Managers and Technology Adoption in Long-Term Care Homes: An Exploratory Study of Key Factors Affecting Health Information Technology Decision-Making
Notice bibliographique
Résumé
Background: Canada has an aging population. With increased life expectancy, older adults become at higher risk of developing chronic disease. Therefore, the need and demand for long-term care (LTC) is expected to continue to increase. LTC homes provide 24-hour nursing care, personal care, and assistance with activities of daily living (Government of Ontario, 2014). Health information technology (IT) presents opportunities to support staff, residents, and their families in LTC environments. This has been particularly emphasized during the latest COVID19 pandemic in light of the challenges that have been observed in LTC homes (e.g., social isolation, limited integration of information, shortage of human resources etc.). Yet, LTC homes still lag significantly behind other areas in healthcare in relation to the adoption of health IT, and little information is available on the factors that affect the decision-making related to health IT solutions in these settings. Objectives and Research Question: This research aims to explore and examine the most relevant and important factors that managers consider when making decisions on the adoption of health IT in LTC homes. Therefore, the research question is: “What are the factors that affect and can inform the decision-making of managers in relation to health IT adoption in LTC homes?” Methods: This research uses an exploratory data-driven Delphi method that consists of a survey of a panel of 19 experts who are decision-makers in their respective LTC homes in Ontario. The Delphi survey is an exploratory approach that elicits the opinion of a panel of experts through iterative feedback consisting of three rounds (brainstorming, narrowing down, and ranking). In this research, the panelists were asked to identify and describe the most important and relevant factors that affect and inform their decision-making regarding health IT solutions in LTC. Then, they were asked to narrow down and rank the list, and a final coefficient of agreement was calculated based on their rankings. Results: The panelists were mostly female, 40-49 years old, and had an average of 9.7 years of experience in their current LTC organization. In addition, participants had an average of 10.5 years of experience in making decisions related to health IT adoption. In the brainstorming round, the panelists provided a total of 102 key factors, which were consolidated into a list of 27 items that varied across 5 general categories: availability of resources, social factors, organizational factors, regulatory requirements, and technical factors of the health IT solution. Fourteen panelists reported key factors related to the cost of technology and ease of use, which were the two key factors that were reported most often by participants. Two items were dropped from the list of key factors in the narrowing down round (i.e., comfort and social environment and vendor reputation) as only items with a rating of 5 out of 7 or greater were retained for the ranking round. In the ranking round, the consensus level was W=0.341. The top 5 factors that were identified and ranked by managers in LTC homes as important in influencing their decisions on the adoption of health IT were (in order of importance): availability of funding, impact on workload and efficiency, value proposition, ease of use, and impact on residents’ outcomes. Contributions: This Delphi survey provides an authoritative list of 25 key factors (and their description) that influence and affect managers’ decisions in LTC homes in relation to the adoption of health IT. This is a first step towards understanding what is happening in the LTC context in relation to health IT decision-making. From a LTC managers’ perspective, the findings provide benchmarking to understand what other LTC homes are doing and the factors that influence their decision-making on health IT solutions. Particular importance should be placed on the highly ranked factors, as they are identified as the factors that are most relevant and most important in facilitating or hindering the decision of LTC managers to adopt health IT solutions. In addition, the results of this thesis may help inform future planning and policies aimed at advancing the use of technology to support the care provided to older adults and the work of health care professionals in LTC. Future studies can use the results of this thesis to develop surveys that assess and monitor the evolution of these factors in broader and more representative samples of LTC managers.
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 | 0,010 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».