What Do Electronic Health Record Vendors Reveal About Their Products: An Analysis of Vendor Websites
Notice bibliographique
Résumé
BACKGROUND: Purchasing electronic health records (EHRs) typically follows a process in which potential adopters actively seek information, compare alternatives, and form attitudes towards the product. A potential source of information on EHRs that can be used in the process is vendor websites. It is unclear how much product information is presented on EHR vendor websites or the extent of its value during EHR purchasing decisions. OBJECTIVE: To explore what features of EHR systems are presented by vendors in Ontario, Canada, on their websites, and the persuasive means they use to market such systems; to compare the online information available about primary care EHR systems with that about hospital EHR systems, and with data compiled by OntarioMD, a regional certifying agency. METHODS: A list of EHR systems available in Ontario was created. The contents of vendor websites were analyzed. A template for data collection and organization was developed and used to collect and organize information on the vendor, website content, and EHR features. First, we mapped information on system features to categories based on a framework from the Institute of Medicine (IOM). Second, we used a grounded theory-like approach to explore information for building consumer confidence in the vendor and product, and the various persuasive strategies employed on vendor websites. All data were first coded by one researcher. A peer reviewer independently analyzed a randomly chosen subset of the websites (10 of 21; 48%) and provided feedback towards a unified coding scheme. All data were then re-coded and categorized into themes. Finally, we compared information from vendor websites and data gathered by OntarioMD. RESULTS: Vendors provided little specific product information on their websites. Only two of five acute care EHR websites (40%) and nine of 16 websites for primary care systems (56%) featured seven or all eight of the IOM components. Several vendor websites included system interface demonstrations: screenshots (six websites), public videos or slideshows (four websites), or for registered viewers only (three websites). Persuasive means used by vendors included testimonials on 14/21 (67%) websites, and directional language. Except for one free system, trial EHR versions were not available. OntarioMD provided more comprehensive information about primary care systems than the vendors' websites. Of 14 points of comparison, only the inclusion of templates and bilingual interfaces were fully represented in both data sources. For all other categories, the vendor websites were less complete than the OntarioMD site. CONCLUSIONS: EHR vendor websites employ various persuasive means, but lack product-specific information and do not provide options for trying systems on a limited basis. This may impede the ability of potential adopters to form perceptions and compare various offerings. Both vendors and clients could benefit from greater transparency and more specific product information on the Web. TRIAL REGISTRATION: N/A.
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 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 | 0,009 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».