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».